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<p>cran.r-project.org</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/jcW-UyB1PbA" height="1">
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<p>johnmackintosh.com</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/jO35NLDCr3w" height="1">
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<p>cran.r-project.org</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/nvcZARxTBaw" height="1">
<p>yihui.name</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/4dQSOTfi-PQ" height="1">
<p>blog.revolutionanalytics.com</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/Dtz_lnDomdA" height="1">
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<p>cran.r-project.org</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/FNxCWWd7wCc" height="1">
<p>cran.r-project.org</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/vBfjJu4AvA4" height="1">
<p>cran.r-project.org</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/OE2xH5rPWrM" height="1">
<p>blog.revolutionanalytics.com</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/hr4O1P17hx4" height="1">
<p>yihui.name</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/h7oriXKItr4" height="1">
<p>cran.r-project.org</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/UEf-CLrlm0w" height="1">
<p>cran.r-project.org</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/PJqmEBP4ToU" height="1">
<p>rviews.rstudio.com</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/6M6XusvT0Jc" height="1">
<p>rud.is</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/lp9xeG7y9N0" height="1">
<p>cran.r-project.org</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/5Njc9KzeAg0" height="1">
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<p>cran.r-project.org</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/wD2MEmgNCaM" height="1">
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<p>dirk.eddelbuettel.com</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/DqvTQAGEAK8" height="1">
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<p>cran.r-project.org</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/wOGDOeHV_-8" height="1">
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<p>dirk.eddelbuettel.com</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/7Ro3hn2544A" height="1">
<p>github.com</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/3D2MJfe5CVQ" height="1">
<p>cran.r-project.org</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/2pFi9BZvVdw" height="1">
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<p>cran.r-project.org</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/56yjceNnTJY" height="1">
<p>www.win-vector.com</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/p9CsQ6D0IIE" height="1">
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<p>rviews.rstudio.com</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/jGfobCZi4mg" height="1">
<p>colinfay.me</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/2ij-D__3S2s" height="1">
<p>blog.rstudio.com</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/_AFzJrwB8nU" height="1">
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<p>developer.r-project.org</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/udJjKWnVec0" height="1">
<p>austinwehrwein.com</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/4OzMalHC2ic" height="1">
<p>cran.r-project.org</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/xa8JgVL64Qo" height="1">
<p>cran.r-project.org</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/jvmEWNeQF6g" height="1">
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<p>blog.revolutionanalytics.com</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/CMrgENrQXdI" height="1">
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<p>cran.r-project.org</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/ImqBVlL7Q2M" height="1">
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<p>cran.r-project.org</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/NeaUzhzuYYo" height="1">
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<p>dirk.eddelbuettel.com</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/EJCDs3hQrJM" height="1">
<p>www.masalmon.eu</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/oIPowROBc3Y" height="1">
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<p>www.win-vector.com</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/5eo9fZILUw4" height="1">
<p>dirk.eddelbuettel.com</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/Oc2qpmO5Ksw" height="1">
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<p>tjmahr.github.io</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/j9N7jFZRPbI" height="1">
<p>rud.is</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/FrzCzqpa2MM" height="1">
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<p>maraaverick.rbind.io</p><img width="1" alt="" src="http://feeds.feedburner.com/~r/RWeeklyLive/~4/TLpnlby2zhA" height="1">
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CategoriesGetting Data
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In this post, I am going to run an exploratory analysis of the plant leaf dataset as made available by UCI Machine Learning repository at this link. The dataset is expected to comprise sixteen samples each of one-hundred plant species. Its analysis was introduced within ref. [1]. That paper describes a method designed to work […]Related Post
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The truth is out there R readers, but often it is not what we have been led to believe. The previous post examined the strong positive results bias in optimism corrected bootstrapping (a method of assessing a machine learning model’s predictive power) with increasing p (completely random features). There were 2 implementations of the method […]
As 2018 nears its end, it’s time for me to look back on my R/Twitter
year with the same simple method as last
year: let me identify and webshoot my 9 best
tweets of 2018!
Downloading and opening my Twitter data
Like in 2017 I tweeted too much and therefore was unable to rely on
rtweet::get_timeline() (or rtweet::get_my_timeline()) to download my
tweets so I exported data via the Tweets tab of
https://analytics.twitter.com/. Last year, I downloaded one file per
quarter but somehow had to download one per month this time. It was
monotonous but not horrible.
library(&quot;magrittr&quot;)
dir(&quot;data&quot;, full.names = TRUE) %__%
purrr::map_df(readr::read_csv) %__%
unique() -__ my_tweets
4196 tweets!
Getting the top 9 tweets
Similarly to my 2017 post, I chose the number of likes as criterion to
define my best tweets.
my_tweets %
magick::image_append(stack = TRUE) %__%
magick::image_border(&quot;salmon&quot;, &quot;20x20&quot;)
Worth noting from my R year are, according to these tweets,
my re-designing my website;
the slides of my R-Ladies Strasbourg talk about how to be a
resilient R user;
my starting work at rOpenSci and Locke
Data (yay!);
my blog post about rainbowing a set of
pictures;
another blog post featuring magick, about combining
hexes but I’d recommend
reading this post by Mitchell O’Hara
Wild
instead;
my blog post about where to get R
help that might be
the personal blog post I’m the proudest of.
What about your R year, on Twitter and elsewhere? Have a good last days
of 2018, and a happy 2019!
In the previous parts of the series we demonstrated a positive results bias in optimism corrected bootstrapping by simply adding random features to our labels. This problem is due to an ‘information leak’ in the algorithm, meaning the training and test datasets are not kept seperate when estimating the optimism. Due to this, the optimism, […]
Recently I wanted to learn how to use emojis as points in a scatterplot points. It seems like the emojifont package is a popular way to do it. However, I couldn’t seem to get it to work on my machine … Continue reading →
During this time of year there is obviously a lot of talk about the Bible. As most people know the New Testament comprises four different Gospels written by anonymous authors 40 to 70 years after Jesus’ supposed crucifiction. Unfortunately we have lost all of the originals but only retained copies of copies of copies (and … Continue reading &quot;Clustering the Bible&quot;
Based on the heatmaps I drew earlier this month, I made a poster of two centuries of data on mortality rates in France for males and females. It turned out reasonably well, I think. I will probably get it blown up to a nice large size and put it up on the wall. I’ve had very good results with PhD Posters for work like this over the years, by the way.
Two centuries of French mortality rates
Here’s a PDF version if you have access to a color printer or plotter and would like a hard copy.
As before, the data are from mortality.org, and the plots were produced in R using ggplot. The code to make them is available on GitHub. The sidebar was added in Adobe Illustrator. And, of course, if you’d like to learn how to make, refine, and understand plots like this, consider buying my book, Data Visualization: A Practical Introduction.
Welcome to part III of debunking the optimism corrected bootstrap in high dimensions (quite high number of features) in the Christmas holidays. Previously we saw with a reproducible code implementation that this method is very bias when we have many features (50-100 or more). I suggest avoiding this method until at some point it has […]
The selloff on Christmas eve was so bad it looked like a typical bear market capitulation. The following rally merely confirmed it. As mentioned in the last post, at the time the correction reached 16%, at the close of December 21st, the oversold indicator was not lighted. What followed was the worst Christmas eve selloff […]
The post The Christmas Eve Selloff was a Classic Capitulation appeared first on Quintuitive.
A cheezy Le Monde mathematical puzzle : (which took me much longer to find [in the sense of locating] than to solve, as Warwick U does not get a daily delivery of the newspaper [and this is pre-Brexit!]): Take a round pizza (or a wheel of Gruyère) cut into seven identical slices and turn one […]
How would I miss to program just a little bit during the holiday season? But I didn’t want to work on something serious, so I decided to checkout some ground work on R-Shiny + JQuery + CSS. The result are some nice holiday greetings inside a shiny app:
An app to greet your family with shinyI just googled CSS + holidays and what I found was this article on CSS for the holidays. The Pixel Art Christmas Tree I found from dodozhang21 really amazed me. But it was missing some shiny features. So I thought why not make it a custom shiny output? I was already writing about custom shiny inputs but never on custom shiny outputs.
Actually, the tree is kind of easy. I found that 83 rows of the CSS file account for the moving pixels of the tree as CSS keyframes. There are 5 keyframes (0% , 25%, 50%, 75%, 100% of the animation). So I read them as data.frames in R. Then I sampled X colors for the number of balls the user wants to find in the tree and replaced X of the 83 rows in 5 data frames. Then I put everything back into HTML by:
glue(&quot;$('head').append(\&quot;{style_css}\&quot;);&quot;)
where glue is the R package that will replace style_css by the CSS I created from my 5 data frames.
To see the whole code, you can go to github.
You think this is not as easy as I thought? Then just get started with R and find the “Jamie” in your company that knows R.
Happy Holidays!
Very shiny holidays!
Very shiny holidays! was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.
Some people are very fond of the technique known as ‘optimism corrected bootstrapping’, however, this method is bias and this becomes apparent as we increase the number of noise features to high numbers (as shown very clearly in my previous blog post). This needs exposing, I don’t have the time to do a publication on […]
In the scorecard development, the area under ROC curve, also known as AUC, has been widely used to measure the performance of a risk scorecard. Given everything else equal, the scorecard with a higher AUC is considered more predictive than the one with a lower AUC. However, little attention has been paid to the statistical […]
There are lots of ways to assess how predictive a model is while correcting for overfitting. In Caret the main methods I use are leave one out cross validation, for when we have relatively few samples, and k fold cross validation when we have more. There also is another method called ‘optimism corrected bootstrapping’, that […]
With the holidays approaching, one of the most discussed questions at STATWORX was whether we’ll have a white Christmas or not. And what better way to get our hopes up, than by taking a look at the DWD Climate Data Center’s historic data on the snow depth on the past ten Christmas Eves? But how to best visualize spatial data? ... Read More
Der Beitrag Dreaming of a white Christmas – with ggmap in R erschien zuerst auf STATWORX.
HAPPY HOLIDAYS!!!🎉⛄🎆🍾❄
In the spirit of the coming new year and new beginnings, we created a tutorial for getting started or restarted with R. If you are new to R or have dabbled in R but haven’t used it much recently, then this post is for you. We will focus on data classes and types, as well as data wrangling, and we will provide basic statistics and basic plotting examples using real data. Enjoy!
By C.Wright
As with most programming tutorials, let’s start with a good’ol “Hello World”.
1) First Command
print(&quot;Hello World&quot;)
## [1] &quot;Hello World&quot;
2) Install and Load Packages and Data
Now we need some data. Packages are collections of functions and/or data. There are published packages that you can use from the community such as these two packages, or you can make your own package for your own private use.
install.packages(&quot;babynames&quot;) install.packages(&quot;titanic&quot;)
Now that we have installed the packages, we need to load them.
library(&quot;babynames&quot;)
library(&quot;titanic&quot;)
Each installation of R comes with quite a bit of data! Now we want to load the “quake” data - there are lots of other options.
data(&quot;quakes&quot;)
data() #this will list all of the datasets available
3) Assigning Objects
Objects can be many different things ranging from a simple number to a giant matrix, but they refer to things that you can manipulate in R.
myString
Deep Learning has revolutionized the fields of image classification, personal assistance, competitive board game play, and many more. However, the financial currency markets have been surprisingly stagnant. In our efforts to create a profitable and accurate trading model, we came upon the question: what if financial currency data could be represented as an image? The [...] continue reading »
Searching for Speed In my previous post, I described the method I use for compiling Fortran (or C) into an R package using the .Call interface. This post will compare the speed of various implementations of the layer loss cost function. The Function Often, insurance or reinsurance is bought in stratified horizontal layers. For example, Read the full article...
The post The Need for Speed Part 2: C++ vs. Fortran vs. C appeared first on Strange Attractors.
Phishing is [still] the primary way attackers either commit a primary criminal act (i.e. phish a target to, say, install ransomware) or is the initial vehicle used to gain a foothold in an organization so they can perform other criminal operations to achieve some goal. As such, security teams, vendors and active members of the... Continue reading →
For the last Riddler of the year I attempt to solve both the Express and Classic Riddlers!
Riddler Express: How Long Will it Take Santa to Place his Reindeer?
We need to find out how long it will take Santa to place his reindeer in the correct sled positions, if he proceeds at random. It takes a minute to harness a raindeer, and the raindeer will grunt to indicate it’s in the right position.
Thinking in terms of a probabiity tree, if we consider the first position in the sled, the expected time that it will take to place the raindeer correctly looks like this:
The expected time that it will take for the first raindeer placed correctly is the sum of all the paths leading to the green circles. For example:
$$
E[T_{8}] = \left (\frac{1}{8} \cdot 1 \right) + \left (\frac{7}{8} \cdot \frac{1}{7} \cdot 2 \right ) + \left (\frac{7}{8} \cdot \frac{6}{7} \cdot\frac{1}{6} \cdot 3 \right ) + …
$$
This process can be repeated for each of the eight deer, with the tree getting one branch shorter for each deer that has been correctly placed. The expected time to place all eight deer when then be the sum of the expected time for each deer:
$$
E[T]=\sum{i=1}^{8}E[T{i}]
$$
To generalize this problem to a sled with N deer, I coded the expected time in R as follows:
raindeer
October and December have been devastating for stocks. It wasn’t until Friday though that we officially reached the depths of a bear market. There are different theories, the most common is 20% pullback in an index. As readers of this blog are aware, I follow a slightly different definition, based on Jack Schannep’s work. Based […]
The post The Bear is Here appeared first on Quintuitive.
‘I can teach you in a minute…’
In a recent post I simulated some simple dice games and promised (or threatened) that this was the first of a series of posts about games of combined luck and chance. The main aim of that post was to show how even ...
This little helper does the same thing as the &quot;Find in files&quot; search within RStudio.
Der Beitrag Day 22 – little helper get_files erschien zuerst auf STATWORX.
Introduction
It is Christmas time! And what better time than this to write about the great tools that are available to all who like R and would like to publish their R work or even blog about it. This post is meant as a praise to the tools that are...
In some scenarios a data scientist may want to train a model for which there exists an abundance of observations, but only a small fraction of is labeled, making the sample size available to train the model rather small. Although there’s plenty of literature on the subject (e.g. “Active learning”, “Semi-supervised learning” etc) one may … Continue reading Does imputing model labels using the model predictions can improve it’s performance?
Week 16 Gold Mining and Fantasy Football Projection Roundup now available. Go get that free agent gold! The post Gold-Mining Week 16 (2018) appeared first on Fantasy Football Analytics.
This blog post is an excerpt of my ebook Modern R with the tidyverse that you can read for
free here. This is taken from Chapter 5, which presents
the {tidyverse} packages and how to use them to compute descriptive statistics and manipulate data.
In the text below, I show how you can use the {tidyverse} functions and principles for the
estimation of \(\pi\) using Monte Carlo simulation.
Going beyond descriptive statistics and data manipulation
The {tidyverse} collection of packages can do much more than simply data manipulation and
descriptive statisics. You can use the principles we have covered and the functions you now know
to do much more. For instance, you can use a few {tidyverse} functions to do Monte Carlo simulations,
for example to estimate \(\pi\).
Draw the unit circle inside the unit square, the ratio of the area of the circle to the area of the
square will be \(\pi/4\). Then shot K arrows at the square; roughly \(K*\pi/4\) should have fallen
inside the circle. So if now you shoot N arrows at the square, and M fall inside the circle, you have
the following relationship \(M = N*\pi/4\). You can thus compute \(\pi\) like so: \(\pi = 4*M/N\).
The more arrows N you throw at the square, the better approximation of \(\pi\) you’ll have. Let’s
try to do this with a tidy Monte Carlo simulation. First, let’s randomly pick some points inside
the unit square:
library(tidyverse)
library(brotools)
n
R 3.5.2, the latest version of the R language for statistical computation and graphics from the R Foundation, was released today. (This release is codenamed &quot;Eggshell Igloo&quot;, likely in reference to this or this Peanuts cartoon.) Compared to R 3.5.1, this update includes only bug fixes, so R scripts and packages compatible with R 3.5.0 or R 3.5.1 should work without modification. Installers for R on Windows and Debian Linux are available now; other platforms should be available from CRAN within the next few days. If you want to see the full list of fixes, check out the announcement from...
In September 2017, I found myself working on a project that required odds data for football. At the time I didn’t know about resources such as Football-Data or the odds-api, so I decided to build a scraper to collect data directly from the bookmakers...
I want to draw your attention to a very valuable (and short!) whitepaper from my colleague, Professor Andrew Ng, where he shares important insights on how to lead companies into the AI era. The five steps outlined in the paper are: Execute pilot projects to gain momentum Build an in-house AI team Provide broad AI … Continue reading &quot;Your AI journey… and Happy Holidays!&quot;
This little helper replaces non-standard characters (such as the German umlaut &quot;ä&quot;) with their standard equivalents (in this case &quot;ae&quot;).
Der Beitrag Day 20 – little helper char_replace erschien zuerst auf STATWORX.
A. Introduction The growth of Crossfit has been one of the biggest developments in the fitness industry over the past decade. Promoted as both a physical exercise philosophy and also as a competitive fitness sport, Crossfit is a high-intensity fitness program incorporating elements from several sports and exercise protocols such as high-intensity interval training, Olympic weightlifting, … Continue reading Examining the Tweeting Patterns of Prominent Crossfit Gyms →
Yet another boring barplot? No! I’ve asked my students from MiNI WUT to visualize some data about their favorite movies or series. Results are pretty awesome. Believe me or not, but charts in these posters are created with ggplot2 (most of them)! Star Wars Fan of StaR WaRs? Find out which color is the most … Czytaj dalej Data, movies and ggplot2
Condenado a estar toda la vida, preparando alguna despedida (Desarraigo, Extremoduro) I live just a few minutes from the Spanish National Museum of Science and Technology (MUNCYT), where I use to go from time to time with my family. The museum is plenty of interesting artifacts, from a portrait of Albert Einstein made with thousands … Continue reading Spinning Pins →
In this previous post, I showed how one can scrape top-level NBA game data from BasketballReference.com. In the post after that, I demonstrated how to scrape play-by-play data for one game. After writing those posts, I thought to myself: why … Continue reading →
by Hong Ooi, senior data scientist, Microsoft Azure A few weeks ago, I introduced the AzureR family of packages for working with Azure in R. Since then, I’ve also written articles on how to use AzureRMR to interact with Azure Resource Manager, how to use AzureVM to manage virtual machines, and how to use AzureContainers to deploy R functions with Azure Kubernetes Service. This article is the next in the series, and covers AzureStor: an interface to Azure storage. The Resource Manager interface: creating and deleting storage accounts AzureStor implements an interface to Azure Resource Manager, which you can use...
Shiny is a great tool for fast prototyping. When a data science team creates a Shiny app, sometimes it becomes very popular. From that point this app becomes a tool used on production by many people, that should be reliable and work fast for many concurrent users. There are many ways to optimize a Shiny app like […]
Article Alternative approaches to scaling Shiny with RStudio Shiny Server, ShinyProxy or custom architecture. comes from Appsilon Data Science | End­ to­ End Data Science Solutions.
So…you want to play a pRank with R? This short post will give you a fun function you can use in R to help you out! How to change a file’s modified time with R Let’s say we have a file, test.txt. What if we want to change the last modified date of the file […]
The post So you want to play a pRank in R…? appeared first on Open Source Automation.
vtreat‘s purpose is to produce pure numeric R data.frames that are ready for supervised predictive modeling (predicting a value from other values). By ready we mean: a purely numeric data frame with no missing values and a reasonable number of columns (missing-values re-encoded with indicators, and high-degree categorical re-encode by effects codes or impact codes). … Continue reading vtreat Variable Importance
This little helper is just a convenience function to set infinite values and NaNs to NA.
Der Beitrag Day 17 – little helper to_na erschien zuerst auf STATWORX.
Introduction Playing around with PyTorch and R Shiny resulted in a simple Shiny app where the user can upload a flower image, the system will then predict the flower species. Steps that I took Download labeled flower data from the … Continue reading →
In a project of developing PPNR balance projection models, I tried to use the Phillips-Ouliaris (PO) test to investigate the cointegration between the historical balance and a set of macro-economic variables and noticed that implementation routines of PO test in various R packages, e.g. urca and tseries, would give different results. After reading through the […]
This report describes several R packages that allow HTML content to be rendered as part of an R plot. The core package is called ‘layoutEngine’, ...
In a new paper in Monthly Weather Review, minimum CRPS and maximum likelihood estimation are compared for fitting heteroscedastic (or nonhomogenous) regression models under different response distributions. Minimum CRPS is more robust to dist...
In our last note we used wrapr::qe() to help quote expressions. In this note we will discuss quoting and code-capturing interfaces (interfaces that capture user source code) a bit more. My position on code-capturing interfaces (or non-standard-evaluation/NSE) is: if poorly handled, they can be a large interface price/risk to pay for the minor convenience of … Continue reading Quoting Concatenate
CategoriesBasic Statistics
Tags
Data Visualisation
Linear Regression
R Programming
Hope you liked the Part 1 ,Part 2 and Part 3 of this Series. In this Part 4, we will go through the tools used during the Improve phase of Six Sigma DMAIC cycle. The most representative tool used during the Improve Phase is DOE-Design of experiments. Proper use of DOE can lead to process […]Related Post
NYC buses: company level predictors with R
Visualizations for correlation matrices in R
Interpretation of the AUC
Simple Experiments with Smoothed Scatterplots
Understanding the Covariance Matrix
Our linl package for writing LaTeX letter with (R)markdown had a fairly minor release today, following up on the previous release well over a year ago. This version just contains one change which Mark van der Loo provided a few months ago with a clea...
I will be pushing a new version of futile.logger (version 1.5) to CRAN in January. This version introduces a number …Continue reading →
Have you ever thought about why the definition of a function in R is different from many other programming languages? The part that causes the biggest difficulties (especially for beginners of R) is that you state the name of the function at the beginning and use the assignment operator – as if functions were like … Continue reading &quot;Learning R: A gentle introduction to higher-order functions&quot;
This little helper returns a set of colors which we often use at STATWORX.
Der Beitrag Day 15 – little helper sci_palette erschien zuerst auf STATWORX.
In case you missed them, here are some articles from November of particular interest to R users. David Gerard assesses the plausibility of a key plot point in 'Jurassic Park' with simulations in R. In-database R is available in Azure SQL Database for private preview. Introducing AzureR, a new suite of R packages for managing Azure resources in R. The AzureRMR package provides an interface for Resource Manager. Roundup of AI, Machine Learning and Data Science news from November 2018. You can now use the AI capabilities of Microsoft Cognitive Services within a container you host. A look back at...
A new version of pdftools has been released to CRAN. Go get it while it’s hot:
install.packages(&quot;pdftools&quot;)
This version has two major improvements: low level text extraction and encoding improvements.
About PDF textboxes
A pdf document may seem to contain paragraphs or tables in a viewer, but this is not actually true. PDF is a printing format: a page consists of a series of unrelated lines, bitmaps, and textboxes with a given size, position and content. Hence a table in a pdf file is really just a large unordered set of lines and words that are nicely visually positioned. This makes sense for printing, but makes extracting text or data from a pdf file extremely difficult.
Because the pdf format has little semantic structure, the pdf_text() function in pdftools has to render the PDF to a text canvas, in order to create the sentences or paragraphs. It does so pretty well, but some users have asked for something more low level.
@opencpu any chance pdftext could return a data frame with one row for each text box with columns giving page, positions, and contents?— Hadley Wickham (@hadleywickham) December 26, 2017
Unfortunately this was not trivial because it required some work in the underlying poppler library. One year later, and this functionality is now finally available in the upcoming poppler version 0.73. The pdftools CRAN binary packages for Windows and MacOS already contain a suitable libpoppler, however Linux users probably have to wait for the latest version of poppler to become available in their system package manager (or compile from source).
Low-level text extraction
We use an example pdf file from the rOpenSci tabulizer package. This file contains a few standard datasets which have been printed as a pdf table. First let’s try the pdf_text() function, which returns a character vector of length equal to the number of pages in the file.
library(pdftools)
pdf_file
This little helper returns the folder structure of a given path. With this, one can for example add a nice overview to the documentation of a project or within a git.
Der Beitrag Day 14 – little helper print_fs erschien zuerst auf STATWORX.
Last weekend, I found out a way to run updated plots within a loop in R, when calling plot() within the loop was never updated in real time. The above suggestion of including a Sys.sleep(0.25) worked perfectly on a simulated annealing example for deter...
The second edition of my book ‘Deep Learning from first principles:Second Edition- In vectorized Python, R and Octave’, is now available on Amazon, in both paperback ($14.99)  and kindle ($9.99/Rs449/-)  versions. Since this book is almost 70% code, all functions, and code snippets have been formatted to use the fixed-width font ‘Lucida Console’. In addition … Continue reading My book ‘Deep Learning from first principles:Second Edition’ now on Amazon
Summary
Are you a data scientist who only wants to focus on modelling and coding and not on setting up a GPU cluster? Then, this blog might be interesting for you. We developed an automated pipeline using gitlab and Kubernetes that is able to run code ...
The Beta-Binomial model is the “hello world” of Bayesian statistics. That is, it’s the first model you get to run, often before you even know what you are doing. There are many reasons for this: It only has one parameter, the underlying pr...
Are multiple births on the increase?
My twin boys turned 5 years old today. Wow, time flies. Life is never dull, because twins are still seen as something of a novelty, so wherever we go, we find ourselves in conversation with strangers, who...
Pipelines in R are popular, the most popular one being magrittr as used by dplyr. This note will discuss the advanced re-usable piping systems: rquery/rqdatatable operator trees and wrapr function object pipelines. In each case we have a set of objects designed to extract extra power from the wrapr dot-arrow pipe %.__%. Piping Piping is … Continue reading Reusable Pipelines in R
As referenced in my last blog post, useR Delhi NCR is all set to host our second meetup on 15th December, i.e. upcoming Saturday. We’ve finalized two exciting speaker sessions for the same. They’re as follows: Basics of Shiny and …
Week 15 Gold Mining and Fantasy Football Projection Roundup now available. Go get that free agent gold! The post Gold-Mining Week 15 (2018) appeared first on Fantasy Football Analytics.
This little helper reads in multiple files of the same type and combines them into a data.table.
Der Beitrag Day 13 – little helper read_files erschien zuerst auf STATWORX.
Since about two weeks, I face a large additional traffic jam every morning due to a construction site on the road. When passing the construction site, often only few people or sometimes nobody seems to be working there. Being an economist, I really won...
For each NBA game, nba.com has a really nice graphic which tracks the point differential between the two teams throughout the game. Here is the lead tracker graphic for the game between the LA Clippers and the Phoenix Suns on … Continue reading →
Visualizing Hurricane Data with Shiny Motivation for Project Around the time that I was selecting a topic for this project, my parents and my hometown found themselves in the path of a Category 1 hurricane. Thankfully, everyone was ok, and there was only minor damage to their property. But this event made me think about […]
Data Visualization: A Practical Introduction will begin shipping next week. I’ve written an R package that contains datasets, functions, and a course packet to go along with the book. The socviz package contains about twenty five datasets and a number of utility and convenience functions. The datasets range in size from things with just a few rows (used for purely illustrative purproses) to datasets with over 120,000 observations, for practicing with and exploring.
A course packet is also included the package. This is a zipped file containing an R Studio project consisting of nine R Markdown documents that parallel the chapters in the book. They contain the code for almost all the figures in the book (and a few more besides). There are also some additional support files, to help demonstrate things like reading in your own data locally in R.
Installing the package
To install the package, you can follow the instructions in the Preface to the book. Alternatively, first download and install R for MacOS, Windows or Linux, as appropriate. Then download and install RStudio. Launch RStudio and then type the following code at the Console prompt (__), hitting return at the end of each line:
my_packages
So, after having held workshops introducing Bayes for a couple of years now, I finally pulled myself together and completed my DataCamp course: Fundamentals of Bayesian Data Analysis in R! 😁
While it’s called a course, it’s more like a 4 ...
It's easy to create a function in R, but what if you want to call that function from a different application, with the scale to support a large number of simultaneous requests? This article shows how you can deploy an R fitted model as a Plumber web service in Kubernetes, using Azure Container Registry (ACR) and Azure Kubernetes Service (AKS). We use the AzureContainers package to create the necessary resources and deploy the service. Fit the model We’ll fit a simple model for illustrative purposes, using the Boston housing dataset (which ships with R in the MASS package). To make...
This little helper is a debug function if you do not work with RStudio.
Der Beitrag Day 12 – little helper dive erschien zuerst auf STATWORX.
Reusable modeling pipelines are a practical idea that gets re-developed many times in many contexts. wrapr supplies a particularly powerful pipeline notation, and a pipe-stage re-use system (notes here). We will demonstrate this with the vtreat data preparation system. Our example task is to fit a model on some arbitrary data. Our model will try … Continue reading Sharing Modeling Pipelines in R
Two years ago, we revealed something sensational: Santa Claus is a data scientist. The secret of his success does not lay in his hundred years of experience as a gift…
A new Le Monde mathematical puzzle in the digit category: Find the largest number such that each of its internal digits is strictly less than the average of its two neighbours. Same question when all digits differ. For instance, n=96433469 is such a number. When trying pure brute force (with the usual integer2digits function!) le=solz=3 […]
by Mark Niemann-Ross, an author, educator, and writer who teaches about R and Raspberry Pi at LinkedIn Learning I spend a LOT of time at r-project.org, in particular the sections for documentation and CRAN. But I hadn’t spent much time in the other areas: R Project, R Foundation, and links. When I recently wandered into the foundation area, I discovered something worth talking about: donations to the R Foundation. I benefit from the work of the R Foundation. They oversee the language, but also encourage a healthy ecosystem. CRAN happens because of them. Updates to R happen because of them....
I have several tables that I would like to load as a sole data frame. Derived functions from read. table () have a lot of convenient features, but it seems like there is a lot of steps in the implementation that would slow things down. The gain in pe...
It’s mid-September and you’re wandering around your preferred supermarket when you stumble across the Christmas section. “Already”, you think. “It wasn’t like this back when I was a kid”. Well, with the power of data science maybe, just maybe, we can definitively say whether Christmas does indeed start earlier every year. Anyone involved in marketing (or who wants to know how much their name gets googled) has probably checked out Google Trends. The main feature on offer is the ability…
A new version 0.4.7 of RQuantLib reached CRAN and Debian. Following up on the recent 0.4.6 release post which contained a dual call for help: RQuantLib was (is !!) still in need of a macOS library build, but also experienced issues on Windows.
Since ...
I’m a casual NBA fan: I don’t have time to watch the games but enjoy viewing the highlights on Instagram/Youtube (especially Shaqtin’ A Fool!); I sometimes read game articles and analyses (e.g. Blogtable). Apart from the game being an amazing … Continue reading →
This little helper removes leading and trailing whitespaces from a string. With R version 3.5.1 trimws was introduced, which does the exact same thing. This just shows, it was not a bad idea to write such a function.
Der Beitrag Day 11 – little helper trim erschien zuerst auf STATWORX.
As a follow-up of my previous study of Australian politicians on Twitter I’ve decided to build a more sophisticated, autonomous solution. The idea at glance: Collect regularly tweets from Members of Australian Parliament Store them in the database Visualize findings (in up-to-date state) in web dashboard A goal here is… The post DB connected R application on open-source Shiny server, part 1 appeared first on Alex Levashov - eCommerce Consultant (Melbourne, Australia).
You've estimated a GLM or a related model (GLMM, GAM, etc.) for your latest paper and, like a good researcher, you want to visualise the model and show the uncertainty in it. In general this is done using confidence intervals with typically 95% converage. If you remember a little bit of theory from your stats classes, you may recall that such an interval can be produced by adding to and subtracting from the fitted values 2 times their standard error. Unfortunately this only really works like this for a linear model. If I had a dollar (even a Canadian one) for every time I've seen someone present graphs of estimated abundance of some species where the confidence interval includes negative abundances, I'd be rich! Here, following the rule of &quot;if I'm asked more than once I should write a blog post about it!&quot; I'm going to show a simple way to correctly compute a confidence interval for a GLM or a related model.
Great post Yash! For those readers interested in getting data from the fitbit API using R I’ve documented the process here: https://towardsdatascience.com/the-gamification-of-fitbit-how-an-api-provided-the-next-level-of-training-eaf7b267af00
The post 5½ Reasons to Ditch Spreadsheets for Data Science: Code is Poetry appeared first on The Lucid Manager.
When I studied civil engineering some decades ago, we solved all our computing problems by writing code. Writing in BASIC or PASCAL, I could quickly perform fundamental engineering analysis, such as reinforced concrete beams, with my home-brew software library. Soon after I started my career, spreadsheets became widely available, and I fully embraced this fantastic … Continue reading &quot;5½ Reasons to Ditch Spreadsheets for Data Science: Code is Poetry&quot;
The post 5½ Reasons to Ditch Spreadsheets for Data Science: Code is Poetry appeared first on The Lucid Manager.
This post will go through 10 different Linux commands and their R alternatives. If you’re interested in learning more R functions for working with files like some of those below, also check out this post. How to list all the files in a directory Linux R What does it do? ls list.files() Lists all the […]
The post 10 R functions for Linux commands and vice-versa appeared first on Open Source Automation.
We’ll be back with Season 2 early in 2019 and to keep you thinking, curious and data focused in between seasons, we’re having a DataFramed challenge. The winner will get to join me on a segment here on DataFramed: the challenge is to listen to as m...
CategoriesRegression Models
Tags
ggplot2
Machine Learning
Prediction
R Programming
In this post, the failure pressure will be predicted for a pipeline containing a defect based solely on burst test results and learning machine models. For this purpose, various Machine Learning models will be fitted to test data under R using the caret package, and in the process compare the accuracy of the models in […]Related Post
Machine learning logistic regression for credit modelling in R
Commercial data analytics: An economic view on the data science methods
Weight loss in the U.S. – An analysis of NHANES data with tidyverse
Machine Learning Results in R: one plot to rule them all! (Part 2 – Regression Models)
Story of pairs, ggpairs, and the linear regression
On December 9 2008, very nearly ten years ago, the first post on Revolutions was published. Way back then, this blog was part of a young startup called Revolution Computing, which later became Revolution Analytics. (That name persists to this day in the URL of this blog.) The idea at that time was to introduce the world to a wonderful but little-known statistics environment called R, by sharing news and applications of R to the broader data analysis community, along with tips and tricks for those that had already discovered R. Since then, this blog has seen several waves shaking...
Previously in this series:
The “lost boarding pass” puzzle
The “deadly board game” puzzle
I’ve recently been enjoying The Riddler: Fantastic Puzzles from FiveThirtyEight, a wonderful book from 538’s Oliver Roeder. Many of the probab...
After living for more than a year on GitHub, we are pleased to announce that the {remedy} package is now on CRAN.  A package for easier Markdown writing About {remedy} Have you ever been frustrated about having to manually add markdown tags to your Rmd? If you are an avid markdown user, no doubt you’ve been complaining about what should be an easy task: adding bold, creating lists, urls, italics… Good news, we’ve got the cure! {remedy} is a package designed to facilitate writing markdown documents in RStudio. The idea is to provide a series of keyboard shortcuts that will make you more efficient withRead More →
The post {remedy} is now on CRAN appeared first on (en) The R Task Force.
The ggmap tutorial has been updated to address the Google Static Map API service issues.
We at STATWORX work a lot with R and we often use the same little helper functions within our projects. These functions ease our daily work life by reducing repetitive code parts or by creating overviews of our projects. At first, there was no plan to make a package, but soon I realised, that it will be much easier to ... Read More
Der Beitrag Day 10 – little helper %nin% erschien zuerst auf STATWORX.
Searching for Speed Everyone who has ever used R has, at one time or another, wished for an increase in R’s speed. If you haven’t, you’re not using R hard enough! Recently, as part of some research on credibility, I was calculating layer loss costs for millions of simulated loss observations. As I progressed, the Read the full article...
The post The Need for Speed Part 1: Building an R Package with Fortran (or C) appeared first on Strange Attractors.
New minor release of the R/exams package to CRAN, containing a range of smaller improvements and bug fixes. Notably scanning of written NOPS exams is enhanced and made more reliable and a new exercise template demonstrates how to use advanced...
I will run an 8-hour course on R and Data Mining at Black Mountain, CSIRO, Australia on 10 &amp; 13 December 2018. The course materials, incl. slides, R scripts and datasets, are available at http://www.rdatamining.com/training/course. Below is outline of the … Continue reading →
Exploratory data analysis is important, everybody knows that. With R, it is also easy. Below you see three lines of code that allow you to interactively explore the Preston Curve, the prominent association of country level real income per capita with life expectancy.
install.packages(&quot;ExPanDaR&quot;)
library(ExPanDaR)
wb
I taught my Data Visualization seminar in Philadelphia this past Friday and Saturday. It covers most of the content of my book, including a unit on making maps. The examples in the book are from the United States. But what about other places? Two of the participants were from Canada, and so here’s an example that walks through the process of grabbing a shapefile and converting it to a simple-features object for use in R. A self-contained R project with this code is available on GitHub.
Mapping data (and, more importantly, converting map data into a format that we can tidily use) requires a bit more heavy lifting behind the scenes than the core tidyverse libraries provide. We start by (if necessary) installing the sf library, which will also bring a number of dependencies with it. We’ll also install a few packages that provide some functions we might use in the conversion and mapping process.
install.packages(&quot;sf&quot;)
install.packages(&quot;rgdal&quot;)
install.packages(&quot;geojsonio&quot;)
install.packages(&quot;spdplyr&quot;)
install.packages(&quot;rmapshaper&quot;)
Then we load the packages, along with others we’ve already installed (as part of the seminar).
library(geojsonio)
library(rmapshaper)
library(rgdal)
library(tidyverse)
library(spdplyr)
library(sf)
library(socviz)
We make a function, theme_map(), that will be our ggplot theme. It consists mostly in turning off pieces of the plot (like axis text and so on) that we don’t need.
theme_map
We at STATWORX work a lot with R and we often use the same little helper functions within our projects. These functions ease our daily work life by reducing repetitive code parts or by creating overviews of our projects. At first, there was no plan to make a package, but soon I realised, that it will be much easier to ... Read More
Der Beitrag Day 09 – little helper object_size_in_env erschien zuerst auf STATWORX.
Exercise template for computing the 2-sided confidence interval (with extended Moodle processing) for the mean based on a random sample.
Name:
confint3
Type:
cloze
Related: confint2 Description:
Computing the 2-sided confidence interval at 95% level for the mean based on a random sample. The exercise is a cloze with two numeric answers for the lower and upper bound of the confidence interval, respectively. Using the 'verbatim' clozetype for Moodle, the exercises yields 100% of the points for the correct solution based on t quantiles but still 50% for a partially correct solution based on normal quantiles.
Solution feedback:
Yes
Randomization:
Random numbers
Mathematical notation:
Yes
Verbatim R input/output:
No
Images:
No
Other supplements:
No
Template:
confint3.Rnw
confint3.Rmd
Raw: (1 random version)
confint3.tex
confint3.md
PDF:
HTML:
(Note that the HTML output contains mathematical equations in MathML. It is displayed by browsers with MathML support like Firefox or Safari - but not Chrome.)
Demo code:
library(&quot;exams&quot;)
set.seed(1090)
exams2html(&quot;confint3.Rnw&quot;)
set.seed(1090)
exams2pdf(&quot;confint3.Rnw&quot;)
set.seed(1090)
exams2html(&quot;confint3.Rmd&quot;)
set.seed(1090)
exams2pdf(&quot;confint3.Rmd&quot;)
This note is a comment on some of the timings shared in the dplyr-0.8.0 pre-release announcement. The original published timings were as follows: With performance metrics: measurements are marketing. So let’s dig in the above a bit. These timings are of the kind of small task large number of repetition breed that Matt Dowle writes … Continue reading Timing Grouped Mean Calculation in R
Introduction
In this post in the R:case4base series we will examine sorting (ordering) data in base R. We will learn to sort our data based on one or multiple columns, with ascending or descending order and as always look at alternatives to base R, namely the tidyverse’s dplyr and data.table to show how we can achieve the same results.
It is recommended to first have a look at the post on subsetting to understand the concepts underlying the sorting process in more detail.
Contents
Introduction
How to use this article
Subsetting as a mechanism for sorting data
Sorting data by contents of a column
Sorting by multiple vectors with different order
Alternatives to base R
Using the tidyverse
Using data.table
Quick benchmarking
TL;DR - Just want the code
References
How to use this article
This article is best used with an R session opened in a window next to it - you can test and play with the code yourself instantly while reading. Assuming the author did not fail miserably, the code will work as-is even with vanilla R, no packages or setup needed - it is a case4base after all!
If you have no time for reading, you can click here to get just the code with commentary
First, let’s read in yearly data on gross disposable income of household in the EU countries into R (click here to download):
gdi
CategoriesProgramming
Tags
Data Visualisation
R Markdown
R Programming
In this article, you learn how to make Automated Dashboard visualizations with distribution in R. First you need to install the `rmarkdown` package into your R library. Assuming that you installed the `rmarkdown`, next you create a new `rmarkdown` script in R. After this you type the following code in order to create a dashboard […]Related Post
Automated Dashboard Visualizations with Ranking in R
Automated Dashboard visualizations with Deviation in R
Automated Dashboard with various correlation visualizations in R
Automated Dashboard with Visualization and Regression for Healthcare Data
Create easy automated dashbords with R and Markdown
… this week that I made a first cameo in the debian/changelog for the Debian R package:
r-base (0.63.1-1) unstable; urgency=low
New upstream release
Linked html directory to /usr/doc/r-base/doc/html (Dirk Eddelbuettel)
– Douglas Bates bates@st...
We at STATWORX work a lot with R and we often use the same little helper functions within our projects. These functions ease our daily work life by reducing repetitive code parts or by creating overviews of our projects. At first, there was no plan to make a package, but soon I realised, that it will be much easier to ... Read More
Der Beitrag Day 08 – little helper intersect2 erschien zuerst auf STATWORX.
TLDR: Begin with N of 10, increase by 10 until p __ 0.05 or max N reached. This design has inflated type-I error. Lower p-value threshold needed to ensure specified type-I error rate. The number of interim analyses and max N affect the type-I error rate. Threshold can be identified using simulation. A recent Facebook … Continue reading &quot;Increase sample size until statistical significance is reached&quot; is not a valid adaptive trial design; but it's fixable. →
CategoriesProgramming
Tags
Data Visualisation
R Markdown
R Programming
In this article, you learn how to make Automated Dashboard Visualizations with Ranking in R. First you need to install the `rmarkdown` package into your R library. Assuming that you installed the `rmarkdown`, next you create a new `rmarkdown` script in R. After this you type the following code in order to create a dashboard […]Related Post
Automated Dashboard visualizations with Deviation in R
Automated Dashboard with various correlation visualizations in R
Automated Dashboard with Visualization and Regression for Healthcare Data
Create easy automated dashbords with R and Markdown
Send Desktop Notifications from R in Windows, Linux and Mac
We at STATWORX work a lot with R and we often use the same little helper functions within our projects. These functions ease our daily work life by reducing repetitive code parts or by creating overviews of our projects. At first, there was no plan to make a package, but soon I realised, that it will be much easier to ... Read More
Der Beitrag Day 07 – little helper count_na erschien zuerst auf STATWORX.
Many of our projects involve getting doctors, nurses, and medical students to collect data on the patients they are looking after. We want to involve many of them in data analysis, without the requirement for coding experience or access to statistical software. To achieve this we have built Shinyfit, a shiny app for linear, logistic, … Continue reading &quot;Shinyfit: Advanced regression modelling in a shiny app&quot;
Time really does fly. It’s been 5 months since Delhi NCR useR group had come into being and our first meetup. It was a successful event which included sessions featuring an R-core member and a veteran data scientist. More importantly, …
Bayesian nonparametrics in NIMBLE: Nonparametric random effects Overview NIMBLE is a hierarchical modeling package that uses nearly the same language for model specification as the popular MCMC packages WinBUGS, OpenBUGS and JAGS, while making the modeling language extensible — you can add distributions and functions — and also allowing customization of the algorithms used to […]
Principal component analysis (PCA) is a dimension-reduction method that can be used to reduce a large set of (often correlated) variables into a smaller set of (uncorrelated) variables, called principal components, which still contain most of the information. PCA is a concept that is traditionally hard to grasp so instead of giving you the n’th … Continue reading &quot;Intuition for principal component analysis (PCA)&quot;
CategoriesProgramming
Tags
Data Visualisation
R Markdown
R Programming
Tips &amp; Tricks
In this article, you learn how to make Automated Dashboard visualizations with Deviation in R. First you need to install the `rmarkdown` package into your R library. Assuming that you installed the `rmarkdown`, next you create a new `rmarkdown` script in R. After this you type the following code in order to create a dashboard […]Related Post
Automated Dashboard with various correlation visualizations in R
Automated Dashboard with Visualization and Regression for Healthcare Data
Create easy automated dashbords with R and Markdown
Send Desktop Notifications from R in Windows, Linux and Mac
CHAID vs. ranger vs. xgboost — a comparison
In this post I will show you how to run an R script on heroku every day. This is a continuation of my previous post on tweeting a death from wikidata.
Why would I want to run a script on heroku?
It is extremely simple, you don’t need to spin up a machine in the cloud on AWS, Google, Azure or Nerdalize. You can just run the script and it works. You can even make it run every day. Heroku does not support R out of the box. So you have to tell heroku to install R (they call that a buildpack).
What do you need?
I combined information from this medium post by Dave Quartey and the description of the buildpack that Dave refers to in that post.
For this to work you need two scripts:
A init.R script that installs the packages and sets up the machine
The script that you want to run
Heroku works a bit like github.
You download and install the heroku command line interface (CLI) and then you can tell heroku what to do, and what scripts need to be moved to heroku.
Setting up your project
I’m assuming you’re starting out in a fresh
folder with only your script in it.
Create an heroku account.
Download and install the heroku CLI
do ‘heroku login’ you make a round trip to the website to verify it is really you
Take your script, look what packages it needs, write those packages down.
Make a script called init.R and modify
it to install packages:
my_packages
This past weekend was the 9th JapanR Conference hosted at LINE
Corporation in Tokyo, Japan!
I’ve been back in Japan for nearly a year now and I’ve been going to
nearly every one of the R user meetups here,
TokyoR, and it’s ...
Many (if not most) tech communities have far more representation from men than from women (and even fewer from nonbinary folk). This is a shame, because everybody uses software, and these projects would self-evidently benefit from the talent and expertise from across the entire community. Some projects are doing better than others, though, and data scientist Reshama Shaikh recently published an in-depth comparison of the representation of women in the R any Python communities. Shaikh's analysis draws from several data sources, which provide evidence that women are better represented in the R community in the Python community. These include: The...
We at STATWORX work a lot with R and we often use the same little helper functions within our projects. These functions ease our daily work life by reducing repetitive code parts or by creating overviews of our projects. At first, there was no plan to make a package, but soon I realised, that it will be much easier to ... Read More
Der Beitrag Day 06 – little helper statusbar erschien zuerst auf STATWORX.
Sometimes it’s useful to be able to extract data from a published figure. If the figure isn’t a vector based format (for which the numeric data is probably still in the file), it’s possible to digitize the image with R, click the points and extract it that way. The digitize package is simple to use […]
A problem with R is that its tables are not good enough to share with non-R users, both in terms of visual attractiveness and ease...
Here you find my slides from last nights TWiML &amp; AI EMEA Meetup about Trust in ML models, where I presented the Anchors paper by Carlos Guestrin et al..
I have also just written two articles for the German IT magazin iX about the same topic of...
CategoriesProgramming
Tags
Correlation
Data Visualisation
R Programming
In this article, you learn how to make Automated Dashboard with various correlation visualizations in R. First you need to install the `rmarkdown` package into your R library. Assuming that you installed the `rmarkdown`, next you create a new `rmarkdown` script in R. After this you type the following code in order to create a […]Related Post
Automated Dashboard with Visualization and Regression for Healthcare Data
Create easy automated dashbords with R and Markdown
Send Desktop Notifications from R in Windows, Linux and Mac
CHAID vs. ranger vs. xgboost — a comparison
Common Mistakes to Avoid When Learning to Code in Python
The ggQC package is a quality control extension for ggplot. Use it to create XmR, XbarR, C and many other highly customizable Control Charts. Additional statistical process control functions include Shewart violation checks as well as capability analysis. If your process is running smoothly, visualize the potential impacted of your next process improvement with a Pareto chart. To learn more, read on! To get started with ggQC, install it from CRAN by running the following code: install.packages(&quot;ggQC&quot;) ggQC Control Charts Control charts are a great way to monitor process outputs, drive improvement, and evaluate measurement systems. The types of control chart types supported by ggQC include: Individuals Charts : mR, XmR Attribute Charts : c, np, p, u Studentized Charts: xBar.rBar, xBar.rMedian, xBar.sBar, xMedian.rBar, xMedian.rMedian Dispersion Charts: rBar, rMedian, sBar The process for building control charts with ggQC is simple. First, load the ggQC and ggplot2 libraries. Next, load your data into R. Your data should be in long-form. The data set below provides an example of long form data if you're not familiar with the term. Finally, make your control chart using standard ggplot layer-by-layer syntax and the stat_QC() command. The example code below, shows how all these steps come together to make an XmR plot. ### Load the Needed Libraries library(ggplot2) library(ggQC) ### Make up some demo data (load your file here instead) set.seed(5555) Process_Data
The post ggQC | ggplot Quality Control Charts – New Release appeared first on R-BAR.
We at STATWORX work a lot with R and we often use the same little helper functions within our projects. These functions ease our daily work life by reducing repetitive code parts or by creating overviews of our projects. At first, there was no plan to make a package, but soon I realised, that it will be much easier to ... Read More
Der Beitrag Day 05 – little helper get_network erschien zuerst auf STATWORX.
Bayesian Nonparametric Models in NIMBLE, Part 1: Density Estimation Bayesian nonparametrics in NIMBLE: Density estimation Overview NIMBLE is a hierarchical modeling package that uses nearly the same language for model specification as the popular MCMC packages WinBUGS, OpenBUGS and JAGS, while making the modeling language extensible — you can add distributions and functions — and […]
This past year in R has been a good one for me; productive, exciting, different. I decided it was worth taking a moment to reflect and share. Goals were set and met. There were also unexpected changes.
CRAN
A year ago, at the tail end of 2017, I pu...
As part of the run-up to the release of Data Visualization (out in about ten days! Currently 30% off on Amazon!), I’ve been playing with graphing different kinds of data. One great source of rich time-series data is mortality.org, which hosts a collection of standardized demographic data for a large number of countries. Mortality rates are often interesting to look at as a heatmap, as we get data for a series of ages (e.g., mortality rates) over some time period. If we make a grid with time on the x-axis and age on the y-axis, we can fill in the boxes with a color representing the rate. A side-effect of that sort of representation is that the diagonals track age cohorts from the bottom left to the upper right of the graph, as people age at the rate of one year per year.
Available mortality data for England and Wales runs back to 1841. Here’s sample code for a plot of that for males:
p
In this article, I hope to inspire you to start exploring satellite imagery datasets. Recently, this technology has gained huge momentum, and we are finding that new possibilities arise when we use satellite image analysis. Satellite data changes the game because it allows us to gather new information that is not readily available to businesses. […]
Artykuł Deep learning in Satellite imagery pochodzi z serwisu Appsilon Data Science | End­ to­ End Data Science Solutions.
Our recent addition to the NLP R universe is called R package ruimtehol which is open sourced at https://github.com/bnosac/ruimtehol This R package is a wrapper around Starspace which provides a neural embedding model for doing the following on text:
Text classification
Learning word, sentence or document level embeddings
Finding sentence or document similarity
Ranking web documents
Content-based recommendation (e.g. recommend text/music based on the content)
Collaborative filtering based recommendation (e.g. recommend text/music based on interest)
Identification of entity relationships
If you are an R user and are interested in NLP techniques. Feel free to test out the framework and provide feedback at https://github.com/bnosac/ruimtehol/issues. The package is not on CRAN yet, but can be installed easily with the command devtools::install_github(&quot;bnosac/ruimtehol&quot;, build_vignettes = TRUE).
Below is an example how the package can be used for multi-label classification on questions asked in Belgian parliament. Each question in parliament was labelled with several of one of the 1785 categories.
library(ruimtehol)data(dekamer, package = &quot;ruimtehol&quot;)## Each question in parliament was labelled with more than 1 category. There are 1785 categories in this datasetdekamer$question_themes
We at STATWORX work a lot with R and we often use the same little helper functions within our projects. These functions ease our daily work life by reducing repetitive code parts or by creating overviews of our projects. At first, there was no plan to make a package, but soon I realised, that it will be much easier to ... Read More
Der Beitrag Day 04 – little helper evenstrings erschien zuerst auf STATWORX.
Our group has done a lot of work with non-standard calling conventions in R. Our tools work hard to eliminate non-standard calling (as is the purpose of wrapr::let()), or at least make it cleaner and more controllable (as is done in the wrapr dot pipe). And even so, we still get surprised by some of … Continue reading Very Non-Standard Calling in R
This post will review Kris Boudt’s datacamp course, along with introducing some concepts from it, discuss GARCH, present an application … Continue reading →
Introduction The Forgotten Age cycle of Arkham Horror is at a close and Fantasy Flight Games already announced the next cycle, The Circle Undone. Not only that, they’ve announced two mythos packs at a rate that… surprised me. A new cycle announcement and two mythos pack announcements in less than two months? Am I the…Read more Making a Profit with Henry Wan in Arkham Horror: The Card Game
This is the next article in my series on AzureR, a family of packages for working with Azure in R. I’ll give a short introduction on how to use AzureVM to manage Azure virtual machines, and in particular Data Science Virtual Machines (DSVMs). Creating a VM Creating a VM is as simple as using the create_vm method, which is available as part of the az_subscription and az_resource_group classes. library(AzureRMR) library(AzureVM) ## using the subscription method sub
On 28 November 2018 I presented a poster at Dutch Demography Day in Utrecht. Here it is:
We at STATWORX work a lot with R and we often use the same little helper functions within our projects. These functions ease our daily work life by reducing repetitive code parts or by creating overviews of our projects. At first, there was no plan to make a package, but soon I realised, that it will be much easier to ... Read More
Der Beitrag Day 03 – little helper multiplot erschien zuerst auf STATWORX.
In all monotonic algorithms that I posted before, I heavily relied on the smbinning::smbinning.custom() function contributed by Herman Jopia as the utility function generating the binning output and therefore feel deeply indebted to his excellent work. However, the availability of smbinning::smbinning.custom() function shouldn’t become my excuse for being lazy. Over the weekend, I drafted a […]
In enterprise environment, we generally need to automate the process of installing multiple R packages so that user does not have to install them separately before submitting your program.
The function below performs the following operations -
First it finds all the already installed R packages
Check packages which we want to install are already installed or not.
If package is already installed, it does not install it again.
If package is missing (not installed), it installs the package.
Loop through steps 2, 3 and 4 for multiple packages we want to install
Load all the packages (both already available and new ones).
Install_And_Load
This report describes three different approaches to communicating between R and MetaPost: importing the PostScript output from MetaPost with the ‘grImport’ package; calling the mpost ...
My suggestions for papers to read during December:Askanazi, R., F. X. Diebold, F. Schorfheide, &amp; M. Shin, 2018. On the comparison of interval forecasts. PIER Working Paper 18-013, Penn. Institute for Economic Research, University of Pennsylvania.Me...
There are literally hundreds of programming languages out there, e.g. the whole alphabet of one letter programming languages is taken. In the area of data science there are two big contenders: R and Python. Now why is this blog about R and not Python? I have to make a confession: I really wanted to like … Continue reading &quot;Why R for data science – and not Python?&quot;
What is homology? Proteins are conserved bio molecules present in all organisms. Some proteins are conserved in many similar species, making them homologues of each other, meaning that the sequence is not the same but there is a degree of similarity. This is measured as homology in percentage. Histone H1 homology in mammals (https://en.wikipedia.org/wiki/Sequence_homology) What … Continue reading How to get the homology of a antibody using R
We at STATWORX work a lot with R and we often use the same little helper function within our projects. These functions ease our daily work life by reducing repetitive code parts or by creating overviews of our projects. At first, there was no plan to make a package, but soon I realised, that it will be much easier to ... Read More
Der Beitrag Day 02 – little helper na_omitlist erschien zuerst auf STATWORX.
I used the Thanksgiving break to push a new update of the TSstudio package to CRAN (version 0.1.3). The new version includes an update for the ts_backtesting function along with two new function - ts_to_prophet for converting time series objects to a prophet input format (i.e., ds and y columns), and ccf_plot for lags plot between two time series. The package can be installed from either CRAN or Github:
# CRAN
install.packages(&quot;TSstudio&quot;)
# Github
# install.packages(&quot;devtools&quot;)
devtools::install_github(&quot;RamiKrispin/TSstudio&quot;)
library(TSstudio)
packageVersion(&quot;TSstudio&quot;)
## [1] '0.1.3'
Converting time series object to a prophet format
The ts_to_prophet function converting ts, xts and zoo objects into prophet input format (i.e., data frame with two columns - ds for date and y for the series values). For instance, convertig the USgas series to a prophet object:
data(&quot;USgas&quot;)
ts_info(USgas)
## The USgas series is a ts object with 1 variable and 225 observations
## Frequency: 12 ## Start time: 2000 1 ## End time: 2018 9
USgas_prophet
This is 3rd part of series about working with Magento 2 REST API in R. If you haven’t read previous posts in this series, I would recommend to do it. This article sample use the functions defined in previous posts. You may find them at R plus Magento 2 REST API… The post R plus Magento 2 REST API revisited: part 3 – more complex samples of use appeared first on Alex Levashov - eCommerce Consultant (Melbourne, Australia).
CategoriesAdvanced Modeling
Tags
Data Management
Data Visualisation
R Programming
We are continuing on with our NYC bus breakdown problem. When we left off, we had constructed a rule-based Cubist regression model with our expanded pool of predictors; but we were still only managing to explain 37% of the data's variance with our model. Given how 'dirty' the target variable 'time_delayed' is (because it is […]Related Post
NYC buses: Cubist regression with more predictors
NYC buses: simple Cubist regression
Visualization of NYC bus delays with R
Explaining Black-Box Machine Learning Models – Code Part 2: Text classification with LIME
Image clustering with Keras and k-Means
Hopefully, one’s coding habits are constantly improving. If you feel any doubt about yourself, I suggest looking back at something you wrote 2011. One thing I’ve changed recently that made my life so much better is a simple silly thing: meaningful name for index and counter variables. Take a look at these pieces of fake […]
We at STATWORX work a lot with R and we often use the same little helper function within our projects. These functions ease our daily work life by reducing repetitive code parts or by creating overviews of our projects. At first, there was no plan to make a package, but soon I realised, that it will be much easier to ... Read More
Der Beitrag Day 01 – little helper checkdir erschien zuerst auf STATWORX.
At the NHS R conference we delivered a session on animating patient flow. This started with a single plot showing all patient movements, and then I demonstrated the ability to create a faceted plot.
But, with many different areas, and a smal...
From the last post, Vancouver has several common genus in its collection, such as Prunus and Acer. So rather than analyzing the diversity of Vancouver tree species on a species level, we could, with the help of some R packages, visualize the variety of taxonomic groups.
First, we format the Vancouver tree dataset similar to before to get the proper scientific name. This is the same formatting done in the previous post.
library(dplyr)
library(purrr)
library(tidyr)
# read in trees shp
# read in trees df
trees_df
Motivation
A couple of weeks back, Branko Milanovic asked on Twitter :
“Does anyone know a link to a calculation on how many people were born … in the entire 20th century?”
Somewhat surprisingly, no-one did. However, there was a calculatio...
So it turns out that the 1990 Michael Crichton novel Jurassic Park is, indeed, a work of fiction. (Personal note: despite the snark to follow, the book is one of my all-time favorites — I clearly remember devouring it in 24 hours straight while ill in a hostel in France.) If the monsters and melodrama didn't give it away, then this chart seals the deal: Not the most glaring issue with #JurassicPark, but this plot from Michael Crichton's original 1990 novel looks pretty darn weird. /1 pic.twitter.com/8dQujwIVNf — David Gerard (@EmpiricalDave) November 29, 2018 That chart seemed like a clever...
CategoriesAdvanced Modeling
Tags
Data Management
Linear Regression
R Programming
We have previously added a set of company identity-agnostic predictors, such as the number of drivers a company employs, or the number of vehicles in the fleet with a hydraulic lift, and so on. we took this approach, rather than having each company as a unique predictor, so that the addition of a new contractor […]Related Post
NYC buses: simple Cubist regression
Visualization of NYC bus delays with R
Explaining Black-Box Machine Learning Models – Code Part 2: Text classification with LIME
Image clustering with Keras and k-Means
‘How do neural nets learn?’ A step by step explanation using the H2O Deep Learning algorithm.
The latest version of pqR and the version before as well use a new garbage collector, and new memory layouts for R objects, which both reduce memory usage and considerably speed up garbage collection. Here, I’ll give an overview of how the new scheme works, and present some performance comparisons with R-3.5.1.  Some more details are presented […]
Week 13 Gold Mining and Fantasy Football Projection Roundup now available. Go get that free agent gold! The post Gold-Mining Week 13 (2018) appeared first on Fantasy Football Analytics.
This is going to be a bit longer than some of my previous tutorials as it covers a walkthrough for sourcing data, scraping tables, cleaning,…Continue ReadingCreate 3D County Maps Using Density as Z-Axis
CategoriesAdvanced Modeling
Tags
Data Manipulation
Machine Learning
R Programming
Tips &amp; Tricks
We continue on from our previously loaded data of NYC bus delays. Although we are fairly sure that our target variable (time_delayed) is not accurate, it is all we have. We can thank the bus drivers of New York that we have anything at all, I suppose, but the inaccuracy of this variable is probably […]Related Post
Visualization of NYC bus delays with R
Explaining Black-Box Machine Learning Models – Code Part 2: Text classification with LIME
Image clustering with Keras and k-Means
‘How do neural nets learn?’ A step by step explanation using the H2O Deep Learning algorithm.
Machine learning: Logistic regression and decision trees for healthcare in R
Recalling Le Monde mathematical puzzle  first competition problem Given yay/nay answers to the three following questions about the integer 13≤n≤1300 (i) is the integer n less than 500? (ii) is n a perfect square? (iii) is n a perfect cube?  n cannot be determined, but it is certain that any answer to the fourth question […]
rstudio::conf 2019, the conference for all things R and RStudio, is less than two months away and we’re looking forward to seeing everyone January 15 - 18 in Austin, TX.
Register for rstudio::conf 2019
Already registered?
Book your r...
ggeffects (CRAN, website) is a package that computes marginal effects at the mean (MEMs) or representative values (MERs) for many different models, including mixed effects or Bayesian models. One of the advantages of the package is its easy-to-use interface: No matter if you fit a simple or complex model, with interactions or splines, the function … Weiterlesen Marginal Effects for (mixed effects) regression models #rstats →
Azure SQL Database, the database-as-a-service based on Microsoft SQL Server, now offers R integration. (The service is currently in preview; details on how to sign up for the preview are provided in that link.) While you've been able to run R in SQL Server in the cloud since the release of SQL Server 2016 by running a virtual machine, Azure SQL Database is a fully-managed instance that doesn't require you to set up and maintain the underlying infrastructure. You just choose the size and scale of the database you want to manage, and then connect to it like any other...
The recent ABC News article Australia’s pollution mapped by postcode reveals nation’s dirty truth is interesting. It contains a searchable table, which is useful if you want to look up your own suburb. However, I was left wanting more: specifically, the raw data and some nice maps. So here’s how I got them, using R. … Continue reading Extracting data from news articles: Australian pollution by postcode
CategoriesBasic Statistics
Tags
Data Management
R Programming
We are following on from our previously loaded data of NYC bus delays, and from our simple Cubist regression fit. We know that we need to add in more predictors, and we have access to company level data. A warning to the faint-hearted: this post is essentially all data manipulation and joins. No pretty graphs, […]Related Post
Visualizations for correlation matrices in R
Interpretation of the AUC
Simple Experiments with Smoothed Scatterplots
Understanding the Covariance Matrix
Six Sigma DMAIC Series in R – Part 3
I’ve been working with the Scottish census recently, to investigate employment in land-based (agriculture, forestry and fishing) industry. A friend of mine has recently moved to Dumfries and Galloway – a rural, farming… Continue reading →
A new RcppArmadillo release arrived at CRAN overnight. The version 0.9.200.5.0 is a minor upgrade and based on the new Armadillo bugfix release 9.200.5 from yesterday. I also just uploaded the Debian version.
Armadillo is a powerful and expressive C...
RStudio recently updated Shiny to allow for editable DataTables. Their implementations allows for editing cells direclty with in the DataTable view. This is fine for many advanced applications, however, for many applications more fine tuned control o...
The latest version of pqR (mostly) solves R’s “else” problem, by modifying the new parser previously introduced in pqR.  I’ll explain the “else” problem and solution here, and also present other advantages of pqR’s parser over the R Core parser, including a big speed advantage in one context. The “else” problem in R Probably most […]
In this post, I present my new package {img2coord}. This package can be used to retrieve coordinates from a scatter plot (as an image). devtools::install_github(&quot;privefl/img2coord&quot;) Have you ever made a plot, saved it as a png and moved on? When you come back to it, it is sometimes difficult to read the values from this plot, especially if there is no grid inside the plot. Making this package was also a good way to practice with clustering. A very simple example Saving a plot as PNG file
CategoriesAdvanced Modeling
Tags
Data Manipulation
Data Visualisation
ggplot2
R Programming
The motivation for choosing this data set to explore is straightforward: I wanted a real-world data set for which additional meta data would be easy to find, making NYC data perfect; I wanted something which did not require specialised domain knowledge, and so buses are a good choice; and I wanted a data set that […]Related Post
Explaining Black-Box Machine Learning Models – Code Part 2: Text classification with LIME
Image clustering with Keras and k-Means
‘How do neural nets learn?’ A step by step explanation using the H2O Deep Learning algorithm.
Machine learning: Logistic regression and decision trees for healthcare in R
Machine Learning Benchmarking with SFA in R
styler 1.1.0 is now available on CRAN. This
release introduces new features and is fully backward-compatible. It also
adapts to changes in the R parser committed into R devel (#419). Major changes
are:
styler can now style roxygen code examples in the source code of package.
This might be helpful for package developers. The option is enabled by
default, so you have to explicitly turn it off if you want styler to leave
the code examples as is:
styler::style_pkg(include_roxygen_examples = FALSE)
In addition to R and Rmd files, styler can now handle Rnw files too. The API
ignores capitalization and dot-prefixing for file types:
styler::style_dir(filetype = c(&quot;r&quot;, &quot;Rmd&quot;, &quot;.rnw&quot;))
the print method for the output of style_text() (print.vertical()) now
returns syntax-highlighted code by default, controllable via the option
styler.colored_print.vertical using the prettycode package. This only
works when ran in a console though, so you can c/p this snippet into yours
to see how it works:
styler::style_text(c(
&quot;add
A few months ago, I wasn’t sure what to expect when looking at fluorescence microscopy images in published papers. I looked at the accompanying graph to understand the data or the point the authors were trying to make. Often, the graph represents one or more measures of the so-called co-localization, but I couldn’t figure out how to interpret them. It turned out; reading the images is simple. Cells are simultaneously stained by two dyes (say, red and green) for two different proteins.
Most liked R posts from last week, sorted based on the number of likes they got on twitter, enjoy: Cluster Analysis – Part 1: Introduction (97 likes) Interactive Graphics with R Shiny (94 likes) Create easy automated dashbords with R and Markdown (87 likes) Introducing vizscorer: a bot advisor to score and improve your ggplot plots (84 likes) R > Python: a Concrete Example (81 likes) How to work with strings in base R – An overview of 20+ methods for daily use. (79 likes) Benford’s Law for Fraud Detection with an Application to all Brazilian Presidential Elections f (79 likes) Statistics Sunday: Reading and Creating a Data Frame with Multiple Text Files (76 likes) Generating data to explore the myriad causal effects that can be estimated in observational (54 likes) Choosing hyper-parameters in penalized regression (47 likes) Animated Population Pyramids in R (46 likes) Scrapping data about Australian politicians with RSelenium (35 likes) OneR – fascinating insights through simple rules (31 likes) OpenCPU 2.1 Release: Scalable R Services (27 likes) Change over time is not “treatment response” (27 likes) Many Factor Models (26 likes) Beautiful Chaos: The Double Pendulum (25 likes) Slides from my talks about Demystifying Big Data […]
The first time we at STATWORX are organizing a workshop for anyone who wants to learn more about data and graphics. Our “Data Visualization in R and ggplot2” workshop is designed for R-beginners and provides the perfect mixture between theory and practice. The workshop will be held at Mindspace in Munich. Participants achieve first insights into the concepts of data ... Read More
Der Beitrag Open Workshop: Data Visualization in R and ggplot2, January 25th in Munich erschien zuerst auf STATWORX.
In a previous article I announced AzureR, a new family of packages for working with Azure from R. This article goes into more detail on how you can use AzureRMR, the base package of the AzureR family, to manage resources with Azure Resource Manager. Before you begin The first thing you have to do is create a service principal. This is a security identity used by apps, services and automation tools for programmatic access to Azure resources. You can think of it as a “user identity” (login and password or certificate) with a role and permissions to access your resources....
Hi there!
Our manuscript about rWind R package has been recently accepted for publication in Ecography! As you know, rWind is a tool used to download and manage wind data, with some utilities that make easy to include wind information in ecological or evolutionary analyses (or others!).
Though there are several examples in the Supporting material of the publication, and others in the vignette included in the CRAN version, here you have a full tutorial with the main utilities of rWind... enjoy!
Computing wind connectivity of Azores Archipelago
In this example, we will compute wind connectivity of Azores Islands with different points of Europe, Africa and North America during June 2018. I will divide each task of this tutorial in different sections for clarification.
 
Downloading wind time series using lubridate and wind.dl_2()
In the last version of rWind (1.0.4), we can use lubridate to create a list of dates which will be used latter with wind.dl_2 to download wind time series. The object obtained with wind.dl_2 is an &quot;rWind_series&quot; &quot;list&quot;. This object can be easily used with other rWind functions as we'll see later.
# Make sure that you have the last version of rWind devtools::install_github(&quot;jabiologo/rWind&quot;) library(rWind) library(lubridate) # Here, we use ymd_hms from lubridate package to create a sequence of dates dt
The new 0.4.6 release of RQuantLib arrived on CRAN and Debian earlier today. It is two-fold update: catching up QuantLib 1.14 while also updating to Boost 1.67 (and newer).
A special thanks goes to Josh for updating to the binary windows library in t...
I’ve released pqR-2018-11-18, a new version of my variant implementation of R.  You can install it on Linux, Windows, or Mac as described at pqR-project.org. Installation must currently be from source, similarly to source installs of R Core versions of R. This version has some major speed improvements, as well as some new features. I’ll details […]
In the post (https://statcompute.wordpress.com/2018/11/23/more-robust-monotonic-binning-based-on-isotonic-regression), a more robust version of monotonic binning based on the isotonic regression was introduced. Nonetheless, due to the loss of granularity, the predictability has been somewhat compromised, which is a typical dilemma in the data science. On one hand, we don’t want to use a learning algorithm that is too greedy […]
We already saw the power of the OneR package in the preceding post. Here we want to give some more examples to gain some fascinating, often counter-intuitive, insights. Shirin Glander of Muenster University tested the OneR package with data from the World Happiness Report to find out what makes people happy: https://shiring.github.io/machine_learning/2017/04/23/one_r. The whole analysis … Continue reading &quot;OneR – fascinating insights through simple rules&quot;
Introduction
This blog posts will use several packages from the
{tidymodels} collection of packages, namely
{recipes},
{rsample} and
{parsnip} to train a random forest the tidy way. I will
also use {mlrMBO} to tune the hyper-parameters of the random forest.
Set up
Let’s load the needed packages:
library(&quot;tidyverse&quot;)
library(&quot;tidymodels&quot;)
library(&quot;parsnip&quot;)
library(&quot;brotools&quot;)
library(&quot;mlbench&quot;)
Load the data, included in the {mlrbench} package:
data(&quot;BostonHousing2&quot;)
I will train a random forest to predict the housing price, which is the cmedv column:
head(BostonHousing2)
## town tract lon lat medv cmedv crim zn indus chas nox
## 1 Nahant 2011 -70.9550 42.2550 24.0 24.0 0.00632 18 2.31 0 0.538
## 2 Swampscott 2021 -70.9500 42.2875 21.6 21.6 0.02731 0 7.07 0 0.469
## 3 Swampscott 2022 -70.9360 42.2830 34.7 34.7 0.02729 0 7.07 0 0.469
## 4 Marblehead 2031 -70.9280 42.2930 33.4 33.4 0.03237 0 2.18 0 0.458
## 5 Marblehead 2032 -70.9220 42.2980 36.2 36.2 0.06905 0 2.18 0 0.458
## 6 Marblehead 2033 -70.9165 42.3040 28.7 28.7 0.02985 0 2.18 0 0.458
## rm age dis rad tax ptratio b lstat
## 1 6.575 65.2 4.0900 1 296 15.3 396.90 4.98
## 2 6.421 78.9 4.9671 2 242 17.8 396.90 9.14
## 3 7.185 61.1 4.9671 2 242 17.8 392.83 4.03
## 4 6.998 45.8 6.0622 3 222 18.7 394.63 2.94
## 5 7.147 54.2 6.0622 3 222 18.7 396.90 5.33
## 6 6.430 58.7 6.0622 3 222 18.7 394.12 5.21
Only keep relevant columns:
boston % select(-medv, -town, -lon, -lat) %__% rename(price = cmedv)
I remove town, lat and lon because the information contained in the column tract is enough.
To train and evaluate the model’s performance, I split the data in two.
One data set, which I call the training set, will be further split into two down below. I won’t
touch the second data set, the test set, until the very end.
train_test_split
I have completed the polishing/correcting/fiddling of the eight statistical analysis related chapters of my evidence-based software engineering book, and an updated draft pdf is now available (download here). The material was in much better shape than I recalled, after abandoning it to the world 2-years ago, to work on the software engineering chapters. Changes include […]
Just for fun, I decided to compare the estimates from lmer and INLA for the variance components of an LMM (this isn’t really something that you would ordinarily do – comparing frequentist and bayesian approaches). The codes are below. A couple of plots are drawn, which show the distribution of the hyperparameters (in this case […]
Another minor release 0.3.3.5.0 of RcppEigen arrived on CRAN today (and just went to Debian too) bringing support for Eigen 3.3.5 to R.
As we now carry our small set of patches to Eigen as diff in our repo, it was fairly straightforward to bring thes...
We already saw the power of the OneR package in the preceding post. Here we want to give some more examples to gain some fascinating, often counter-intuitive, insights. Shirin Glander of Muenster University tested the OneR package with data from the World Happiness Report to find out what makes people happy: https://shiring.github.io/machine_learning/2017/04/23/one_r. The whole analysis is … Continue reading &quot;OneR – fascinating insights through simple rules&quot;
Introduction
In this post in the R:case4base series we will look at string manipulation with base R, and provide an overview of a wide range of functions for our string working needs.
We will use simple examples to learn to perform basic string operations, concatenate strings, work with substrings, switch cases, quote, find and replace within strings and more. Some interesting bonuses will also be included.
As always, some popular alternatives to base R will also be suggested and many useful references provided for further reading.
Contents
Quick overview of the very basics
String concatenation
String manipulation and properties
Basic pattern matching and replacement
Bonuses
Alternatives to base R
TL;DR - Just want the code
References
Quick overview of the very basics
This post is aimed to serve as an overview of functionality provided by base R to work with strings. Note that the term “string” is used somewhat loosely and refers to character vectors and character strings. In R documentation, references to character string, refer to character vectors of length 1.
Also since this is an overview, we will not examine the details of the functions, but rather list examples with simple, intuitive explanations trading off technical precision.
# String constants can be assigned using
# double quotes a
CategoriesVisualizing Data
Tags
R Programming
Text Mining
WordCloud
Having state election coming soon in Victoria (Australian state where I live) I decided to make a quick analysis and compare what politicians from major Australian party’s post in their Twitter accounts. I have collected Twitter hanldes of Australian Parliament members (also referred in text and code as MP that states for Member of Parliament) […]Related Post
Introducing vizscorer: a bot advisor to score and improve your ggplot plots
Visualize your CV’s timeline with R (Gantt chart style)
Analysing UK Traffic Trends with PCA
Time series visualizations with wind turbine energy data in R
Visualizations for credit modeling in R
I had the pleasure of attending the EARL (Enterprise Applications of the R Language) Conference held in Seattle on 2018-11-07, and the honour of being one of the speakers. The EARL conferences occupy a unique niche in the R conference universe, bringin...
Well, R is definitively here to stay and made its way into the data science tool zoo. For me as a statistician, I often feel alienated surrounded by these animals, but R is still also the statistician’s tool of choice (yes, it has come to age, but where are the predators ..?) What was usually […]
Since publishing the monotonic binning function based upon the isotonic regression (https://statcompute.wordpress.com/2017/06/15/finer-monotonic-binning-based-on-isotonic-regression), I’ve received some feedback from peers. A potential concern is that, albeit improving the granularity and predictability, the binning is too fine and might not generalize well in the new data. In light of the concern, I revised the function by imposing two […]
In this post, I’m evaluating some ways of choosing hyper-parameters (\(\alpha\) and \(\lambda\)) in penalized linear regression. The same principles can be applied to other types of penalized regresions (e.g. logistic). Model In penalized linear regression, we find regression coefficients \(\hat{\beta}_0\) and \(\hat{\beta}\) that minimize the following regularized loss function \[L(\lambda, \alpha) = \underbrace{ \frac{1}{2n} \sum_{i=1}^n \left( y_i - \hat{y}_i \right)^2 }_\text{Loss function} + \underbrace{ \lambda \left((1-\alpha)\frac{1}{2}\|\hat{\beta}\|_2^2 + \alpha \|\hat{\beta}\|_1\right) }_\text{Penalization} ~,\] where \(\hat{y}_i=\hat{\beta}_0 + x_i^T\hat{\beta}\), \(0 \le \alpha \le 1\) and \(\lambda __ 0\). This regularization is called elastic-net and has two particular cases, namely LASSO (\(\alpha = 1\)) and ridge (\(\alpha = 0\)). So, in elastic-net regularization, hyper-parameter \(\alpha\) accounts for the relative importance of the L1 (LASSO) and L2 (ridge) regularizations. There is another hyper-parameter, \(\lambda\), that accounts for the amount of regularization used in the model. I won’t discuss the benefits of using regularization here. Data In this blog post, I’m using the Breast cancer gene expression data from The Cancer Genome Atlas. dir.create(dir
Most liked R posts from last week, sorted based on the number of likes they got on twitter, enjoy: Dimensionality Reduction Methods Using FIFA 18 Player Data (192 likes) Visualize your CV’s timeline with R (Gantt chart style) (154 likes) The tidy caret interface in R (127 likes) Make Beautiful Tables with the Formattable Package (120 likes) Utilizing K-means to extract colours from your favourite images (108 likes) Easy time-series prediction with R: a tutorial with air traffic data from Lux Airport (101 likes) Discourse Network Analysis: Undertaking Literature Reviews in R (72 likes) Voronoi diagram with ggvoronoi package with Train Station data (72 likes) Installing RStudio &amp; Shiny Servers (64 likes) Preview my new book: Introduction to Reproducible Science in R (64 likes) RATest. A Randomization Tests package is available on CRAN (60 likes) Analysing UK Traffic Trends with PCA (59 likes) Convert Data Frame to Dictionary List in R (50 likes) Example of Overfitting (49 likes) A Mathematician’s Perspective on Topological Data Analysis and R (46 likes) Data Science With R Course Series – Week 9 (46 likes) How to de-Bias Standard Deviation Estimates (43 likes) SOMs and ggplot (35 likes) Shiny 1.2.0: Plot caching (34 likes) Quoting […]
In the post (https://statcompute.wordpress.com/2018/11/17/growing-list-vs-growing-queue), it is shown how to grow a list or a list-like queue based upon a dataframe. In the example, the code snippet was heavily relied on the FOR loop to do the assignment item by item, which I can’t help thinking of potential alternatives afterwards. For instance, is there an implementation […]
This post is dedicated to the beautiful chaos created by double pendulums. I have seen
a great variety of animated versions, implemented with different tool but never in R.
Thanks to the amazing package gganimate, it is actually not that hard to produce them in R.
library(tidyverse)
library(gganimate)
I am not going to attempt to explain the math behind the double pendulum. If you are interested
in the details, check out a complete walkthrough here.
The code presented here is a straightforward adaption from python.
First, we need to set up some basic constants and the starting conditions.
# constants
G
Recently, I came up with Thoen’s law. It is an empirical one, based on several years of doing data science projects in different organisations. Here it is: The probability that you have worked on a data science project that failed, approaches one very quickly as the number of projects done grows. I think many, far more than we as a community like to admit, will deal with projects that don’t meet their objectives. This blog does not explore why data science projects have a high risk of failing. Jonathan Nolis already did this adequately. Rather, I’ll look for strategies how we might deal with projects that are failing. Disappointing as they may be, failed projects are inherently part of the novel and challenging discipline data science is in many organisations. The following approach might reduce the probability of failure, but that is not the main point. Their objective is to prevent failing in silence after too long a period of project time. In which you try to figure out things on your own. They will shift failure from the silent personal domain to the public collective one. Hopefully, reducing stress and blame by yourself and others.
I am pleased to announce that the RFishBC package has been released to CRAN. This package is intended to help fisheries scientists gather age and measurement data from digital images of calcified structures and, possibly, back-calculate p...
Week 12 Gold Mining and Fantasy Football Projection Roundup now available. Go get that free agent gold! The post Gold-Mining Week 12 (2018) appeared first on Fantasy Football Analytics.
A Le Monde mathematical puzzle from after the competition: A sequence of five integers can only be modified by subtracting an integer N from two neighbours of an entry and adding 2N to the entry.  Given the configuration below, what is the minimal number of steps to reach non-negative entries everywhere? Is this feasible for […]
OpenCPU provides a mature and robust system for hosting R based services. The server exposes a simple HTTP API for calling R functions, scripts and managing data. The Cloud Server is completely free and scales up to many concurrent users. T...
Ruth Thomson, Interim Director of Strategic Innovation sat down with Jelena, one of Mango’s machine learning experts. Thanks Jelena for your time. It is an absolute pleasure to have this opportunity to discuss machine learning today. Tell me about your background with machine learning I’ve been using machine learning for many years. I first started using machine learning as part of my PhD in Physics over 12 years ago using a neural net for mainly pattern recognition in particle physics.…
While there is more and more data available in structured formats (CSV, JSON) through initiatives like OpenData, sometimes nicely formatted data still not publicly available. When I decided to conduct a little study of what Australian politicians from the major party post in Twitter. So I decide to find list… The post Scrapping data about Australian politicians with RSelenium appeared first on Alex Levashov - eCommerce Consultant (Melbourne, Australia).
I like both Python and R, and teach them both, but for data science R is the clear choice. When asked why, I always note (a) written by statisticians for statisticians, (b) built-in matrix type and matrix manipulations, (c) great graphics, both base and CRAN, (d) excellent parallelization facilities, etc. I also like to say … Continue reading R __ Python: a Concrete Example →
How does driving an electric car compare to driving a similar gasoline car in terms of total pollution damages? While some comments about the future of mobility may suggest that electric cars are already clearly much more environmental friendly, the an...
On November 7th, Uwe Friedrichsen and I gave our talk from the JAX conference 2018: Deep Learning - a Primer again at the W-JAX in Munich.
A few weeks before, I gave a similar talk at two events about Demystifying Big Data and Deep Learning (and how to...
Imagine you are a fish ecologist who compiled a list of fish species for your country. 🐟
Your list could be useful to others, so you publish it as a supplementary file to an article or in a research repository. That is fantastic, but it might be difficult for others to discover your list or combine it with other lists of species. Luckily there’s a better way to publish species lists: as a standardized checklist that can be harvested and processed by the Global Biodiversity Information Facility (GBIF).
I've posted several examples here of using Azure Cognitive Services for data science applications. You can upload an an image or video to the service and extract information about faces and emotions, generate a caption describing a scene from a provided photo, or speak written text in a natural voice. (If you haven't tried the Cognitive Services tools yet, you can try them out using the instructions in this notebook using only a browser.) But what if you can't upload an image or text to the cloud? Sending data outside your network might be subject to regulatory or privacy policies....
This will be a non-technical post illustrating the problems with identifying treatment responders or non-responders using inappropriate within-group analyses. Specifically, I will show why it is pointless to try to identify a subgroup of non-responders using a naïve analysis of data from one treatment group only, even though we have weekly measures over time.
This type of unwarranted and misleading causal language is surprisingly common. You see this type of language even when the data comes from an RCT, often in secondary analyses of mediators, predictors, or in even trendier studies where the authors have a strong urge to mix omics (e.g., genomics) and artificial intelligence/machine learning for vague reasons. Defining treatment response
When we talk about &quot;treatment response&quot; we usually mean the causal estimand, i.e., the counterfactual comparison of the effect of receiving the treatment versus not-receiving the treatment. Deep down we all know that a treatment outcome is a relative contrast, and not simply a patient's change from pre to posttest. We can (usually) only estimate the effect of the treatment on the group level, by randomizing patients to a treatment or a control group and estimating the average treatment effect by comparing the expected outcome in the two groups. The rate at which we tend to &quot;forget&quot; this basic knowledge seems directly related to our drive to publish in high impact journals.
Simulating the problem
# Packages used
library(tidyverse)
library(powerlmm) # dev version
library(ggrepel)
I will use powerlmm to simulate some longitudinal treatment data. In this setup, the control group has a ~40% reduction in symptoms at posttest, which in this case would give a Cohen's d of 0.5. And the actual treatment effect is d = 0.5, i.e., a 7 point difference at posttest between the treatment and control group. These would not be surprising numbers in some psychiatric trials.
p
Introduction These past few weeks I’ve been writing about a new package I created, MCHT. Those blog posts were basically tutorials demonstrating how to use the package. (Read the first in the series here.) I’m done for now explaining the technical details of the package. Now I’m going to use the package for purpose I…Read more The Distribution of Time Between Recessions: Revisited (with MCHT)
Introduction Domain squatting or URL hijacking is a straightforward attack that requires little skill. An attacker registers a domain that is similar to the target domain and hopes that a user accidentally visits the site. For example, if the domain is example.com, then a typo-squatter would register similar domains such as common misspelling: examples.com misspellings
The post Hacking Bioconductor appeared first on Jumping Rivers.
No hables de futuro, es una ilusión cuando el Rock &amp; Roll conquistó mi corazón (El Rompeolas, Loquillo y los Trogloditas) In this post I create flowers inspired in the Julia Sets, a family of fractal sets obtained from complex numbers, after being iterated by a holomorphic function. Despite of the ugly previous definition, the … Continue reading Flowers for Julia →
Another maintenance release of RcppMsgPack got onto CRAN today. Two new helper functions were added and not unlike the previous 0.2.2 release in, some additional changes are internal and should allow compilation on all CRAN systems.
MessagePack itsel...
You can file this one under “I may have the very specific solution if you’re having exactly the same problem.” So: if you’re running some R code and you see a warning like this: Warning message: In checkMatrixPackageVersion() : Package version inconsistency detected. TMB was built with Matrix version 1.2.14 Current Matrix version is 1.2.15 … Continue reading Using OSX? Compiling an R package from source? Issues with ‘-fopenmp’? Try this.
First Statistics Sunday in far too long! It's going to be a short one, but it describes a great trick I learned recently while completing a time study for our exams at work.To give a bit of background, this time study involves analzying time examinees ...
At Business Science, we are developing a revolutionary system for teaching Business Analysis with Python (Business Analysis with Python is a new course we are developing at Business Science University).
The system is revolutionary for a number of re...
### GROWING LIST ###
base_lst1
Last week I blogged about some different ways of dealing with data in a cross tab that has been suppressed as a means of disclosure control, when the count in a cell is less than six. I tried simple replacement of those cells with “3”, two differen...
The libcurl library (the foundational library behind the RCurl and curl packages) has switched to using OpenSSL’s default ciphers since version 7.56.0 (October 4 2017). If you’re a regular updater of curl/httr you should be fairly current with these cipher suites, but if you’re not a keen updater or use RCurl for your web-content tasks,... Continue reading →
By Gabriel Vasconcelos and Yuri Fonseca The intuition Let us begin with a brief explanation about Benford’s law and why should it work as a fraud detector method. Given a set of numbers, the first thing we need to do … Continue reading →
In R, there are a couple ways to convert the column-oriented data frame to a row-oriented dictionary list or alike, e.g. a list of lists. In the code snippet below, I would show each approach and how to extract keys and values from the dictionary. As shown in the benchmark, it appears that the generic […]
A second and minor update for the RcppGetconf package for reading system configuration — not unlike getconf from the libc library — is now on CRAN.
Changes are minor. We avoid an error on a long-dead operating system cherished in one particular c...
Among most popular off-the-shelf machine learning packages available to R, caret ought to stand out for its consistency. It reaches out to a wide range of dependencies that deploy and support model building using a uniform, simple syntax. I have been using caret extensively for the past three years, with a precious partial least squares (PLS) tutorial in … Continue reading The tidy caret interface in R →
I occasionally see queries on various social media as to overfitting — what is it?, etc. I’ll post an example here. (I mentioned it at my talk the other night on our novel approach to missing values, but had a bug in the code. Here is the correct account.) The dataset is prgeng, on wages of … Continue reading Example of Overfitting →
by Mark Niemann-Ross Mango Solutions presented the EARL conference in Seattle this November and I was fortunate enough to have time to attend. During the conference I took notes on the presentations, which I’ll pass along to you. Thoughts on the future of R in industry The EARL conference started with a panel discussion on R. Moderated by Doug Ashton from Mango Solutions, the panel included Julia Silge with Stack Overflow, David Smith with Microsoft, and Joe Cheng with Rstudio. Topics ranged from the tidyverse, R certification, R as a general purpose language, Shiny, and the future of R. I...
Most liked R posts from last week, sorted based on the number of likes they got on twitter, enjoy: New R Cheatsheet: Data Science Workflow with R (295 likes) Coding Regression trees in 150 lines of R code (90 likes) ‘How do neural nets learn?’ A step by step explanation using the H2O Deep Learning algorithm. (88 likes) Detailed introduction of “myprettyreport” R package (83 likes) How to Sort Data in R (76 likes) Explore Your Dataset in R (75 likes) Tesseract 4 is here! State of the art OCR in R! (74 likes) “A Guide to Working With Census Data in R” is now Complete! (70 likes) 4 ways to be more efficient using RStudio’s Code Snippets, with 11 ready to use examples (69 likes) Show R shiny app in WordPress (67 likes) Integrating R and Telegram (57 likes) Working with US Census Data in R (51 likes) R shiny stock analysis (48 likes) How to Use Simulated Data to Check Choice Model Experimental Designs Using R (47 likes) anytime – dates in R (46 likes) Practical statistics books for software engineers (43 likes) One-arm Bayesian Adaptive Trial Simulation Code (42 likes) T-mobile uses R for Customer Service AI […]
In case you missed them, here are some articles from October of particular interest to R users. Peter Provost ports some 80's-era BASIC programs for kids to R. In a podcast for Fringe FM, I discuss the ethics of AI, Microsoft and Open Source, and the R Community. Roundup of AI, Machine Learning and Data Science news from October 2018. In this episode of &quot;Guy in a Cube&quot;, R is used to visualize Anscombe's Quartet via Power BI. Di Cook suggests using computer vision to automate statistical model assessment for machine learning in the 2018 Belz Lecture. R provides the...
Many R users appear to be big fans of &quot;code capturing&quot; or &quot;non standard evaluation&quot; (NSE) interfaces. In this note we will discuss quoting and non-quoting interfaces in R. The above terms are simply talking about interfaces where a name to be used is captured from the source code the user typed, and thus does … Continue reading Quoting in R
I’m writing a series of posts on various function options of the glmnet function (from the package of the same name), hoping to give more detail and insight beyond R’s documentation. In this post, we will focus on the standardize … Continue reading →
Week 11 Gold Mining and Fantasy Football Projection Roundup now available. Go get that free agent gold! The post Gold-Mining Week 11 (2018) appeared first on Fantasy Football Analytics.
I love the formattable package, but I always struggle to remember its syntax. A quick Google search reveals that I’m not alone in this struggle....
Right now Rcpp stands at 1500 reverse-dependencies on CRAN. The graph is on the left depicts the growth of Rcpp usage (as measured by Depends, Imports and LinkingTo, but excluding Suggests) over time. What an amazing few days this has been as we als...
Literature reviews are the cornerstone of science. Keeping abreast of developments within any given field of enquiry has become increasingly difficult given the enormous amounts of new research. Databases and search technology have made finding relevant literature easy but, keeping a coherent overview of the discourse within a field of enquiry is an ever more … Continue reading &quot;Discourse Network Analysis: Undertaking Literature Reviews in R&quot;
This note is just a quick follow-up to our last note on correcting the bias in estimated standard deviations for binomial experiments. For normal deviates there is, of course, a well know scaling correction that returns an unbiased estimate for observed standard deviations. It (from the same source): … provides an example where imposing the … Continue reading More on Bias Corrected Standard Deviation Estimates
A new minor clean-up release of the anytime package arrived on CRAN overnight. This is the fourteenth release, and follows the 0.3.2 release a good week ago.
anytime is a very focused package aiming to do just one thing really well: to convert anythi...
I recently picked up a copy of my favorite game Stardew Valley again.
If you don’t know the game, I can highly recommend it! You inherit a pixel farm and
you are in charge of everything. Crops, animals, fishing, mining and never forget to socialize.
My plan was to shut off work for at least a few hours while playing. But at one point
you inevitably start optimizing your farm. In most cases, the layout of crops.
Aaaaaaaand that’s how you turn a farming game into an optimization problem you try to solve with R.
library(tidyverse)
Your farm
If you start a standard farm, this is what you can work with.
The first step is to get the layout into R. Luckily, I found a
reddit post,
where someone posted the layout as an excel file. I converted the file into a matrix and
a long data frame. The data frame is for plotting and the matrix for the actual optimization.
farm_blank
Despite their now inherent evil status, GitHub has some tools other repository aggregators do not. One such tool is the free vulnerability alert service which will scan repositories for outdated+vulnerable dependencies. Now, “R” is nowhere near a first-class citizen in the internet writ large, including software development tooling (e.g. the Travis-CI and GitLab continuous integration... Continue reading →
by Angelika Stefan &amp; Felix Schönbrodt Almost all researchers have experienced the tingling feeling of suspense that arises right before they take a look at long-awaited data: Will they support their favored hypothesis? Will they yield interesting or even groundbreaking results? In a perfect world (especially one without publication bias), the cause of this suspense […]
The post Gazing into the Abyss of P-Hacking: HARKing vs. Optional Stopping appeared first on Nicebread.
The windows clipboard is a quick way to get data in and out of R. How can we exploit this feature to accomplish our basic data exploration needs and when might its use be inappropriate? Read on. Overview Paste Data from Clipboard to R HTML Tables Spreadsheets Copy Data from R to Clipboard Summary and Usage Notes Paste Data from Clipboard to R We won't be using it in this post, but you can see the contents of the Windows clipboard in R using the readClipboard() command. Going through the documentation you'll note a variety of formats that can be read. For our purposes, we are looking at moving text based data into R from HTML tables or spreadsheets that happen to be open on our desktop. HTML Tables HTML tables are easy to copy into R via the windows clipboard if you're using Chrome or Firefox. To demonstrate, copy the simple HTML table shown below by highlighting the text and pressing CTRL + C on your keyboard (or whatever copy method works best for you.) Items Inventory Apple 5 Kiwi 7 Meat Balls 253 Next, grab the windows clipboard content and “paste” it into an R data frame using the following R-code: My_HTML_Data
The post Windows Clipboard Access with R appeared first on R-BAR.
Dean Attali wrote this nice post a few years ago describing knitr’s spin function. This function allows a regular R file, with comments written with the roxygen2-style comment tag #' to
be rendered as an HTML document with the comments rendered as text and the results of
the R code rendered in place, much as a RMarkdown document would. The basic rules for this are (from Dean’s post):
Any line beginning with #' is treated as a markdown directive (#' # title will be a header, #' some **bold** text results in some bold text)
Any line beginning with #+ is parsed as code chunk options
Run knitr::spin on the file
In effect, this “spinnable” R script is the complement of a RMarkdown document with
respect to format, since the RMarkdown document is primarily a text (Markdown) document with code chunks, and the R script is primarily a code document with text chunks.
RMarkdown is nice when you want to create a final document to report your analysis, but can be burdensome when you’re developing code. The R script is much nicer for developing code, but it would be nice to get a nice document at the end of development without too much hassle. It turns out we can flip back and forth between the two complementary formats through functions in knitr, where fname denotes the respecive source file:
RMarkdown to R : knitr::purl(fname, documentation = 2)
R to RMarkdown : knitr::spin(fname, knit = FALSE, format = &quot;Rmd&quot;)
Why would you want to convert R to RMarkdown when it renders to HTML or Word or PDF automatically (within RStudio), you ask? Because spin uses knit2html/knit2pdf/knit for rendering the documents, rather than rmarkdown::render, which uses the power of Pandoc and hence has richer options in terms of formatting. You could create a pipeline that starts with your R script, creates the intermediate Rmd file using spin, and then uses rmarkdown::render to create the final product.
In the first article of this series, I built an Alpine-based Docker image with R base packages from Alpine’s native repositories, as well as one image with R compiled from source code. The images are hosted on Docker Hub, velaco/alpine-r repository...
I did a remote install of Ubuntu Server today. This was somewhat novel because it’s the first time that I have not had physical access to the machine I was installing on. The server install went very smoothly indeed.
The next tasks were to install RStudio Server and Shiny Server. The installation process for each of these is well documented on the RStudio web site:
Installing RStudio Server and
Installing Shiny Server.
These are my notes. Essentially the same, with some small variations.
RStudio Server
Install a recent version of R.
Download the distribution.
wget https://download2.rstudio.org/rstudio-server-1.1.463-amd64.deb
Install the server.
sudo dpkg -i rstudio-server-1.1.463-amd64.deb
Verify the installation.
sudo rstudio-server verify-installation
RStudio Server runs on port 8787, so you should be able to access it in a browser at http://:8787.
Shiny Server
Become root and install the shiny package.
sudo su
R -e &quot;install.packages('shiny', repos='https://cran.rstudio.com/')&quot;
exit
Download the distribution.
wget https://download3.rstudio.org/ubuntu-14.04/x86_64/shiny-server-1.5.9.923-amd64.deb
Install the server.
sudo dpkg -i shiny-server-1.5.9.923-amd64.deb
Shiny Server runs on port 3838, so you should be able to access it in a browser at http://:3838.
Last week at the ODSC West conference, I was thrilled with the interest in my Using AI for Good workshop: it was wonderful to find a room full of data scientists eager to learn how data science and artificial intelligence can be used to help people and the planet. The workshop was focused around projects from the Microsoft AI for Good program. I've included some details about the projects below, and you can also check out the workshop slides and the accompanying Jupyter Notebooks that demonstrate the underlying AI methods used in the projects. The projects were drawn from the...
We’re pleased to announce the CRAN release of Shiny v1.2.0! This release features Plot Caching, an important new tool for improving performance and scalability in Shiny apps.
If you’re not familiar with the term “caching”, it just means that when we perform a time-consuming operation, we save (cache) the results so that the next time that operation is requested, we can skip the actual operation and instantly fetch the previously cached results. Shiny’s reactive expressions do some amount of caching for you already, and you can use more explicit techniques to cache the various operations you might do to your data (using the memoise package, or manually saving intermediate data frames to disk as CSV or RDS, for two examples).
Plots, being very common and (potentially) expensive-to-compute outputs, are great candidates for caching, and in theory you can use renderImage to accomplish this. But because Shiny’s renderPlot function contains a lot of complex infrastructure code, it’s actually quite a difficult task. Despite some valiant attempts, all of the examples we’ve seen in the wild have had serious limitations we wanted to overcome.
Shiny v1.2.0 introduces a new function, renderCachedPlot, that makes it much easier to add plot caching to your app.
Using renderCachedPlot
Let’s take a simple, but expensive, plot output:
output$plot
At the upcoming This week in machine learning and AI European online Meetup, I’ll be presenting and leading a discussion about the Anchors paper, the next generation of machine learning interpretability tools. Come and join the fun! :-)
Date: Tuesda...
So you know lapply, sapply, and apply…but…what about rapply, vapply, or eapply? These are generally a little less known as far as the apply family of functions in R go, so this post will explore how they work. rapply Let’s start with rapply. This function has a couple of different purposes. One is to recursively […]
The post Those “other” apply functions… appeared first on Open Source Automation.
The glmnet function (from the package of the same name) is probably the most used function for fitting the elastic net model in R. (It also fits the lasso and ridge regression, since they are special cases of elastic net.) … Continue reading →
Antarctic/Southern Ocean science and rOpenSci Collaboration and reproducibility are fundamental to Antarctic and Southern Ocean science, and the value of data to Antarctic science has long been promoted. The Antarctic Treaty (which came into force in 1...
Introduction Over the past few weeks I’ve published articles about my new package, MCHT, starting with an introduction, a further technical discussion, demonstrating maximized Monte Carlo (MMC) hypothesis testing, bootstrap hypothesis testing, and last week I showed how to handle multi-sample and multivariate data. This is the final article where I explain the capabilities of…Read more Time Series and MCHT
I’m pleased to share Part I of my new book “Introduction to Reproducible Science in R“. The purpose of this …Continue reading →
This note is about attempting to remove the bias brought in by using sample standard deviation estimates to estimate an unknown true standard deviation of a population. We establish there is a bias, concentrate on why it is not important to remove it for reasonable sized samples, and (despite that) give a very complete bias … Continue reading How to de-Bias Standard Deviation Estimates
There are only two more weeks in the course! This week will extend what you learned from the Expected Value by performing an optimization and sensitivity analysis.
The optimization and sensitivity analysis will teach you how to identify the maximum bu...
This blog post introduces the RATest package we released a while back on CRAN with my colleague and good friend Mauricio Olivares-Gonzalez. The package contains a collection of randomization tests, data sets and examples. The current version focuses on two testing problems and their implementation in empirical work, mostly related to economics. First, it facilitates the empirical researcher to test for particular hypotheses, such as comparisons of means, medians, and variances from k populations using robust permutation tests, which asymptotic validity holds under very weak assumptions, while retaining the exact rejection probability in finite samples when the underlying distributions are identical. Second, it implements Canay and Kamat (2017) permutation test for testing the continuity assumption of the baseline covariates in the sharp regression discontinuity design (RDD).
I’ve always been curious to make Voronoi diagram, I just think they are beautiful! When I came across data set with train stations in Japan. I instantly thought this would be great data sets to make Voronoi diagram! I’ve gotten data sets from (Ekidata)[http://www.ekidata.jp/] site. I’m amazed how many train stations we have in Japan, as well as coverage of train systems in Japan.
There are couple of packages I could’ve used to make Voronoi diagram, but I’ve utilized package ggvoronoi. I really like using “outline” inside of geom_voronoi function to mask out the shape! (Which I wasn’t sure how to do before using deldir package).
Voronoi Diagram with Train Station as a seed.
ggvoronoi makes it easy to plot voronoi diagram! All I really needed to produce voronoi diagram was longitude &amp; latitude.
Initially I’ve plotted all the train station as a point (using geom_point), you can see that station will reveal shape of Japan, as JR (Japan Railway) really covers coast line of Japan. There are total of 10828 points, as there were 10828 stations listed in most recent data set downloaded today.
I also used treemap package to create treemap.
I’ve colour coded rectangle inside of treemap with company types. 47% of 10K+ stations are JR Japan Railway stations in Japan.
Tokyo (area: 2,188 sq.km) has 943 stations all together, followed by Hokkaido 650 stations, but Hokkaido is the biggest prefecture in terms of area (83,456.87 sq.km) . It would be interesting to get area data for each prefecture, so we can calculate stations per area.
jp
A new RcppArmadillo release, now at 0.9.200.4.0, based on the new Armadillo release 9.200.4 from earlier this week, is now on CRAN, and should get to Debian very soon.
Armadillo is a powerful and expressive C++ template library for linear algebra ai...
Week 10 Gold Mining and Fantasy Football Projection Roundup now available. Go get that free agent gold! The post Gold-Mining Week 10 (2018) appeared first on Fantasy Football Analytics.
Package introduction: Package myprettyreport helps to export ggplot2 graphs into a good-looking PDF file in a clear and easy way with a wide range of flexibility. It has a modular […]
Recently at work I’ve been asked to help some clinicians understand why my risk model classifies specific patients as high risk. Just prior to this work I stumbled across the work of some data scientists at the University of Washington called lime. LIME stands for “Local Interpretable Model-Agnostic Explanations”. The idea is that I can answer those questions I’m getting from clinicians for a specific patient by locally fitting a linear (aka “interpretable”) model in the parameter space just around my data point. I decided to pursue lime as a solution and the last few months I’ve been focusing on implementing this explainer for my risk model. Happily, I also discovered an R package that implements this solution that originated in python.
Sample Data
So the first step to this blog was to find some public data for illustration. I remembered an example used in an Introduction to Statistical Learning by James, Witten, Hastie and Tibshirani. I will use the Heart.csv data which can be downloaded using the link in the code below:
library(readr)
library(ranger)
library(tidyverse)
library(lime)
dat
Introduction
In this post we will look at yet another productivity increasing feature of the RStudio IDE - Code Snippets. Code Snippets let us easily insert and potentially execute predefined pieces of code and work not just for R code, but many other languages as well.
In this post we will cover 4 different ways to increase productivity using Code Snippets and provide 11 real-life examples of their use that you can take advantage of instantly.
How do Code Snippets work
Using, Viewing and editing snippets
In RStudio, we can browse and define snippets under Tools -__ Global Options... -__ Code -__ Edit Snippets window
When typing code, the snippet will appear as an auto-complete option (similar to function names) if we type the first few letters of its name
Use Shift+Tab to insert the snippet immediately or pick the snippet from the auto-complete list (by clicking or scrolling on it and pressing Tab)
Note that as there is no auto-completion when editing R Markdown documents, we need to use the Shift+Tab method exclusively in that case.
Sharing and exporting Snippets
Once we customize some snippets they are automatically saved to our ~/.R/snippets directory, one file per language. We can use these files to share our snippets with others, but also to edit them directly in the respecive file, without the need to click through the RStudio menus. RStudio will automatically load the Snippets from the .snippets files first, for languages that have the file present.
Four common use-case scenarios
1. Automatically insert boilerplate or template-style code
The first and probably most frequent use of the Code Snippets feature is to quickly insert predefined pieces of code that require a lot of typing with little alternation, a.k.a. boilerplate code. A good illustration is a snippet covering a tryCatch block:
snippet tryc
${1:variable}
I had an opportunity recently to design a Bayesian adaptive trial with several interim analyses that allow for early stopping due to efficacy or futility. The code below implements the one-arm trial described in the great introductory article by Ben Saville et al. Three of the coauthors are current or former colleagues at Vandy. The … Continue reading One-arm Bayesian Adaptive Trial Simulation Code →
The 4.1.0 release of simmer, the Discrete-Event Simulator for R, is on CRAN. As per request in the mailing list, now get_global() is able to work inside a generator function. Moreover, the new add_global() method attaches a global attribute to a simulator. library(simmer) env % set_global(&quot;interarrival&quot;, 1, mod=&quot;+&quot;) generator
Motivation There are dozens of machine learning algorithms out there. It is impossible to learn all their mechanics, however, many algorithms sprout from the most established algorithms, e.g. ordinary least squares, gradient boosting, support vector machines, tree-based algorithms and neural networks. At STATWORX we discuss algorithms daily to evaluate their usefulness for a specific project. In any case, understanding these ... Read More
Der Beitrag Coding Regression trees in 150 lines of R code erschien zuerst auf STATWORX.
T-Mobile, the global telecommunication company, is using R in production to automatically classify text messages to customer service and route them to an agent that can help. The AI@T-mobile team used the keras library in R to build a natural language processing engine with Tensorflow, and deployed it to production as a docker container. The MRAN Time Machine ensures the container gets fixed R package versions for reproducibility. While you may think first of Python for building production AI models, the keras library in R has already proven to be a first-class interface for building AI models. The T-mobile team...
If you’re an R/RStudio user who has migrated to Mojave (macOS 10.14) or are contemplating migrating to it, you will likely eventually run into an issue where you’re trying to access resources that are in Apple’s new hardened filesystem sandboxes. Rather than reinvent the wheel by blathering about what that means, give these links a... Continue reading →
In this blog post, I’ll explain the new functionality of the OpenImageR package, SLIC and SLICO superpixels (Simple Linear Iterative Clustering) and their applicability based on an IJSR article. The author of the article uses superpixel (SL...
I just saw an announcement on R Bloggers about the anytime package. It looks to be a very handy package to convert dates in pretty much any format to Date or POSIX classes, without the need to define the format – it’s guessed by an underlying C++ library. It certainly seems to be flexible… putting […]
Introducing Webhooks — Fastest Way to Collect DataToday we’re introducing webhooks, the fastest way you can send data to a JSON file from anywhere.The number 1 most taxing task when it comes to data analysis or taking an initiative to become m...
by Hong Ooi, senior data scientist, Microsoft Azure This post is to announce a new family of packages we have developed as part of the CloudyR project for talking to Azure from R: AzureR. As background, some of you may remember the AzureSMR package, which was written a few years back as an R interface to Azure. AzureSMR was very successful and gained a significant number of users, but it was never meant to be maintainable in the long term. As more features were added it became more unwieldy until its design limitations became impossible to ignore. The AzureR family...
As mentioned here two days ago, the Rcpp package turned ten on Monday—and we used to opportunity to mark the current version as 1.0.0! Thanks to everybody who liked and retweeted our tweet about this. And of course, once more a really big Thank You...
So you have read my (draft) book on evidence-based software engineering and want to learn more about the statistical techniques used, but are not interested lots of detailed mathematics. What books do I suggest? All the following books are sitting on the shelf next to where I write (not that they get read that much […]
Real-time computation (or estimation) of the “probability to win” is difficult. We’ve seem that in soccer games, in elections… but actually, as a professor, I see that frequently when I grade my students. Consider a classical multiple choice exam. After each question, imagine that you try to compute the probability that the student will pass. Consider here the case where we have 50 questions. Students pass when they have 25 correct answers, or more. Just for simulations, I will assume that students just flip … Continue reading The “probability to win” is hard to estimate… →
Get notify when an R script finishes on Telegram.
A new minor release of the anytime package arrived on CRAN this morning. This is the thirteenth release, and the first since July as the package has gotten feature-complete.
anytime is a very focused package aiming to do just one thing really well: t...
I’ve recently begun reading through the book Modern Data Science with R, by Benjamin S. Baumer, Daniel T. Kaplan, and Nicholas J. Horton. It’s quite clear and informative. One of the things I especially appreciate about it is that I’m not finding the math to be too cumbersome. That is, even for someone like me, […]
The post Data viz challenge: Recreating FiveThirtyEight’s ‘Deadest Names’ graphic with ggplot2 appeared first on my (mis)adventures in R programming.
For us, R is not about packages and data analysis. It is about a vibrant community that creates all those wonderful toys. As the Why R? Foundation we want to take part in the growth of the R user base. One of our first efforts is the Why R? conference ...
If you need data about the American populace, there's no source more canonical than the US Census Bureau. The bureau publishes a wide range of public sets, and not just from the main Census conducted every 10 years: there are more than 100 additional surveys and programs published as well. To help R users access this rich source of data, Ari Lamstein and Logan Powell have published A Guide to Working with US Census Data in R, a publication of the R Consortium Census Working Group. The guide provides an overview of the data available from the US census bureau...
UI Update — DatazarAs your projects get more and more files added to it, keeping track of your datasets and analysis can become tedious. We’ve updated your project file list so you can see what type of files you have in your project before you ...
If you’ve read our previous R Tip on using sigr with linear models, you might have noticed that the lm() summary object does in fact carry the R-squared and F statistics, both in the printed form: model_lm
I wrote a post about getting Magento 2 data to R using REST API last year. Now I provide more examples of use and a wrapper over API that you can re-use to get data from Magento 2 to R in a bit more convenient way. Prerequisites Magento 2 I… The post R plus Magento 2 REST API revisited: part 1- authentication and universal search appeared first on Alex Levashov - eCommerce Consultant (Melbourne, Australia).
The Summary
In this short note I will write about the httr package and my need to detect whether or not an HTTP request had been redirected or not - it turns out this is quite easy. Along the way I will also show how to access information of an HTTP-...
Help! The same annotations go on every facet! (with thanks to a student for sending me her attempt). This is a question I get fairly often and the answer is not straightforward especially for those that are relatively new to R and ggplot2. In this post, I will show you how to add different annotations… Continue reading Adding different annotation to each facet in ggplot →
Logfile analysis allows website owners a deep insight about how Google and other search engines crawl their pages. How often does a bot come by a page, which pages are rarely or not at all visited by the bots? For which pages does the bot get errors? All these and more questions can be answered
Der Beitrag Can we predict the crawling of the Google-Bot? erschien zuerst auf Keyword Hero - The SEO Revolution.
Most liked R posts from last week, sorted based on the number of likes they got on twitter, enjoy: From webscraping data to releasing it as an R package to share with the world: a full tutorial (129 likes) How to Create a Correlation Matrix in R (124 likes) Machine Learning Basics – Random Forest (119 likes) Running R Code in Parallel (94 likes) Time series visualizations with wind turbine energy data in R (92 likes) Book Review – Sound Analysis and Synthesis with R (86 likes) Multilevel Modeling Solves the Multiple Comparison Problem: An Example with R (78 likes) Master R shiny: One trick to build maintainable and scalable event chains (75 likes) Communicating results with R Markdown (74 likes) Visualize the Business Value of your Predictive Models with modelplotr (72 likes) R shiny custom docker server with caching (58 likes) Automated Email Reports with R (58 likes) Segmenting Customers by their Purchase Histories Using Non-Negative Matrix Factorization (57 likes) Scatterplot matrices (pair plots) with cdata and ggplot2 (53 likes) Feature Selection with the Boruta Algorithm (52 likes) How to Make a Pie Chart in R (52 likes) New Course: Analyzing Election and Polling Data in R (51 likes) […]
xts version 0.11-2 was published to CRAN yesterday. xts provides data structure and functions to work with time-indexed data.  This is a bug-fix release, with notable changes below:
The xts method for shift.time() is now registered. Thanks to Philippe Verspeelt for the report and PR (#268, #273). An if-statement in the xts constructor will no longer try to use a logical vector with length __ 1. Code like if (c(TRUE, TRUE)) will throw a warning in an upcoming R release, and this patch will prevent that warning. Thanks to Hugh Parsonage for the report and PR (#270, #272). Fix subset when index(i) and i contain duplicates. Observations were being incorrectly dropped, and behavior is now consistent with zoo. Thanks to Stack Overflow user scs for the report, and Philippe Verspeelt for the help debugging (#275). Make column names for merge() results with unnamed objects shorter and more like zoo (#248). Previously, column names could be hundreds, even thousands, of characters. This change has the added benefit of making na.fill() much faster (#259). NOTE: This may BREAK existing code for integer unnamed objects. The to.period() family of functions now use the index timezone when converting intraday index values to daily values (or lower frequency). Previously, the dates would be calculated as UTC dates, instead of dates in the local timezone (as they are now). Thanks to Garrett See and Gabor Grothendieck for the reports (#53, #277). As always, I'm looking forward to your questions and feedback! If you have a question, please ask on Stack Overflow and use the [r] and [xts] tags. Or you can send an email to the R-SIG-Finance mailing list (you must subscribe to post). Open an issue on GitHub if you find a bug or want to request a feature, but please read the contributing guide first!
Ten years ago today I wrote the NEWS.Rd entry in this screenshot for the very first Rcpp_release:
First Rcpp release
So Happy Tenth Birthday, Rcpp !! It has been quite a ride. Nearly 1500 packages on CRAN, or about one in nine (!!), rely on Rcpp to...
This is a post about dealing with tables that have been subject to cell suppression or perturbation for confidentiality reasons. While some of the data world is only just waking up to issues of confidentiality and privacy, national statistics offices ...
After more than a decade of consistent losses from rolling a long NG position, the cumulative return has been positive for the past 12 months.  This has only occurred briefly during the 'polar vortex' of early 2014 and during the 2008 commodities 'super cycle' peak.
For years, long only positions in NG have experienced losses on average in the last month before rolling the position.  The traditional explanation for this has been an oversupplied market relative to storage capacity.  Producers sell into the short term market; storage owners earn a 'negative convenience yield' for storing this into the future.Fracking costs have increased, partly driven by input costs Fracking PPI and increasing interest rates. Industrial demand has recovered according to the EIA.It seems likely that the natural gas market is in transition towards an equilibrium in which there is no longer an excess of supply being stored into the future relative to demand.Could this winter be the first test of this phenomenon?  Will we see significant backwardation in the curve as convenience yield accrues to holders of the physical commodity?
Simple exploratory data analysis (EDA) using some very easy one line commands in R.
Introduction I’ve spent the past few weeks writing about MCHT, my new package for Monte Carlo and bootstrap hypothesis testing. After discussing how to use MCHT safely, I discussed how to use it for maximized Monte Carlo (MMC) testing, then bootstrap testing. One may think I’ve said all I want to say about the package,…Read more Beyond Univariate, Single-Sample Data with MCHT
This is a fun part of the course where you learn how to add value to the business by providing ROI-Driven Data Science!
Week 8 will teach you how to calculate a simple policy change - Implementing a No Overtime Policy. You will calculate the expected ...
Motivation There are dozens of machine learning algorithms out there. It is impossible to learn all their mechanics, however, many algorithms sprout from the most established algorithms, e.g. ordinary least squares, gradient boosting, support vector machines, tree-based algorithms and neural networks. At STATWORX we discuss algorithms daily to evaluate their usefulness for a specific project or problem. In any case, ... Read More
Der Beitrag Coding Gradient boosted machines in 100 lines of code erschien zuerst auf STATWORX.
Introduction This is an analysis of the Kaggle 2018 survey dataset. In my analysis I am trying to understand the similarities and differences between men and women users from US and India, since these are the two biggest segments of the respondent population. The number of respondents who chose someting other than Male/Female is quite […]
Can you tell us about your upcoming keynote at EARL and what the key take-home messages will be for delegates? I’m going to talk about functional programming which I think is one of the most important programming techniques used with R. It’s not something you need on day 1 as a data scientist but it gives you some really powerful tools for repeating the same action again and again with code. It takes a little while to get your head…
Two weeks ago I mentioned that I was clearing my calendar until I finished writing A Guide to Working with Census Data in R. Today I’m...
The post “A Guide to Working With Census Data in R” is now Complete! appeared first on AriLamstein.com.
It was always my hope that the Fisheries Stock Assessment (FSA) R package would become a community endeavor. With the recent release on CRAN of v0.8.21 that hope has finally come to fruition. This version has two new co-authors.
Alexis Din...
R is designed to make working with statistical models fast, succinct, and reliable. For instance building a model is a one-liner: model
Teaching R is our mission at Business Science University because R is the most efficient language for exploring data, performing business analysis, and applying data science to business to extract ROI for an organization. R has an amazing ecosystem of ...
The Rocker Project maintains the official Docker images of interest to R users. I use their images as a base to deploy containerized Shiny apps, but the virtual size of the images I build tends to fall in the range between 400 and 600 MB. To reduce t...
A new release 0.4.13 of RProtoBuf got onto CRAN a few hours ago. RProtoBuf provides R with bindings for the Google Protocol Buffers (“ProtoBuf”) data encoding and serialization library used and released by Google, and deployed fairly widely in nu...
coalesce is a classic useful SQL operator that picks the first non-NULL value in a sequence of values. We thought we would share a nice version of it for picking non-NA R with convenient operator infix notation wrapr::coalesce(). Here is a short example of it in action: library(&quot;wrapr&quot;) NA %?% 0 # [1] 0 A … Continue reading coalesce with wrapr
R might not be the most obvious tool when it comes to analysing audio data. However, an increasing number of packages allows analysing and synthesising sounds. One of such packages is seewave. Jerome Sueur, one of the authors of seewave, now wrote a book about working with audio data in R. The book is entitled […]
And here is Le Monde mathematical puzzle  last competition problem Find the number of integers such that their 15 digits are all between 1,2,3,4, and the absolute difference between two consecutive digits is 1. Among these numbers how many have 1 as their three-before-last digit and how many have 2? Combinatorics!!! While it seems like […]
GHCN  version 4   beta is available.  Using the GHS  population dataset the ~27000  GHCNV4 sites were filtered  to collect only rural stations.   GHS  combines two datasets,  a  10meter  built surface  satellite dataset and a human population dataset.   https://ghsl.jrc.ec.europa.eu/.    using site locations the population within 10km  of the site was extracted.  two cases were considered:  […]
Automatically map complex operations safely or quietly, and quickly see the captured side effects.
R is an amazing tool to perform advanced statistical analysis and create stunning visualizations. However, data scientists and analytics practitioners do not work in silos, so these analysis have to be copied and emailed to senior managers and partners teams. Cut-copy-paste sounds great, but if it  is a daily or periodic task, it is more […]
Introduction Writing appealing interactive web applications – one of STATWORX's many competences – is an ease with R shiny. Just a few lines of code in one R script create the whole logic you need to let the whole magic of shiny happen. It is so simple that you can make a hello world app in a heartbeat, like so. ... Read More
Der Beitrag Master R shiny: One trick to build maintainable and scalable event chains erschien zuerst auf STATWORX.
We have our latest note on the theory of data wrangling up here. It discusses the roles of “block records” and “row records” in the cdata data transform tool. With that and the theory of how to design transforms, we think we have a pretty complete description of the system.
This report describes support for a new type of variable-width line in the ‘vwline’ package for R that is based on Bezier curves. There is ...
A new release of RcppAnnoy is now on CRAN.
RcppAnnoy is the Rcpp-based R integration of the nifty Annoy library by Erik. Annoy is a small and lightweight C++ template header library for very fast approximate nearest neighbours—originally developed ...
Here is the course link.
Course Description
This is an introductory course to the R programming language as applied in the context of political data analysis. In this course students learn how to wrangle, visualize, and model data with R by applying data science techniques to real-world political data such as public opinion polling and election results. The tools that you'll use in this course, from the dplyr, ggplot2, and choroplethr packages, among others, are staples of data science and can be used to analyze almost any dataset you get your hands on. Students will learn how to mutate columns and filter datasets, graph points and lines on charts, make maps, and create models to understand relationships between variables and predict the future. This course is suitable for anyone who already has downloaded R and knows the basics, like how to install packages.
Chapter 1: Presidential Job Approval Polls (Free)
Chapter one uses a dataset of job approval polling for US presidents since Harry Truman to introduce you to data wrangling and visualization in the tidyverse.
Chapter 2: U.S. House and Senate Polling
In this chapter, you will embark on a historical analysis of &quot;generic ballot&quot; US House polling and use data visualization and modeling to answer two big questions: Has the country changed over time? Can polls predict elections?
Chapter 3: Election Results and Political Demography
This chapter teaches you how to make maps and understand linear regression in R. With election results from the United States and the United Kingdom, you'll also learn how to use regression models to analyze the relationship between two (or more!) variables.
Chapter 4: Predicting the Future of Politics
In this ensemble of applied statistics and data analysis, you will wrangle, visualize, and model polling and prediction data for two sets of very important US elections: the 2018 House midterms and 2020 presidential election.
Prerequisites
Introduction to the Tidyverse
Dr. Gentleman’s work at 23andMe focuses on the exploration of how human genetic and trait data in the 23andMe database can be used to identify new therapies for disease. Dr. Gentleman is also recognised as one of the originators of the R programming language and has been awarded the Benjamin Franklin Award, a recognition for Open Access in the Life Sciences presented by the Bioinformatics Organisation. His keynote will focus on the History of R and some thoughts on data…
In this earlier post we analysed the location of meteorite impacts from this dataset, including plotting their fall locations on a globe. Forming part of...
At Jumping Rivers we run a lot of R courses. Some of our most popular courses revolve around the tidyverse, in particular, our Introduction to the tidyverse and our more advanced mastering course. We even trained over 200 data scientists NHS – see our case study for more details. As you can imagine, when giving
The post What R version do you really need for a package? appeared first on Jumping Rivers.
Azure ML Studio, the collaborative drag-and-drop data science workbench, now supports R 3.4 in the Execute R Script module. Now you can combine the built-in data manipulation and analysis modules of ML Studio with R scripts to accomplish other data tasks, as for example in this workflow for oil and gas tank forecasting. With the Execute R Script module you can immediately use more than 650 R packages which come preinstalled in the Azure ML Studio environment. You can also use other R packages (including packages not on CRAN) and source in R scripts you develop elsewhere (as shown above),...
Coming on the heels of last week’s RcppTOML 0.1.4 release bringing support for TOML v0.5.0, we have a new release 0.1.5 on CRAN with better encoding support as well as support for the time type.
RcppTOML brings TOML to R. TOML is a file format that...
Week 9 Gold Mining and Fantasy Football Projection Roundup now available. Go get that free agent gold! The post Gold-Mining Week 9 (2018) appeared first on Fantasy Football Analytics.
(translated from original French version)
The Rencontres Hivernales du Libre (RHL) (Winter Meeting of Freedom) takes place 25-27 January 2019 at St-Cergue.
Swisslinux.org invites the free software community to come and share workshops, great meals and ...
Dear R Programmers,Those who all use data.table for your data readings, good news is that now, fread supports direct reading of zip formats like&quot;gz&quot; and &quot;bz2&quot;.To all my followers and readers, as mentioned earlier several times, good way for saving both space and reading fast is achievable by first saving raw files into &quot;gz&quot; format and their after reading the same into R and convert them into to fst format for all further reads/loading.Happy R Programming!
In a prior post, we covered how to run Python from the Task Scheduler on Windows. This article is similar, but it’ll show how to run R from the Task Scheduler, instead. Similar to before, let’s first cover how to R from the command line, as knowing this is useful for running it from the […]
The post How to run R from the Task Scheduler appeared first on Open Source Automation.
Need something to distract the kids while they're waiting to head out Trick-or-Treating? You could have them try out a Creepy Computer Game in R! Engineer and social scientist Dr Peter Provost translated one of the old BASIC games from the classic book Creepy Computer Games into R: just have them type in this file (and yes, they do need to type it character-by-character to get the full experience) and then source it into R to play the game. The goal is to move your character * to the exit at the SE corner of the graveyard populated by gravestones...
For anyone in the NYC area, I will be holding an in-person training session December 3rd on doing data analysis with Python. We will be covering the pandas, pyodbc, and matplotlib packages. Please register at Eventbrite here: https://www.eventbrite.com/e/data-analysis-with-python-how-to-read-wrangle-and-analyze-data-tickets-51945542516. Overview Learn how to apply Python to read, wrangle, visualize, and analyze data!  This course provides a […]
The post Data Analysis with Python Course: How to read, wrangle, and analyze data appeared first on Open Source Automation.
The penultimate Le Monde mathematical puzzle  competition problem is once again anti-climactic and not worth its points: For the figure below [not the original one!], describing two (blue) half-circles intersecting with a square of side 20cm, and a (orange) quarter of a circle with radius 20cm, find the radii of both gold circles, respectively tangent […]
- Its been almost two years since I first hosted this blog as an alternative outlet of my writings. Everything is currently being built using jekyll and hosted in github. It works fine but I want to have more control over the dep...
The final results of the Data + Art STEAM project with Forest Hill Junior and Senior Public School in Toronto!
One of the concepts we teach in both Practical Data Science with R and in our theory of data shaping is the importance of identifying the roles of columns in your data. For example, to think in terms of multi-row records it helps to identify: Which columns are keys (together identify rows or records). Which … Continue reading Use Pseudo-Aggregators to Add Safety Checks to Your Data-Wrangling Workflow
What are the best R posts to read from last week? To help you decide, here is a list of the top R posts, sorted based on the number of likes they got on twitter, enjoy : Drawing beautiful maps programmatically with R, sf and ggplot2 — Part 1: Basics (219 likes) Drawing beautiful maps programmatically with R, sf and ggplot2 — Part 2: Layers (173 likes) Doing a Twitter Analysis with R (169 likes) Visualizations for correlation matrices in R (153 likes) How to perform merges (joins) on two or more data frames with base R, tidyverse and data.table (146 likes) Machine learning: Logistic regression and decision trees for healthcare in R (146 likes) Time series prediction – with deep learning (124 likes) A gentle introduction to reinforcement learning or what to do when you don’t know what to do (119 likes) How to import multiple data files (the fast way) (104 likes) Machine learning logistic regression for credit modelling in R (100 likes) How to import a subset of a (too huge) csv file (94 likes) Drawing beautiful maps programmatically with R, sf and ggplot2 — Part 3: Layouts (80 likes) Create a Glossary in R Markdown (74 […]
A few colleagues of mine and I from codecentric.ai are currently working on developing a free online course about machine learning and deep learning. As part of this course, I am developing a series of videos about machine learning basics - the first v...
Petrol is a product that used by most of Australians. So people are pretty sensitive to price changes, especially when the fuel become more expensive. With prices reaching $1.6 for unleaded the debates are becoming more and more hot. Are greedy petrol traders ripping off Australians or the price changes… The post Are petrol prices in Australia fair? appeared first on Alex Levashov - eCommerce Consultant (Melbourne, Australia).
carbon.js is the easiest way to create beautiful images of your source code, and carbonate creates an api in R to interact with it. The package gives a new simple way to share your script with the rest of the R community on posters, slides, Shiny apps and social media!
This little snippet combines SO MANY of my favorite #rstats 📦s:✨ @jimhester_'s glue🥛 @yoniceedee's carbonate🐦 @kearneymw's rtweet😎 @hadleywickham's emo📍 @krlmlr's here https://t.co/ploHThYu82
— 𝓛𝓾𝓬𝔂::maternity_leave🤱 (@LucyStats) August 30, 2018
Installation
#remotes::install_github('yonicd/carbonate')
install.packages('carbonate')
Usage
Initialize new carbon object
library(carbonate)
The default code in the carbon object is taken from the clipboard.
x
After week 7, you will be able to communicate confidently which model features are the most important.
Interpretability is a very important topic in machine learning. The automated machine learning tool, H2O, makes a data scientist’s life easier, ho...
Abstract:
As an applied statistician you get in touch with many challenging problems in need of a statistical solution. Often, your client/colleague already has a working solution and just wants to clarify a small statistical detail with you. Equally o...
Introduction Now that we’ve seen MCHT basics, how to make MCHTest() objects self-contained, and maximized Monte Carlo (MMC) testing with MCHT, let’s now talk about bootstrap testing. Not much is different when we’re doing bootstrap testing; the main difference is that the replicates used to generate test statistics depend on the data we feed to…Read more Bootstrap Testing with MCHT
I recently tried out Data Camp’s project called “Phyllotaxis: Draw flowers using mathematics”. Now I’m hooked on drawing spirals using golden angle. Also the mathematical art on instructor’s blog, Fronkostin is just amazing!
Separately on twitter, I’ve gotten message on art with prime number, and that got me thinking to experiment with prime number &amp; phyllotaxis flowers. I couldn’t figure out how I’d generate prime numbers in R, but I came across site where you can download prime numbers.
library(tidyverse)
library(patchwork)
## Read first 10000 digits of prime number!
prime
R is an interpreted programming language with vectorized data structures. This means a single R command can ask for very many arithmetic operations to be performed. This also means R computation can be fast. We will show an example of this using Conway’s Game of Life. Conway’s Game of Life is one of the most … Continue reading Conway’s Game of Life in R: Or On the Importance of Vectorizing Your R Code
In the 1980s I spent my time writing code on my 8-bit ZX81 and Atari computers. I learnt everything I know about programming from copying and modifying printed code listings from books with computer games. The games in these books are mostly simple text-based games, but the authors gave them enticing names, often imaginatively illustrated … Continue reading &quot;Celebrate Halloween with Creepy Computer Games in R&quot;
Abstract
You can find the data used in this blog post here: https://github.com/b-rodrigues/elections_lux
This is a follow up to a previous blog post
where I extracted data of the 2018 Luxembourguish elections from Excel Workbooks.
Now that I have the data, I will create a map of Luxembourg by commune, with pie charts of the
results on top of each commune! To do this, I use good ol’ {ggplot2} and another packages
called {scatterpie}. As a bonus, I have added the code to extract the data from the 2013
elections from Excel. You’ll find this code in the appendix at the end of the blog post.
Introduction
Before importing the data for the elections of 2018, let’s install some packages:
install.packages('rgeos', type='source') # Dependency of rgdal
install.packages('rgdal', type='source') # To read in the shapefile
These packages might be very tricky to install on OSX and Linux, but they’re needed to import the
shapefile of the country, which is needed to draw a map. So to make things easier, I have
created an rds object, from the shapefile of Luxembourg, that you can import natively in R without
needing these two packages. But if you want to use them, here is how:
communes
In my previous post, I showed how to use cdata package along with ggplot2‘s faceting facility to compactly plot two related graphs from the same data. This got me thinking: can I use cdata to produce a ggplot2 version of a scatterplot matrix, or pairs plot? A pairs plot compactly plots every (numeric) variable in … Continue reading Scatterplot matrices (pair plots) with cdata and ggplot2
Full many a gem of purest ray serene, The dark unfathomed caves of ocean bear; Full many a flower is born to blush unseen, And waste its sweetness on the desert air. Thomas Gray, An Elegy Written In A Country Churchyard Introduction It is finally here! cricpy, the python avatar , of my R package cricketr is now … Continue reading Introducing cricpy:A python package to analyze performances of cricketers
A new minor release of RcppRedis arrived on CRAN earlier today. RcppRedis is one of several packages to connect R to the fabulous Redis in-memory datastructure store (and much more). RcppRedis does not pretend to be feature complete, but it may do so...
What’s a Lectionary?
A lectionary, according to Wikipedia, is a listing of scripture readings for Christian or Judaic worship on a given day. The Roman Catholic Lectionary will contain a list of readings for a specific day that are on a 3-year cyc...
Introduction
In this post in the R:case4base series we will look at one of the most common operations on multiple data frames - merge, also known as JOIN in SQL terms.
We will learn how to do the 4 basic types of join - inner, left, right and full join with base R and show how to perform the same with tidyverse’s dplyr and data.table’s methods. A quick benchmark will also be included.
Joins
Contents
Merging (joining) two data frames with base R
The arguments of merge
Merging multiple data frames
Alternatives to base R
Quick benchmarking
TL;DR - Just want the code
References
Merging (joining) two data frames with base R
To showcase the merging, we will use a very slightly modified dataset provided by Hadley Wickham’s nycflights13 package, mainly the flights and weather data frames. Let’s get right into it and simply show how to perform the different types of joins with base R.
First, we prepare the data and store the columns we will merge by (join on) into mergeCols:
dataurl
Julia is a Data Scientist at Stack Overflow, has a PhD in astrophysics and an abiding love for Jane Austen (which we totally understand!). Before moving into Data Science and discovering R, Julia worked in academia and ed tech, and was a NASA Datanaut. She enjoys making beautiful charts, programming in R, text mining, and communicating about technical topics with diverse audiences. In fact, she loves R and text mining so much, she literally wrote the book on it: Text…
It is a relatively rare event, and cause for celebration, when CRAN gets a new Task View. This week the r-miss-tastic team: Julie Josse, Nicholas Tierney and Nathalie Vialaneix launched the Missing Data Task View. Even though I did some research on R packages for a post on missing values a couple of years ago, I was dumbfounded by the number of packages included in the new Task View.
This single page not only describes what R has to offer with respect to coping with missing data, it is probably the world’s most complete index of statistical knowledge on the subject. But, the task view is not just devoted to esoterica. The Exploration of missing data section contains a number of tools that should be useful in any data wrangling effort like this plot diagnostic plot from the dlookr package.
The downside that may curb one’s enthusiasm, is that mastering missing data techniques requires some serious study. Missing data problems are among the most vexing in statistics, and the newer techniques for tackling these problems are relatively sophisticated. So, unless you are a na.omit() kind of guy/gal (a data scientist?) coming to grips with NAs may involve a subtle inferential task embedded in the usual data wrangling effort.
It is also the case that it is not easy to find good introductory material on the subject. The notable exception is Stef van Buuren’s authoritative R-based textbook, Flexible Imputation of Missing Data which he has graciously made available online.
I also found the review papers by Dong and Peng and by Horton and Kleinman to be helpful. And, if you read French a little better than I do, the review by Imbert and Vialaneix looks like it covers all of the basic material.
Finally, I should mention that the Missing Data Task View is part of the R Consortium funded project A unified platform for missing values methods and workflows. Many thanks to the ISC for making it possible for the expert r-miss-tastic team to do the work.
Week 8 Gold Mining and Fantasy Football Projection Roundup now available. Go get that free agent gold! The post Gold-Mining Week 8 (2018) appeared first on Fantasy Football Analytics.
Summary
The data science community, Kaggle, recently announced the Google Analytics Customer Revenue Prediction competition. The competition uses data from the Google Merchandise store, and the challenge is to create a model that will predict the tota...
For the last months, ThinkR has been involved (with Mango, Procogia and the Linux Foundation) in a working group for an RConsortium R Certification.  Why this Working Group (WG)? No doubt the demand for R (and for Data Science) has been increasing over the past few years — Data Scientist became the sexiest job of the century according to Harvard Business Review, and Glassdoor listed it as the “Best Job in America” in 2016, 2017 and 2018. No need to argue on that point: data science is now one of the top skills to have on the market. With the growth in interest came a growth ofRead More →
The post RConsortium — Building an R Certification appeared first on (en) The R Task Force.
Authors: John Mount, and Nina Zumel 2018-10-25 As a followup to our previous post, this post goes a bit deeper into reasoning about data transforms using the cdata package. The cdata packages demonstrates the &quot;coordinatized data&quot; theory and includes an implementation of the &quot;fluid data&quot; methodology for general data re-shaping. cdata adheres to the so-called … Continue reading Designing Transforms for Data Reshaping with cdata
view raw Rmd
This tutorial is the third part in a series of three:
General concepts illustrated with the world map
Adding additional layers: an example with points and polygons
Positioning and layout for complex maps (this document)
After th...
Spatial visualizations really come to life when you have a real map as a background. In R, ggmap is the package that you’ll want to use to get these maps. In what follows, we’ll demonstrate how to use ggmap with … Continue reading →
At DataCamp, we pride ourselves on having the best platform and the best curriculum for learning data science. To this end, we put a lot of effort into ensuring that every exercise is both effective at educating and enjoyable. After a course is launche...
Anscombe's Quartet is a famous collection of four small data sets — just 11 (x,y) pairs each — that was developed in the 1970s to emphasize the fact that sometimes, numerical summaries of data aren't enough. (For a modern take on this idea, see also the Datasaurus Dozen.) In this case, it takes visualizing the data to realize that the for data sets are qualitatively very different, even though the means, variances, and regression coefficients are all the same. In the video below for Guy in a Cube, Buck Woody uses R to summarize the data (which is conveniently built...
I was thinking about creating a glossary in bookdown and found out that there was already an issue about it. I like Yihui’s recommendation: use Pandoc’s definition lists. This was exactly what I had been doing, but I quickly found out that there was a major drawback – the definition lists won’t order alphabetically unless written in that way.
So I wrote an R function to reorder the definition lists written in R Markdown. Note that this functions only works for R Markdown files containing defintion lists exclusively. If the R Markdown files aren’t whole-definition-lists, the function will fail.
Usage
To order the definition lists alphabetically, simply put the Rmd file path in the function. To have a different output file, provide the output file path as the second argument.
sort_def_list(&quot;glossary.Rmd&quot;)
# sort_def_list(&quot;glossary.Rmd&quot;, &quot;reordered.Rmd&quot;)
The output in PDF looks like this (I used the multicol package)1:
Source Code
Notes
To see the source R Markdown file, visit glossary.rmd. To see the output PDF, visit glossary.pdf↩
Visit R-bloggers
Last updated: 2018-10-24
first steps: how to For those who will laugh at seeing deep learning with one hidden layer and the Iris data set of 150 records, I will say: you’re perfectly right 🙂 The goal at this stage is simply to take the first steps fit a regression model manually (hard way) Subject: predict Sepal.Length given … Continue reading automl package: part 2/2 first steps how to
Join us at the EARL US Roadshow – a conference dedicated to the real-world usage of R EARL, the Enterprise Applications of the R Language Conference is set to embark on a US roadshow following a successful London conference in September, with dates in Seattle, Houston and Boston between 7th and 13th November. This cross-sector conference hosted and organised by Mango Solutions focuses on the commercial use of the R programming language and showcases some of the world’s leading practitioners…
rOpenSci’s software engineer / postdoc Jeroen Ooms will explain what images are, under the hood, and showcase several rOpenSci packages that form a modern toolkit for working with images in R, including opencv, av, tesseract, magick and pdftools.
🕘 Thursday, November 15, 2018, 10-11AM PST; 7-8PM CET (find your timezone)
☎️ Find all details for joining the call on our Community Calls page.
Everyone is welcome. No RSVP needed.
Agenda
Welcome (Stefanie Butland, rOpenSci Community Manager, 5 min)
Working with images in R (Jeroen Ooms, 35 min)
Q &amp; A (20 min)
Abstract
Images in various forms are used for numerous applications across scientific disciplines. Whether you are observing through satellite or microscope, looking at MRI scans or petri dishes, trying to find patterns or abnormalities, the data is in the image. Unfortunately the tools for working with images are traditionally highly fragmented by field, and often narrow in scope. At rOpenSci we are working on a suite of general purpose packages based on powerful c/c++ libraries. These provide an extensible and interoperable foundation for working with images in R, which can be used to implement domain specific-methods. This talk gives a taste of things we can currently do with images in R, and highlights some of the ongoing developments and challenges.
Speaker
Jeroen Ooms is a postdoc and software engineer at rOpenSciJeroen on GitHub, Twitter
Following the highly successful M4 Forecasting Competition, there will be a conference held on 10-11 December at Tribeca Rooftop, New York, to discuss the results. The conference will elaborate on the findings of the M4 Competition, with prominent sp...
CategoriesRegression Models
Tags
Logistic Regression
R Programming
ROC
Machine learning logistic regressions is a widely popular method to model credit modeling. There are excellent and efficient packages in R, that can perform these types of analysis. Typically you will first create different machine learning visualizations before you perform the machine learning logistic regression analysis. This article is the second step of a credit […]Related Post
Commercial data analytics: An economic view on the data science methods
Weight loss in the U.S. – An analysis of NHANES data with tidyverse
Machine Learning Results in R: one plot to rule them all! (Part 2 – Regression Models)
Story of pairs, ggpairs, and the linear regression
Extract FRED Data for OLS Regression Analysis: A Complete R Tutorial
One of the differences between statistical data scientists and machine learning engineers is that while the latter group are concerned primarily with the predictive performance of a model, the former group are also concerned with the fit of the model. A model that misses important structures in the data — for example, seasonal trends, or a poor fit to specific subgroups — is likely to be lacking important variables or features in the source data. You can try different machine learning techniques or adjust hyperparameters to your heart's content, but you're unlikely to discover problems like this without evaluating the...
I use generalized additive models (GAMs) in my research work. I use them a lot! Simon Wood’s mgcv package is an excellent set of software for specifying, fitting, and visualizing GAMs for very large data sets. Despite recently dabbling with brms, mgcv is still my go-to GAM package. The only down-side to mgcv is that it is not very tidy-aware and the ggplot-verse may as well not exist as far as it is concerned. This in itself is no bad thing, though as someone who uses mgcv a lot but also prefers to do my plotting with ggplot2, this lack of awareness was starting to hurt. So, I started working on something to help bridge the gap between these two separate worlds that I inhabit. The fruit of that labour is gratia, and development has progressed to the stage where I am ready to talk a bit more about it.
gratia is an R package for working with GAMs fitted with gam(), bam() or gamm() from mgcv or gamm4() from the gamm4 package, although functionality for handling the latter is not yet implement. gratia provides functions to replace the base-graphics-based plot.gam() and gam.check() that mgcv provides with ggplot2-based versions. Recent changes have also resulted in gratia being much more tidyverse aware and it now (mostly) returns outputs as tibbles.
In this post I wanted to give a flavour of what is currently possible with gratia and outline what still needs to be implemented.
A new version of our RcppTOML package just arrived on CRAN. It wraps an updated version of the cpptoml parser which, after a correction or two, now brings support for TOML v0.5.0 – which is still rather rare.
RcppTOML brings TOML to R. TOML is a fi...
It’s been a while since I shared any information about the R Consortium’s Census Working Group. Today I’d like to share an update on three...
The post Update on the R Consortium Census Working Group appeared first on AriLamstein.com.
Welcome to week 6 of the Data Science with R Course Series.
This week’s curriculum is an extension of week 5, where we started analyzing model performance.
This week will cover the following:
Learn techniques to communicate and measure model pe...
Introduction I introduced MCHT two weeks ago and presented it as a package for Monte Carlo and boostrap hypothesis testing. Last week, I delved into important technical details and showed how to make self-contained MCHTest objects that don’t suffer side effects from changes in the global namespace. In this article I show how to perform…Read more Maximized Monte Carlo Testing with MCHT
If you have any basic experience with R, you probably noticed that R uses three dots ellipsis (…) to allow functions to take arguments that weren’t pre-defined or hard-coded when the function was built. Even though R beginners are usually aware of this behavior, especially due to some common functions that implement it (for example, […]
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Well, there are actually more like 74 posts in this list, sorted based on number of likes on twitter, but you can focus on the first 7 to not get overrun by the variation, enjoy:   Title Number of likes R for Beginners: Logistic Regression 163 How to plot animated maps with gganimate 139 Generalized Linear Models understanding the link function 129 A simple example of webscraping with R : the top 5% economist ranking 120 p-values and null hypotheses 97 R for Beginners: Modeling Regression Effects Using REffects Library 94 Use R with Excel: Importing and Exporting Data 91 Introduction to statistical thinking 89 Loading and visualizing Shapefiles in R 88 Obtaining the number of components from cross validation of principal components regression 86 R Training – Data Manipulation 81 R for Beginners: Creating a Twitter App for Scraping Data 76 R Training – Data Visualization 72 Prophets of gloom: Using NLP to analyze Radiohead lyrics 72 Running R scripts within in-database SQL Server Machine Learning 70 Modifying Excel Files using openxlsx 67 5 Alternatives to the Default R Outputs for GLMs and Linear Models 66 shinytest – Automated testing for Shiny apps 51 How to change the number […]
This summer we had five interns participate in our internship program. Each intern was here for 10 weeks and worked closely with a mentor or team. Everyone jumped right in and contributed quickly. We are excited about the progress our interns made and wanted to share it with you here!
Fanny Chow (@Fannystats) worked with Max Kuhn on bootstrapping methods in the rsample package. Check out her blog post here.
Alex Hayes (@alexpghayes) work on a major new release of the `broom` package featuring more modern behavior, new tidiers, new documentation and some refactoring of package internals. He wrote a blog post about his experience.
Dana Seidel (@dpseidel) worked with Hadley this summer preparing the scales 1.0.0 release and improving themes and secondary axis functionality in ggplot2. Read more about her summer projects on her blog and take a look at slides from her September 19th talk introducing the scales package to RLadies-SF.
Timothy Mastny (@timmastny) worked with Winston Chang and the Shiny team on Sass. Sass is a CSS compiler that will help make dynamic themes for Shiny apps and other R packages. He made this R package from the internship.
Irene Steves (@i_steves) worked with Jenny Bryan on The Tidies of March. Here’s a link to her blog post. Irene also presented at R-Ladies in Paris.
Thank you again to our interns and Hadley, Max, Jenny, Winston, and Dave!
Introduction: The purpose of this Shiny web app is to better advise clients in residential property investments in New York City by visualizing sales and rental market trends in major areas. Check out the app HERE, and find the code HERE. Data: Sales and rental data are compiled from OneBlockOver by Streeteasy. The dataset used […]
The first update in a little while brings us release 0.0.4 of RApiDatetime which got onto CRAN this morning via the lovely automated sequence of submission, pretest-recheck and pretest-publish.
RApiDatetime provides seven entry points for C-level fun...
Why &amp; how automl package automl package provides: Deep Learning last tricks (those who have taken Andrew NG’s MOOC on Coursera will be in familiar territory) hyperparameters autotune with metaheuristic (PSO) experimental stuff and more to come (you’re welcome as coauthor!) Deep Learning existing frameworks, disadvantages Deploying and maintaining most Deep Learning frameworks means: Python… … Continue reading automl package: part 1/2 why and how
In between client work, John and I have been busy working on our book, Practical Data Science with R, 2nd Edition. To demonstrate a toy example for the section I’m working on, I needed scatter plots of the petal and sepal dimensions of the iris data, like so: I wanted a plot for petal dimensions … Continue reading Faceted Graphs with cdata and ggplot2
Two statistical indices crossed my inbox in the last week, both of which use fast food restaurants to measure a concept indirectly.First up, in the wake of recent hurricanes, is the Waffle House Index. As The Economist explains:Waffle House, a breakfas...
Some pre-Halloween post today. It started actually while I was in Barcelona : kids wanted to go back to some store we’ve seen the first day, in the gothic part, and I could not remember where it was. And I said to myself that would be quite long to do all the street of the neighborhood. And I discovered that it was actually an old problem. In 1962, Meigu Guan was interested in a postman delivering mail to a number of streets such that … Continue reading Solving the chinese postman problem →
A Lazy Function
I have just submitted a revised version of survHE on CRAN — it should be up very shortly. This will be version 1.0.64 and its main feature is a major restructuring in the way the rstan/HMC stuff works.
Basically, this is due to a change in the default C++ compiler. I don’t think much will change in terms of how survHE works when running full Bayesian models using HMC, but R now compiles it without problems. After the advice of Ben Goodrich, I have also modified the package so that it compiles the Stan programs serially as opposed to gluing them all together, which should optmise the use of the memory.
In 2016, 2.1 million Americans were found to have an opioid use disorder (according to SAMHSA), with drug overdose now the leading cause of injury and death in the United States. But some of the country’s top minds are working to fight this epidemic, and statisticians are helping to lead the charge.  In This is Statistics’ second annual … Continue reading Statistics Challenge Invites Students to Tackle Opioid Crisis Using Real-World Data
CategoriesVisualizing Data
Tags
Data Visualisation
ggplot2
R Programming
tidyverse
Visualization is a great way to get an overview of credit modeling. Typically you will start by making data management and data cleaning and after this, your credit modeling analysis will start with visualizations. This article is, therefore, the first part of a credit machine learning analysis with visualizations. The second part of the analysis […]Related Post
Decision Trees and Random Forests in R
Add value to your visualizations in R
Visualize your Portfolio’s Performance and Generate a Nice Report with R
Exploring San Francisco Bay Area’s Bike Share System
Analysis and Visualization of Blue Bikes Sharing in Boston
An Introduction to Loops in R - Loops in R
First, if you are new to programming, you should know that loops are a
way to tell the computer that you want to repeat some operation for a
number of times. This is a very common task t...
Week 7 Gold Mining and Fantasy Football Projection Roundup now available. Go check out our cheat sheet for this week. The post Gold-Mining Week 7 (2018) appeared first on Fantasy Football Analytics.
Here is the course link.
Course Description
This course will help you take your data visualization skills beyond the basics and hone them into a powerful member of your data science toolkit. Over the lessons we will use two interesting open datasets t...
Here is the course link.
Course Description
Data visualization is an integral part of the data analysis process. This course will get you introduced to rbokeh: a visualization library for interactive web-based plots. You will learn how to use rbokeh l...
Game of Chefs is an Israeli reality cooking competition show, where chefs compete for the title of &quot;Israel's most talented chef&quot;. The show has four stages: blind auditions, training camp, kitchen battles, and finals. Here I will examine the 4th season's (Game of Chefs: Confectioner) inter-rater reliability in the blind auditions, using some simple R code.The Format
In the blind auditions, candidates have 90 minutes to prepare a dessert, confection or other baked good, which is then sent to the show's four judges for a blind taste test. The judges don't see the candidate, and know nothing about him or her, until after they deliver their decision - A &quot;pass&quot; decision is signified by the judge granting the candidate a kitchen knife; a &quot;fail&quot; decision is signified by the judge not granting a knife. A candidate who receives a knife from at least three of the judges continues on to the next stage, the training camp.
The 4 judges and host, from left to right: Yossi Shitrit, Erez Komarovsky, Miri Bohadana (host), Assaf Granit, and Moshik Roth
Inter-Rater Reliability
I've watched all 4 episodes of the blind auditions (for purely academic reasons!), for a total of 31 candidates. For each dish, I recorded each of the four judges' verdict (fail / pass).Let's load the data.
df
CategoriesData Management
Tags
Databases
Import Data
R Programming
Tips &amp; Tricks
In this article, you learn how to connect R with Excel by importing and exporting data between the two programs. Excel: pros and cons Excel is still very popular among companies and organizations. One of the main advantages in a spreadsheet is ease of providing the user with a rapid overview of a dataset, using […]Related Post
Combining data in R: the skill of merging, joining, and stacking
Efficient data management and SQL data selection in R
Essential data cleaning for ad-hoc tasks in R
Proteomics Data Analysis (2/3): Data Filtering and Missing Value Imputation
Clean Your Data in Seconds with This R Function
The Professional Footballers’ Association (PFA) Team of the Year is released in England at the end of each season, picking the 11 most influential players in each of Britain’s leagues. The Team of the Year award was launched in the 1973-1974 season, meaning there was 44 years worth of data to web scrape. Using Wikipedia’s PFA […]
Are you looking for some more information on building a data warehouse? The Intellixus website is the place you need to be today. A dedicated individual called Claude set up this blog in order to share some of his valuable... Read More ›
The post Building a data warehouse appeared first on Claude Seidman - The Data Guy.
A couple of weeks ago, the first of September we had satRday in Amsterdam (The Netherlands) a fantastic event hosted by GoDataDriven. Now the great talks, including my 10 minute lightning talk on text2vec are online. My talk The satRday … Continue reading →
The standard summary outputs from the glm and lm summary methods are a case in point. If you have been using R for as long...
It’s been pretty quiet over here for a long time. But I have been busy beavering away at many interesting projects at NIWA, including a project where we developed a new method for identifying when your regression model is starting … Continue reading →
During my stay in London for the m3 conference, I also gave a talk at the R-Ladies London Meetup on Tuesday, October 16th, about one of my favorite topics: Interpretable Deep Learning with R, Keras and LIME.
Keras is a high-level open-source deep lear...
In August of 2003 Thomas Lumley added bquote() to R 1.8.1. This gave R and R users an explicit Lisp-style quasiquotation capability. bquote() and quasiquotation are actually quite powerful. Professor Thomas Lumley should get, and should continue to receive, a lot of credit and thanks for introducing the concept into R. In fact bquote() is … Continue reading Quasiquotation in R via bquote()
I’ve been creating several output tables for a paper, which I usually store as
sheets in an Excel file, since my collaborators are entirely in the Microsoft Office ecosystem.
One issue I often run into is having to modify a single sheet in that file with updated data, while keeping the rest of the file intact. This is necessary since I’ve perhaps done some custom formatting in Excel on some of the tables, and I don’t
want to re-format them everytime I modify a single sheet. This problem can be alleviated by creating output functions in R that properly format the output tables in the first place, making the entire process reproducible. Working on this one, stay tuned!
But for now, how to modify a single Excel sheet in a file? The openxlsx package allows this to be done very easily. As an aside, if you interact with R and Excel and are not using openxlsx, why aren’t you? This doesn’t depend on Java and has several powerful features.
The following code reads an existing Excel file, checks if a particular sheet exists,
creates it if it doesn’t, and writes data from a data.frame results to it and then saves it back on disk:
library(openxlsx)
boldHeader
I came across this post which gives a method to estimate Pi by using a circle, it’s circumscribed square and (lots of) random points within said square. Booth used Stata to estimate Pi, but here’s some R code to do the same thing… x
This is the last update to this strange saga… I hope. Easily two of the most popular posts on my blog are this one and this one describing a couple of ways in which I managed to hack together using...Continue Reading →
Petros and his colleagues at DARTH have organised a workshop on Decision Modeling in R, in Toronto (Canada) — the exact dates are 5-7 December 2018.
Here’s Petros’ original advert
We are excited to announce that the Decision Analysis in R for Technologies in Health (DARTH) workgroup, in collaboration with the Hospital for Sick Children and partnering institutions, will be giving a 3-day workshop on Decision modeling using R in Toronto, Canada!
When: Wednesday December 5th to Friday December 7th, 2018
Location: The Hospital for Sick Children, Toronto, Canada
Please note that you may register to attend in-person or by webinar
For novice R users, a 1-day ‘Introduction to R’ course will be offered on Tuesday December 4th, 2018.
For more information and registration details, please visit: http://darthworkgroup.com/workshops/ or visit: the Workshop’s website.
Please feel free to share this information with anyone you think will be interested.
For further information, please contact us here.
I went to give an intense lecture on BCEA and then a seminar on our work on EVPPI a couple of years back — and I did have a lot of fun, despite the very cold weather (but then it was in February, so possibly much worse than this time around).
More importantly, I think the guys at DARTH are doing a very nice and complementary work on pushing people around so that they use R as much as possible in economic evaluation and so I think events like this are absolutely great!
In R, the logical || (OR) and &amp;&amp; (AND) operators are unique in that they are designed only to work with scalar arguments. Typically used in statements like while(iter __ 1000 &amp;&amp; eps __ 0.0001) continue_optimization() the assumption is that the objects on either side (in the example above, iter and eps) are single values (that is, vectors of length 1) — nothing else makes sense for control flow branching like this. If either iter or eps above happened to be vectors with more than one value, R would silently consider only the first elements when making the AND comparison....
Today I received in the mail a copy of the short book published by edp sciences after the courses we gave last year at the astrophysics summer school, in Autrans. Which contains a quick introduction to ABC extracted from my notes (which I still hope to turn into a book!). As well as a longer […]
The culmination of the previous weeks has been preparation for machine learning modeling. At this stage, we have defined the business problem, we’ve explored and understood how the data relates to the business problem, and we’ve preprocessed the da...
Introduction Last week I announced the first release of MCHT, an R package that facilitates bootstrap and Monte Carlo hypothesis testing. In this article, I will elaborate on some important technical details about making MCHTest objects, explaining in the process how closures and R environments work. To recap, last week I made a basic MCHTest-class…Read more MCHT, Closures, and R Environments: Making MCHTest Objects Self-Contained
Amazing artworks people made in R:See their source code and more arts at:http://www.r-graph-gallery.com/286-antonio-sanchez-dataart/
dplyr is much more useful than tapply, read on to find out why.
I love R and I love Shiny. One of the things I really like about shiny is the ease with which you can incorporate other Javascript based tools and libraries. By my own admission, my JavaScript skills are definitely lacking but there are so many cool libraries out there which can really make visualisation and
The post Voice Control your Shiny Apps appeared first on Jumping Rivers.
Once upon a time, we at STATWORX used Slack just as a messenger, but than everything changed when emojis came… Since then, we use them for all kinds of purposes. For example we take polls with them to see were we will eat lunch or we capture unforgettable moments by creating new emojis. The possibilities are limitless! But since we ... Read More
Der Beitrag How we use emojis erschien zuerst auf STATWORX.
Shiny modules are short (well, usually short) server and UI functions, that can be connected to each other by a common namespace, and be embedded within a regular Shiny app. You can’t run a Shiny module without a parent Shiny app. The modules can contain both inputs and outputs, and are usually centered around a […]
Related exercise sets:
Building Shiny App exercises part 3 Parallel Computing Exercises: Foreach and DoParallel (Part-2) Parallel Computing Exercises: Snow and Rmpi (Part-3) Explore all our (__1000) R exercises
Find an R course using our R Course Finder directory
A new bugfix release 0.2.5 of RcppCCTZ got onto CRAN this morning – just a good week after the previous release.
RcppCCTZ uses Rcpp to bring CCTZ to R. CCTZ is a C++ library for translating between absolute and civil times using the rules of a time...
Principal components (PC) regression is a common dimensionality reduction technique in supervised learning. The R lab for PC regression in James et al.’s Introduction to Statistical Learning is a popular intro for how to perform PC regression in R: it is … Continue reading →
Manfred te Grotenhuis passed away. He was a respected sociologist, statistician, and teacher. I’ll leave it to others to comment on these achievements. To me, he was my teacher and mentor in statistics, and a ...
¡Ay que ver cómo se estropean los cuerpos! (Pilar, my beloved grandmother) My grandmother was a master of sewing. When she was young, she worked as dressmaker, and her profession became a hobby with the passage of time. I remember her doing cross-stitch, embroidering tablecloths and doing crochet. I have some of her artworks at … Continue reading Crochet Patterns →
I’ve recently added new bloggers to the site, leading to an influx of posts (142 new posts on the site, just last week). And while I love R, I do not have the time to read them all. Luckily, these posts are also published on R-bloggers twitter page, where over 62K followers see the new posts and decide if to give them a “like” or not. So I thought it might be helpful to sort all of last weeks posts (that got at least 15 likes), just to make sure I will not miss out on reading a particular popular post. I’m posting it here in the hopes that this would be helpful to others as well. Here is the list:   Title Number of likes R and Python: How to Integrate the Best of Both into Your Data Science Workflow 223 How to build your own Neural Network from scratch in R 209 Introduction to ggplot2 110 Making Calendar with ggplot + Moon Phase Calendar for fun 105 How to import a directory of csvs at once with base R and data.table. Can you guess which way 99 Geocoding with ggmap and the Google API 99 neuralnet: Train and […]
In previous posts (https://statcompute.wordpress.com/2017/01/22/monotonic-binning-with-smbinning-package) and (https://statcompute.wordpress.com/2017/06/15/finer-monotonic-binning-based-on-isotonic-regression), I’ve developed 2 different algorithms for monotonic binning. While the first tends to generate bins with equal densities, the second would define finer bins based on the isotonic regression. In the code snippet below, a third approach would be illustrated for the purpose to generate bins with roughly equal-sized […]
I’d like to introduce to you the New Zealand Trade Intelligence Dashboard. This is one of the New Zealand government shiny web apps that should have been mentioned in Peter Ellis’ post. If you are only interested in TRADE, you may … Continue reading →
Having all the R functions, all libraries and any kind of definitions (URL, links, working directories, environments, memory, etc) in one file is nothing new, but sometimes a lifesaver. Using R function source is the function to achieve this. Storing…Read more ›
Statistics Sunday: Some Psychometrics Tricks in R It's been a long time since I've posted a Statistics Sunday post! Now that I'm moved out of my apartment and into my house, I have a bit more time on my hands, but work has been quite busy. Today, I'm preparing for 2 upcoming standard-setting studies by drawing a sample of items from 2 of our exams. So I thought I'd share what I'm up to in order to pass on some of these new psychometric tricks I've learned to help me with this project.Because I can't share data from our item banks, I'll generate a fake dataset to use in my demonstration. For the exams I'm using for my upcoming standard setting, I want to draw a large sample of items, stratified by both item difficulty (so that I have a range of items across the Rasch difficulties) and item domain (the topic from the exam outline that is assessed by that item). Let's pretend I have an exam with 3 domains, and a bank of 600 items. I can generate that data like this:domain1
From the first time I listened to Radiohead’s The Bends, the band has been my favorite. I was a grad student in England at the time, and I recall listening to “Fake Plastic Trees” on repeat as I made my way to and from the library each day. By the time OK Computer came out, […]
The post Prophets of gloom: Using NLP to analyze Radiohead lyrics appeared first on my (mis)adventures in R programming.
In our wrapr pipe RJournal article we used piping into ggplot2 layers/geoms/items as an example. Being able to use the same pipe operator for data processing steps and for ggplot2 layering is a question that comes up from time to time (for example: Why can’t ggplot2 use %__%?). In fact the primary ggplot2 package author … Continue reading Piping into ggplot2
A new package of ours, RcppNLoptExample, arrived on CRAN yesterday after a somewhat longer-than-usual wait for new packages as CRAN seems really busy these days. As always, a big and very grateful Thank You! for all they do to keep this community hum...
- Its been a while since I write a CRAN package and this weekend I decided
to work on a idea I had some time ago. The result is package
PkgsFromFiles.
When working with different computers at home or work, one of the
problems I ...
Introduction
Inspired by a recent post on how to import a directory of csv files at once using purrr and readr by Garrick, in this post we will try achieving the same using base R with no extra packages, and with data·table, another very popular package and as an added bonus, we will play a bit with benchmarking to see which of the methods is the fastest, including the tidyverse approach in the benchmark.
Let us show how to import all csvs from a folder into a data frame, with nothing but base R
To get the source data, download the zip file from this link and unzip it into a folder, we will refer to the folder path as data_dir.
Quick import of all csvs with base R
To import all .csv files from the data_dir directory and place them into a single data frame called result, all we have to do is:
filePaths
Four years ago I referenced the Seinfeld Streak used in an earlier post of regular updates to to the Rcpp Gallery:
This is sometimes called Jerry Seinfeld’s secret to productivity: Just keep at it. Don’t break the streak.
and then showed the fi...
STATWORX welcomes you to our open workshop “Deep Learning in R and Keras” ! The workshop is designed for advanced R-users who are fluent in importing, processing and manipulating data. The workshop will be held in german at our office in Frankfurt. You will learn how to construct, estimate and evaluate both basic and more complex neural networks in R ... Read More
Der Beitrag Open Workshop: Deep Learning in R and Keras, November 14th in Frankfurt erschien zuerst auf STATWORX.
My prior post on animated flow in the Delaware River contained the last work I performed on my previous lap top just before it gave up the ghost.  When a colleague asked me to share it, I was distressed to realize that the script was lost.  I decided to recreate the script from scratch and to fix one the limitations of the older version by making it easily transportable to other river systems.  Specifically, all the USGS gage numbers, drainage areas, and river miles are contained in a .csv file.  To apply the new script to the different river, I just need to populate the .csv with local information.  I'm glad I did that, since there were several high flow events at other rivers that I wanted to inspect using this script.Above is the Neuse River after Hurricane Florence.  I also applied this to the Susquehanna River and the Schuylkill River.
The Economist's Big Mac Index (also described on Wikipedia if you're not a subscriber) was created (somewhat tongue-in-cheek) as a measure to compare the purchasing power of money in different countries. Since Big Macs are available just about everywhere in the world, the price of a Big Mac in Sweden — expressed in US dollars — gives an American traveler a sense of how much more expensive things will be in Stockholm. And comparing the price of a Big Mac in several countries converted to a single baseline currency is a measure of how over-valued (or undervalued) those other currencies...
In this post we’re going to model the prices of Airbnb appartments in London. In other words, the aim is to build our own price suggestion model. We will be using data from http://insideairbnb.com/ which we collected in April 2018. This work is inspired from the Airbnb price prediction model built by Dino Rodriguez, Chase Davis, and Ayomide Opeyemi. Normally we would be doing this in R but we thought we’d try our hand at Python for a change. We present a shortened version…
I have had a long history with Excel TV and, like Excel TV (and Excel itself) the channel has changed over the years. Gone are their regular live-streaming interviews with leading Excel authorities. Excel TV’s main product is now online courses. These are pre-recorded classes taught by the same caliber of talent as that of […]
The third release of the binb package just arrived on CRAN, and it comes with a new (and very crispy) theme: Monash. With that we are also thrilled to welcome Rob Hyndman as a co-author.
Here is a quick demo combining all (by now four) themes:
Also,...
These are my sketchnotes for Sam Charrington’s podcast This Week in Machine Learning and AI about Evaluating Model Explainability Methods with Sara Hooker:
Sketchnotes from TWiMLAI talk: Evaluating Model Explainability Methods with Sara Hooker
You...
A recent project of mine has been setting up a Twitter bot on innovation quotes. I enjoy this project because in addition to curating a great set of content and growing an audience around it, I have also learned a lot about coding. From web scraping to regular expressions to social media automation, I’ve learned […]
In the previous STT5100 course, last week, we’ve seen how to use monte carlo simulations. The idea is that we do observe in statistics a sample , and more generally, in econometrics . But let’s get back to statistics (without covariates) to illustrate. We assume that observations are realizations of an underlying random variable . We assume that are i.id. random variables, with (unkown) distribution . Consider here some estimator – which is just a function of our sample . So is a real-valued … Continue reading Monte Carlo techniques to create counterfactuals →
Week 6 Gold Mining and Fantasy Football Projection Roundup now available. Go get that free agent gold! The post Gold-Mining Week 6 (2018) appeared first on Fantasy Football Analytics.
I wasn't at the Use of R in Official Statistics (uRos2018) conference in the Netherlands last month, but I'm thankful to Jeroen Ooms for sharing the slides from his keynote presentation. In addition to being a postdoc staffer at ROpenSci, Jeroen maintains the official repository for the daily R builds on Windows — you might recognize his name from the verification certificate that pops up when installing R on Windows. His uRos2018 talk provides a fascinating glimpse into the complex systems, dependencies, and processes that come together to make installing R as easy as as double-click. On thing I found...
We’ve now discussed the improved support in RStudio v1.2 for SQL, D3, and Python. Today, we’ll talk about IDE support for C/C++ and Rcpp.
The IDE has had excellent support for C/C++ since RStudio v0.99, including:
Tight integration with t...
The third quarter of the year just ended, and our data science content library has never grown faster. We now have a content library of +180 courses, +35 projects, and +20 practice modules. In addition, we greatly improved the feedback messages learner...
- Last year I released package GetLattesData. This package is very handy
for anyone that researches bibliometric data of Brazilian scholars. You
could easily import the whole academic history of any researcher
registered at the p...
Earlier today, digest version 0.6.18 arrived on CRAN. It will get uploaded to Debian in due course.
digest creates hash digests of arbitrary R objects (using the md5, sha-1, sha-256, sha-512, crc32, xxhash32, xxhash64 and murmur32 algorithms) permitt...
Rewording Le Monde mathematical puzzle  fifth competition problem For the 3×3 tables below, what are the minimal number of steps to move from left to rights when the yellow tokens can only be move to an empty location surrounded by two other tokens? In the 4×4 table below, there are 6 green tokens. How many […]
A decision was made early in DataCamp's history to outsource our instructors so we can attract the best talent and build as many high-quality courses and projects as quickly as possible. DataCamp instructors -- academics, data scientists in industry, a...
Saghir Bashir of ilustat recently shared a nice getting started with R and tidyverse guide. In addition they were generous enough to link to Dirk Eddelbuette’s later adaption of the guide to use data.table. This type of cooperation and user choice is what keeps the R community vital. Please encourage it. (Heck, please insist on … Continue reading Some R Guides: tidyverse and data.table Versions
Find out how to use R to summarise electricity use by hour or day.
Here is the course link.
Course Description
In this course, you will learn the end-to-end process for creating an R package from scratch. You will start off by creating the basic structure for your package, and adding in important details like func...
The CBC covered the recent (as of the original post-time on this blog entry) Quebec elections and used a well-crafted hex grid map to display results: They have a great ‘splainer on why they use this type of map. Thinking that it may be useful for others, I used a browser Developer Tools inspector to... Continue reading →
This week in the Data Science With R Course Series we’ll cover Data Preparation, where we structure the data in preparation for modeling. This week’s modules will teach you:
How to create a preprocessing pipeline with the recipes package
How ...
Here is the course link.
Course Description
Mixture modeling is a way of representing populations when we are interested in their heterogeneity. Mixture models use familiar probability distributions (e.g. Gaussian, Poisson, Binomial) to provide a c...
The first time we at STATWORX are opening our doors for anyone who wants to learn more about data. Our “Data Visualization in R and ggplot2” workshop is designed for R-beginners and provides the perfect mixture between theory and practice. The workshop will be held in german at our office in Frankfurt. Participants achieve first insights into the concepts of ... Read More
Der Beitrag Open Workshop: Data Visualization in R and ggplot2, November 8th in Frankfurt erschien zuerst auf STATWORX.
In 2012, Danielle Herbert, Adrian Barnett, Philip Clarke and Nicholas Graves published an article entitled “on the time spent preparing grant proposals: an observational study of Australian researchers“, whose conclusions had been included in Nature under a more explicit title, “Australia’s grant system wastes time” ! In this study, they included 3700 grant applications sent to the National Health and Medical Research Council, and showed that each application represented 37 working days: “Extrapolating this to all 3,727 submitted proposals gives an estimated 550 working … Continue reading October, grant proposal season →
A number of key assumptions underlie the linear regression model - among them linearity and normally distributed noise (error) terms with constant variance In this post, I consider an additional assumption: the unobserved noise is uncorrelated with any covariates or predictors in the model.
In this simple model:
\[Y_i = \beta_0 + \beta_1X_i + e_i,\]
\(Y_i\) has both a structural and stochastic (random) component. The structural component is the linear relationship of \(Y\) with \(X\). The random element is often called the \(error\) term, but I prefer to think of it as \(noise\). \(e_i\) is not measuring something that has gone awry, but rather it is variation emanating from some unknown, unmeasurable source or sources for each individual \(i\). It represents everything we haven’t been able to measure.
Our goal is to estimate \(\beta_1\), which characterizes the structural linear relationship of \(X\) and \(Y\). When we estimate the model, we get a quantity \(\hat{\beta_1}\), and we hope that on average we do pretty well (i.e. if we were to estimate \(\beta_1\) repeatedly, \(E[\hat{\beta_1}] = \beta_1\)). In order for us to make sure that is the case, we need to assume that \(e_i\) and \(X_i\) are independent. In other words, the sources that comprise \(e_i\) must not be related in way to whatever it is that \(X_i\) is measuring.
Correlation without causation
First, I’ll generate \(X's\) and \(e's\) that are correlated with a using a data generation process that makes no assumptions about the underlying causal process. The provides a picture of how \(\hat{\beta_1}\) might diverge from the true \(\beta_1\).
library(simstudy)
set.seed(3222)
dT
In our next MünsteR R-user group meetup on Tuesday, November 20th, 2018, titled Using R to help plan the future of transport, Mark Padgham will provide an overview of several inter-related R packages for analysing urban dynamics.
You can RSVP here...
While some English Premier League matches are bitterly even in nature, others are historically more one-sided. Using the engsoccerdata package in R, I looked at EPL results data from 1888 to 2017, and sought to find the answer to the question: what have been the most uneven matches in the English first division? Observing matches…
Since 2015, the R Consortium has funded projects of benefit to, and proposed by, the R community. Twice a year, the R Consortium Infrastructure Steering Committee reviews grant proposals and makes awards based on merit and funds available. (Those funds come, in turn, from the annual dues paid by R Consortium members.) If you'd like to propose a project of your own, the deadline for submission for the Fall 2018 Call for Proposals is October 31. Not sure what kind of project might be approved? In general, the R Consortium grants fund projects in these categories: Creation of general infrastructure...
This post is a continuation of my earlier post Big Data-1: Move into the big league:Graduate from Python to Pyspark. While the earlier post discussed parallel constructs in Python and Pyspark, this post elaborates similar and key constructs in R and SparkR. While this post just focuses on the programming part of R and SparkR it … Continue reading Big Data-2: Move into the big league:Graduate from R to SparkR
Last week I ran across this great post on creating a neural network in Python. It walks through the very basics of neural networks and creates a working example using Python. I enjoyed the simple hands on approach the author used, and I was interested to see how we might make the same model using R.
In this post we recreate the above-mentioned Python neural network from scratch in R. Our R refactor is focused on simplicity and understandability; we are not concerned with writing the most efficient or elegant code.
Our very basic neural network will have 2 layers. Below is a diagram of the network:
For background information, please read over the Python post. It may be helpful to open the Python post and compare the chunks of Python code to the corresponding R code below. The full Python code to train the model is not available in the body of the Python post, but fortunately it is included in the comments; so, scroll down on the Python post if you are looking for it.
Let’s get started with R!
Create Training Data
First, we create the data to train the neural network.
# predictor variables
X
According to a KDD poll fewer respondents (by rate) used only R in 2017 than in 2018. At the same time more respondents (by rate) used only Python in 2017 than in 2016. Let’s take this as an excuse to take a quick look at what happens when we try a task in both systems. … Continue reading Running the Same Task in Python and R
Have you ever wanted to make your Shiny tables interactive, more functional and look better? The DT package, which stands for “DataTables”, provides an R interface to the JavaScript library “DataTables”. It allows creating high standard tables by implementing the functionalities and design features that are available through the “DataTables” library. Even though the DT […]
Related exercise sets:
Parallel Computing Exercises: Foreach and DoParallel (Part-2) Parallel Computing Exercises: Snow and Rmpi (Part-3) Bayesian Inference – MCMC Diagnostics using coda : Exercises Explore all our (__1000) R exercises
Find an R course using our R Course Finder directory
In the last couple of years, real estate companies have shifted their focus to the digital world, and now almost all investments have an online system showing what apartments are available. This is very convenient for their potential clients, as they can easily become familiar with the apartments on offer. Things become interesting when all […]
Artykuł Are you buying an apartment? How to hack competition in the real estate market with data monitoring pochodzi z serwisu Appsilon Data Science | We Provide End­ to­ End Data Science Solutions.
MCHT is an R package for bootstrap and Monte Carlo hypothesis testing currently available on GitHub.
In this post, I show some results of predicting height based on DNA mutations. This analysis aims at reproducing the analysis of this paper using my own analysis tools in.
I use a new dataset composed of 500,000 adults from UK, and genotyped over hund...
From Executive Business Leadership to Data Scientists, we all agree on one thing: A data-driven transformation is happening. Artificial Intelligence (AI) and more specifically, Data Science, are redefining how organizations extract insights from their ...
The PALM tree algorithm for partially additive (generalized) linear model trees is introduced along with the R package palmtree. One potential application is modeling of treatment-subgroup interactions while adjusting for global additive effe...
R has been around long time and the packages have evolved through the years as well. From the initial releases, updates, to new packages. Like many open-source and community driven languages, R is not an exception. And getting the first…Read more ›
How can we use data analytics to increase our self-knowledge? Along with biofeedback from digital devices like FitBit, less structured sources such as sent emails can provide insights.
E.g. here it seems my communication took a sudden more positive tu...
rOpenSci post-doc hacker Jeroen Ooms has just released a cool new package, av, that he wrote “will become the video counterpart of the magick package which [rOpenSci uses] for working with images.”. av provides bindings to the FFmepg libraries for editing videos. It’s already become a renderer for gganimate by Thomas Lin Pedersen, but av allows more than making videos of graphics. In this post, I’ll show how to use av and webshot to make a trailer/sneak preview of a slidedeck, i.e. a short video featuring the first few slides on music!
Why make a trailer of your slidedeck?
No, making a trailer of a slidedeck isn’t only a pretext for trying out av! In case you hadn’t noticed, I’m all about promoting your work once you’ve put in the work. I’ve even written a whole post about blog promotion. Promoting a slidedeck after a talk, or a talk before the event, is quite similar to promoting a blog post both in terms of benefits and technically. In particular, a tweet about it will be more powerful if featuring an illustration, and why not making it animated?
For more tips about advertising for a thing via a talk, check out the excellent “Carpe Talk” article on tidyverse.org by Jenny Bryan and Mara Averick.
Capture the first few slides
In this post, I’ll use the slidedeck that Jeroen shared after giving a talk about R infrastructure after the uRos conference. I’ll capture the first few slides minus the ones where he introduced himself, because I wanted the focus of the trailer to be on the talk topic. I didnd’t know webshot::webshot() was vectorized, thanks to Jeroen for telling me that which shortened the code below.
fs::dir_create(&quot;slides&quot;)
urls
A critical tweet the other day by Nicolas Sommet reminded me of the book The Spirit Level: Why Equal Societies Almost Always Do Better”. That book argues that:
“…there are ‘pernicious effects that inequality has on societies: eroding trust...
In R, we can subset a data frame df easily by putting the conditional in square brackets after df. For example, if I want all the rows in df which have value equal to 1 in the column colA, all … Continue reading →
This is the second part of Analyzing the Greatest Strikers in Football. In the first part,
we created the function get_goals() which allows us to conveniently scrape detailed information of players
career goals from transfermarkt.co.uk. In this part,
we are going to explore the data.
library(tidyverse) # for data wrangling
library(lubridate) # for date formats
library(ggimage) # adding images to ggplot
library(patchwork) # attaching ggplot objects
library(viridis) # viridis color schemes
We start by getting the data of Zlatan with the get_goals() function.
zlatan_tbl % ggplot(aes(x = reorder(provider,goals),y = goals))+
geom_col(fill=&quot;#FF7F00&quot;)+
scale_y_continuous(breaks=seq(0,12,3))+
theme_goals(grid = &quot;x&quot;)+
labs(x = &quot;&quot;, y = &quot;count&quot;,subtitle=&quot;Top ten providers&quot;)+
coord_flip() -__ p_prov
zlatan_tbl$data %__% dplyr::filter(type!=&quot;&quot;) %__% group_by(type) %__% summarise(goals = n()) %__% ggplot(aes(x = reorder(type,goals),y = goals))+
geom_col(fill=&quot;#FF7F00&quot;)+
theme_goals(grid = &quot;x&quot;)+
labs(x = &quot;&quot;, y = &quot;count&quot;,subtitle=&quot;Type of goal&quot;)+
coord_flip() -__ p_type
p_prov + p_type + plot_layout(nrow = 1) + plot_annotation(theme=theme(
plot.background=element_rect(fill=&quot;#666666&quot;,colour=&quot;#666666&quot;),
plot.title = element_text(family = &quot;Roboto Condensed&quot;,color=&quot;white&quot;,face = &quot;bold&quot;,size=rel(1.5))),
title=zlatan_tbl$name)
According to his transfermarkt profile,
Zlatan is “both-footed”, but he seems to prefer the right foot for scoring.
For comparison, here are the plots for Messi and CR7.
Scorecard
You can neatly put all plots together to create an informative scorecard for Zlatan.
p
The “Gold Standard” for Data Science Project ManagementThe inspiration for this post came most recently from a slide-deck by Ming Tang, a Bioinformatician at Harvard, and a new Chromebook Data Science course offered by Jeffery Leek from John Hopkin...
Each time we face real applications in an applied econometrics course, we have to deal with categorial variables. And the same question arise, from students : how can we combine automatically factor levels ? Is there a simple R function ? I did upload a few blog posts, over the pas years. But so far, nothing satistfying. Let me write down a few lines about what could be done. And if some wants to write a nice R function, that would be awesome. To … Continue reading Combining automatically factor levels in R →
A new release 0.2.4 of RcppCCTZ is now on CRAN.
RcppCCTZ uses Rcpp to bring CCTZ to R. CCTZ is a C++ library for translating between absolute and civil times using the rules of a time zone. In fact, it is two libraries. One for dealing with civil tim...
CategoriesProgramming
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Best R Packages
R Programming
Tips &amp; Tricks
In the age of smartphones, Notifications have become an integral part of life that Smart watches have started popping up to handle our notifications. Desktop Notification is a very good way of letting the user know about a task completion or some information. Say, your model has been running and at the end, you just […]Related Post
CHAID vs. ranger vs. xgboost — a comparison
Common Mistakes to Avoid When Learning to Code in Python
Top Python Libraries for Machine Learning
Get Popular Programming Languages from TIOBE Index using R
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Introduction Let’s take a quick look at a very important and common experimental problem: checking if the difference in success rates of two Binomial experiments is statistically significant. This can arise in A/B testing situations such as online advertising, sales, and manufacturing. We already share a free video course on a Bayesian treatment of planning … Continue reading Quick Significance Calculations for A/B Tests in R
A while ago, I wrote two blogposts about image classification with Keras and about how to use your own models or pretrained models for predictions and using LIME to explain to predictions.
Recently, I came across this blogpost on using Keras to extract learned features from models and use those to cluster images. It is written in Python, though - so I adapted the code to R. You find the results below.
One use-case for image clustering could be that it can make labelling images easier because - ideally - the clusters would pre-sort your images, so that you only need to go over them quickly and check that they make sense.
Libraries
Okay, let’s get started by loading the packages we need.
library(keras)
library(magick) # for preprocessing images
library(tidyverse)
library(imager)
Pretrained model
And we load the VGG16 pretrained model but we exclude the laste layers.
model
Here are some conferences focused on R taking place in the next few months: Oct 26: Nor'eastR Conference (Providence, RI). A one-day R conference, organized by grassroots R community members in the Northeastern US. Oct 27: SatRdays Belgrade (Serbia). Another city joins the popular SatRDays series of one-day, community led conferences. Nov 7: EARL Seattle (Seattle, WA). The EARL London conference goes on a Stateside road trip, with a 1-day stop in Seattle. Nov 8-9: DC R Conference (Washington, DC). From the same organizers as the fantastic New York R Conference, this DC-based outpost will feature speakers including Mara Averick,...
This started out as a “hey, I wonder…” sort of thing, but as usual, they tend to end up as interesting voyages into the deepest depths of code, so I thought I’d write it up and share. Shoutout to @coolbutuseless...Continue Reading →
It’s October, time for spooky Twitter names! If you’re on this social
media platform, you might have noticed some of your friends switching
their names to something spooky and punny. Last year I was “Maelstrom
Salmon”, which I find scary but is arguably not that funny. Anyhow, what
if you want to switch your name but have no inspiration? In this post,
we shall explore R’s abilities to help us with that with the help of
webscraping, phonetic spelling and string distance algorithms, and the
magic of randomness!
My strategy for spooky name generation will be to replace each part of a
name (e.g. first and last names) with the phonetically closest
Halloween-related words. Therefore, the technical challenges here were
to find a list of Halloween-related words, and to measure phonetical
string distance.
Getting spooky words
The otherwise very useful package
rcorpora doesn’t have a
“spooky” category, so I decided to scrape this webpage of Halloween
words for
kids.
It was one of the first search results, and being for kids it couldn’t
be offensive. Like the last two times I webscraped, I used the very
cool
polite package!
There were two less useful sections on the page, with French and Spanish
words, that I very inelegantly removed after looking up their div ID in
the page source.
# introduce myself
session
CategoriesRegression Models
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Data Visualisation
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Logistic Regression
R Programming
The most demanding skill in data science is statistical regression analysis, followed by clustering methods, and the third most required skill is visualization. So if the data scientist learns these analytical skills and focuses on how to use them it will be of great value to both the data scientist, the companies/ organizations as well […]Related Post
Weight loss in the U.S. – An analysis of NHANES data with tidyverse
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Extract FRED Data for OLS Regression Analysis: A Complete R Tutorial
MNIST For Machine Learning Beginners With Softmax Regression
I do not always come up with new ideas for my blog, but rather get inspired by the great work of others.
In this case, it was a reddit post by u/Cheapo_Sam, who charted world footballs greatest goal scorers in a marvelous way.
According to the post, the data was gathered manually which I thought is too tedious (Ain’t nobody got time for that!).
So I decided to automatize this step.
This post exclusively deals with acquisition and cleaning of the data. The next (one or two)
posts will handle the analysis.
library(tidyverse) # for data wrangling
library(rvest) # for web scraping
library(lubridate) # for date formats As a data source, I found transfermarkt.co.uk to be a good place.
It has very detailed records of goals scored by players, dating back to the 1960’s.
As an example, we will get the data of Zlatan Ibrahimovic.
url and a . The html_node() in conjunction with html_text() allows
us to extract the birthday as plain text by navigating along these css classes.
The str_squish() eliminates all the excessive whitespace.
This allows us now to add yet another column to the data frame which holds the exact age of
Zlatan for each goal.
tab$age
Mango Solutions are pleased to be exhibiting at ACoP 9 on 7-10th October at the Loews Coronado Bay Resort near San Diego, CA and welcome our customers and conference attendees to booth 27 where we will be showcasing our PK/PD model informed drug development tool – Navigator Workbench. Navigator Workbench provides a powerful, validated platform for PK/PD model development, execution, evaluation and reporting tasks.  Alongside Mango’s ModSpace product – a proven document, model and code repository which supports cross-functional teams…
Do you have code that accompanies a research project or manuscript? How do you review and archive that code before you submit a paper? Our next Community Call will present different perspectives on this hot topic, with plenty of time for Q&amp;A.
What’s the culture of the group around feedback and code collaboration?
What are the use cases?
What are some practices that can adopted?
🕘 Tuesday, October 16th, 9-10 AM PDT (find your timezone)
☎️ Find all details for joining the call on our Community Calls page.
Everyone is welcome. No RSVP needed.
There’s an ongoing rich discussion on this topic, in our public forum, with contributions from the speakers, as well as other principal investigators, students and government staff.
Have a question for the speakers? Add yours to the discussion thread. See a question you’d like to have answered? Give it a ❤️. The moderator will select some of these questions to ask during Q&amp;A.
Agenda
Welcome from Stefanie Butland, rOpenSci Community Manager, (5 min)
Dr Carl Boettiger (10 min)
Dr Melanie Frazier &amp; Dr Julia Stewart-Lowndes (10 min)
Dr Hao Ye (10 min)
Q &amp; A (20 min, moderated by Carl Boettiger)
Speakers
Carl Boettiger is Assistant Professor in the Department of Environmental Science, Policy and Management at UC Berkeley, a co-founder and leadership team member of rOpenSciCarl on GitHub, Twitter, Website
Melanie Frazier is the lead analyst for the global Ocean Health Index at the National Center for Ecological
Analysis and Synthesis (NCEAS), University of California, Santa Barbara.Melanie on Website
Julia Stewart-Lowndes is Science Program Lead for the Ocean Health Index, a marine data scientist at the National Center for Ecological Analysis and Synthesis (NCEAS), and a Mozilla Open Science Fellow.Julia on GitHub, Twitter, Website
Hao Ye is a Postdoctoral Associate and Moore Data Fellow in the Weecology Lab at the University of Florida.Hao on GitHub, Twitter, Website
With another brilliant EARL London behind us, we are now looking forward to bringing the conference to the US with three one day EARL Roadshow events! We couldn’t be more excited to bring the latest and greatest in R to three cities across America: Seattle – 7 November Houston – 9 November Boston – 13 November With speakers from a huge range of industries, including R giants Hadley Wickham, Julia Silge, Robert Gentleman and Bob Rudis, you can expect to…
NOTE: This is a repost of an article that was first published in 2016
Introduction
Every so often while doing data analysis, I have come across a situation where I have
two datasets, which have the same structure but with small differences in the ac...
In case you missed them, here are some articles from September of particular interest to R users. R code by Barry Rowlingson to replicate an XKCD comic about curve fitting. The rayshader package creates 3-D relief maps in R with perspective, shadows, and depth of field. The R Developer's Guide to Azure, with links to documentation for Azure cloud services integrating R. A review of many commercial applications of R presented at EARL London 2018. Roundup of AI, Machine Learning and Data Science news from September 2018. A Shiny app using the Custom Vision API to identify pictures (or not)...
Week 5 Gold Mining and Fantasy Football Projection Roundup now available. Go get that free agent gold! The post Gold-Mining Week 5 (2018) appeared first on Fantasy Football Analytics.
Here is the course link.
Course Description
The world is full of unobservable variables that can't be directly measured. You might be interested in a construct such as math ability, personality traits, or workplace climate. When investigating const...
Once upon a time, there was a team of fearless R knights who started a new quest: they were missioned to bring the almighty Google Auth into the wonderful world of ShinyProxy. Here is their story, told from the inside.   In the beginning, there was a container For those amongst our readers who do not know ShinyProxy, this open source project by Open Analytics is designed to deploy a server which can run multiple Shiny Apps, and where each new shiny app is launched in a new session. In a few words, what ShinyProxy does is basically connecting a server to a list ofRead More →
The post A Tale of Two Shiny Apps: Google Auth &amp; ShinyProxy appeared first on (en) The R Task Force.
Last call for the 2-day course on Text Mining with R, held next week (08-09 October 2018) in Brussels, Belgium. Subscribe at https://www.eventbrite.co.uk/e/dsb2018-text-mining-with-r-jan-wijffels-bnosac-session-03-04-tickets-50586501588
You'll learn during that course the following:
Cleaning of text data, regular expressions
String distances
Graphical displays of text data
Natural language processing: stemming, parts-of-speech tagging, tokenization, lemmatisation, dependency parsing, noun phrase detection and keyword extraction
Entity recognition &amp; chunking using Conditional Random Fields
Sentiment analysis
Statistical topic detection modelling (latent dirichlet allocation)
Visualisation of correlations &amp; topics
Automatic classification using predictive modelling based on text data
Word and Text embeddings
Document similarities &amp; Text alignment
Training on Text Mining Are you interested in how text mining techniques work, then you might be interested in the following data science courses that are held in the coming months.
08-09/10/2018: Text mining with R. Brussels (Belgium). Subscribe at https://www.eventbrite.co.uk/e/dsb2018-text-mining-with-r-jan-wijffels-bnosac-session-03-04-tickets-50586501588
15-16/10/2018: Statistical machine learning with R. Leuven (Belgium). Subscribe here
05-06/11/2018: Text mining with R. Leuven (Belgium). Subscribe here
19-20/12/2018: Applied spatial modelling with R. Leuven (Belgium). Subscribe here
21-22/02/2018: Advanced R programming. Leuven (Belgium). Subscribe here
13-14/03/2018: Computer Vision with R and Python. Leuven (Belgium). Subscribe here
     15/03/2019: Image Recognition with R and Python: Subscribe here
01-02/04/2019: Text Mining with R. Leuven (Belgium). Subscribe here
A purely (?) algorithmic Le Monde mathematical puzzle For the table below, what is the minimal number of steps required to reach equal entries when each step consists in adding ones to three entries sitting in a L, such as (7,11,12) or (5,6,10)? Same question for the inner table of four in yellow. For the […]
In a previous post, we showed how using vectorization in R can vastly speed up fuzzy matching. Here, we will show you how to use R’s vectorization functionality to efficiently build a logistic regression model. Now we could just use the caret or stats packages to create a model, but building algorithms from scratch is […]
The post How to build a logistic regression model from scratch in R appeared first on Open Source Automation.
Microsoft Ignite, Microsoft's annual developer conference, wrapped up last week and many of the big announcements focused on artificial intelligence and machine learning. The keynote presentation from Microsoft's Cloud AI lead Eric Boyd showcases the major developments, or you can check out his accompanying blog post for a written summary. In this post, I'll dive down into the details of the specific announcements and provide links to the associated recorded presentations from Ignite and other technical documents. Tools for AI developers Azure Machine Learning Service (Azure ML) adds several new capabilities. These include a new Python package that developers can...
In last night’s post, I introduced prVis(), a new visualization tool which we have invented, available in our polyreg package. Recall that prVis() is intended as a simpler alternative to recent visualization tools like t-SNE and UMAP. Here I will post another example. The dataset is prgeng, included in the package. It consists of wage … Continue reading Manifold Visualization: Second Example →
With the increase in R usage over the past few years and the sheer diversity of industries employing data-science and analytics, the backgrounds of the people using R day-to-day has also changed. Different background, from hard-sciences to software-eng...
It has been just a few months since I reviewed five free and open-source point-and-click graphical user interfaces (GUIs) to the R language. I plan to keep those reviews up to date as new features are added. BlueSky’s interface would … Continue reading →
The next bi-monthly update in the 0.12.* series of Rcpp releases landed on CRAN early this morning. It was actually released on September 20, but I made a first cut (see #887 below) at a deprecation which CRAN and I decided to reverted for now, then ...
By Gabriel Vasconcelos &amp; Yuri Fonseca Introduction This post is the second of a series of examples of the BooST (Boosting Smooth Trees) model. You can see an introduction to the model here and the first example here. Our objective … Continue reading →
vtreat is a powerful R package for preparing messy real-world data for machine learning. We have further extended the package with a number of features including rquery/rqdatatable integration (allowing vtreat application at scale on Apache Spark or data.table!). In addition vtreat and can now effectively prepare data for multi-class classification or multinomial modeling. The two … Continue reading Modeling muti-category Outcomes With vtreat
Hello, everyone! Did you have fun at June 25th’s workshop with H2O and Lime? If you were there, we’re sure you did. If you weren’t… here’s a recap, with the explanation, the link to the live event and all the materials too! The workshop started at 19 in Mikamai, and it began with a bang! […]
The post Machine Learning in R with H2O and LIME: slides, photos and video appeared first on MilanoR.
Our arXiv paper and the associated R package polyreg caused a bit of a stir, both pro and con, when we first announced them here in June. The discussion even spread as far as Twitter, Reddit and Hacker News. We’ll be announcing a revised paper, and various new features to the package, very soon. But … Continue reading Manifold Visualization: Polynomials to the Rescue →
Data Science and Machine Learning in business begins with R. Why? R is the premier language that enables rapid exploration, modeling, and communication in a way that no other programming language can match: SPEED! This is why you need to learn R. Time ...
By providing a set of wrappers to existing functions, the stringr package allows for simple, consistent and efficient manipulations of strings in R. Even though there are some more basic packages that offer strings-related functions, you might find yourself in need for a more complete and straightforward solution for handling strings in R. With a […]
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Bayesian Inference – MCMC Diagnostics using coda : Exercises Practice Your ggplot Skills: Exercises Parallel Computing Exercises: Snow and Rmpi (Part-3) Explore all our (__1000) R exercises
Find an R course using our R Course Finder directory
(Disclaimer: I’ve no grudge against python programming language per se. I think its equally great. In the following post, I’m merely recounting my experience.)
It’s been quite a while since I last posted. The reasons are numerous, notable being,...
A minor maintenance release of the nanotime package for working with nanosecond timestamps just arrived on CRAN.
nanotime uses the RcppCCTZ package for (efficient) high(er) resolution time parsing and formatting up to nanosecond resolution, and the b...
A new version of RcppAPT – our interface from R to the C++ library behind the awesome apt, apt-get, apt-cache, … commands and their cache powering Debian, Ubuntu and the like – is now on CRAN.
This version is a bit of experiment. I had asked on...
CategoriesVisualizing Data
Tags
Data Visualisation
dplyr
ggplot2
lares
R Programming
There are few things more exciting than seeing your stocks values going up! I started investing last year in stocks and, like visualization and R lover, I couldn’t help but create some nice plots and functions to automate the process of watching it happen. Some of you might already know the lares package; I’ve included […]Related Post
Exploring San Francisco Bay Area’s Bike Share System
Analysis and Visualization of Blue Bikes Sharing in Boston
Analysis of Los Angeles Crime with R
Mapping the Prevalence of Alzheimer Disease Mortality in the USA
Animating the Goals of the World Cup: Comparing the old vs. new gganimate and tweenr API
You probably saw this XKCD last week, which brought a grimace of recognition to statisticians everywhere: It's so realistic, that Barry Rowlingson was able to reproduce all but two of the &quot;charts&quot; above with a simple R function (and a little help from the xkcd ggplot2 theme): And now for @revodavid et al, with the xkcd package and font! (still two more to do...) pic.twitter.com/3aVHis23Gl — Barry Rowlingson (@geospacedman) September 21, 2018 You can find the R code behind Barry's reproductions here, and I'm sure he'd welcome contributions for the remaining two charts :).
Beginner, Intermediate and Advanced R courses February 2019
This is an announcement of our next set of R courses. There are 4 separate courses taught over 5th-8th February 2019.
You can register with the Centre for Applications in Resource Management...
In my semi-daily run of brew update I noticed that proj4 had been updated to 5.2. I kinda “squeee“‘d since (as the release notes show) the Equal Earth projection was added to it (+proj=eqearth). As the team who created the projection describes it: “The Equal Earth map projection is a new equal-area pseudocylindrical projection for... Continue reading →
The 4.0.1 release of simmer, the Discrete-Event Simulator for R, is on CRAN since a couple of weeks ago. There are few changes, notably new getters (get_sources(), get_resources(), get_trajectory()) for simmer environments and some improvements in resource selection policies (see details in help(select)). A new convenience function, when_activated, makes it easier to generate arrivals on demand, triggered from trajectories. Let us consider,… Continuar leyendo simmer 4.0.1 →
Week 4 Gold Mining and Fantasy Football Projection Roundup now available. Go get that free agent gold! The post Gold-Mining Week 4 (2018) appeared first on Fantasy Football Analytics.
Cost-effectiveness analysis with individual level data
You can do this practical either directly in BUGS, or running the scripts from R. If you are using the BUGS graphical interface, then you will need to open the relevant model files (e.g. normal-mo...
As tweeted three days ago, our still-new binb package with crisper Beamer themes for RMarkdown now contains presento. Versions 0.0.2 with this addition just arrived on CRAN.
Here is a quick demo combining all themes:
Otherwise, we also added links t...
On Wednesday, September 26th, I gave a talk about ‘Decoding The Black Box’ at the Frankfurt Data Science Meetup.
My slides were created with beautiful.ai and can be found here.
DECODING THE BLACK BOX
And finally we will have with us Dr.Shiri...
Data scientists often work with geographic data that needs to be visualized on a map, and sometimes the maps themselves are the data. The data is often located in two-dimensional space (latitude and longitude), but for some applications we have a third dimension as well: elevation. We could represent the elevations using contours, color, or 3-D perspective, but with the new rayshader package for R by Tyler Morgan-Wall, it's easy to visualize such maps as 3-D relief maps complete with shadows, perspective and depth of field: 👌 Dead-simple 3D surface plotting in the next version of rayshader! Apply your hillshade...
Cost-effectiveness analysis with individual level data
You can do this practical either directly in BUGS, or running the scripts from R. If you are using the BUGS graphical interface, then you will need to open the relevant model files (e.g. normal-mo...
Here is the course link.
Course Description
Bayesian estimation offers a flexible alternative to modeling techniques where the inferences depend on p-values. In this course, you’ll learn how to estimate linear regression models using Bayesian met...
Recently I began to look further into Time Series(TS). During the course of my Master’s degree, I used the forecast package quite a bit (Thanks to Prof. Hyndman), and TS got my attention. So, after reading lots of publications about everything you can imagine about TS, I came across one publication from Prof. Eamonn, of the University of … Continue reading Time Series with Matrix Profile
Here is the course link.
Course Description
Linear regression serves as a workhorse of statistics, but cannot handle some types of complex data. A generalized linear model (GLM) expands upon linear regression to include non-normal distributions inc...
So I’ve been holidaying in Sri Lanka for a while now, and thought I would do a brief blog post using some public tourism data. Tourism has grown rapidly in Sri Lanka since the civil war finished in 2009. Travelling around, it’s easy to see why....
The theme for this year’s workshop will be “Moving beyond supervised learning in healthcare”. This will be a great forum for those who work on computational solutions to the challenges facing clinical medicine. The submission deadline is Friday Oct 26, 2018. Hope to see you there! https://ml4health.github.io/2018/pages/call-for-papers.html
And here is the third Le Monde mathematical puzzle  open for competition: Consider for this puzzle only integers with no zero digits. Among these an integer x=a¹a²a³… is refined if it is a multiple of its scion, the integer defined as x without the first digit, y=a²a³…. Find the largest refined integer x such the […]
If you want to run R in the cloud, you can of course run it in a virtual machine in the cloud provider of your choice. And you can do that in Azure too. But Azure provides seven dedicated services that provide the ability to run R code, and you can learn all about them in the new R Developer's Guide to Azure at Microsoft Docs. The services include: Data Science Virtual Machine: a customized VM to use as a data science workstation or as a custom compute target. This includes Microsoft R Open, Microsoft Machine Learning Server, and Microsoft...
I am happy to announce that a new version of afex (version 0.22-1) has appeared on CRAN. This version comes with two major changes, for more see the NEWS file. To get the new version including all packages used in the examples run: install.packages(&quot;afex&quot;, dependencies = TRUE) First, afex does not load or attach package […]
@drsimonj here with a quick share on making great use of the secondary y axis with ggplot2 – super helpful if you’re plotting groups of time series!
Here’s an example of what I want to show you how to create (pay attention to the numbers of the ...
Our interference from then environment issue was a bit subtle. But there are variations that can be a bit more insidious. Please consider the following. library(&quot;dplyr&quot;) # unrelated value that happens # to be in our environment z % select(-z) # x y # … Continue reading A Better Example of the Confused By The Environment Issue
Correctly interpreting predictive models can be tricky. One solution to this problem is to create interactive simulators, where users can manipulate the predictor variables and see...
When I plotted the PCA results (e.g. scatter plot for PC1 and PC2) and was about to annotate the dataset with different covariates (e.g. gender, diagnosis, and ethic group), I noticed that it's not straightforward to annotate __2 covariates at the same time using ggplot. Here is what works for me in ggplot:pcaData
Two weeks ago was our most successful EARL London conference in its 5-year history, which I had the pleasure of attending for both days of talks. Now I must admit, as a Python user, I did feel a little bit like I was being dragged along to an event where everyone would be talking about the latest R packages for customising RMarkdown and Shiny applications (… and there was a little bit of that – I’m pretty sure I heard…
Enhance your data storytelling skills by creating display tables with pizazz.
An updated version of the simstudy package (0.1.10) is now available on CRAN. The impetus for this release was a series of requests about generating correlated binary outcomes. In the last post, I described a beta-binomial data generating process that uses the recently added beta distribution. In addition to that update, I’ve added functionality to genCorGen and addCorGen, functions which generate correlated data from non-Gaussian or normally distributed data such as Poisson, Gamma, and binary data. Most significantly, there is a newly implemented algorithm based on the work of Emrich &amp; Piedmonte, which I mentioned the last time around.
Limitation of copula algorithm
The existing copula algorithm is limited when generating correlated binary data. (I did acknowledge this when I first introduced the new functions.) The generated marginal means are what we would expect. though the observed correlation on the binary scale is biased downwards towards zero. Using the copula algorithm, the specified correlation really pertains to the underlying normal data that is used in the data generation process. Information is lost when moving between the continuous and dichotomous distributions:
library(simstudy)
set.seed(736258)
d1
DataCamp is now offering a discount on unlimited access to their course curriculum. Access over 170+ course in R, Python, SQL and more taught by experts and thought-leaders in data science such as Mine Cetinkaya-Rundel (R-Studio), Hadley Wickham (R-Studio), Max Kuhn (caret) and more. Check out this link to get the discount! Below are some … Continue reading Save On an Annual DataCamp Subscription (Less Than 2 Days Left)
A Twitter Thread turned into a blog post.
Data Science and Machine Learning in business begins with R. Why? R is the premier language that enables rapid exploration, modeling, and communication in a way that no other programming language can match: SPEED! This is why you need to learn R. Time ...
It is no great secret: I like value oriented interfaces that preserve referential transparency. It is the side of the public debate I take in R programming. &quot;One of the most useful properties of expressions is that called by Quine referential transparency. In essence this means that if we wish to find the value of … Continue reading A Subtle Flaw in Some Popular R NSE Interfaces
A reader, e.g. Mr. Wayne Zhang, of my previous post (https://statcompute.wordpress.com/2018/09/03/playing-map-and-reduce-in-r-by-group-calculation) made a good comment that “Why not use directly either Spark or H2O to derive such computations without involving detailed map/reduce”. Although Spark is not as flexible as R in the statistical computation (in my opinion), it does have advantages for munging large-size data […]
Notes on the here package - The here package is pretty simple ( only 3 functions), but I cannot remember how to use it to navigate folders, so this is my aide-memoire.
It might be useful for others too.
Here finds the root of your current fo...
Several people wanted to have the slides from Betancourt's lectures at SMLP2018. It is possible to recreate most of the course from his writings:1. Intro to probability:https://betanalpha.github.io/assets/case_studies/probability_theory.html2. Workflow...
SCImago Journal &amp; Country Rank provides valuable estimates of academic journals’ prestige. The data is freely available at the project website and is distributed for deeper analysis in forms of .csv and .xlsx files. I downloaded all the files a...
On Friday, while working on a project that I needed to union multiple data.frames with different column names, I realized that the base::rbind() function doesn’t take data.frames with different columns names and therefore just quickly drafted a rbind2() function on the fly to get the job done based on the idea of MapReduce that I […]
I’ve ended up (almost accidentally) collecting a number of different solutions to the “use a column to choose values from other columns in R” problem. Please read on for a brief benchmark comparing these methods/solutions. What we did is: build a 1,000,000 row variation of the original example. In this variation we ensured that the … Continue reading Timing Column Indexing in R
R is not just a tool for the data science elite. It is an immensely powerful tool that can be used by market researchers in...
During the EARL (Enterprise Applications of the R Language) conference in London last week, the organizers asked me how I thought the conference had changed over the years. (This is the conference's fifth year, and I'd been to each one.) My response was that it reflected the increasing maturity of R in the enterprise. The early years featured many presentations that were about using R in research and the challenges (both technical and procedural) for integrating that research into the day-to-day processes of the business. This year though, just about every presentation was about R in production, as a mainstream...
You can find members of the rOpenSci team at various meetings and workshops around the world. Come say ‘hi’, learn about how our software packages can enable your research, or about our process for open peer software review and onboarding, how you can get connected with the community or tell us how we can help you do open and reproducible research.
Where’s rOpenSci?
When
Who
Where
What
Sep 21, 2018
Julia Gustavsen
🇨🇭 Bern, CH
OpenCon Switzerland
Sep 24 - 25, 2018
Karthik Ram
🇺🇸 New York, NY
NumFOCUS Project Forum
Oct 26, 2018
Nick Tierney (representing rOpenSci)
🇰🇷 Seoul, KR
R User Conference in Korea 2018
Nov 22 - 23, 2018
Nick Tierney
🇦🇺 Melbourne, AU
rOpenSci OzUnconf 2018
Jan 15 - 18, 2019
Karthik Ram, Jenny Bryan
🇺🇸 Austin, TX
rstudio::conf
Jan 24 - 26, 2019
Maëlle Salmon
🇨🇷 San Jose, CR
ConectaR2019
View full calendar
The following document details how to create a treemap in R using the treemap package. What are they &amp; when do we use them In the most basic terms a treemap is generally used when we want to visualize proportions. It can be thought of a pie map where the slices are replaced by rectangles. … Continue reading Simple Steps to Create Treemap in R
I’m pretty sure most readers of this blog are already familiar with Rmarkdown and Github. In this post I don’t pretend to invent the wheel but rather give a quick run-down of how I set-up and use these tools to produce high quality and scalable (in human time) reproducible data science development code. Github While … Continue reading Reproducible development with Rmarkdown and Github
In a recent blog post our CEO Sebastian Heinz wrote about Google's newest stroke of genius – AutoML Vision. A cloud service &quot;that is able to build deep learning models for image recognition completely fully automated and from scratch&quot;. AutoML Vision is part of the current trend towards the automation of machine learning tasks. This trend started with automation of ... Read More
Der Beitrag A Performance Benchmark of Different AutoML Frameworks erschien zuerst auf STATWORX.
We recently saw a great recurring R question: “how do you use one column to choose a different value for each row?” That is: how do you use a column as an index? Please read on for some idiomatic base R, data.table, and dplyr solutions. Let’s say we have some example data: df
Week 3 Gold Mining and Fantasy Football Projection Roundup now available. Go get that free agent gold! The post Gold-Mining Week 3 (2018) appeared first on Fantasy Football Analytics.
Last week I spent some time reminiscing about my PhD and looking through some old R code. This trip down memory lane led to some of my old R scripts that amazingly still run. My R scripts were fairly simple and just created a few graphs. However now that I’ve been programming in R for
The post R from the turn of the century appeared first on Jumping Rivers.
You created some great Shiny applications, following our advice of Shiny packaging for example, and you want to put them into production, self-hosting, so that others can enjoy them, without limitations, on the Internet or on an internal server of your company? ShinyProxy is for you! ShinyProxy v2.0 has recently been released. What a great opportunity to talk about its implementation! A major interest of ShinyProxy is its ability to create an independent Docker container for each user who connects to your Shiny application. This overrides the limitations on the number of users faced with ShinyServer. Are you interested? Follow the installation procedure…. ShinyProxy installationRead More →
The post Shiny application in production with ShinyProxy, Docker and Debian appeared first on (en) The R Task Force.
Data Science and Machine Learning in business begins with R. Why? R is the premier language that enables rapid exploration, modeling, and communication in a way that no other programming language can match: SPEED! This is why you need to learn R. Time ...
Here is the course link.
Course Description
In this course, you will learn the foundations of A/B testing, including hypothesis testing, experimental design, and confounding variables. You will also be exposed to a couple more advanced topics, sequ...
Following a teaser tweet two days ago, we are thrilled to announce that binb version 0.0.1 arrived on CRAN earlier this evening.
binb extends a little running joke^Htradition I created a while back and joins three other CRAN packages offering RMarkdo...
This report discusses some problems that can arise when attempting to import PostScript images into R, when the PostScript image contains coordinate transformations that skew ...
Despite having sailed through the core components of this year’s Talk Like A Pirate Day R post a few months ago, time has been an enemy of late so this will be a short post that others can build off of, especially since there’s lots more knife work ground to cover from the data. DMC-WhAt?... Continue reading →
Customers lifetime value (LTV or CLV) is one of the cornerstones of product analytics because we need to make a lot of decisions for which the LTV is a necessary or at least very significant factor. In this article, we will focus on products/services/applications with recurring subscription paymentsCustomers lifetime value (LTV or CLV) is one […]
The post LTV prediction for a recurring subscription with R appeared first on AnalyzeCore by Sergey Bryl' - data is beautiful, data is a story.
In a web application, routing is the process of using URLs to drive the user interface. Routing adds more possibilities and flexibility while building a complex and advanced web application, offering dividing app into separated sections. New features Contributing to open source is incorporated into Appsilon mission. Last week we updated i18n internationalization package, now it’s […]
Artykuł Official release of shiny.router and its new features pochodzi z serwisu Appsilon Data Science | We Provide End­ to­ End Data Science Solutions.
Registration is now open for my 1.5-day workshop on deep learning with Keras and TensorFlow using R.
It will take place on November 8th &amp; 9th in Munich, Germany.
You can read about one participant’s experience in my workshop:
Big Data – ...
I have yet another Meetup talk to announce:
On Wednesday, October 26th, I’ll be talking about ‘Decoding The Black Box’ at the Frankfurt Data Science Meetup.
Particularly cool with this meetup is that they will livestream the event at www.youtube....
The second Le Monde mathematical puzzle in the new competition is sheer trigonometry: When in the above figures both triangles ABC are isosceles and the brown segments are all of length 25cm, find the angle in A and the value of DC², respectively. This could have been solved by R coding the various possible angles […]
Intro One of the features of functions of smooth package is the ability to work with intermittent data and the data with periodically occurring zeroes. Intermittent time series is a series that has non-zero values occurring at irregular frequency (Svetuknov and Boylan, 2017). Imagine retailer who sells green leap sticks. The demand on such a […]
I had a great time at the EARL Conference in London last week, and as always came away invigorated by all of the applications of R that were presented there. I'll do a full writeup of the conference later this week, but in the meantime I wanted to share the materials from my own presentation there, &quot;Not Hotdog: Image Recognition with R and the Custom Vision API&quot;. I've embedded the slides below: This is an embedded Microsoft Office presentation, powered by Office Online. In the presentation, I showed how easy it is to build a Shiny app to perform image...
Artificial intelligence (AI) has the potential to change industries across the board, yet few organizations are able to capture its value and realize a real return-on-investment. The reality is that the transition to AI and data driven analysis is diff...
When I first started working as a data scientist (or something like it) I was told to program in C++ and Java. Then R came along and it was liberating, my ability to do data analysis increased substantially. As my applications grew in size and complexity, I started to miss the structure of Java/C++. At the time, Python felt like a good compromise so I switched again. After joining Mango Solutions I noticed I was not an anomaly, most data…
The wayback🔗 package has had an update to more efficiently retrieve mementos and added support for working with the Internet Archive’s advanced search+scrape API. Search/Scrape The search/scrape interface lets you examine the IA collections and download what you are after (programmatically). The main function is ia_scrape() but you can also paginate through results with the... Continue reading →
We are thrilled to announce our (my and Nina Zumel’s) paper on the dot-pipe has been accepted by the R-Journal! A huge “thank you” to the reviewers and editors for helping us with this! You can find our article here (pdf here)!
This post will investigate using Principal Components as part of a momentum strategy. Recently, I ran across a post from … Continue reading →
The breakDown package explains predictions from black-box models, such as random forest, xgboost, svm or neural networks (it works for lm and glm as well). As a result you gets decomposition of model prediction that can be attributed to particular variables. The version 0.3 has a new function break_down. It identifies pairwise interactions of variables. … Czytaj dalej Break Down: model explanations with interactions and DALEX in the BayArea
Back in the “olden days” of the 1970s it was apparently not unknown for statisticians to create 3D visualizations using tinkertoys. For some inexplicable reason, the advent...
Today I am very happy to announce that during my stay in London for the m3 conference, I’ll also be giving a talk at the R-Ladies London Meetup on Tuesday, October 16th, about one of my favorite topics: Interpretable Deep Learning with R, Keras and L...
The shinydashboard provides a well-designed dashboard theme for Shiny apps, and allows for an easy assembly of a dashboard from a couple of basic building blocks. The package is widely used in commercial environments as well, due to its neat features for building convenient and robust layouts. This exercise set will help you practice all […]
Related exercise sets:
Spatial Data Analysis: Introduction to Raster Processing (Part 1) Parallel Computing Exercises: Snow and Rmpi (Part-3) Mathematical Expressions in R Plots: Tutorial Explore all our (__1000) R exercises
Find an R course using our R Course Finder directory
One thing that is sure to get lost in my long note on macros in R is just how concise and powerful macros are. The problem is macros are concise, but they do a lot for you. So you get bogged down when you explain the joke. Let’s try to be concise. Below is an … Continue reading Parameterizing with bquote
This blog post is one of a series highlighting specific images from my book Data Visualization: charts, maps and interactive … More
In the post (https://statcompute.wordpress.com/2018/09/15/how-to-avoid-for-loop-in-r), I briefly introduced the idea of vectorization and potential use cases. One might be wondering why we even need the Vectorize() function given the fact that it is just a wrapper and whether there is any material efficiency gain by vectorizing a function. It is true that the Vectorize() function is […]
This tutorial will introduce the Deep Learning classification task with Keras.
With focus on one-hot encoding, layer shapes, train &amp; evaluate the model.How to create a sequential model in Keras for R
You did an experiment or a survey, or you collected data, and you are ready to analyse them. But what is the first step? Everyone will tell you that you have to visualise your data with graphs. Yes, but how? and why? What are the common pitfalls to avoid? We are very grateful to Marion Louveaux, bio-image data analyst for this combination, adaptation and translation of the two French articles “Les pièges de la représentation de données” and “Comment se passer d’un barbarplot ?”. Let’s imagine that your dataset contains the sizes of two groups of individuals, A and B. You want to know ifRead More →
The post Data visualisation pitfalls: how to avoid barbarplots ? appeared first on (en) The R Task Force.
When You Need a New Book to Read I log all of my books on Goodreads. On top of that, whenever I hear about a new book I have to read, I add it on Goodreads, so I remember it. Of course, this means my Goodreads bookshelves are a little out of control. Fortunately, I can use R to dig through my Goodreads to-read shelf and figure out the next book to read and/or buy.If you're on Goodreads, you can easily download your entire bookshelf, including your to-read books, by going to &quot;My Books&quot; then clicking &quot;Import and Export&quot;. On the right side of the screen will be a link for &quot;Export Library&quot;. Click that and give it a minute (or several). Soon, a link will appear to download your entire library in a CSV file. You can then bring that into R.If I'm ever stuck for the next book to read, I can use this file to randomly select a book from my to-read list to check out next. Because I own a lot of books on my to-read list, I'd like to filter that dataset to only include books I own. (Note: You can add books to your &quot;owned&quot; list by clicking on &quot;My Books&quot; then &quot;Owned Books&quot; to select which books you already have in your library. Otherwise, you can keep running the sample function until you get a book you already own or have ready access to. You'd just want to skip the &quot;reading_list&quot; part below.)
setwd(&quot;~/Dropbox&quot;)library(tidyverse)
books
Boosted binary generalized additive models (GAMs) with stability selection and corresponding MCMC-based credibility intervals are discussed in a new MWR paper as a probabilistic forecasting method for the occurrence of thunderstorms.
...
A FOR loop is the most intuitive way to apply an operation to a series by looping through each item one by one, which makes perfect sense logically but should be avoided by useRs given the low efficiency. In R, there are two ways to implement the same functionality of a FOR loop. The first […]
I’ve been thinking a bit on “competition” in the R ecosystem. I guess the closest I can come to a fair and coherent view on “competition” in the R ecosystem is some variation of the following. I, of course, should not be treating things as a competition. We are all doing work and hoping for … Continue reading On “Competition” in the R Ecosystem
Our R package wrapr supplies a &quot;piping operator&quot; that we feel is a real improvement in R code piped-style coding. The idea is: with wrapr‘s &quot;dot arrow&quot; pipe &quot;%.__%&quot; the expression &quot;A %.__% B&quot; is treated very much like &quot;{. % B(.)&quot; as a … Continue reading Better R Code with wrapr Dot Arrow
Introduction
We all know that feeling. We have this great idea about a new project, feature, function, piece of code.
What do we want? Write that amazing new code! When do we want it? Right NOW!
The aim of this post is to try and give you at 3 good...
The AUC* or concordance statistic c is the most commonly used measure for diagnostic accuracy of quantitative tests. It is a discrimination measure which tells us how well we can classify patients in two groups: those with and those without the outcome of interest. Since the measure is based on ranks, it is not sensitive […]Related Post
Simple Experiments with Smoothed Scatterplots
Understanding the Covariance Matrix
Six Sigma DMAIC Series in R – Part 3
Prediction Interval, the wider sister of Confidence Interval
Six Sigma DMAIC Series in R – Part 2
CategoriesBasic Statistics
Tags
AUC
Data Visualisation
Logistic Regression
R Programming
ROC
rstudio::conf, the conference on all things R and RStudio, will take place January 17 and 18, 2019 in Austin, Texas, preceded by Training Days on January 15 and 16.
We’ve received requests from a number of you for permission to submit talk/e-poster abstracts after the deadline (this Saturday, September 15). In response, we’re extending the deadline by a week for everyone; the new submission deadline is September 22, a week from Saturday. We’ll still notify you of our decision on October 1.
See our earlier post for submission guidelines.
Apply now!
President Trump is once again causing distress by downplaying the number of deaths caused by Hurricane Maria's devastation of Puerto Rico last year. Official estimates initially put the death toll at 15 before raising it to 64 months later, but it was clear even then that those numbers were absurdly low. The government of Puerto Rico commissioned an official report from the Millikan Institute of Public Health at George Washington University (GWU) to obtain a more accurate estimate, and with its interim publication official toll stands at 2,975. Why were the initial estimates so low? I read the interim GWU...
I am running a couple of online events through BayesCamp next month that might interest you if you want to … More
Week 2 Gold Mining and Fantasy Football Projection Roundup now available. Go get that free agent gold! The post Gold-Mining Week 2 (2018) appeared first on Fantasy Football Analytics.
digest version 0.6.17 arrived on CRAN earlier today after a day of gestation in the bowels of CRAN, and should get uploaded to Debian in due course.
digest creates hash digests of arbitrary R objects (using the md5, sha-1, sha-256, sha-512, crc32, xx...
wrapr 1.6.2 is now up on CRAN. We have some neat new features for R users to try (in addition to many earlier wrapr goodies). The first is the %in_block% alternate notation for let(). The wrapr let()-block allows easy replacement of names in name-capturing interfaces (such as transform()), as we show below. library(&quot;wrapr&quot;) column_mapping
Recalling Le Monde mathematical puzzle  first competition problem For the X table below, what are the minimal number of lights that are on (green) to reach the minimal and maximal possible numbers of entries (P) with an even (P as pair) number of neighbours with lights on? In the illustration below, there are 16 lights […]
Over the last couple of days, I’ve been fettling the build scripts for the TM351 VM, which typically uses vagrant to build a VirtualBox VM from a set of shell scripts, so they can be used to build a single Docker container that runs all the TM351 services, specifically Jupyter notebooks, OpenRefine, PostgreSQL and MongoDB. Docker […]
Author and research engineer Joel Grus kicked off an important conversation about Jupyter Notebooks in his recent presentation at JupyterCon: There's no video yet available of Joel's talk, but you can guess the theme of that opening slide, and walking through the slides conveys the message well, I think. Yuhui Xie, author and creator of the rmarkdown package, provides a detailed summary and response to Joel's talk, where he lists Joel's main critiques of Notebooks: Hidden state and out-of-order execution Notebooks are difficult for beginners Notebooks encourage bad habits Notebooks discourage modularity and testing Jupyter’s autocomplete, linting, and way of...
xts version 0.11-1 was published to CRAN this morning. xts provides data structure and functions to work with time-indexed data.  This release contains some awesome features that will transparently make your xts code even faster!
There's a new window.xts() method, thanks to Corwin Joy (#100, #240). Corwin also refactored and improved the performance of the binary search algorithm used to subset xts objects. Tom Andrews reported and fixed a few related regressions (#251, #263, #264).
The na.locf.xts() method loops over columns of multivariate objects in C code, for improved speed and memory performance. Thanks to Chris Katsulis and Tom Andrews for their reports and patches (#232, #233, #234, #235, #237).
After many years, merge.xts() can finally handle multiple character or complex xts objects. Thanks to Ken Williams for the report (#44).
You can use &quot;quarters&quot; to specify tick/grid mark locations on plots. Thanks to Marc Weibel for the report (#256).
There are also a few notable bug fixes:
make.index.unique() always returns a unique and sorted index. Thanks to Chris Katsulis for the report and example (#241).
Plots have better axis tick mark locations, thanks to Dirk Eddelbuettel (#246).
periodicity() now warns instead of errors if the xts object contains less than 2 observations (#230).
first() and last() now keep dims when they would otherwise be dropped by a regular row subset. This is consistent with head() and tail(). Thanks to Davis Vaughan for the report (#226).
An invalid ISO8601 range subset now returns no data instead of all rows (#96).
As always, I'm looking forward to your questions and feedback! If you have a question, please ask on Stack Overflow and use the [r] and [xts] tags. Or you can send an email to the R-SIG-Finance mailing list (you must subscribe to post). Open an issue on GitHub if you find a bug or want to request a feature, but please read the contributing guide first!
I was chatting with some cyber-mates at a recent event and the topic of cyber attacks on the U.S. power-grid came up (as it often does these days). The conversation was brief, but the topic made its way into active memory and resurfaced when I saw today’s Data Is Plural newsletter which noted that “Utility... Continue reading →
It is now possible to create public dashboards, based on R code, for free! To illustrate how it works, I’ve used the free version of Displayr...
The secret is out: Nina Zumel and I are busy working on Practical Data Science with R2, the second edition of our best selling book on learning data science using the R language. Our publisher, Manning, has a great slide deck describing the book (and a discount code!!!) here: We also just got back our … Continue reading Practical Data Science with R2
Here is the course link.
Course Description
In this course, you'll learn to work with data using tools from the tidyverse in R. By data, we mean your own data, other people's data, messy data, big data, small data - any data with rows and columns that comes your way! By work, we mean doing most of the things that sound hard to do with R, and that need to happen before you can analyze or visualize your data. But work doesn't mean that it is not fun - you will see why so many people love working in the tidyverse as you learn how to explore, tame, tidy, and transform your data. Throughout this course, you'll work with data from a popular television baking competition called &quot;The Great British Bake Off.&quot;
Chapter 1: Explore your data (Free)
You will start this course by learning how to read data into R. We'll begin with the readr package, and use it to read in data files organized in rows and columns. In the rest of the chapter, you'll learn how to explore your data using tools to help you view, summarize, and count values effectively. You'll see how each of these steps gives you more insights into your data.
Chapter 2: Tame your data
In this chapter, you will learn some basics of data taming, like how to tame your variable types, names, and values.
Chapter 3: Tidy your data
Now that your data has been tamed, it is time to get tidy. In this chapter, you will get hands-on experience tidying data and combining multiple tidying functions together in a chain using the pipe operator.
Chapter 4: Transform your data
In this chapter, you will learn how to tame specific types of variables that are known to be tricky to work with, such as dates, strings, and factors.
Prerequisites
Introduction to the Tidyverse
As I spent an exciting day at satRday conference in Amsterdam I would like to share with you my thoughts. TL;DR: Event was cool, my presentation went fine 😉 General Wow, I don’t remember an event where I had so much fun. From most of the talks, I took out something valuable. People were really […]
Artykuł What you missed at satRday @Amsterdam pochodzi z serwisu Appsilon Data Science | We Provide End­ to­ End Data Science Solutions.
Here is the course link.
Course Description
As a data scientist, you will often find yourself working with non-numerical data, such as job titles, survey responses, or demographic information. This type of data is qualitative and can be ordinal, if...
Background Information Brands like Rolex employ a number of methods to maintain their position as a luxury watchmaker. They are selective about the brand ambassadors they hire, the location and decoration of flagship stores, the events they sponsor, and, most importantly, the price tag of the watches. With such effort, Rolex has become one of […]
This blogpost announces the release of the udpipe R package version 0.7 on CRAN. udpipe is an R package which does tokenization, parts of speech tagging, lemmatization, morphological feature tagging and dependency parsing. It's main feature is that it is a lightweight R package which works on more than 50 languages and gives you rich NLP output out of the box. 
The package was updated mainly in order to more easily work with the crfsuite R package which does entity/intent recogntion and chunking. The user-visible changes that were made are that udpipe now has a shorthand for working with text in the TIF format and it now also allows to indicate the location of the token inside the original text. Next to this, version 0.7 also caches the udpipe models.
Example
Using udpipe (version __= 0.7) works as follows. First download the model of your language and next do the annotation.
library(udpipe)udmodel
Azure HDInisght was recently updated with version 9.3 of ML Services in HDInsight, which provides integration with R and Python. In particular, it makes it possible to run R and Python within HDInsight's managed Spark instance. The integration provides: R and Python support, with interaction via Visual Studio, VS Code, or RStudio Specialized distributed analytics libraries for R and Python Simple operationalization of R and Python with Spark Interoperability with Sparklyr, pyspark and H2O.ai In the episode of Data Exposed embedded below, you can learn more about the ML Service 9.3 in HDInsight and see some demos of the R...
When modeling frequency outcomes, we often need to go beyond the standard Poisson regression due to the strict distributional assumption and to consider more flexible alternatives. In general, there are two broad categories of modeling approaches in light of practical concerns about frequency outcomes. The first category of models are mainly intended to address the […]
Is your deep convolutional network misclassifying images? You can find out why with a heatmap of class activation overlaid on its misclassified pictures.
A heatmap overlay shows parts of an image most activated in a neural network’s last convolution...
A second Riddle(r), with a puzzle related with the integer set Ð={,12,3,…,N}, in that it summarises as Given a random walk on Ð, starting at the middle N/2, with both end states being absorbing states, and a uniform random move left or right of the current value to the (integer) middle of the corresponding (left […]
A first update to the AsioHeaders package arrived on CRAN today. Asio provides a cross-platform C++ library for network and low-level I/O programming. It is also included in Boost – but requires linking when used as part of Boost. This standalone v...
The shinydashboard package provides a well-designed dashboard theme for Shiny apps and allows for an easy assembly of a dashboard from a couple of basic building blocks. The package is widely used in commercial environments as well, due to its neat features for building convenient and robust layouts. This exercise set will help you practice […]
Related exercise sets:
Spatial Data Analysis: Introduction to Raster Processing (Part 1) Mathematical Expressions in R Plots: Tutorial Parallel Computing Exercises: Snow and Rmpi (Part-3) Explore all our (__1000) R exercises
Find an R course using our R Course Finder directory
Congested streets and slow-crawling traffic are a fact of life in many metropolitan areas, such as New York City, Los Angeles, and Chicago. Bike sharing is an innovative solution for such problems, and it works by dispersing a large fleet of publicly-available bikes throughout crowded cities for personal transport. Implemented in 2013, Ford GoBike is […]
Related Post
Analysis and Visualization of Blue Bikes Sharing in Boston
Analysis of Los Angeles Crime with R
Mapping the Prevalence of Alzheimer Disease Mortality in the USA
Animating the Goals of the World Cup: Comparing the old vs. new gganimate and tweenr API
Machine Learning Results in R: one plot to rule them all! (Part 1 – Classification Models)
CategoriesVisualizing Data
Tags
Data Frames
Data Visualisation
ggmap
ggplot2
R Programming
Tips &amp; Tricks
R users who also use the dplyr package will be able to quickly understand the following code that adds an estimated area column to a data.frame. suppressPackageStartupMessages(library(&quot;dplyr&quot;)) iris %__% mutate( ., Petal.Area = (pi/4)*Petal.Width*Petal.Length) %__% head(.) ## Sepal.Length Sepal.Width Petal.Length Petal.Width Species Petal.Area ## 1 5.1 3.5 1.4 0.2 setosa 0.2199115 ## 2 4.9 3.0 … Continue reading A Quick Appreciation of the R transform Function
Introduction The Kolmogorov distribution (which I call ) is as follows: There is no known simpler form and we have to work with this sum as it is. This is an infinite sum. How can we compute the value of this infinite sum numerically? Naïvely we can do the following: In other words, sum up…Read more Naïve Numerical Sums in R
Because I have been preparing an extended presentation on (Bayesian) survival analysis in health economic evaluation, I took the opportunity to make some tweaks to survHE — nothing major, but I was aware of couple of imprecisions in the code or things I wanted to make a bit better, so while I was knitring my slides, I made the changes.
As soon as I get a moment, I’ll also update my repository (which can be used to update the package), but for now, I’ve uploaded the changes on GitHub, so the development version (1.0.63) is now available by simply typing
devtools::update_packages(&quot;survHE&quot;)
on the R terminal (assuming you have installed survHE).
The changes are the following:
I’ve fixed a minor problem in the print method; up until now, if you were fitting an Exponential model with no covariates using MLE (ie via flexsurv), then the resulting output from the call to print was simply showing the values (mean, 95% confidence interval and SE) but no label for the relevant parameter (it should have read rate). The print method was doing OK when the underlying model was run using HMC (via rstan) or INLA. Now it consistently report the name of the parameters.
I’ve made some addition to the model.fit.plot, which can be used to visualise the model fitting using Information Criteria, as recommended by the NICE DSU. Now, there’s an additional option scale, which by default is set to “absolute” (and nothing changes in comparison to the previous version) but can now also take on the value “relative” (or “rel”). This in turn creates barplots with the percentage increase in the *IC selected with comparison to the best fitting model (among the lot selected). This makes it easier to check how much better the “better” model is.
I may make a few more changes in the next few days, as I keep working on this — and if so, I’ll update the GitHub repository.
The sergeant🔗 package has a minor update that adds REST API coverage for two “new” storage endpoints that make it possible to add, update and remove storage configurations on-the-fly without using the GUI or manually updating a config file. This is an especially handy feature when paired with Drill’s new, official Docker container since that... Continue reading →
I’ve written a post about rendering IPA symbols properly regardless of the output format of the R Markdown document. I implemented the ideas into an R package, linguisticsdown. linguisticsdown provides a Shiny interface to facilitate inserting IPA s...
In the previous post (https://statcompute.wordpress.com/2018/09/03/playing-map-and-reduce-in-r-by-group-calculation), I’ve shown how to employ the MapReduce when calculating by-group statistics. Actually, the same Divide-n-Conquer strategy can be applicable to other use cases, one of which is the subsetting operation. In the example below, let’s still use the same iris data for the demonstration purpose. In R, the most convenient […]
If your R or dplyr work is taking what you consider to be a too long (seconds instead of instant, or minutes instead of seconds, or hours instead of minutes, or a day instead of an hour) then try data.table. For some tasks data.table is routinely faster than alternatives at pretty much all scales (example … Continue reading R Tip: Give data.table a Try
In US politics news, the New York Times took the unusual step this week of publishing an anonymous op-ed from a current member of the White House (assumed to be a cabinet member or senior staffer). Speculation about the identity of the author is, of course, rife. Much of the attention has focused on the use of specific words in the article, but can data science provide additional clues? In the last 48 hours, several R users have employed text similarity analysis to try and identify likely culprits. David Robinson followed a similar process to the one he used to...
With EARL just next week, we have just one more speaker interview to share! In today’s interview, Ruth Thomson, Practice Lead for Strategic Advice spoke to Jasmine Pengelly, whose career includes teaching Data Analysis and Data Science at General Assembly and permanent positions as a Data Analyst at Stack Overflow and DAZN. Jasmine will be presenting a lightning talk “Putting the R in Bar” where she will show how businesses can make data-driven decisions using the example of a Cocktail Bar.…
Yesterday I tried to start RStudio and something weird happened: the window launched but it was blank and unresponsive.
I tried dpkg --remove and then re-installed. Same problem.
I tried dpkg --remove followed by dpkg --purge and then re-installed. S...
In the last post of this series of the STATWORX Blog, we explored ggplot2's themes, which control the display of all non-data components of a plot. Ggplot comes with several inbuilt themes that can be easily applied to any plot. However, one can tweak these out-of-the-box styles using the theme() function. We did this last time. Furthermore, one also can ... Read More
Der Beitrag Custom themes in ggplot2 erschien zuerst auf STATWORX.
This is the third part of my blog series on fitting the 4-parameter Wiener model with brms. The first part discussed how to set up the data and model. The second part was concerned with (mostly graphical) model diagnostics and the assessment of the adequacy (i.e., the fit) of the model. This third part will […]
Sin ese peso ya no hay gravedad Sin gravedad ya no hay anzuelo (Mira cómo vuelo, Miss Caffeina) I love messing around with R to generate mathematical patterns. I always get surprised doing it and gives me lot of satisfaction. I also learn lot of things doing it: not only about R, but also about … Continue reading Tweetable Mathematical Art With R →
In case you missed them, here are some articles from August of particular interest to R users. A guide to installing R and RStudio with packages and multithreaded BLAS on various platforms. Some tips for Excel users migrating to R for data analysis. Videos of presentations from the New York R Conference. The Chartmaker Directory compares and provides examples of data visualizations for dozens of tools, including R. Siraj Raval's video overview of Azure machine learning services. A simple R script based on the gganimate package illustrates the luminance illusion. Roundup of AI, Machine Learning and Data Science news from...
For ref, when running IRkernel Jupyter R notebooks, media objects can be embedded by making use of the shiny::tags function, that can return HTML elements with appropriate MIME types, and are renderable using _repr_html machinery (h/t @flying-sheep): For example: PS By the by, I notice the existence of another R kernel for Jupyter notebooks, JuniperKernel. […]
I'm happy to announce that the following list of courses for R users is ready to be booked. All courses are face-to-face courses held in Belgium.
08-09/10/2018: Text mining with R. Brussels (Belgium). http://di-academy.com/bootcamp + send mail to training@di-academy.com
15-16/10/2018: Statistical machine learning with R. Leuven (Belgium). Subscribe here
05-06/11/2018: Text mining with R. Leuven (Belgium). Subscribe here
19-20/12/2018: Applied spatial modelling with R. Leuven (Belgium). Subscribe here
21-22/02/2018: Advanced R programming. Leuven (Belgium). Subscribe here
13-14/03/2018: Computer Vision with R and Python. Leuven (Belgium). Subscribe here
     15/03/2019: Image Recognition with R and Python: Subscribe here
01-02/04/2019: Text Mining with R. Leuven (Belgium). Subscribe here
For a list of all R courses which can be given in-house at your site, visit http://www.bnosac.be/index.php/training and get in touch to schedule the course.
A tutorial showing how to get started with R in IBM Watson Studio. It provides a free, hosted R environment. What’s not to love?
Analyzing the relationship between the sample size and how it impacts on the accuracy in a classification model
A penultimate Le Monde mathematical puzzle  before the new competition starts [again!] For a game opposing 40 players over 12 questions, anyone answering correctly a question gets as reward the number of people who failed to answer. Alice is the single winner: what is her minimal score? In another round, Bob is the only lowest […]
Blue Bikes is a bicycle sharing system in the Boston, Massachusetts. The bikes sharing program started on 28 July 2011. This program aimed for individuals to use it for short-term basis for a price. The program allows individuals to borrow a bike from a dock station and retrun to another dock station after using it. […]
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If you want to do statistical analysis or machine learning with data in SQL Server, you can of course extract the data from SQL Server and then analyze it in R or Python. But a better way is to run R or Python within the database, using Microsoft ML Services in SQL Server 2017. Why? It's faster. Not only to you get to use the SQL Server instance (which is likely to be faster than your local machine), but it also means you no longer have to transport the data over a network, which is likely to be the biggest...
Week 1 Gold Mining and Fantasy Football Projection Roundup now available. Go get that free agent gold! The post Gold-Mining W1 (2018) appeared first on Fantasy Football Analytics.
Thanks to the second post of the series where we obtained data from
eBird we know
what birds were observed in the county of Constance. Now, not all
speciesâ€™ names mean a lot to me, and even if they did, there are a lot
of them. In this post, we shall use rOpenSciâ€™s packages accessing
taxonomy and trait data in order to summarize some characteristics of
the birdsâ€™ population of the county: armed with scientific and common
names of birds, we have access to plenty of open data!
Getting and filtering the occurrence data
For more details about the following code, refer to the previous post
of the series.
The single difference is our adding a step to keep only data for the
most recent years.
# polygon for filtering
landkreis_konstanz
As I mentioned in July, my proposal to the R Consortium to create aÂ Guide to Working with Census Data in RÂ was accepted. Today I’d like...
The post Update on R Consortium Census Guide appeared first on AriLamstein.com.
There will be a symposium on ABC in Montréal this coming December, the day before NIPS, in a continuation of past years NIPS workshops. While invited speakers and panelists have been selected by the committee, a call for papers is open. Note that in continuation with the best “ABC in…” tradition, registration is free! I […]
Clojure is such an interesting programming language that it can not only enhance our skill set but also change the way how we should write the program. After learning Clojure, I canâ€™t help thinking about how to employ the functional programming and MapReduce paradigm to improve our experience with other programming languages, e.g. R in […]
When our Chief Data Scientist, Rich Pugh, offered to write a blogpost about the DiagrammeR package, he asked what format weâ€™d like it in.Â  â€œA poem?â€� we retorted. He didnâ€™t disappointâ€¦.   Gather ye and listen to my tale of daring do, Of heroes, villains, diagrams â€“ and a chunk of R code too, Wherefore my tale involves the noble Knight, Sir DiagrammeR, Who brought the power of Knights of old â€“ our own true Excalibur!   My tale begins…
Why? Have you ever created a multilingual Shiny app? It is very likely that the answer isÂ no, because Shiny just doesnâ€™t have any good tools for that. InÂ AppsilonÂ we came across the internationalization problem many times, so we decided to make a tool which makes a live easier when it comes to mulitlingual.Â shiny.i18nÂ is the new kid […]
ArtykuÅ‚ Internationalization of shiny apps has never been easier! pochodzi z serwisu Appsilon Data Science | We Provide EndÂ­ toÂ­ End Data Science Solutions.
Ever wonder what an evolving gif of each premier league teamâ€™s goal difference vs points would look like made in R? Look no further! Most of this is going to be setting up the data (as always) instead of actually plotting the data. To get the data into shape, we’re going to be using the
The post Animating the Premier League using gganimate appeared first on Jumping Rivers.
I am soon embarking on a trip around the world to speak at the World Water Congress in Tokyo, visit family and friends in the Netherlands and some tourism in San Francisco. One of the workshops I am involved with is about Fake News in the water industry. Fake news and conspiracies are threatening western … Continue reading &quot;Flat Earth Mathematics in the R Language&quot;
The post Flat Earth Mathematics in the R Language appeared first on The Lucid Manager.
R tip : how to pass a formula to lm(). Often when modeling in R one wants to build up a formula outside of the modeling call. This allows the set of columns being used to be passed around as a vector of strings, and treated as data. Being able to treat controls (such as … Continue reading R tip: How to Pass a formula to lm
Calling functions in R usually involves typing brackets. And since many of our actions in R involve calling a function, we will have to type a lot of brackets working with R. Often it would make our life a lot easier if we could omit the need to type brackets where convenient. We will do exactly that today.
Work in R faster with custom bracketless commands
A good starting example is, well, quitting R altogether. Usually, one may do:
quit()
Which will in turn likely get you and extra question regarding saving a workspace image. So you then finally type n and are done with it. If you want to be a bit faster, you may do:
q(&quot;no&quot;)
Better, but still an awful lot of typing just to quit R, especially when working in a terminal-like environment with multiple sessions.
Let us be a bit craftier and make R quit just by typing qq
To make a bracketless command, we will (mis)use the fact that typing an object name into R console and pressing enter will often invoke a print method specific for the class of that object.
All we have to do to create our very first bracketless command is to create a custom print method for a funky class made for this single purpose. Then we make an object of that class and type its name to the console:
qq
Nothing is more frustrating than a long piece of code with no standard way of naming elements, presenting code or organizing files. It’s not only unreadable but more importantly not reusable. Unfortunately, unlike other programming languages, R has no widely accepted coding best practices. Instead there has been various attempts to put together a few […]
Creating a spectrogram is a basic step in every analysis of audio signals. Spectrograms visualise how frequencies change over a time period. Luckily, there is a selection of R packages that can help with this task. I will present a selection of packages that I like to use. This post is not an introduction to […]
R is an amazing language with 25 years of development behind it, but you can make the most from R with additional components. An IDE makes developing in R more convenient; packages extend R's capabilities; and multi-threaded libraries make computations faster. Since these additional components aren't included on the official R website, getting the ideal R environment set up can be a bit tricky. Fortunately, there's a handy R installation guide by Mauricio Vargas that explains how to get everything you need set up on Windows, Mac and Ubuntu Linux. On each platform, the guide describes how to install: The...
Here is the course link.
Course Description
Communication is a key part of the data science process. Dashboards are a popular way to present data in a cohesive visual display. In this course you'll learn how to assemble your results into a polished...
Just after starting at Mango I made the decision to start learning Italian. I have always been interested in learning languages and I was really keen to go back to Italy, so I thought it would be something fun to do out of work. It turned out to have a much greater impact on my work than I expected â€“ and not just because of project work based in Italy. In the early stages of my learning, I read “Fluent…
Here is the course link.
Course Description
The Bayesian approach to statistics and machine learning is logical, flexible, and intuitive. In this course, you will engineer and analyze a family of foundational, generalizable Bayesian models. These r...
Today weâ€™re releasing Datazarâ€™s desktop application for Mac. This is the debut for the desktop app and weâ€™re thrilled to finally get it in the hands of everyone. The program gives you the might of the cloud while providing you an e...
Here is the course link.
Course Description
People make choices everyday. They choose products like orange juice or a car, decide who to vote for, and choose how to get to work. Marketers, retailers, product designers, political scientists, transpo...
Motivation
So, Iâ€™m in the process of moving to Melbourne and have a personal interest in the rental market there. But Iâ€™m also more generally interested in the economics of housing. Purchase costs get most of the attention, but rental ...
This report describes an R package called ‘dvir’ that aims to use TeX as a layout engine, but performs all rendering within R. The package ...
If you're familiar with analyzing data in Excel and want to learn how to work with the same data in R, Alyssa Columbus has put together a very useful guide: How To Use R With Excel. In addition to providing you with a guide for installing and setting up R and the RStudio IDE, it provide a wealth of useful tips for working with Excel data in R, including: To import Excel data into R, use the readxl package To export Excel data from R, use the openxlsx package How to remove symbols like &quot;$&quot; and &quot;%&quot; from currency and...
What should I cite?
How should I cite it?
Previous blogposts
Citing the packages, modules and softwares you used for your analysis is important, both from a reproducibility perspective (statistical routines are often implemented in different w...
In this post, I compare different approaches to get first principal components of large matrices in R. Comparison library(bigstatsr) library(tidyverse) Data # Create two matrices, one with some structure, one without n
We’re streaming live right now! Thursday 08:45-17:30: YouTube Link Friday 09:00-17:00: YouTube Link Timezone is Eastern European Summer Time (EEST) +0300 UTC Here’s a link to the full program. There have already been some great talks and they’ll all be posted with slides and runnable source code after the conference on the Stan web site.
The post StanCon Helsinki streaming live now (and tomorrow) appeared first on Statistical Modeling, Causal Inference, and Social Science.
I am sure you must have heard of Six Sigma quality standard or Six Sigma experts. But, what is Six Sigma? Six Sigma is a set of techniques used by organizations to improve their processes and optimize operations. Six Sigma was popularized by manufacturing organizations and Jack Welch, former CEO of GE, was one of … Continue reading Using Control Charts in R
In a paper recently published in the Condor, titled Evaluating time-removal models for estimating availability of boreal birds during point-count surveys: sample size requirements and model complexity, we assessed different ways of controlling for point-count duration in bird counts using data from the Boreal Avian Modelling Project. As the title indicates, the paper describes a cost-benefit analysis to make recommendations about when to use different types of the removal model. The paper is open access, so feel free to read the whole paper here).
Today’s R tip is: put your values in columns. Some R users use different seemingly clever tricks to bring data to an analysis. Here is an (artificial) example. chamber_sizes
Anyone who has a long commute or who has to travel with work knows the importance of podcasts. Podcasts allow you turn otherwise useless time, into something both productive and interesting. In this blog post, we’ll describe some of our favourite podcasts. This list isn’t meant to be exhaustive, it’s just what we are currently
The post Podcast recommendations appeared first on Jumping Rivers.
I made the “capital” mistake of walking across the entire NUS campus this morning, which is quite green and pretty, but which almost enjoys an additional dimension brought by such an intense humidity that one feels having to get around this humidity!, a feature I have managed to completely erase from my memory of my […]
Managing packages in production is a lot of work: you have to juggle between versions, internal packages, CRAN updates, Bioconductor, GitHub sourcesâ€¦ Let’s have a look into RStudio Package Manager, one of the tools available that helps you dealing with this. What is love RSPM (Baby donâ€™t hurt me, no more ðŸŽ¶) RStudio Package Manager (or RSPM for short) is a solution designed to help you to deal with the installation of packages in your organisation. Wether you need to make available to your team the whole CRAN through a proxy, a selection of the CRAN, GitHub packages, internal packages, etc, you can use RSPMRead More →
The post Playing around with RStudio Package Manager appeared first on (en) The R Task Force.
29.08.2018 Recently I discovered the package PyRserve for Python which connects Python with R using Rserve. This is extremly useful because Bio7 already integrates Rserve and has special GUI interfaces available to transfer, e.g.,  data from spreadsheets, ImageJ image and selection data (also georeferenced),  Java simulation data, etc. With this new Rserve connection this data […]
On November 7th, Iâ€™ll be in Munich for the W-JAX conference where Iâ€™ll be giving the talk that my colleague Uwe Friedrichsen and I gave at the JAX conference this April again: Deep Learning - a Primer.
Let me know if any of you here are g...
As a journal editor, I often receive studies in which the investigators fail to describe, analyse, or even acknowledge missing data. This is frustrating, as it is often of the utmost importance. Conclusions may (and do) change when missing data is accounted for.  A few seem to not even appreciate that in conventional regression, only … Continue reading &quot;Five steps for missing data with Finalfit&quot;
In the previous post (https://statcompute.wordpress.com/2018/08/26/adjacent-categories-and-continuation-ratio-logit-models-for-ordinal-outcomes), weâ€™ve shown alternative models for ordinal outcomes in addition to commonly used Cumulative Logit models under the proportional odds assumption, which are also known as Proportional Odds model. A potential drawback of Proportional Odds model is the lack of flexibility and the restricted assumption of proportional odds, of which the […]
Hi everyone! In this series we are going to work with a gene called the Mitochondrial Control Region. This series will be about getting some insights into it from sequence analysis. We are going through the entire process, from getting data to reach some conclusions and more importantly raise some questions. All of the bash […]
The videos from the NYC R conference have been published, and there are so many great talks there to explore. I highly recommend checking them out: you'll find a wealth of interesting R applications, informative deep dives on using R (and a few other applications as well), and some very entertaining deliveries. In this post, I wanted to highlight a couple of talks in particular. The talk by Jonathan Hersh (Chapman University), Applying Deep Learning to Satellite Images to Estimate Violence in Syria and Poverty in Mexico is both fascinating and a real technical achievement. In the first part of...
In this new post, weâ€™re taking a break from modern birding data in our
birderâ€™s seriesâ€¦ letâ€™s explore
gorgeous drawings from a natural history collection! Armed with
rOpenSciâ€™s packages binding powerful C++ libraries and open taxonomy
data, how much information can we automatically extract from images?
Maybe not much, but weâ€™ll at least have explored image manipulation,
optical character recognition (OCR), language detection, taxonomic name
resolution with rOpenSciâ€™s packages.
Free natural history images and appropriate R tooling!
A long time ago I had bookmarked the Flickr account of the Biodiversity
Heritage Library (BHL).
So many beautiful images of biodiversity, moreover free to use! In
particular, I downloaded all pictures from one of the Birds of
Australia
albums.
I wanted to try to extract the bird names from images using packages of
Jeroen Oomsâ€™, rOpenSciâ€™s post-doc hacker &amp; C(++)-bindings wizard. For
that I worked with magick for
image manipulation, tesseract
for optical character recognition (OCR),
cld2/cld3
for language detectionâ€¦ Quite the armory! Weâ€™ll also sprinkle some
taxonomy magic by Scott Chamberlain, one of rOpenSciâ€™s co-founders, to
resolve the names extracted.
OCR bird naming workflow, piece by piece
In this section, we explain the different elements of our R workflow:
preparing images, extracting text, resolving taxonomic names.
Image preparation
I saved the pictures locally in a â€œbirdsâ€� folder. Yes, I click-buttoned
instead of using the Flickr API
for which e.g.Â Jim Hester wrote a minimal R
wrapperâ€¦ I donâ€™t do everything
with R scripts (yet).
library(&quot;magrittr&quot;)
filenames %
magick::image_contrast() # we'll need the filename later
attr(image, &quot;filename&quot;)
Welcome to Part Two of the three-part tutorial series on proteomics data analysis. The ultimate goal of this exercise is to identify proteins whose abundance is different bewteen the drug-resistant cells and the control. In other words, we are looking for a list of differentially regulated proteins that may shed light on how cells escape […]
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A collection of some commonly used and some newly developed methods for the visualization of outcomes in oncology studies include Kaplan-Meier curves, forest plots, funnel plots, violin plots, waterfall plots, spider plots, swimmer plot, heatmaps, circos plots, transit map diagrams and network analysis diagrams (reviewed here). Previous articles in this blog presented an introduction to … Continue reading Visualization of Tumor Response – Spider Plots
Centered around Bookdown, we will review some non-standard customizations in order to self-publish a book.
Nowadays (I’ve seen that word used so much in journal articles lately that I could not resist using it) I’m using world tile grids more frequently as the need arises to convey the state of exposure of various services at a global (country) scale. Given that necessity fosters invention it seemed that having a ggplot2... Continue reading →
Statistics Sunday: Visualizing Regression I had some much needed downtime this weekend, after an exhausting week, along with some self-care - Saturday I had a one-hour deep tissue massage, which left me a little bruised but much more relaxed, and Sunday I spent a few hours in the salon chair having my color touched up, which left me much blonder. Which is why I'm a little late with my Statistics Sunday post, but today, I'm introducing another recently discovered r package: rpart. Short for &quot;recursive partitioning,&quot; this package creates decision trees for classification, regression, and survival analyses. Today, I'm going to demonstrate using the rpart package for visualizing regression.To demonstrate this technique, I'm using my 2017 reading dataset. A reader requested I make this dataset available, which I have done - you can download it here. This post describes the data in more detail, but the short description is that this dataset contains the 53 books I read last year, with information on book genre, page length, how long it took me to read it, and two ratings: my own rating and the average rating on Goodreads. Look for another dataset soon, containing my 2018 reading data; I made a goal of 60 books and I've already read 50, meaning lots more data than last year. I'm thinking of going for broke and bumping my reading goal up to 100, because apparently 60 books is not enough of a challenge for me now that I spend so much downtime reading.First, I'll load my dataset, then conduct the basic linear model I demonstrated in the post linked above.
setwd(&quot;~/R&quot;)library(tidyverse)
## Warning: Duplicated column names deduplicated: 'Author' =__ 'Author_1' [13]
## Parsed with column specification:## cols(## .default = col_integer(),## Title = col_character(),## Author = col_character(),## G_Rating = col_double(),## Started = col_character(),## Finished = col_character()## )
## See spec(...) for full column specifications.
colnames(books)[13]
Multinomial processing trees are recursively partitioned to capture heterogeneity in latent cognitive processing steps. Accompanied by the R function mpttree in the psychotree package, combining partykit::mob and psychotools::mpt.
...
Happy birthday R! We’re celebrating your 25th anniversary this month. In 1997, when your developers Ross Ihaka and Robert Gentleman – whose two first names probably gave rise to the…
ï»¿In the previous post (https://statcompute.wordpress.com/2018/01/28/modeling-lgd-with-proportional-odds-model), I’ve shown how to estimate a standard Cumulative Logit model with the ordinal::clm function and its use case in credit risk models. To better a better illustration of the underlying logic, an example is also provided below, showing how to estimate a Cumulative Logit model by specifying the log likelihood […]
Gaussian clusters are found in a range of fields and simulating them is important as often we will want to test a given class discovery tools performance under conditions where the ground truth is known (e.g. K=6). However, a flexible Gaussian cluster simulator for simulating Gaussian clusters with defined variance, spacing, and size does not […]
The annual Melbourne Data Science Initiative (or MeDaScIn, pronounced medicine) is on again next month (24-27 September) with lots of tutorials, and the annual datathon.
This year there will be a “Forecasting with R” workshop (25 September) led my two of my Monash colleagues – George Athanasopoulos and Elena Sanina.
Another great tutorial will be led by Steph Kovalchik (from Tennis Australia) on sports analytics with R (24 September).
For the full list of tutorials, see the MeDaScIn website.
I’m about to show you an R trick. Various readers may find it cool, useful and interesting, or stupid, useless and an evil deed undermining the sanctity of R’s functional programming nature (“All bow”). But I hope many of you will find the material here rather intriguing if not useful. All this involves a trick … Continue reading What, No Parentheses? →
R and Python scripts you upload or create on Datazar are now executable.R files with anÂ .r orÂ .rscript and Python files withÂ .pyextensions will be active for running. You can now analyze your datasets, import other peopleâ€™s public data,...
An overview of my experience in the NASA Datanaut kickoff session
Working with a new data visualization tool, and wondering how to create a specific type of chart? The Chartmaker Directory (designed by Andy Kirk) indexes more than 35 tools and over 50 charts, and provides links to examples from each combination. Here's an intentionally small detail from the index, where each hollow dot in a column represents a sample chart created a tool (the rightmost column is R, for example), and solid dots also include code. The visualization tools include applications like Excel, Power BI and Tableau; languages and libraries including R, Stata, and Python's matplotlib); and frameworks like D3....
In this post I simulate a population and a complex survey of it. The survey has stratification, two-stage sampling and post-collection calibration to marginal population totals. The original idea was to follow up on last weekâ€™s post on relativ...
Apple has run the death bell on 32-bit macOS apps and, if you’re running a recent macOS version on your Mac (which you should so you can get security updates) you likely see this alert from time-to-time: If you’re like me, you click through that and keep working but later ponder just how many of... Continue reading →
Everybody talks about them, many people know how to use them, few people understand them: Long Short-Term Memory Neural Networks (LSTM). At STATWORX, with the beginning of the hype around AI and projects with large amounts of data, we also started using this powerful tool to solve business problems. In short, an LSTM is a special type of recurrent neural ... Read More
Der Beitrag CodeR: an LSTM that writes R Code erschien zuerst auf STATWORX.
By Thiago Milagres Preallocating Memory This will be a short post about a simple, but very important concept that can drastically increase the speed of poorly written codes. It is very common to see R loops written as follows: This â€¦ Continue reading â†’
Itâ€™s been over a year since Headless Chrome was introduced and it has matured greatly over that time and has acquired a pretty large user base. The TLDR on it is that you can now use Chrome as you would any command-line interface (CLI) program and generate PDFs, images or render javascript-interpreted HTML by supplying... Continue reading â†’
Hi, Just to play a bit with the gganimate package, and to celebrate National Coupling Day, the above plot shows different couplings of two univariate Normal distributions, Normal(0,1) and Normal(2,1). That is, each point is a pair (x,y) where x follows a Normal(0,1) and y follows a Normal(2,1). Below Iâ€™ll recall briefly how each coupling [â€¦]
City of Los Angeles or â€œThe Birthplace of Jazzâ€ is one of the most populous city in the United States of America with the population estimated over four million. With the city of this size, it is worth the effort to explore the crime rate in this city. The current project is aimed to explore [â€¦]
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Many illusions are based on the fact that our perceptions of color or brightness of an object are highly dependent on the background surrounding the object. For example, in this image (an example of the Cornsweet illusion) the upper and lower blocks are exactly the same color, according to the pixels displayed on your screen. Mind = blown. These two blocks are exactly the same shade of grey. Hold your finger over the seam and check. pic.twitter.com/OqAnforGqs â€” David Smith (@revodavid) December 5, 2013 Here's another simpler representation of the principle, created by Colin Fay (in response to this video...
digest version 0.6.16 arrived on CRAN earlier today, and was just prepared for Debian as well.
digest creates hash digests of arbitrary R objects (using the 'md5', 'sha-1', 'sha-256', 'sha-512', 'crc32', 'xxhash32', 'xxhash64' and 'murmur32' algorith...
Introduction This note shares an experiment comparing the performance of a number of data processing systems available in R. Our notional or example problem is finding the top ranking item per group (group defined by three string columns, and order defined by a single numeric column). This is a common and often needed task. Comparisons â€¦ Continue reading Timings of a Grouped Rank Filter Task
In our previous article we have showed you how you can make your daily work easier with the right solution. The optimal support of different data science languages, a scalable…
I recently had the wonderful opportunity to chat with Hadley Wickham. He is an immensely prolific, yet humble guy who has not only contributed heavily to the advancement and development of R as a language and environment, but who also cares and has thought a lot about the process of doing data science the right … Continue reading Advice to Young (and Old) Programmers: A Conversation with Hadley Wickham
When you signed up a brand new Salesforce account, you can see lots of dummy dataset (such as Acme Inc.). If you are a Salesforce developer or administrator, you might have to delete all records prior to release the account…Read more ›
The latest R tip in Win-Vector Blog encourages you to Use Radix Sort based on a simple benchmark showing a x35 speedup compared to the default method, but with no further explanation. In my opinion, though, the complete tip would be, instead, use radix sort… if you know what you are doing, because a quick benchmark shouldn’t spare you the effort of actually reading the� Continuar leyendo Read the docs before questioning R�s defaults ?
Udemy is offering readers of R-bloggers access to its global online learning marketplace for $10 per course! This deal (offering over 50%-90% discount) is for hundreds of their courses – including many R-Programming, data science, machine learning etc. Click here to browse ALL (R and non-R) courses Advanced R courses: Regression modelling Comprehensive Linear Modeling with R (15 Hours of video) Linear regression in R for Data Scientists (7 hours) Linear Mixed-Effects Models with R (11 Hours of video) Linear Regression, GLMs and GAMs with R (8 hours) Forecasting Models with R (5 hours) Time Series Analysis and Forecasting in R (3 Hours of video) Regression, Data Mining, Text Mining, Forecasting using R (32 hours) Statistical/machine learning Bayesian Computational Analyses with R (12 Hours of video) Data Science and Machine Learning Bootcamp with R (17 hours) Decision Tree - Theory, Application and Modeling using R (5 hours) Data Mining with R: Go from Beginner to Advanced! (12 hours) Machine Learning A-Z : Hands-On Python &amp; R In Data Science (40 hours) Applied Multivariate Analysis with R (13 Hours of video) Visualization Graphs in R (ggplot2, plotrix, base R) - Data Visualization with R Programming Language (5 Hours of video) Multivariate Data Visualization […]
In the Riddler of August 18, two riddles connected with the integer set Ã�={2,3,…,10}: Given a permutation of Ã�, what is the probability that the most likely variation (up or down) occurs at each term? Given three players choosing three different integers in Ã� sequentially, and rewards in Ã� allocated to the closest of the […]
R tip: use radix sort. The “method = &quot;radix&quot;” option can greatly speed up sorting and ordering tables in R. For a 1 million row table the speedup is already as much as 35 times (around 9.6 seconds versus 3 tenths of a second). Below is an excerpt from an experiment sorting showing default settings … Continue reading R tip: Use Radix Sort
Styling ggplot2 graphics In my previous post, we demonstrated that contrary to popular opinion, it is possible to generate attractive looking plots using just base graphics. Although we did confess, that it did take a lot of time and effort. In this post, we repeat the same exercise. Using the dreaded iris data set, we’ll
The post Styling ggplot2 Graphics appeared first on Jumping Rivers.
@drsimonj here to share a (sort of) readable version of my presentation at the amst-R-dam meetup on 14 August, 2018: �Exploring correlations in R with corrr???.
Those who attended will know that I changed the topic of the talk, originally a...
General description of transformers functionality in {glue} with some potentially useful examples.
Prologue
Package {glue} is designed as â€œsmall, fast, dependency freeâ€� tools to â€œglue strings to data in Râ€�. To put simply, it provides concise and flexible alternatives for paste() with some additional features:
library(glue)
x Its â€œtransformerâ€� functionality is an interesting way to manipulate string output by supplying custom modification function.
Functional programming with {purrr} can be very helpful in creating concise and extensible code.
sessionInfo()
sessionInfo()
## R version 3.4.4 (2018-03-15)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.5 LTS
## ## Matrix products: default
## BLAS: /usr/lib/openblas-base/libblas.so.3
## LAPACK: /usr/lib/libopenblasp-r0.2.18.so
## ## locale:
## [1] LC_CTYPE=ru_UA.UTF-8 LC_NUMERIC=C ## [3] LC_TIME=ru_UA.UTF-8 LC_COLLATE=ru_UA.UTF-8 ## [5] LC_MONETARY=ru_UA.UTF-8 LC_MESSAGES=ru_UA.UTF-8 ## [7] LC_PAPER=ru_UA.UTF-8 LC_NAME=C ## [9] LC_ADDRESS=C LC_TELEPHONE=C ## [11] LC_MEASUREMENT=ru_UA.UTF-8 LC_IDENTIFICATION=C ## ## attached base packages:
## [1] methods stats graphics grDevices utils datasets base ## ## other attached packages:
## [1] stringr_1.3.1 purrr_0.2.5 glue_1.3.0 ## ## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.18 bookdown_0.7 crayon_1.3.4 digest_0.6.15 ## [5] rprojroot_1.3-2 backports_1.1.2 magrittr_1.5 evaluate_0.11 ## [9] blogdown_0.8 rlang_0.2.1.9000 stringi_1.2.4 rmarkdown_1.10 ## [13] tools_3.4.4 xfun_0.3 yaml_2.2.0 compiler_3.4.4 ## [17] htmltools_0.3.6 knitr_1.20
Here I am sharing the slides for a webinar I gave for SAP about Explaining Keras Image Classification Models with LIME.
Slides can be found here: https://www.slideshare.net/ShirinGlander/sap-webinar-explaining-keras-image-classification-models-with-lim...
Data Science is an amazing field of research that is under active development both from the academia and the industry. One of the saddest facts in the real-world is that most data science projects in organizations fail. Here I�ll present a new i...
The Monti et al. (2003) consensus clustering algorithm is one of the most widely used class discovery techniques in the genome sciences and is commonly used to cluster transcriptomic, epigenetic, proteomic, and a range of other types of data. It can automatically decide the number of classes (K), by resampling the data and for each [?]
tl;dr: A list of useful resources aimed to self-publish a book on Amazon using Bookdown.
rstudio::conf, the conference on all things R and RStudio, will take place January 17 and 18, 2019 in Austin, Texas, preceded by Training Days on January 15 and 16. We are pleased to announce that this yearâ€™s conference includes contrib...
By Gabriel Vasconcelos and Yuri Fonseca Introduction This is the first of a series of post on the BooST (Boosting Smooth Trees). If you missed the first post introducing the model click here and if you want to see the … Continue reading →
Today is the first day of the new academic year at the University of Utah. This semester I am teaching MATH 3070: Applied Statistics I, the fourth time I've taught this course.
FOAAS upstream recently went to release 2.0.0, so here we are catching up bringing you all the new accessors from FOAAS 2.0.0: bag(), equity(), fts(), ing(), particular(), ridiculous(), and shit(). We also added off_with() which was missing previous...
The rvest package allows for simple and convenient extraction of data from the web into R, which is often called “web scraping.” Web scraping is a basic and important skill that every data analyst should master. You’ll often see it as a job requirement. In the following exercises, you will practice your scraping skills on […]
Related exercise sets:
Spatial Data Analysis: Introduction to Raster Processing (Part 1) Advanced Techniques With Raster Data: Part 1 – Unsupervised Classification Spatial Data Analysis: Introduction to Raster Processing: Part-3 Explore all our (__1000) R exercises
Find an R course using our R Course Finder directory
Why build a recommender system? The most wonderful and most frustrating characteristic of the Internet is its excessive supply of content. As a result, many of todayâ€™s commercial giants are not content providers, but content distributors. The success of companies such as Amazon, Netflix, YouTube and Spotify relies on their ability to effectively deliver relevant and novel content to users. However, with such a vast array of content at their fingertips, the search space becomes near impossible to navigate with…
ThisÂ  work is still in progres. I think, however, it can already resonate with some people in the community. The communication I am hopeful for should lead to a better design and maybe getting valuable tools faster. The main goal is to extend the base R’s history mechanism (see ?history) which currently gives access to … More Reproducible research and a repository of artifacts, a RFC
Using Text Analysis to Become a Better Writer We all have words we love to use, and that we perhaps use too much. As an example: I have a tendency to use the same transitional statements, to the point that, before I submit a manuscript, I do a find all to see how many times I've used some of my favorites, e.g., additionally, though, and so on.I'm sure we all have our own words we use way too often.
Text analysis can also be used to discover patterns in writing, and for a writer, may be helpful in discovering when we depend too much on certain words and phrases. For today's demonstration, I read in my (still in-progress) novel - a murder mystery called Killing Mr. Johnson - and did the same type of text analysis I've been demonstrating in recent posts.To make things easier, I copied the document into a text file, and used the read_lines and tibble functions to prepare data for my analysis.
setwd(&quot;~/Dropbox/Writing/Killing Mr. Johnson&quot;)library(tidyverse) KMJ_text
The analysis of high frequency stock transactions has played an important role in the algorithmic trading and the result can be used to monitor stock movements and to develop trading strategies. In the paper “An Ordered Probit Analysis of Transaction Stock Prices” (1992), Hausman, Lo, and MacKinlay discussed estimating trade-by-trade stock price changes with the […]
Version 2.5-0 of the R package 'sandwich' is available from CRAN now with enhanced object-oriented clustered covariances (for lm, glm, survreg, polr, hurdle, zeroinfl, betareg, ...). The software and corresponding vignette have been improved ...
Some more Practical Data Science with R news. Practical Data Science with R is the book we wish we had when we started in data science. Practical Data Science with R, Second Edition is the revision of that book with the packages we wish had been available at that time (in particular vtreat, cdata, and … Continue reading More Practical Data Science with R Book News
In comparison with other statistical software (e.g., SAS, STATA, and SPSS), R is the best for data visualization. Therefore, in all posts I have written for DataScience+ I take advantage of R and make plots using ggplot2 to visualize all the findings. For example, previously I plotted the percentiles of body mass index in the […]
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Introduction
In this summertime post in the case4base series, we will look at useful tools in base R, which let us profile our code without any extra packages needed to be installed. We will cover simple and easy to use speed profiling, more complex profiling of performance and memory and, as always, look at alternatives to base R as well, with a special shout out to profiling integration in RStudio.
Contents
Simple time profiling with system.time
Profile R execution with Rprof
Non-sampling memory use profiling with Rprofmem
Profiling integration within RStudio
Background profiling with base R via an RStudio addin
Alternatives to base R
References
Simple time profiling with system.time
Base function system.time returns the difference between two proc.time calls within which it evaluates an expression provided as argument. The simplest usage can be seen below:
system.time(runif(10^8))
## user system elapsed ## 4.660 0.392 5.186
For the purpose of processing the results, we can of course store and examine them within a variable where we can see that it is in fact a numeric vector with 5 elements with a proc_time class. It uses summary as its print method via the print.proc_time. For most our purposes, we would be interested in the â€œelapsedâ€� element of the result, giving us the â€˜realâ€™ elapsed time since the process was started:
tm
I came across this question on X validated that wondered about one of our examples in Monte Carlo Statistical Methods. We have included a section on Laplace approximations in the Monte Carlo integration chapter, with a bit of reluctance on my side as this type of integral approximation does not directly connect to Monte Carlo […]
This is a review of James Picerno’s Quantitative Investment Portfolio Analytics in R. Overall, it’s about as fantastic a book … Continue reading →
Step 1: Create a Tidy data frame The very first step in creating visualizations is to get the data in a useful format. In the...
A new RcppArmadillo release 0.9.100.5.0, based on the new Armadillo release 9.100.5 from earlier today, is now on CRAN and in Debian.
It once again follows our (and Conrad's) bi-monthly release schedule. Conrad started with a new 9.100.* series a fe...
This post tries to explain the difference between odds ratios and relative risk ratios; and how just a few letters in the code fitting a generalized linear model mean the difference between extracting one or the other. There are plenty of other explan...
I was at the EdinbR talk this week by the RStudio community lead – Curtis Kephart. It was really interesting, but I disagree with his suggestion to point and click different parameters when… Continue reading →
A while back, I started to create an R package that would help me and my collegues at STATWORX with our daily work. After writing the DESCRIPTION file, I did not want to do this ever again. I found the desc package which let's you parse, manipulate and reformat this file via some functions and ergo a script. If you ... Read More
Der Beitrag An R package to create NEWS.md files erschien zuerst auf STATWORX.
A new RcppArmadillo release 0.9.100.5.0, based on the new Armadillo release 9.100.5 from earlier today, is now on CRAN and in Debian.
It once again follows our (and Conrad's) bi-monthly release schedule. Conrad started with a new 9.100.* series a fe...
Every wanted to make R talk to you? Now you can, with the mscstts package by John Muschelli. It provides an interface to the Microsoft Cognitive Services Text-to-Speech API (hence the name) in Azure, and you can use it to convert any short piece of text to a playable audio file, rendering it as speech using a number of different voices. Before you can generate speech yourself, you'll need a Bing Speech API key. If you don't already have an Azure account, you can generate a free 7-day API key in seconds by visiting the Try Cognitive Services page, selecting...
Apache Drill 1.14.0 was recently released, bringing with it many new features and a temporary incompatibility with the current rev of the MapR ODBC drivers. The Drill community expects new ODBC drivers to arrive shortly. The sergeant🔗 is an alternative to ODBC for R users as it provides a dplyr interface to the REST API... Continue reading →
Great post!I wanted to mention that although many previous studies have used the area under receiver operating characteristic curve (auROC) statistic to benchmark the precision, it misleads evaluators when the test data is (highly) imbalanced see: PLOS...
Introduction
There are many different R packages for dealing with spatial data. The main distinctions between them involve the types of data they work with — raster or vector — and the sophistication of the analyses they can do. Raster data can be thought of as pixels, similar to an image, while vector data consists of points, lines, or polygons. Spatial data manipulation can be quite complex, but creating some basic plots can be done with just a few commands. In this post, we will show simple examples of raster and vector spatial data for plotting a watershed and gage locations, and link to some other more complex examples.
Setting up
First, let’s download an example shapefile (a polygon) of a HUC8 from western Pennsylvania, using the sbtools package to access ScienceBase. The st_read function from the sf (for “simple features”) package reads in the shapefile. We will be using sf throughout these examples to manipulate the points and polygon for the gages and HUC. Then we’ll retrieve gages with discharge from this watershed using the dataRetrieval package. Both dataRetrieval and sbtools are covered in our USGS Packages curriculum.
library(sbtools)
library(dataRetrieval)
library(sf)
item_file_download(sb_id = &quot;5a83025ce4b00f54eb32956b&quot;, names = &quot;huc8_05010007_example.zip&quot;, destinations = &quot;huc8_05010007_example.zip&quot;, overwrite_file = TRUE)
## [1] &quot;huc8_05010007_example.zip&quot;
unzip('huc8_05010007_example.zip', overwrite = TRUE)
#note st_read takes the folder, not a particular file
huc_poly
16.08.2018 A new release of Bio7 is available. The new Bio7 2.9 release comes with a plethora of new R features and bugfixes. Release Notes: General: Based on Eclipse 4.8 Improved the dark theme and the layout of the dark theme in many places All editor font colors are now changed automatically to default optimized […]
A blogpost inspired by a tweet and a YouTube video. ‘Luminance-gradient-dependent lightness illusion’ In the last days, I’ve stumbled upon this tweet: A demo of lightness perception pic.twitter.com/BSVpgcuIw1 — Akiyoshi Kitaoka (@AkiyoshiKitaoka) August 12, 2018 Which is a demonstration of how our perception of color is affected by the lightness surrounding this color. Also recently, I’ve been watching the useR 2018 YouTube recordings, which contains a video called The Grammar of Animation, presenting {gganimate} a package by Thomas Lin Pedersen extending the {ggplot2} grammar of graphics to include animation. If you want to know more about {gganimate}, feel free to browse the GitHub repo, and watch theRead More →
The post Remaking ‘Luminance-gradient-dependent lightness illusion’ with R appeared first on (en) The R Task Force.
My slides from the R/Pharma conference on &quot;Modeling in the Tidyverse&quot; are in pdf format as well as the HTML version. (Joe Cheng just killed it in his shiny presentation - see this...
Imagine a situation where you are asked to predict the tourism revenue for a country, let’s say India. In this case, your output or dependent or response variable will be total revenue earned (in USD) in a given year. But, what about independent or predictor variables? You have been provided with two sets of predictor … Continue reading Dealing with The Problem of Multicollinearity in R
In this exercise, we will continue to solve problems from the last exercise about GLM here. Therefore, the exercise number will start at 9. Please make sure you read and follow the previous exercise before you continue practicing. In the last exercise, we knew that there was over-dispersion over the model. So, we tried to […]
Related exercise sets:
Spatial Data Analysis: Introduction to Raster Processing (Part 1) Advanced Techniques With Raster Data: Part 1 – Unsupervised Classification Spatial Data Analysis: Introduction to Raster Processing: Part-3 Explore all our (__1000) R exercises
Find an R course using our R Course Finder directory
We are pleased and excited to announce that we are working on a second edition of Practical Data Science with R! Manning Publications has just announced the launching of the MEAP (Manning Early Access Program) for the second edition. The MEAP allows you to subscribe to drafts of chapters as they become available, and give … Continue reading Announcing Practical Data Science with R, 2nd Edition
I'll be giving a talk at the R/Medicine conference on Sept 7th in New Haven CT. My talk is on modeling in the tidyverse but there are some excellent speakers. Rob Tibshirani, Mike...
I just came across a nice little post on acquiring and visualizing geodata in R using the Max Planck Institute of Ornithology as an example. It’s by the rOpenSci guys. Some useful code in there by the look of it… 🙂 Worth a look…
Here is the course link.
Course Description
You've taken a survey (or 1000) before, right? Have you ever wondered what goes into designing a survey and how survey responses are turned into actionable insights? Of course you have! In Analyzing Surve...
The language used by data scientists can be confusing to anyone encountering it for the first time. Ever changing best practices and constantly evolving technologies and methodologies have given rise to a range of nuanced terms used throughout casual data conversation. Unfamiliarity with these terms often leads to disconnected expectations across different parts of a business when undertaking projects involving data and analytics. To make the most out of any data science project, it is important that participants have a…
By Gabriel Vasconcelos and Yuri Fonseca   We are happy to introduce our new machine learning method called Boosting Smooth Trees (BooST) (full article here). This model was a joint work with professors Marcelo Medeiros and Álvaro Veiga. The BooST … Continue reading →
Microsoft R Open 3.5.1 has been released, combining the latest R language engine with multi-processor performance and tools for managing R packages reproducibly. You can download Microsoft R Open 3.5.1 for Windows, Mac and Linux from MRAN now. Microsoft R Open is 100% compatible with all R scripts and packages, and works with all your favorite R interfaces and development environments. This update brings a number of minor fixes to the R language engine from the R core team. It also makes available a host of new R packages contributed by the community, including packages for downloading financial data, connecting...
This post is the 1st post of a series showcasing various rOpenSci
packages as if Maëlle were a birder trying to make the most of R in
general and rOpenSci in particular. Although the series use cases will
mostly feature birds, it’ll be the occasion to highlight rOpenSci’s
packages that are more widely applicable, so read on no matter what your
field is! Moreoever, each post should stand on its own.
A logical first step in this birder’s guide to rOpenSci is to use R to
find where to observe birds! In this blog post, we shall use rOpenSci
packages accessing open geographical data to locate and visualize
where to observe birds near a given location.
First of all, where are we now?
The Max Planck Institute for Ornithology (henceforth shortened to MPI),
where Maëlle will give a talk is located at Am Obstberg 1 78315
Radolfzell. Let’s geolocate it using rOpenSci’s opencage
package that interfaces the
OpenCage Geocoder, a commercial service
based on open data. When choosing to
get only one result via limit = 1, one gets what the API considers to
be the best one.
mpi %
magick::image_annotate(&quot;Map data © OpenStreetMap contributors&quot;,
color = &quot;white&quot;,
boxcolor = &quot;black&quot;,
size = 15,
gravity = &quot;south&quot;)
Quite pretty! The lakes can be seen because of the absence of roads and
buildings on them.
Now, let’s plot the bird hide and the MPI on them. Since we used
osmdata::osmdata_sf, we had gotten the data in a receivable class,
sf.
points_map
A couple of months ago I blogged about consumer spending on vehicle fuel by income. The impetus for that post was the introduction on 1 July 2018 of an 11.5 cent per litre “regional fuel tax” in Auckland.
One vehement critic of the levy, largely ...
Introduction As part of our effort to provide users ways to replicate our analyses and improve their performance in fantasy football, we are continuously looking at ways we can improve[...]
The post 2018 Update of ffanalytics R Package appeared first on Fantasy Football Analytics.
The data.table R package is really good at sorting. Below is a comparison of it versus dplyr for a range of problem sizes. The graph is using a log-log scale (so things are very compressed). But data.table is routinely 7 times faster than dplyr. The ratio of run times is shown below. Notice on the … Continue reading data.table is Really Good at Sorting
The development version of splashr now support authenticated connections to Splash API instances. Just specify user and pass on the initial splashr::splash() call to use your scraping setup a bit more safely. For those not familiar with splashr and/or Splash: the latter is a lightweight alternative to tools like Selenium and the former is an... Continue reading →
We are less than one month away from the R/Medicine conference! Tickets are still available to connect with the R users who are advancing the way we think about human health.
The goal of the R/Medicine conference is to promote the use of th...
Model interpretability is critical to businesses. If you want to use high performance models (GLM, RF, GBM, Deep Learning, H2O, Keras, xgboost, etc), you need to learn how to explain them. With machine learning interpretability growing in importance, s...
I love Arkham Horror; The Card Game. I love it more than I really should;it's ridiculously fun. It's a cooperative card game where you build a deck representing a character in the Cthulhu mythos universe, and with that deck you play scenarios in a narrative campaign where you grapple with the horrors of the mythos. Your actions in (and between) scenarios have repercussions for how the campaign plays out, changing the story, and you use experience points accumulated in the scenarios to upgrade your deck with better cards.
A Deluge of Content Over the last two decades, the accessibility of media has increased dramatically. One of the main reasons for this surge in available content is the evolution of online streaming platforms. Services like Netflix, Hulu, Amazon Prime, and others enable access for millions of consumers to a seemingly countless number of movies […]
IRA Tweet Data You may have heard that two researchers at Clemson University analyzed almost 3 millions tweets from the Internet Research Agency (IRA) - a &quot;Russian troll factory&quot;. In partnership with FiveThirtyEight, they made all of their data available on GitHub. So of course, I had to read the files into R, which I was able to do with this code:
files %arrange(desc(tf_idf))tweet_tfidf %__%mutate(word = factor(word, levels = rev(unique(word)))) %__%group_by(account_category) %__%top_n(15) %__%ungroup() %__%ggplot(aes(word, tf_idf, fill = account_category)) +geom_col(show.legend = FALSE) +labs(x = NULL, y = &quot;tf-idf&quot;) +facet_wrap(~account_category, ncol = 2, scales = &quot;free&quot;) +coord_flip()
## Selecting by tf_idf
But another method of examining terms and topics in a set of documents is Latent Dirichlet Allocation (LDA), which can be conducted using the R package, topicmodels. The only issue is that LDA requires a document term matrix. But we can easily convert our wordcounts dataset into a DTM with the cast_dtm function from tidytext. Then we run our LDA with topicmodels. Note that LDA is a random technique, so we set a random number seed, and we specify how many topics we want the LDA to extract (k). Since there are 6 account types (plus 1 unknown), I'm going to try having it extract 6 topics. We can see how well they line up with the account types.tweets_dtm %cast_dtm(account_category, word, n)library(topicmodels)tweets_lda %ungroup() %__%arrange(topic, -beta)top_terms %__%mutate(term = reorder(term, beta)) %__%ggplot(aes(term, beta, fill = factor(topic))) +geom_col(show.legend = FALSE) +facet_wrap(~topic, scales = &quot;free&quot;) +coord_flip()
Based on these plots, I'd say the topics line up very well with the account categories, showing, in order: news feed, left troll, fear monger, right troll, hash gamer, and commercial. One interesting observation, though, is that Trump is a top term in 5 of the 6 topics.
Visualising potential winners in the Scottish Championship - Last August, I was given a surprise gift of a season ticket to watch my local football team, Inverness Caledonian Thistle.
They’d been relegated from the top league in Scotland t...
Ceteris Paribus package is a part of DALEX family of model explainers. Version 0.3 just gets to CRAN. It’s equipped with new functions for very elastic visual exploration of black box models. Its grammar generalizes Partial Dependency Plots, Individual Conditional Expectations, Wangkardu Plots and gives a lot of flexibility in model comparisons, groups comparisons and … Czytaj dalej Ceteris Paribus v0.3 is on CRAN
The latest Redmonk Language Rankings are out. These rankings are published twice a year, and the top three positions in the June 2018 rankings remain unchanged from last time: JavaScript at #1, Java at #2, and Python at #3. The Redmonk rankings are based on Github repositories (as a proxy for developer activity) and StackOverflow activity (as a proxy for user discussion), as shown in the chart below. Compared to two quarters ago, R has dropped 2 places to #14. But as Stephen O'Grady notes, this ranking volatility is typical: R is essentially a case study at this point for...
Boxplots are often used to show data distributions, and ggplot2 is often used to visualize data. A question that comes up is what exactly do the box plots represent? The ggplot2 box plots follow standard Tukey representations, and there are many references of this online and in standard statistical text books. The base R function to calculate the box plot limits is boxplot.stats. The help file for this function is very informative, but it’s often non-R users asking what exactly the plot means. Therefore, this blog post breaks down the calculations into (hopefully!) easy-to-follow chunks of code for you to make your own box plot legend if necessary. Some additional goals here are to create boxplots that come close to USGS style. Features in this blog post take advantage of enhancements to ggplot2 in version 3.0.0 or later.
First, let’s get some data that might be typically plotted in a USGS report using a boxplot. Here we’ll use chloride data (parameter code “00940”) measured at a USGS station on the Fox River in Green Bay, WI (station ID “04085139”). We’ll use the package dataRetrieval to get the data (see this tutorial for more information on dataRetrieval), and plot a simple boxplot by month using ggplot2:
library(dataRetrieval)
library(ggplot2)
# Get chloride data using dataRetrieval:
chloride
In a previous post, I studied gender diversity in the film industry, I did this by focusing on some key behind-the-camera roles and measuring the evolution of the gender diversity in the last decade. The conclusion was not great: women are under-represented, especially in the most important roles of directors and writers, as these key roles determine the way women are portrayed in front of the camera. I was curious about the TV series industry too: as the TV series industry…
Do you plan to upgrade your server installation from Ubuntu 16.04 to Ubuntu 18.04 LTS ? It is also the best time to migrate to R 3.5 ! By the way, if you always found difficult to install R packages for geographical data on Ubuntu, this time is over. Just follow the guide… New Ubuntu version and new R version If you use Ubuntu on your laptop or on your servers in your company, chances are high that you use a Long Term Support (LTS) version. Using a LTS version is safer for companies having stability constraints. This avoids possible breaking changes that would compromiseRead More →
The post Installation of R 3.5 on Ubuntu 18.04 LTS and tips for spatial packages appeared first on (en) The R Task Force.
A brand new release of RQuantLib, now at version 0.4.5, just arrived on CRAN, and will get to Debian shortly. This release re-enables Windows builds thanks to a PR by Jeroen who now supplies a QuantLib library build in his rwinlib repositories. (Sadl...
The analysis of DNA and RNA, the blueprint of life and its carbon copy, has become a staple in the burgeoning field of molecular biology. An emerging and exciting area of study that adds another dimension to our understanding of cellular biology is that of proteomics. The use of mass spectrometry has enabled the identification […]
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The debate about business confidence as a possible leading indicator of economic growth reminded me of a question that has long been on my back-burner - what data make the best leading indicators (say, a quarter of a year ahead) of economic growth? Th...
In this post I show how to enrich a ggplot map with data obtained from the Open Street Map (OSM) API. After adding elevation details to the map, I add water bodies and elements identifying human activity. To highlight the areas more densely inhabitated, I propose to use a density-based clustering algorithm of OSM features. […]
Here is the course link.
Course Description
Bayesian data analysis is an approach to statistical modeling and machine learning that is becoming more and more popular. It provides a uniform framework to build problem specific models that can be used...
For today’s interview, Ruth Thomson, Practice Lead for Strategic Advice spoke to Catherine Gamble, Data Scientist at Marks and Spencer. Catherine is presenting “Using R to Drive Revenue for your Online Business” at EARL London and we got the chance to get a preview of the use case she’ll be presenting. Thanks Catherine for this interview. What was the business need or opportunity that led to this project? As an online retailer, we know that the actions we take, for…
A simple (summertime?!) arithmetic Le Monde mathematical puzzle A “powerful integer” is such that all its prime divisors are at least with multiplicity 2. Are there two powerful integers in a row, i.e. such that both n and n+1 are powerful?  Are there odd integers n such that n² – 1 is a powerful integer […]
In case you missed them, here are some articles from July of particular interest to R users. A program to validate quality and security for R packages: the Linux Foundation's CII Best Practices Badge Program. R scripts to generate images in the style of famous artworks, like Mondrian's. A 6-minute video tour of the AI and Machine Learning services in Azure, including R. The July roundup of AI, Machine Learning and Data Science news. An R package for tiling hexagon-shaped images, used to create a striking banner of hex stickers for useR!2018. Highlights and links to videos from the useR!2018...
Click here to access the map 1 - Background and motivation During the last few years, the sharing economy has become more and more ubiquitous, from taxi riding applications to DIY and so on. The Real Estate market is no exception and in the Hotels and Hospitality sector we have seen more of these examples. […]
Thanks to @LukeBornn &amp; Tim Swartz, I had the opportunity to present the #HawkesHand🔥🏀at #CASSIS18 . I learned a lot, had a hell of a time &amp; met some great people #RuffRuff . My Hawkes Hand slides can be viewed here https://t.co/RVkyoNc2IY pic.twitter.com/hPfBcpDhcv — MikeJackTzen (@MKJCKTZN) August 8, 2018 Thanks to Luke Bornn &amp; Tim […]
I have a dashboard at work that plots the number of contracts my office handles over time, and I wanted to add a trendline to show growth. However, trendline on the entire dataset skews low because our fiscal year just restarted. It took a little trial and error to figure out how to exclude the most recent year’s count so the trendline is more accurate for this purpose, so I thought I’d share my solution in a short post. Here’s the code with a dummy dataset, with both the original trendline and the leave-one-out version: x
We are happy to report that our MODIStsp package for
automatic preprocessing of MODIS time series has been recently approved for being included in the rOpenSci ecosystem of R packages for reproducible science!
We wish to thank reviewers Leah Wasser and...
This blog post illustrates the new functionality of the OpenImageR package (Gabor Feature Extraction). The Gabor features have been used extensively in image analysis and processing (Character and Face recognition). Gabor (Nobel prize winner,...
Python retains its top spot in the fifth annual IEEE Spectrum top programming language rankings, and also gains a designation as an &quot;embedded language&quot;. Data science language R remains the only domain-specific slot in the top 10 (where it as listed as an &quot;enterprise language&quot;) and drops one place compared to its 2017 ranking to take the #7 spot. Looking at other data-oriented languages, Matlab as at #11 (up 3 places), SQL is at #24 (down 1), Julia at #32 (down 1) and SAS at #40 (down 3). Click the screenshot below for an interactive version of the chart where...
One of the few remaining hurdles when working with R in the enterprise is consistent access to CRAN. Often desktop class systems will have unrestricted access while server systems might not have any access at all. This inconsistency often stems from security concerns about allowing servers access to the internet. There have been many different approaches to solving this problem, with some organisations reluctantly allowing outbound access to CRAN, some rolling their own internal CRAN-like repositories, and others installing a…
Three months have passed since the initial release of [crayon-5b6a25a3704bb539673860-i/] on CRAN. I would not say that the package develops like crazy, but there have been some changes since May. Let’s have a look. We start by loading both [crayon-5b6a25a3704ca424011901-i/] and [crayon-5b6a25a3704cf992980275-i/]: [crayon-5b6a25a3704d3644912402/] Rolling Origin First of all, [crayon-5b6a25a3704d7036937777-i/] function now has its own class […]
I recently attended the 2018 Association of Public Data Users (APDU) conference. This was my second time attending the conference, and I enjoyed learning more...
The post Notes from the 2018 APDU Conference appeared first on AriLamstein.com.
We were very excited when Home Credit teamed up with Kaggle to host the Home Credit Default Risk Challenge. Default risk is a topic that impacts all financial institutions, one that machine learning can help solve. We decided to flip the goal of this c...
Welcome to Part 3 of my series on “Visualizing the World Cup with R”! This is the culmination of this mini project that I've been working on throughout the World Cup (You can check out the Github Repo here). In addition, from having listened to Thomas Pedersen's excellent keynote at UseR! 2018 in Brisbane on […]
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Highlighting Specific Cases in ggplot2 Here's my belated Statistics Sunday post, using a cool technique I just learned about: gghighlight. This R package works with ggplot2 to highlight a subset of data. To demonstrate, I'll use a dataset I analyzed for a previous post about my 2017 reading habits. [Side note: My reading goal for this year is 60 books, and I'm already at 43! I may have to increase my goal at some point.]
setwd(&quot;~/R&quot;)library(tidyverse) books 'Author_1' [13]
## Parsed with column specification:## cols(## .default = col_integer(),## Title = col_character(),## Author = col_character(),## G_Rating = col_double(),## Started = col_character(),## Finished = col_character()## )
## See spec(...) for full column specifications.
One analysis I conducted with this dataset was to look at the correlation between book length (number of pages) and read time (number of days it took to read the book). We can also generate a scatterplot to visualize this relationship.
cor.test(books$Pages, books$Read_Time)
## ## Pearson's product-moment correlation## ## data: books$Pages and books$Read_Time## t = 3.1396, df = 51, p-value = 0.002812## alternative hypothesis: true correlation is not equal to 0## 95 percent confidence interval:## 0.1482981 0.6067498## sample estimates:## cor ## 0.4024597
scatter 14) +geom_label(aes(label = Title),hjust = 1,vjust = 1,fill = &quot;blue&quot;,color = &quot;white&quot;,alpha = 0.5)
Derek Jones recently discussed a possible future for the R ecosystem in “StatsModels: the first nail in R’s coffin”. This got me thinking on the future of CRAN (which I consider vital to R, and vital in distributing our work) in the era of super-popular meta-packages. Meta-packages are convenient, but they have a profoundly negative … Continue reading Meta-packages, nails in CRAN’s coffin
The possibility of saving several tables in a single file is a nice feature of Excel. When sharing results with colleagues, it might be useful to compact everything in a single file. As a bioinformatician, I am too lazy to do that manually, and I searched the web for tools that allow doing that. I […]
Using R to turn raw data into browsable and reusable content.
Yes, I am finally blogging this. 🙂 This blog post is slighty different, since it brings you the tittle of the book, that my dear friend Julie Koesmarno (blog | twitter) and I have written in and it was published…Read more ›
Hi everyone!   In this post we’re going to work with time series data, and write R functions to aggregate hourly and daily time series in monthly time series to catch a glimpse of their underlying patterns. For this analysis we’re going to use public meteorological data recorded by the government of the Argentinian province of […]
Introduction The speed or run-time of models in R can be a critical factor, especially considering the size and complexity of modern datasets. The number of data points as well as the number of features can easily be in the millions...
In 2012, when I decided to write a book on evidence-based software engineering, R was the obvious system to use for data analysis. At the time, lots of new books had “using R” or “with R” added at the end of their titles; I chose “using R”. When developers tell me they need to do […]
Not a full R article, but a quick note demonstrating by example the advantage of being able to collect many expressions and pack them into a single extend_se() node. This example may seem extreme or unnatural. However we have seen once you expose a system to enough users you see a lot more extreme use … Continue reading Collecting Expressions in R
Originally posted on The Geokook.: When I was considering submitting my paper on psd to J. Stat. Soft. (JSS), I kept noticing that the time from “Submitted” to “Accepted” was nearly two years in many cases.  I ultimately decided that was much too long of a review process, no matter what the impact factor might be (and in two…
She dreams in colour, she dreams in red, can’t find a better man (Better Man, Pearl Jam) Today I bring another experiment based on The Quick Draw! Data from Google, one of my most fortunate discoveries of the last times. The Quick Draw! is a web game developed by Google, that can be played on a computer, … Continue reading How Do We Draw a Line? →
During our research on the effect of prednisone consumption during pregency on health outcomes of the baby (Palmsten K, Rolland M, Hebert MF, et al., Patterns of prednisone use during pregnancy in women with rheumatoid arthritis: Daily and cumulative dose. Pharmacoepidemiol Drug Saf. 2018 Apr;27(4):430-438. https://www.ncbi.nlm.nih.gov/pubmed/29488292) we developed a custom plot to visualize for each patient … Continue reading Longitudinal heat plots
By Henrique Helfer Hoeltgebaum Introduction I am happy to introduce the package HCmodelSets, which is now available on CRAN. This package implements the methods proposed by Cox, D.R. and Battey, H.S. (2017). In particular it performs the reduction, exploratory and … Continue reading →
✨ I had to processes a bunch of emails for a $DAYJOB task this week and my “default setting” is to use R for pretty much everything (this should come as no surprise). Treating mail as data is not an uncommon task and many R packages exist that can reach out and grab mail from... Continue reading →
Introduction
Profiling our code is a very useful tool to determine how well the code performs on different metrics.
The addin we will create in this article will let us use a keyboard shortcut to run profiling on R code selected in RStudio without blocking the session or requiring any external packages.
Specifically for very simple overview use, it may be beneficial to look at the time needed for a set of expressions to compute, e.g. how fast the code is. Secondly, especially important in case of computing on big datasets in-memory, the amount of memory utilized, e.g. how much RAM was used.
The addin in action
Contents
Profiling options provided by base R
Asynchronous execution and communication of the results with the session
Results of the profiling
The addin formalities
TL;DR - Just give me the package
References
Profiling options provided by base R
Without going into any detail at all, we have 2 very nice options to profile our code with base R:
base::system.time(expr) - returns CPU and other times that expr used
utils::Rprof - can serve as a switch to enable and disable profiling, with a variety of options, saving the results into a file on disk, by default &quot;Rprof.out&quot;
For the use of our addin, we will utilize the second approach, as we are interested not only in time spent, but also in memory utilization of the profiled expressions.
After finishing the profiling, we will use utils::summaryRprof to summarize the results provided to us by the Rprof functionality mentioned above. To get an overview, we will examine only the total time the selected expressions took to execute and the maximum memory.
The very simplistic implementation can look as follows:
profileExpression
Predicting the outcome of the different teams in the FIFA World Cup has been of great interest to the general public, and predicting the outcome has also attracted quite some attention in the R community. The World Cup has ended and by now, everyone knows that France managed to take home the trophy that slipped through their fingers when they hosted the UEFA Euro 2016 championship. But who won the more important competition of predicting the outcome?
]]__
Did you know that you can run R and Python code within a SQL Server instance? Not only does this give you a powerful server to process your data science calculations, but it makes things faster by eliminating the need to move data between client and server. Best of all, you can access this power via your favourite interface on your local machine, like the RStudio IDE for R or the VS Code Python extensions. To make this work, all you need is the necessary functions to launch computations in SQL Server, provided in the revoscaler package for R, and...
The uRos2018 conference is aimed at professionals and academics who are involved in producing or consuming official (government) statistics. We are happy to announce that we recently posted the full program of the 6th international conference on the use of … Continue reading →
Occasionally R might not be the tool you want to use (hard to believe, but apparently that happens). Then you may need to export some data from R via a csv file. When the data is stored as a ts object, the time index can easily get lost. So I wrote a l...
At the Insurance Data Science conference, both Eric Novik and Paul-Christian Bürkner emphasised in their talks the value of thinking about the data generating process when building Bayesian statistical models. It is also a key step in Michael Betancourt’s Principled Bayesian Workflow.
In this post, I will discuss in more detail how to set priors, and review the prior and posterior parameter distributions, but also the prior predictive distributions with brms (Bürkner (2017)).
While I understand the languages I need well enough, I don't know much about programming best practices. This goes from function naming to code organization, along with all the tools others created to manage projects (git, make, ctabs, etc.). For short scripts and blog posts, this is fine. Even for a research paper where you're using tools rather than making new ones, this is okay. But when projects start to get big and highly innovative, my lack of knowledge of programming practices starts to bite me in the butt.
In this exercise, we will try to handle the model that has been over-dispersed using the quasi-Poisson model. Over-dispersion simply means that the variance is greater than the mean. It’s important because it leads to inflation in the models and increases the possibility of Type I errors. We will use a data-set on amphibian road […]
Related exercise sets:Spatial Data Analysis: Introduction to Raster Processing (Part 1) Advanced Techniques With Raster Data: Part 1 – Unsupervised Classification Spatial Data Analysis: Introduction to Raster Processing: Part-3 Explore all our (__1000) R exercisesFind an R course using our R Course Finder directory
The Joint Statistical Meetings are being held in the beautiful city of
Vancouver, British Columbia this year. I gave a talk on data visualization this year, which is a new one for me, but an area I’m quite excited about. I’ve been looking into the newer toolsets using Javascript graphics for a while now for different projects, and this talk gave me a chance to coalesce my thoughts about data visualization principles and usage, and how the new toolsets can help.
The slides for my talk are available here.
What we couldn’t cover
In the data cleaning portion of our Intro to R class, we cover a variety of common data manipulation tasks. Most of these were achieved using the package dplyr, including removing or retaining certain columns (select), filtering out rows by column condition (filter), creating new columns (mutate), renaming columns (rename), grouping data by categorical variables (group_by), and summarizing data (summarize).
But often, data users need to do more complex manipulation of their data, like changing the shape of the data or creating a new column conditional on values in another column. In this post, we will use USGS stream data to demonstrate data munging techniques that are beyond the basics taught in our Intro to R course. If you’re following along and see a new function and want to learn more, a reminder that you can see the help file for that function by executing a ? followed by the function name in your console to see the help file and examples.
What we want to achieve
In this example, a data user is interested in how nutrient concentrations are related to discharge in a stream within a mixed use watershed of urban and agricultural land use. The site is located in Dane County near Madison, WI, and we’re focusing on daily values from a 20-year period from 1998 to 2017. We’ll use these data to demonstrate a variety of data munging techniques that are beyond the basics.
How to get there
Get the data
The first step falls within what we’ve already learned in the Intro to R class: pull data from NWIS using the USGS package dataRetrieval, select the columns we want to keep, and rename columns.
# load libraries
library(dplyr)
library(dataRetrieval)
library(lubridate)
library(tidyr)
library(ggplot2)
library(viridis)
# Get data for the Yahara River at Windsor in Dane County, Wisconsin
yahara_no
In our next MünsteR R-user group meetup on Tuesday, August 28th, 2018 Jenny Saatkamp will give a talk titled Blog Mining: Deriving the success of blog posts from metadata and text data. You can RSVP here: http://meetu.ps/e/F7zDN/w54bW/f
In our next MünsteR Meetup, Jenny Saatkamp will present her Blog Mining analysis, which is based on 1.500 blog posts from the codecentric company blog (https://blog.codecentric.de/) and makes use of different mining techniques for metadata and text data. The presentation is divided into two parts: First, new insights into the blog posts and their success is gained using metadata. Therefore, diverse methods and R-packages are used such as H2O automatic machine learning, lime, clustering and basic explorative statistics. The sources of metadata are the blog itself, Google Analytics and the SEO-Tool Ahrefs. Second, a text mining analysis is performed using the texts and titles of each blog post. You will learn about common preprocessing steps like stemming, removal of stop words and bag of words and how to visualize text data. As the text mining analysis is still in progress, Jenny might also be able to show you results of a machine learning algorithm that is able to derive the success of blog posts based on text data.
About the speaker: Jenny Saatkamp is currently studying business informatics at the University of Applied Science in Münster. She is a junior Data Scientist, but enthusiastic about it and has already done several Data Science projects in her study and internships. One of her projects is the presented analysis of metadata, which is the topic of her bachelor thesis. The text analysis is also a project as a part of her study.
The August 2018 issue of Significance Magazine includes a retrospective feature on the R language. (I suggest reading the PDF version, also available for free access.) The article by Nick Thieme looks back at the 25 years since the R language was first conceived at Auckland University in 1992. It follows the history of R through the first public announcement in 1993, its first release as open source software in 1995, the formation of the R Core Group and the launch of CRAN in 1997, and the release of R version 1.0.0 on February 29, 2000. Image credit: Significance (Wiley),...
With 6 weeks to go until the 2018 London EARL Conference, we can officially announce it will be the biggest and best yet. Launched in 2014, EARL focuses on the commercial use of R, an idea born from our experience of hosting the LondonR user group. Established in 2007, the user group has grown from 25 people meeting in a bar to a membership of well over 2,000, representing an increasingly wide and varied range of industry sectors. Members tell…
Exploratory data analysis (EDA) the very first step in a data project. We will create a code-template to achieve this with one function.
If you have been studying or working with Machine Learning for at least a week, I am sure you have already played with the Titanic dataset! Today I bring some fun DALEX (Descriptive mAchine Learning EXplanations) functions to study the whole set’s response to the Survival feature and some individual explanation examples. Before we start, […]Related PostK-fold cross-validation in StanAutomated Text Feature Engineering using textfeatures in RExplaining Keras image classification models with LIMEImage classification with keras in roughly 100 lines of codeR vs Python: Image Classification with Keras
Andrew and I both received a note like this from NVIDIA: We have reviewed your NVIDIA GPU Grant Request and are happy support your work with the donation of (1) Titan Xp to support your research. Thanks! In case other people are interested, NVIDA’s GPU grant program provides ways for faculty or research scientists to […]
The post Thanks, NVIDIA appeared first on Statistical Modeling, Causal Inference, and Social Science.
The Source Code chapter of my evidence-based software engineering book has been added to the draft pdf (download here). This chapter has suffered from coming last and there is still lots of work to be done. Almost all the source code related data has been plundered to fill up earlier chapters. Some data did not […]
A tutorial showing how to highlight a subset of your data within the context of the full data set. We review the old school way (layering) and new school way (gghighlight).
There are multiple add-on packages available in R to fit choice models in a Bayesian framework.  These include bayesm, choiceModelR, and flipChoice. In this article,...
Today I am happy to announce that my proposal to the R Consortium was accepted! I first announced that I was submitting a proposal back...
The post R Consortium Proposal Accepted! appeared first on AriLamstein.com.
debugr is a new package designed to support debugging in R. It mainly provides the dwatch() function which prints a debug output to the console or to a file. A debug output can consist of a static text message, the values of one or more objects (p...
There are more than 12,000 packages for R available on CRAN, and many others available on Github and elsewhere. But how can you be sure that a given R package follows best development practices for high-quality, secure software? Based on a recent survey of R users related to challenges in selecting R packages, the R Consortium now recommends a way for package authors to self-validate that their package follows best practices for development. The CII Best Practices Badge Program, developed by the Linux Foundation's Core Infrastructure Initiative, defines a set of criteria that open-source software projects should follow for quality...
One of the great additions to the R ecosystem in recent years is RStudio's Shiny package. With it, you can easily whip up and share a user interface for a new statistical method in just a few hours. Today I want to share some of the methods and challenges that come up when the actual […]
Hugo Bowne-Anderson and I spoke about about data science in pharmaceuticals, the tidyverse, and more for the excellent DataFramed podcast from DataCamp. Listen to it here or throug...
tl;dr Rj Editor lets you analyse data in jamovi with R, and make use of your favourite R packages from within the jamovi statistical spreadsheet jmvconnect makes it easy to access jamovi data sets from R
Comparing multiple models is one of the core but also one of the trickiest element of data analysis. Under a Bayesian framework the loo package in R allows you to derive (among other things) leave-one-out cross-validation metrics to compare the predictive abilities of different models. Cross-validation is basically: (i) separating the data into chunks, (ii) […]Related PostAutomated Text Feature Engineering using textfeatures in RExplaining Keras image classification models with LIMEImage classification with keras in roughly 100 lines of codeR vs Python: Image Classification with KerasUpdate: Can we predict flu outcome with Machine Learning in R?
We’ve released the newest version of NIMBLE on CRAN and on our website. Version 0.6-12 is primarily a maintenance release with various bug fixes. Changes include: a fix for the bootstrap particle filter to correctly calculate weights when particles are not resampled (the filter had been omitting the previous weights when calculating the new weights); […]
The American Birder For the millions of bird watchers in America, relevant and useful resources are always a welcome sight. Range maps and ecological histories enhance the bird watching experience by adding a layer of conservation awareness and help hobbyists become more acquainted with the birds they observe. As a birder myself, I am always […]
  Have you ever been wine shopping and wondered if the ratings actually mean anything? Do only high-priced wines get good reviews? This analysis attempts to demystify some of the confusion behind these ratings by examining a wide range of wines reviewed by a popular wine publication and showing ways to choose a wine based […]
Project background Land Registry publishes data for each housing sale transaction that is registered in England &amp; Wales. This data has been used extensively for many analysis, from price evolution in time to the assessment of price differences between areas. This dataset is publicly available under the government licence and dates back to 1995. The main […]
In an earlier post, I focused on an in-depth visit with CHAID (Chi-square automatic interaction detection). Quoting myself, I said “As the name implies it is fundamentally based on the venerable Chi-square test – and while not the most powerful (in terms of detecting the smallest possible differences) or the fastest, it really is easy […]Related PostCommon Mistakes to Avoid When Learning to Code in PythonTop Python Libraries for Machine LearningGet Popular Programming Languages from TIOBE Index using RHow to make Seaborn Pairplot and Heatmap in R (Write Python in R)Topic Modeling in Python with NLTK and Gensim
The co-integration is an important statistical concept behind the statistical arbitrage strategy named “Pairs Trading”. While projecting a stock price with time series models is by all means difficult, it is technically feasible to find a pair of (or even a portfolio of) stocks sharing the common trend such that a linear combination of two […]
Recently, the R-consortium accepted a new project called histoRicalg. The main goal of the project is to document and transfer knowledge of some older algorithms used by R and by other computational systems. There is a lot of R written in Fortran—much of which is in the old F77 format—and in C whose original implementations Read the full article...
The post histoRicalg: The effort to document historic and historical numerical algorithms in R appeared first on Strange Attractors.
After reading this interesting analysis of “How Often Are Americans’ Accounts Breached?” by Gaurav Sood (which we need more of in cyber-land) I gave in to the impulse to do some gg-doodling with the “Have I Been Pwnd” JSON data he used. It’s just some basic data manipulation with some heavy ggplot2 styling customization, so... Continue reading →
Why forecast sales? Humans have the magical ability to plan for future events, for future gain. It’s not quite a uniquely human trait. Because apparently ravens can match a 4-year-old. An abundance of data, and some very nice R packages, make our ability to plan all the more powerful. A couple of months ago we looked … Continue reading &quot;But can ravens forecast?&quot;
The post But can ravens forecast? appeared first on thinkr.
Hi everyone, I'm at JSM 2018 right now, so feel free to drop by my session or drop by in the halls! Just give me a tweet!
Back to the meat-and-potatoes of this post. A while ago I was running good old sample and comparing its performance to my lpm2_kdtree function in the BalancedSampling package (Grafström and Lisic, 2016). In this comparison I noticed that sample was in some cases slower than my balanced sampling method when using sampling weights.
library(BalancedSampling)
library(ggplot2)
# sampling proportion
f
By Gabriel Vasconcelos In this previous post I discussed some of the parameters we have to tune to estimate a boosting model using the xgboost package. In this post I will discuss the two parameters that were left out in … Continue reading →
Finding the neural stem cell populations in mouse brain   Introduction The main objective of this project is to identify new stem cell populations in mouse brain. Characterizing the unique gene expression signature of these cells could be the starting point for finding and defining cancer stem cells (CSC) in human tumors. In particular, glioblastoma, […]
A simple Le Monde mathematical puzzle none too geometric: Find square triangles which sides are all integers and which surface is its perimeter. Extend to non-square rectangles. No visible difficulty by virtue of Pythagore’s formula: for (a in 1:1e4) for (b in a:1e4) if (a*b==2*(a+b+round(sqrt(a*a+b*b)))) print(c(a,b)) produces two answers 5 12 6 8 and in […]
For our club meeting today we were going to summarize the Demystifying Data Science conference but we forgot that the videos are not released yet.
Oops, we'll have to postpone our blog post. We didn't read the fine print that talk recordings will be available sometime next week. Sorry about that!— LIBD rstats club (@LIBDrstats) July 27, 2018
So we adjusted plans and decided to continue our work on the UpSetR (Gehlenborg, 2016) package by Nils Gehlenborg.
Yesterday we discussed various options for visualizing large numbers of overlapping groups. We explored uses of the #venneuler package from Lee Wilkinson and Simon Urbanek and the #UpSetR package from Jake Conway, Alexander Lex, and @ngehlenborg. #rstats pic.twitter.com/k55YfihmiP— LIBD rstats club (@LIBDrstats) June 16, 2018
What you can currently do
First, let’s install the version we used for this post:
devtools::install_github('hms-dbmi/UpSetR@fe2812c')
Our ultimate goal is to submit a pull request that enables UpSetR users to specify a color by row for the dots instead of the actual rows. We had already identified an example that we could work with.
library('UpSetR')
movies
Looking for something original to decorate your wall? Art With Code, created by Harvard University bioinformatician Jean Fan, provides a collection of R scripts to generate artistic images in the style of famous artworks, for example this randomly-generated piece in the style of Mondrian: Other art generators include &quot;Tunnel&quot; (rotated and scaled designs in the style of Päivi Julin), Pointillism (sylizes photographs as a dots), and Pixel (an interesting multi-scale filter for photos). The transformations using only the built-in R grid and lattice packages, which made it easy for me to try Pixel on a photo of my own dog,...
Based on a paper published in JAMA last year, the weight gain is increasing among US adults while there is no difference in the percentage of people that were trying to lose weight. The authors used the data from the National Health and Nutrition Examination Survey NHANES from 1988 to 2014 and calculated the proportion […]Related PostMachine Learning Results in R: one plot to rule them all! (Part 2 – Regression Models)Story of pairs, ggpairs, and the linear regressionExtract FRED Data for OLS Regression Analysis: A Complete R TutorialMNIST For Machine Learning Beginners With Softmax RegressionTime-dependent ROC for Survival Prediction Models in R
In an earlier post, I focused on an in depth visit with CHAID (Chi-square automatic interaction detection). Quoting myself, I said “As the name implies it is fundamentally based on the venerable Chi-square test – and while not the most powerful (in terms of detecting the smallest possible differences) or the fastest, it really is easy to manage and more importantly to tell the story after using it”. In this post I’ll spend a little time comparing CHAID with a random forest algorithm in the ranger library and with a gradient boosting algorithm via the xgboost library. I’ll use the exact same data set for all three so we can draw some easy comparisons about their speed and their accuracy.
This year the UseR conference took place in Brisbane, Australia. UseR is my favorite conference and this one was mine 11th (counting from Dortmund 2008).  Every UseR is unique. Every UseR is great. But my feelings are that European UseRs are (on average) more about math, statistics and methodology while US UseRs are more about … Czytaj dalej No worries! Afterthoughts from UseR 2018
MaxDiff is a type of best-worst scaling. Respondents are asked to compare all choices in a given set and pick their best and worse (or...
As noted elsewhere, sometimes beauty matters. A plot that’s pleasing to the eye will be considered more gladly, and thus might be understood more thoroughly. Also, since we at STATWORX oftentimes need to subsume and communicate our results, we have come to appreciate how a nice plot can upgrade any presentation. So how make a plot look good? How make ... Read More
Der Beitrag Using themes in ggplot2 erschien zuerst auf STATWORX.
Introduction It is 35 degree Celsius out side, we are in the middle of the ‘slow news season’, in many countries also called cucumber time.  A period typified by the appearance of less informative and frivolous news in the media. … Continue reading →
Continuing the blog’s UDF theme of late, there are two new UDF kids in town: drill-url-tools🔗 for slicing &amp; dicing URI/URLs (just going to use ‘URL’ from now on in the post) drill-domain-tools🔗 for slicing &amp; dicing internet domain names (IDNs). Now, if you’re an Apache Drill fanatic, you’re likely thinking “Hey hrbrmstr: don’t you... Continue reading →
A big thank you to Databricks for working with us and sharing:
rquery: Practical Big Data Transforms for R-Spark Users
How to use rquery with Apache Spark on Databricks
rquery on Databricks is a great data science tool.
I just got back from attending this amazing conference in Paris:http://www.go-isop.org/stan-for-pharmacometrics---paris-franceA few people were disturbed/surprised by the fact that I am linguist (&quot;what are you doing at an pharmacometrics conference?&quot;)....
Since we announced the bookdown package in 2016, there have been a large number of books, reports, notes, and tutorials written with this package and published to https://bookdown.org. We were excited to see that! At the same time, however, ...
Hi Pawel, I’m glad you enjoyed it. I was trying to play around with facet_grid() earlier but I guess I didn’t stumble upon the proper parameters. Your suggestion works perfectly; not only does it keep each grid x-axis width proportional to its length, but it also keeps appropriate space-between-variables. Thank you for sharing that!
Here is the course link.
Course Description
When working with data, we often want to create models to predict future events, but we also want an even deeper understanding of how our data is connected or structured. In this course, you will explore ...
Here is the course link.
Course Description
Experimental design is a crucial part of data analysis in any field, whether you work in business, health or tech. If you want to use data to answer a question, you need to design an experiment! In this c...
Here is the project link.
Project Description
Do women live longer than men? How long? Does it happen everywhere? Is life expectancy increasing? Everywhere? Which is the country with the lowest life expectancy? Which is the one with the highest? In...
The year 2017 has completely turned the film industry upside down. The allegations of harassment and sexual assault against Harvey Weinstein have raised the issue of sexism and misogyny in this industry to the eyes of the general public. In addition, it has helped raise awareness of the poor gender diversity and under-representation of women in Hollywood. One of the main problems posed by the low presence of women behind the camera is that this is then reflected in the fictional…
Last year I announced a collaboration with the Census Bureau to help with their new online training platform. This website, called “Census Academy”, is designed to...
The post Census Academy Update! appeared first on AriLamstein.com.
For More posts on Bayesian models, Bayesian Nonparametrics, and causal inference follow me on twitter @stablemarkets. My advisor and his collaborator are teaching a short course on Bayesian Nonparametric Methods for Causal Inference at JSM next week. A...
Given the number of people interested in my first post for visualizing Classification Models Results, I’ve decided to create and share some new function to visualize and compare whole Linear Regression Models with one line of code. These plots will help us with our time invested in model selection and a general understanding of our […]Related PostStory of pairs, ggpairs, and the linear regressionExtract FRED Data for OLS Regression Analysis: A Complete R TutorialMNIST For Machine Learning Beginners With Softmax RegressionTime-dependent ROC for Survival Prediction Models in Rxplain – R package for interpretations and explanations of statistical results
The first Insurance Data Science event was held at Cass Business School last week, 16 - 17 July 2018.
The conference followed on from five iterations of the R in Insurance events, which have the aim of bringing together practitioners and academics toge...
Educators who teach science using R tend to face common pedagogical problems, regardless of their scientific domain. Yet instructors who teach with R often feel isolated at their institutions. They may be the only ones in their de...
If you are a user who needs to work with Rcpp-based packages, or you are a maintainer of one of such packages, you may be interested in the recent development of the unwind API, which can be leveraged to boost performance since the last Rcpp update. In a nutshell, until R 3.5.0, every R call from C++ code was… Continuar leyendo Boost the speed of R calls from Rcpp →
Hi there!  In 2010, when I was studying my Biology Degree at Universidad Complutense in Madrid, I fell in love with a documentary miniseries called Great Migrations(National Geographic). Their episodes talk about awesome migrations of animals around the globe. One of these chapters is “Great Migrations: Science Of Migrations”. It shows how scientists study the patterns and processes of animal migration. One of these researchers is John Fryxell, from Gelph University in Canada. John explains how mathematical models can help to understand movement patterns. Through simulations, he shows how wildebeests maintaining a clustered movement pattern can more effectively avoid to be hunted by a virtual lion (here is the Spanish version in YouTube). I have to confess that each time I see those images, I think that I would be completely fulfilled with this kind of jobs.I modified a little bit my previous “go” function to reproduce the experiment shown by John. This is a small tribute to this documentary series and to all those people who inspire and motivate us and arouse our curiosity, as John.You can find the code to perform these simulations with R below. To perform the gif of this post, you can follow this procedures. And if you want to be updated with my posts, find me on my new Twitter account @javi_ferlopThat is all, enjoy! # First, we prepare packages and raster layers to be used later. We also load # the species class to define the hunter inital location and his environmental # optimum (grass cover in this case), as in previous posts. library (raster) library(dismo) gc
Another bi-monthly update in the 0.12.* series of Rcpp landed on CRAN early this morning following less than two weekend in the incoming/ directory of CRAN. As always, thanks to CRAN for all the work they do so well.
So once more, this release follow...
For today’s interview, Ruth Thomson, Practice Lead for Strategic Advice spoke to Willem Ligtenberg, Data Scientist at CZ, a health insurance company in the Netherlands. Willem is presenting “Developing a Shiny application to predict work load” at EARL London and we got the chance to get a preview of some of the areas he will be covering. Thanks Willem for taking the time for this interview. What was the business need or opportunity that led to this project? We are…
In the previous post, I have showed how to visualize near real-time data using Python and Dash module.  And it is time to see one of the many ways, how to do it in R. This time, I will not…Read more ›
The gifski package which was demonstrated in May at eRum 2018 in Budapest is now on CRAN. Gifski is a simple but powerful package which can hopefully take away an important performance bottleneck for generating animated graphics in R.
What is Gifski
Gifski is a multi-threaded high-quality GIF encoder written in Rust. It can create animated GIF images with thousands of colors per frame and do so much faster than other software. The Gifski Website has more technical details and beautiful examples.
The R package wraps the Rust crate and can be installed in the usual way from CRAN. One of the major benefits of Rust is that it has no runtime, so the R package has no dependencies.
install.packages(&quot;gifski&quot;)
On Linux you need to install cargo to compile the rust code, but the package does not require any external libraries. Cargo automatically compiles and links all Rust code when building the R package. If you are on MacOS, try installing from source to see how it works:
install.packages(&quot;gifski&quot;, type = &quot;source&quot;)
How to Use
The R interface is very simple: either generate a GIF from a set of images, or directly from the R graphics device. The ?gifski manual page contains example of both. The gifski() function combines a set of PNG images into a single animated GIF file:
# Convert png files to gif
png(&quot;frame%03d.png&quot;)
par(ask = FALSE)
for(i in 1:10)
plot(rnorm(i * 10), main = i)
dev.off()
png_files
Last time I blogged, I offered my obnoxious helpful advice for blog content and promotion. Today, let me again be the agony aunt you didn’t even write to! Imagine you have an R question, i.e. a question related to how you can do something with R,...
1. Global trend lines One of the simplest methods to identify trends is to fit a ordinary least squares regression model to the data. The...
The 4.0.0 release of simmer, the Discrete-Event Simulator for R, is on CRAN under a new license: we decided to switch to GPL __= 2. Most notably in this major release, the C++ core has been refactorised and exposed under inst/include. This is not a big deal for most users, but it enables extensions. As an example… Continuar leyendo simmer 4.0.0 →
I am happy to announce that our paper is published today in The Lancet.
Kashnitsky, I., &amp; Schöley, J. (2018). Regional population structures at a glance. The Lancet, 392(10143), 209–210. https://doi.org/10.1016/S0140-6736(18)31194-2
At a...
Bill Venables, member of the R Foundation, co-author of the Introduction to R manual, R package developer, and one of the smartest and nicest (a rare combo!) people you will ever meet, received some gifts from the user!2018 conference committee in recognition of his longtime service to the R community. He received a book of reminiscences from those whose lives he has touched, and this blanket: This morning we took care of some unfinished #user2018 business. 😊 @visnut @tslumley @_jessie_roberts pic.twitter.com/gW0wX2PLF2 — Miles McBain (@MilesMcBain) July 19, 2018 The design on the blanket is the &quot;Hex Sticker Wall&quot; that was...
In this post, I try to show you in which situations using a data frame is appropriate, and in which it’s not. Learn more with the Advanced R book. What is a data frame? A data frame is just a list of vectors of the same length, each vector being a column. This may convince you: str(iris) ## 'data.frame': 150 obs. of 5 variables: ## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ... ## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ... ## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ... ## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ... ## $ Species : Factor w/ 3 levels &quot;setosa&quot;,&quot;versicolor&quot;,..: 1 1 1 1 1 1 1 1 1 1 ... is.list(iris) ## [1] TRUE length(iris) ## [1] 5 sapply(iris, typeof) ## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## &quot;double&quot; &quot;double&quot; &quot;double&quot; &quot;double&quot; &quot;integer&quot; sapply(iris, length) ## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## 150 150 150 150 150 What is a list? A list is just a vector of references to objects in memory. x
A previous post explored how to deal with Amazon Athena queries asynchronously. The function presented is a beast, though it is on purpose (to provide options for folks). In reality, nobody really wants to use rJava wrappers much anymore and dealing with icky Python library calls directly just feels wrong, plus Python functions often return... Continue reading →
Summer holidays are over and work is calling. We will get you back on track by bringing you our popular R trainings and data science talks to the South of…
Feature Selection Feature Selection is one of the most interesting fields in machine learning in my opinion. It is a boundary point of two different perspectives on machine learning – performance and inference. From a performance point of view, feature selection is typically used to increase the model performance or to reduce the complexity of the problem in order to ... Read More
Der Beitrag Benchmarking Feature Selection Algorithms with Xy() erschien zuerst auf STATWORX.
This report discusses ways to combine graphics output from the ‘graphics’ package and the ‘grid’ package in R and introduces a new function echoGrob in ...
A griddy Le Monde mathematical puzzle: On a 4×5 regular grid, find how many nodes need to be turned on to see all 3×4 squares to have at least one active corner in case of one arbitrary node failing.  Repeat for a 7×9 grid. The question is open to simulated annealing, as in the following […]
It could be the era of Deep Learning where it really doesn’t matter how big is your dataset or how many columns you’ve got. Still, a lot of Kaggle Competition Winners and Data Scientists emphasis on one thing that could put you on the top of the leaderboard in a Competition is “Feature Engineering”. Irrespective […]Related PostExplaining Keras image classification models with LIMEImage classification with keras in roughly 100 lines of codeR vs Python: Image Classification with KerasUpdate: Can we predict flu outcome with Machine Learning in R?Evaluation of Topic Modeling: Topic Coherence
Installing R package Because flipAPI does not require external libraries that use Java or Perl, installation is very straightforward. Simply open R and type the...
We are delighted to announce that the full EARL US Roadshow agendas are now available! Each city has a different line-up of world-class speakers and R experts for you to explore. EARL focuses on the business application of R and will showcase many fascinating and inspiring use cases from a range of industry sectors. We wanted to share with you just some of the talks we are looking forward to in each city: Seattle – 7 November See the full…
A generalized linear model (GLM) is a flexible generalization of an ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution. The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a link function and by allowing the magnitude […]
Related exercise sets:Spatial Data Analysis: Introduction to Raster Processing (Part 1) Advanced Techniques With Raster Data – Part 3: Exercises Advanced Techniques With Raster Data: Part 1 – Unsupervised Classification Explore all our (__1000) R exercisesFind an R course using our R Course Finder directory
A new maintenance release of the nanotime package for working with nanosecond timestamps just arrived on CRAN.
nanotime uses the RcppCCTZ package for (efficient) high(er) resolution time parsing and formatting up to nanosecond resolution, and the bit64 package for the actual integer64 arithmetic. Initially implemented using the S3 system, it now uses a more rigorous S4-based approach thanks to a rewrite by Leonardo Silvestri.
This release re-disables tests for xts use. At some point we had hoped a new xts version would know what nanotime is. That xts version is out now, and it doesn’t. Our bad for making that assumption.
Changes in version 0.2.2 (2018-07-18)
Unit tests depending on future xts behaviour remain disabled (Dirk in #41).
We also have a diff to the previous version thanks to CRANberries. More details and examples are at the nanotime page; code, issue tickets etc at the GitHub repository.
This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. Please report excessive re-aggregation in third-party for-profit settings.
I am involved with a stepped-wedge designed study that is exploring whether we can improve care for patients with end-stage disease who show up in the emergency room. The plan is to train nurses and physicians in palliative care. (A while ago, I described what the stepped wedge design is.)
Under this design, 33 sites around the country will receive the training at some point, which is no small task (and fortunately as the statistician, this is a part of the study I have little involvement). After hearing about this ambitious plan, a colleague asked why we didn’t just randomize half the sites to the intervention and conduct a more standard cluster randomized trial, where a site would either get the training or not. I quickly simulated some data to see what we would give up (or gain) if we had decided to go that route. (It is actually a moot point, since there would be no way to simultaneously train 16 or so sites, which is why we opted for the stepped-wedge design in the first place.)
I simplified things a bit by comparing randomization within site with randomization by site. The stepped wedge design is essentially a within-site randomization, except that the two treatment arms are defined at different time points, and things are complicated a bit because there might be time by intervention confounding. But, I won’t deal with that here.
Simulate data
library(simstudy)
# define data
cvar
We all hate the experience of calling a service provider and being placed on hold for a very long time. Organisations that take their level of service seriously plan their call centres so that the waiting times for customers is within acceptable limits. Having said this, making people wait for something can in some instances increase the level of perceived value.
Call centre performance can be expressed by the Grade of Service, which is shown in the percentage of calls that are answered within a specific time, for example, 90% of calls are answered within 30 seconds. This Grade of Service depends on the volume of calls made to the centre, the number of available agents and the time it takes to process a contact. Although working in a call centre can be chaotic, the Erlang C formula describes the relationship between the Grade of Service and these variables quite accurately.
This article explains how to use the Erlang C formula in the R language to manage a contact centre by calculating the number of agents needed to meet a given service level. This approach is extended with a Monte Carlo situation to understand the stochastic nature of the real world better.
The post Call Centre Workforce Planning Using Erlang C in R language appeared first on The Lucid Manager.
he fourteenth annual worldwide R user conference, useR!2018, was held last week in Brisbane, Australia and it was an outstanding success. The conference attracted around 600 users from around the world and — as the first held in the Southern hemisphere — brought many first-time conference-goers to useR!. (There were also a number of beginning R users as well, judging from the attendance at the beginner's tutorial hosted by R-Ladies.) The program included 19 3-hour workshops, 6 keynote presentations, and more than 200 contributed talks, lightning talks and posters on using, extending, and deploying R. If you weren't able to...
To automate the process of modeling selection and evaluate the results with visualization, I have created some functions into my personal library and today I’m sharing the codes with you. I run them to evaluate and compare Machine Learning models as fast and easily as possible. Currently, they are designed to evaluate binary classification models […]Related PostSeaborn Categorical Plots in PythonMatplotlib Library Tutorial with Examples – PythonVisualize the World Cup with R! Part 1: Recreating Goals with ggsoccer and ggplot2Creating Slopegraphs with RHow to use paletteR to automagically build palettes from pictures
On est les champions ! Si nous n’avons pas eu le temps de faire un modèle de prédiction pour cette coupe du monde de football 2018 (mais FiveThirtyEight en a fait un très sympa, voir ici), cela ne nous a pas empêché de faire un concours de pronostics entre collègues et ex-collègues statisticiens, sur le … Continue reading Analyse de pronostics pour le Mondial 2018
Sydney’s congestion at ‘tipping point’ blares the headline and to illustrate, an interactive chart with bars for city population densities, points for commute times and of course, dual-axes. Yuck. OK, I guess it does show that Sydney is one of three cities that are low density, but have comparable average commute times to higher-density cities. … Continue reading Just use a scatterplot. Also, Sydney sprawls.
Earlier this year at the QCon.ai conference, I gave a short presentation, &quot;The Case for R, for AI developers&quot;. I also presented an interactive workshop, using R and the Microsoft Cognitive Services APIs, to automatically generate captions for images, and to create a tool to recognize images of hotdogs. Video from both the presentation, and the workshop (which starts at the 10:00 mark), is now available to view the QCon.ai website. You can find the slides for the presentation here. The R code for the &quot;Not Hotdog&quot; workshop is available as an Azure Notebook which you can clone and use...
R packages are widely used in science, yet the code behind them often does not come under scrutiny. To address this lack, rOpenSci has been a pioneer in developing a peer review process for R packages. The goal of pkginspector is to help that process by providing a means to better understand the internal structure of R packages. It offers tools to analyze and visualize the relationship among functions within a package, and to report whether or not functions’ interfaces are consistent. If you are reviewing an R package (maybe your own!), pkginspector is for you.
We began building pkginspector during unconf18, with support from rOpenSci and guidance from Noam Ross. The package focuses on facilitating a few of the many tasks involved in reviewing a package; it is one of a collection of packages, including pkgreviewr (rOpenSci) and goodpractice, among others, that are devoted to this project. (The division of labor among these packages is under discussion). If you’re not familiar with rOpenSci’s package review process, “How rOpenSci uses Code Review to Promote Reproducible Science” provides context.
Function calls
rev_fn_summary() helps you analyze function calls. It takes a package path and returns a table of information about its functions. Consider this example included in pkginspector:
# devtools::install_github(&quot;ropenscilabs/pkginspector&quot;)
library(pkginspector)
path
In Displayr, Visualizations of chart type Column, Bar, Area, Line and Scatter all support trend lines.  Trend lines can be  linear or non-parametric (cubic spline,...
All data needs to be clean before you can explore and create models. Common sense, right. Cleaning data can be tedious but I created a function that will help. The function do the following: Clean Data from NA’s and Blanks Separate the clean data – Integer dataframe, Double dataframe, Factor dataframe, Numeric dataframe, and Factor […]Related PostHands-on Tutorial on Python Data Processing Library Pandas – Part 2Hands-on Tutorial on Python Data Processing Library Pandas – Part 1Using R with MonetDBRecording and Measuring Your Musical Progress with RSpark RDDs Vs DataFrames vs SparkSQL – Part 4 Set Operators
A small feature release of our pinp package for snazzier one or two column vignettes get onto CRAN a little earlier.
It offers two new options. Saghir Bashir addressed a longer-standing help needed! issue and contributed code to select papersize opti...
I love it when researchers take the time to share their knowledge of the computational tools that they use. So first, let me point you at Environmental Computing, a site run by environmental scientists at the University of New South Wales, which has a good selection of R programming tutorials. One of these is Making … Continue reading Using leaflet, just because
To post your R job on the next post Just visit  this link and post a new R job  to the R community. You can post a job for  free  (and there are also “featured job” options available for extra exposure). Current R jobs Job seekers:  please follow the links below to learn more and apply for your R job of interest: Featured Jobs Freelance Data Analytics Instructor Level Education From Northeastern University – Posted by LilyMeyer Boston Massachusetts, United States 16 Jul 2018 Full-Time Financial Systems Analyst National Audit Office – Posted by ahsinebadian London England, United Kingdom 13 Jul 2018 Full-Time Data Scientist National Audit Office – Posted by ahsinebadian London England, United Kingdom 13 Jul 2018 Freelance Senior Data Scientist Data Science Talent – Posted by damiendeighan Frankfurt am Main Hessen, Germany 6 Jul 2018 Full-Time Lead Quantitative Developer The Millburn Corporation – Posted by The Millburn Corporation New York New York, United States 15 Jun 2018   All New R Jobs Freelance R programmer &amp; population data scientist United Nations, DESA, Population Division – Posted by pgerland Anywhere 17 Jul 2018 Freelance Data Analytics Instructor Level Education From Northeastern University – Posted by LilyMeyer Boston Massachusetts, United States 16 Jul 2018 Freelance Regex enthusiast needed for summer job MoritzH Anywhere 13 Jul 2018 Full-Time Financial […]
“We demonstrate HMC’s sensitivity to these parameters by sampling from a bivariate Gaussian with correlation coefficient 0.99. We consider three settings (ε,L) = {(0.16; 40); (0.16; 50); (0.15; 50)}” Ziyu Wang, Shakir Mohamed, and Nando De Freitas. 2013 In an experiment with my PhD student Changye Wu (who wrote all R codes used below), we […]
The problem
pkgdown is an R package that can create a beautifully looking website for your own R package. Built and maintained by Hadley Wickham and his gang of prolific contributors, this package can parse the documentation files and vignettes for yo...
In summary: useR! the conference for users of R was held in Brisbane earlier this month it sounded like a lot of fun and here’s an analysis of tweets that used the #useR2018 hashtag during the week The code that generated the report (which I’ve used heavily and written about before) is at Github too. … Continue reading Twitter coverage of the useR! 2018 conference
I am currently experiencing problems with getting my posts in full length on
r-bloggers. You can continue here with
reading in case only the first paragraph is rendered.
One cool thing that happens if you work resonates in the community is that you
see other people using it. In this blog post I am going to address a typical
question people have when they want to use a source code formatter - in
particular styler:
I don’t like rule xyz of the tidyverse style guide, which is the default
style guide implemented in styler. How can I tell styler not to apply it?
Theory
First, I think reading the docs would be a good approach. There are two
resources:
The help file
for the function tidyverse_style(), which returns the transformer functions that prettify your code. It has a few interesting arguments, some of which are rather complex.1
If you can’t get styler behaving the way you want using the arguments of
tidyverse_style(), you have another option, which is described in a vignette: Creating your own style guide. Yes, I admit, it’s pretty long and if you don’t want to become a styler expert, it may be a little bit overwhelming.
If you don’t care about how to create new rules but you simply want to remove
a rule, I have good news for you: There is a quick way to do it. These are the
steps you need to complete in order to do it:
Figure out which transformer function in the transformers returned
by tidyerse_style() corresponds to the rule you want to remove.
Set that element in the list to NULL, which is equivalent to removing it.
Pass the list to style_text as a transformer.
Practice
Lets assume you want to remove the rule that turns = into
I was trying to find an apt image to accompany this post, but googling “sleek and shiny” mostly brought up images of women with ridiculously, well, sleek and shiny hair (and magical products to achieve just that), which has nothing to do with what I want to talk about here (not that this has ever stopped me including pics that have nothing to do with the actual post, of course…).
Anyway, Andrea has been doing lots of work and has suggested an upgrade to the BCEAweb webapp, mainly involving a move towards Shiny navbarPage. I think the results are quite nice — well, dare I say sleek &amp; shiny?
All the code we have used in our GitHub repository, of course.
These include both global methods, which involve fitting a regression over the whole time series; and more flexible local methods, where we relax the constraint...
A new maintenance release, now at version 0.9.11, of the RcppClassic package arrived earlier today on CRAN. This package provides a maintained version of the otherwise deprecated initial Rcpp API which no new projects should use as the normal Rcpp AP...
Given the vast amount of R packages available today, it makes sense (at least to me, as a trained economist) to ask a simple yet difficult question: How much value has been created by all those packages? As all R stuff on CRAN is open-source (...
Hope you liked the Part 1 and Part 2 of this Series. In this Part 3, we will go through the tools used during the analyze phase of Six Sigma DMAIC cycle. In this phase, available data is used to identify the key process inputs and their relation to the output. We will go through […]Related PostPrediction Interval, the wider sister of Confidence IntervalSix Sigma DMAIC Series in R – Part 2Six Sigma DMAIC Series in R – Part 1Implementation and Interpretation of Control Charts in RTime series model of forecasting future power demand
I’ve blogged about how to use Amazon Athena with R before and if you are a regular Athena user, you’ve likely run into a situation where you prepare a dplyr chain, fire off a collect() and then wait. And, wait. And, wait. And, wait. Queries that take significant processing time or have large result sets... Continue reading →
In this short post, I talk about why I’m moving away from using function apply. With matrices It’s okay to use apply with a dense matrix, although you can often use an equivalent that is faster. N
Many users have tried to do a seasonal decomposition with a short time series, and hit the error “Series has less than two periods”.
The problem is that the usual methods of decomposition (e.g., decompose and stl) estimate seasonality using at least as many degrees of freedom as there are seasonal periods. So you need at least two observations per seasonal period to be able to distinguish seasonality from noise.
[…]
It is exciting for us to see the amazing growth of the R Markdown ecosystem over the four years since the creation of R Markdown in 2014. Now you can author many types of documents, and build a wide range of applications based on R Markdown. As an effort to unite and improve the documentation of the R Markdown base package (rmarkdown) and several other extensions (such as bookdown, blogdown, pkgdown, flexdashboard, tufte, xaringan, rticles, and learnr) in one place, we authored a book titled “R Markdown: The Definitive Guide”, which is to be published by Chapman &amp; Hall/CRC in about two weeks.
You can pre-order a copy now if you like. Our publisher is generous enough to allow us to provide a complete online version of this book at https://bookdown.org/yihui/rmarkdown/, which you can always read for free. The full source of this book is also freely and publicly available in the Github repo https://github.com/rstudio/rmarkdown-book.
Please feel free to let us know if you have any questions or suggestions regarding this book. You are always welcome to send Github pull requests to help us improve the book.1 We hope you will find this book useful.
When you are reading the online version of the book, you may click the Edit button on the toolbar to edit the source file of a page, and follow the guide on Github to create a pull request. ↩
Stencila launches the first version of its (open source) word processor and spreadsheet editor designed for researchers. By Michael Aufreiter, Substance, and Aleksandra Pawlik and Nokome Bentley, Stencila Stencila is an open source office suite designed for researchers. It allows the authoring of interactive, data-driven publications in visual interfaces, similar to those in conventional office … Continue reading Stencila – an office suite for reproducible research
In the first part of Introducing the Kernelheaping Package I showed how to compute and plot kernel density estimates on rounded or interval censored data using the Kernelheaping package. Now, let’s make a big leap forward to the 2-dimensional case. Interval censoring can be generalised to rectangles or alternatively even arbitrary shapes. That may include counties, zip codes, electoral districts or administrative districts. Standard area-level mapping methods such as chloropleth maps suffer from very different area sizes or odd area shapes which can greatly distort the visual impression. The Kernelheaping package provides a way to convert these area-level data to a smooth point estimate. For the German capital city of Berlin, for example, there exists an open data initiative, where data on e.g. demographics is publicly available. We first load a dataset on the Berlin population, which can be downloaded from: https://www.statistik-berlin-brandenburg.de/opendata/EWR201512E_Matrix.csv ```r library(dplyr) library(fields) library(ggplot2) library(Kernelheaping) library(maptools) library(RColorBrewer) library(rgdal) gpclibPermit() ``` ```r data
Machine Learning (ML) is still an underdog in the field of economics. However, it gets more and more recognition in the recent years. One reason for being an underdog is, that in economics and other social sciences one is not only interested in predicting but also in making causal inference. Thus many &quot;off-the-shelf&quot; ML algorithms are solving a fundamentally different ... Read More
Der Beitrag Using Machine Learning for Causal Inference erschien zuerst auf STATWORX.
Introduction During a recent negotiation of an informed consent form for use in a clinical trial, the opposing lawyer and I skirmished over the applicability of the Genetic Information Nondiscrimination Act of 2008, commonly known as GINA. Specifically, the opposing lawyer thought that guidance issued by the U.S. Office for Human Research Protections in 2009 was now outdated, in part because enforcement efforts were erratic. The argument was primarily driven by policy, rather than data. Being a data-driven guy, I wanted to see whether the data supported the argument advanced by the other lawyer. Fortunately, the U.S. Equal Employment Opportunity …
The process involves two stages. First, collate individual cases of raw data together with a grouping variable. Second, perform which calculation you want on each...
Just a little fun today… the R console isn’t the most interesting of things… text is typically either black or red (assuming default settings in RStudio). There’s a package though called crayon which allows one to change the style of text in terms of colour, background and some font-type settings. It could be an interesting […]
Microsoft has released a number of data sets produced by Microsoft Research and made them available for download at Microsoft Research Open Data. The Datasets in Microsoft Research Open Data are categorized by their primary research area, such as Physics, Social Science, Environmental Science, and Information Science. Many of the data sets have not been previously available to the public, and many are large and useful for research in AI and Machine Learning techniques. Many of the datasets also include links to associated papers from Microsoft Research. For example, the 10Gb DESM Word Embeddings dataset provides the IN and the...
Some time ago, Maëlle Salmon published a very nice post showing how she scraped her mathematical family tree from the Mathematics Genealogy Project. Of course I immediately wanted to produce my own! I am not a mathematician myself, but one of my PhD supervisor has a PhD in mathematics. Which makes me the indirect descendant of a long lineage of famous mathematicians! As Maëlle kindly invited me to share my tree on Twitter, I decided to write this post to...Lire la suite Lire la suite
Richard McElreath inquires: I was helping a colleague recently fix his MATLAB code by using log_sum_exp and log1m tricks. The natural question he had was, “where do you learn this stuff?” I checked Numerical Recipes, but the statistical parts are actually pretty thin (at least in my 1994 edition). Do you know of any books/papers […]
The post Where do I learn about log_sum_exp, log1p, lccdf, and other numerical analysis tricks? appeared first on Statistical Modeling, Causal Inference, and Social Science.
1. Improve sampler efficiency by picking the right model We need to address how we specify our models before even discussing writing code that is...
Here is the course link.
Course Description
Python and R have seen immense growth in popularity in the &quot;Machine Learning Age&quot;. They both are high-level languages that are easy to learn and write. The language you use will depend on your background and field of study and work. R is a language made by and for statisticians, whereas Python is a more general purpose programming language. Regardless of the background, there will be times when a particular algorithm is implemented in one language and not the other, a feature is better documented, or simply, the tutorial you found online uses Python instead of R. In either case, this would require the R user to work in Python to get his/her work done, or try to understand how something is implemented in Python for it to be translated into R. This course helps you cross the R-Python language barrier.
Chapter 1: The Basics (Free)
Learn about some of the most important data types (integers, floats, strings, and booleans) and data structures (lists, dictionaries, numpy arrays, and pandas DataFrames) in Python and how they compare to the ones in R.
Chapter 2: Control flow, Loops, and Functions
This chapter covers control flow statements (if-else if-else), for loops and shows you how to write your own functions in Python!
Chapter 3: Pandas
In this chapter you will learn more about one of the most important Python libraries, Pandas. In addition to DataFrames, pandas provides several data manipulation functions and methods.
Chapter 4: Plotting
You will learn about the rich ecosystem of visualization libraries in Python. This chapter covers matplotlib, the core visualization library in Python along with the pandas and seaborn libraries.
Chapter 5: Capstone
As a final capstone, you will apply your Python skills on the NYC Flights 2013 dataset.
Prerequisites
Writing Functions in R
rquery and rqdatatable are new R packages for data wrangling; either at scale (in databases, or big data systems such as Apache Spark), or in-memory. The speed up both execution (through optimizations) and development (though a good mental model and up-front error checking) for data wrangling tasks. Win-Vector LLC‘s John Mount will be speaking on … Continue reading John Mount speaking on rquery and rqdatatable
Let’s welcome the viridis palette into the new version of {ggplot2}! Viri-what ? viridis is one of the favorite color palettes of one of the member of the team (guesswho). The viridis palette was first developed for the python package matplotlib, and has been implemented in R since. The strengths of this palette are that: plots are beautiful (which is good enough a reason to use it) colors are perfectly perceptually-uniform, even when printed in black and white (yes, people still print stuffs in 2018…) colors are perceived by the most common forms of color blindness (an important feature) This palette is now the default colorRead More →
The post [ggplot2] Welcome viridis ! appeared first on (en) The R Task Force.
In case you missed them, here are some articles from June of particular interest to R users. An animated visualization of global migration, created in R by Guy Abel. My take on the question, Should you learn R or Python for data science? The BBC and Financial Times use R — without post-processing — for publication graphics. &quot;Handling Strings in R&quot;, a free e-book by Gaston Sanchez, has been updated. The AI, Machine Learning and Data Science roundup for June 2018. The PYPL Popularity of Languages Index ranks R as the 7th most popular programming language. The &quot;lime&quot; package for...
If you don’t know rOpenSci, then I recommend checking them out. They write a lot of really good packages for R. A relatively new seems to be drake. I’ve not played with it yet, but it looks to be very useful at giving indications about which parts of an analysis are subject to changes, and […]
Experienced and wannabe speakers; R professionals and enthusiasts; people with good ideas and will to share them: this is who we are looking for. For what? For our Call for Submissions!
The post Call for submissions open! appeared first on MilanoR.
In my last post, I’d pointed out the importance of a local community and described how Delhi NCR useR group came about. It’s been a month since we’d started the group and I’m excited to announce that our first meetup is scheduled to take place on 14th July 2018, i.e. upcoming Saturday. Venue for the event is SocialCops headquarters in Saket, New Delhi. As a budding community, support from established organizations is a big boost, and we’re grateful to SocialCops for coming onboard as mentors and venue partners. Event details can be found on meetup and you can RSVP here …
The file contains 2017 face-to-face post-election survey responses along with explanatory notes. Read the Stata DTA file into R with two these two lines: The...
Introduction The R Commander is a free and open source user interface for the R software, one that focuses on helping users learn R commands by point-and-clicking their way through analyses. The R Commander is available on Windows, Mac, and Linux; there is … Continue reading →
The Spatial Equilibrium concept is well known to urban economists. In a nutshell, it states that in equilibrium there are no rents to be gained by changing locations. Ed Glaeser begins Chapter 2 of his book: “Cities, Agglomeration, and Spatial Equilibrium” with the well known Alonso-Muth-Mills model. In this post, I want to summarize it briefly following Ed Glaeser presentation and reproduce his 2.1. and 2.2. figures. This is the perfect excuse to play around with the “tidycensus” package.
Last week the R Core Team released the latest update to the R statistical data analysis environment, R version 3.5.1. This update (codenamed &quot;Feather Spray&quot; — a Peanuts reference) makes no user-visible changes and fixes a few bugs. It is backwards-compatible with R 3.5.0, and users can find updates for Windows, Linux and Mac systems at their local CRAN mirror. (The update to Microsoft R Open featuring the R 3.5.1 engine is scheduled for release on August 29.) The complete list of fixes to R 3.5.1 is included in the release announcement, found at the link below. R-announce mailing list:...
Our diligent readers already know how important having well formatted data is for efficient statistical analyses. Here we gathered some advice on how to make a well structured database, in order to perform accurate analyses, and avoid driving your fellow analysts crazy. We are very grateful to Marion Louveaux, bio-image analyst for this translation of french version “Une base de données réussie” on our ThinkR website. An example is worth a thousand words. Let’s imagine you are a medical intern working on an experimental assay (any resemblance to people who actually existed …). We could have given the following title to our article: “How toRead More →
The post The Ten Commandments for a well-formatted database appeared first on (en) The R Task Force.
As promised, how to conduct mixed effects meta-analysis in R:Code used in the video is available here. And I'd recommend the following posts to provide background for this video:What is meta-analysis?Introduction to effect sizesVariance and weights in ...
Euler Problem 33 takes us back to the world of fractions from our primary school days. Many kids hate and struggle learning about fractions but once you master...
The post Euler Problem 33: Digit Cancelling Fractions and Ford Circles appeared first on The Lucid Manager.
A security vulnerability in a third-party library used by RStudio Connect was uncovered during a security audit last week. We have confirmed that this vulnerability has not been used against any of the RStudio Connect instances we host, and are unaware of it being exploited on any customer deployments. Under certain conditions, this vulnerability could compromise the session of a user that was tricked into visiting a specially crafted URL. The issue affects all versions of RStudio Connect up to and including 1.6.4.1, but none of our other products. We have prepared a hotfix: v1.6.4.2.
RStudio remains committed to providing the most secure product possible. We regularly perform internal security audits against RStudio Connect in order to ensure the product’s security.
As part of the responsible disclosure process, we will provide additional details about the vulnerability and how to ensure that you have not been affected, in the coming weeks once customers have had time to update their systems. For now, please update your RStudio Connect installations to version 1.6.4.2 as soon as possible.
The most recent riddle on the Riddler consists in finding the shorter sequence of digits (in 0,1,..,9) such that all 10⁴ numbers between 0 (or 0000) and 9,999 can be found as a group of consecutive four digits. This sequence is obviously longer than 10⁴+3, but how long? On my trip to Brittany last weekend, […]
Pipes have been a fundamental aspect of computer programming for many decades. In short, the semantics of pipes can be thought of as taking the output from the left-hand side and passing it as input to the right-hand side. For example, in a linux shell, you might cat example.txt | unique | sort to take … Continue reading Writing Pipe-friendly Functions →
Introduction In this note we will show how to speed up work in R by partitioning data and process-level parallelization. We will show the technique with three different R packages: rqdatatable, data.table, and dplyr. The methods shown will also work with base-R and other packages. For each of the above packages we speed up work … Continue reading Speed up your R Work
Introduction
In this part of the primer we discuss creating and using custom .jar archives within our R scripts and packages, handling of Java exceptions from R and a quick look at performance comparison between the low and high-level interfaces provided by rJava.
In the first part we talked about using the rJava package to create objects, call methods and work with arrays, we examined the various ways to call Java methods and calling Java code from R directly via execution of shell commands.
R
The first meeting of R users in Cuba, HavanaR! Club 2018, will take place from 29-10-2018 to 02-11-2018. The workshop is organized jointly by the Group of Probability and Statistics of Universidad de La Habana, the __eR-Biostat initiative, the Center...
Both my osqueryr and macthekinfe packages have had a few updates and I wanted to put together a fun example (it being Friday, and all) for what you can do with them. All my packages are now on GitHub and GitLab and I’ll be maintaining them on both so I can accommodate the comfort-level of... Continue reading →
June 22 By Debbie Liske This is Part One of a three part tutorial series originally published on the DataCamp online learning platform in which you will use R to perform a variety of analytic tasks on a case study of musical lyrics by the legendary artist, Prince. The three tutorials cover the following: Part … Continue reading Lyric Analysis with NLP and Machine Learning using R: Part One – Text Mining
This morning during the lecture on nonlinear regression, I mentioned (very) briefly the case of convex regression. Since I forgot to mention the codes in R, I will publish them here. Assume that where is some convex function. Then is convex if and only if , , Hidreth (1954) proved that ifthen is unique. Let , then where. I.e. is the projection of onto the (closed) convex cone . The projection theorem gives existence and unicity. For convenience, in the application, we will consider … Continue reading Convex Regression Model →
Another quick riddle from the riddler: solve the equation EXMREEK + EHKREKK = ?K?H?X?E which involves every digit between 0 and 9. While the puzzle can be unravelled by considering first E and K, which must be equal to 6 and 3, a simple R code also leads to the conclusion isok
There are over 12,700 R packages on CRAN. How to find the right one for you? The new package 'packagefinder' helps you search for packages on CRAN right from the R console. With 'packagefinder' you can search for multiple keywords in the name, tit...
This July we had the great honor to present mlr and its ecosystem at the http://whyr2018.pl/ in Wroclaw in Poland.
You can find the slides here.
We want to thank the organizers for inviting us, providing us with great food and coffee and also many thanks to all participants for showing great interest in mlr.
Here is the course link.
Course Description
Get ready to have some fun with maps! Interactive Maps with leaflet in R will give you the tools to make attractive and interactive web maps using spatial data and the tidyverse. In this course, you will cre...
I notice from the binder-examples/r repo that Binderhub now appears to offer all sorts of R goodness out of the can, if you specify a particular R build. From the same repo root, you can get: Jupyter notebook+R: http://beta.mybinder.org/v2/gh/binder-examples/r/master?filepath=index.ipynb RStudio: http://beta.mybinder.org/v2/gh/binder-examples/r/master?urlpath=rstudio A Shiny app: http://beta.mybinder.org/v2/gh/binder-examples/r/master?urlpath=shiny/bus-dashboard/ And from previously, here’s a workaround for displaying R/HTMLwidgets in […]
Survey your audience and visualise the results with R and Google forms
I wanted to make my presentation on dataviz at the UQ School of
Bioinformatics
more interactive.
A quiz is a good way to engage your audience. Given I was giving a talk
about R datavisuals I thought it would be fun to visualise the quiz
results using R live with the audience. To top it off, we posted the
results to Twitter.
This blog describes is how I did that.
Our next R course is at University of Queensland (Australia) from Feburary 5th 2019. Beginner, intermediate and advanced classes available.
You could also use this system to survey our audience and share the
results live. Just prepare you R code and set it to run at a certain
time during your talk with a task scheduling algorithm.
Setting up the survey
I used Google Forms to do my quiz. You can take it
here.
I posed a few questions that challenged the audience to think about the
best way to visualise data.
It is pretty easy to set up a survey if you have a gmail account. A few
tips:
You can add images, which is great posing questions about results.
I used the ‘short answer’ input for numeric answers. If you click
the validation tab at the bottom of each ‘short answer’ question you
can require users enter certain types of numbers (e.g. within a
range).
Think carefully about limiting required inputs if you want to avoid
bugs that might arise from unexpected answers.
There is a green button at the top of the form that let’s you link
it to a google sheet. Do this.
You can make the sheet public, so other people can use it, but
changing the sharing settings.
Connecting to your survey answers in R
I used the googlesheets package to read my survey answers from the
(public) spreadsheet. You will need to authenticate yourself first:
library(googlesheets)
gs_ls()
This will prompt you to login to your google account and authenticate an
app that allows the connection to happen.
Now we can load our data:
sheet_url
Generalised Additive Models (GAM) are non-parametric models that add smoother to the data. On this exercise, we will look at GAMs using cubic spline using the mgcv package. Dataset used can be downloaded here. The dataset is the experiment result at grassland richness over time in Yellowstone National Park (Skkink et al. 2007). Answers to […]
Related exercise sets:Advanced Techniques With Raster Data – Part 3: Exercises Spatial Data Analysis: Introduction to Raster Processing (Part 1) Spatial Data Analysis: Introduction to Raster Processing: Part-3 Explore all our (__1000) R exercisesFind an R course using our R Course Finder directory
Here in Belgium, World Cup fever is at fever pitch, but with matches starting
during work hours, how is a desk worker supposed to follow along? By leaving
the R environment? Blasphemy.
Today we show how to use R to generate live desktop notifications for The
Beautiful Game.
A notification system preview, free of local bias.
Overview
We break the process of producing a live score notification into the following
steps:
Get the score
Check if the score has changed
If yes, show a notification
Repeat steps 1-3 every minute
Step 1: Getting the Score
FIFA provides an API
with detailed information about matches. The API provides a list of each day’s
matches in JSON. A full description of the fields is provided in the API
documentation.
For the purposes of this exercise, we need the scores
(AggregateHomeTeamScore, AggregateAwayTeamScore, HomeTeamPenaltyScore,
AwayTeamPenaltyScore), and team names (HomeTeam.TeamName,
AwayTeam.TeamName).
Additionally, we subset the data to the active World Cup matches by filtering to
matches with IdSeason of 254645 (the World Cup competition
ID) and MatchStatus of 3 (the live match status ID).
As functions, this looks like:
readLiveMatchScore
Calibration in survey sampling is a wonderful tool, and today I want to show you how we can use it in some Machine Learning applications, using the R package Icarus. And because ’tis the season, what better than a soccer dataset to illustrate this? The data and code are located on this gitlab repo: https://gitlab.com/haroine/weighting-ml … Continue reading Weighting tricks for machine learning with Icarus – Part 1
R language supports several null-able values and it is relatively important to understand how these values behave, when making data pre-processing and data munging. In general, R supports: NULL NA NaN Inf / -Inf NULL is an object and is returned when an expression or function results in an undefined value. In R language, NULL … Continue reading R null values: NULL, NA, NaN, Inf
Once in a while, or a wee bit more frequently (!), it proves impossible to communicate with a contributor of a question on X validated. A recent instance was about simulating from a multivariate kernel density estimate where the kernel terms at x¹,x²,… are Gaussian kernels applied to the inverses of the norms |x-x¹|, |x-x²|,… […]
Dear readers, I have been consolidating my online presence and the Devil is in the Data blog has moved to the Lucid Manager website. This will be the last post on this domain and I hope to see you again... Continue Reading →
The post The Devil is in the Data is moving appeared first on The Devil is in the Data.
Test driving the new release of ggplot2 - A new release of ggplot2
Now that tidy evaluation is baked into ggplot2, as of TODAY, let’s take it for a spin:
ggplot2 3.0.0 %__%
create function %__%
test function %__%
end
library(dplyr)
library(ggplot2)
library(tidyr)
library(tibble)
data
Following up the follow up - After my previous post, my good friend David Henderson asked a good question on Twitter ( it happens :))
To be honest, I hadn’t even noticed there was no tilde (the ‘~’ ) in my call to facet_wrap.
So I did a little experiment :
library(dplyr)
library(ggplot2)
library(tidyr)
library(tibble)
data
After posting a couple of my World Cup viz on Twitter, I thought I'll collate some of them into a blog post. This will be Part 1 of a series as the World Cup goes on and I keep improving my viz skills throughout the tournament. I will also explain how I made improvements in […]Related PostCreating Slopegraphs with RHow to use paletteR to automagically build palettes from picturesVisualize Market Basket analysis in RLattice-Like Forest Plot using ggplot2 in RHow does visualization in Plotly differ from Seaborn
Hi there!Just a few lines to inform you about the new release of rWind R package (v1.0.2). This version have several new features. Here you have an example of one of them, the function wind.dl_2 to download time series of wind data. In this example we create a bunch of PNG files to be converted later into a GIF of wind speed of east Asia region.Enjoy! # We will create a gif of wind speed for east Asia during the end of June (2018) # First, we should load the packages that we will use. Use install.packages() to # download and install the new version of rWind (v1.0.2) install.package(&quot;rWind&quot;) library(rWind) library(fields) library(shape) library(rworldmap) library(lubridate) # Now, we use lubridate package to create a sequence of dates/times (each three # hours) dt
A recent blog post regarding data visualization had some barplots I liked the look of (aesthetically…for research purposes, they wouldn’t be suitable). They look as if they’ve be coloured in with a pencil, rather than having solid blocks of colour… I wondered whether it’s possible with R, and indeed it is. There’s a github project […]
The next DALEX workshop will take place in 4 days at UseR. In the meantime I am working on a new explainer for a single observation. Something like a diagnostic plot for a single observation. Something that extends Ceteris Paribus Plots. Something similar to Individual Conditional Expectation (ICE) Plots. An experimental version is implemented in ceterisParibus … Czytaj dalej Local Goodness-of-Fit Plots / Wangkardu Explanations – a new DALEX companion
Data Description R CodeThis function and package is long pending for publishing from my side, this time expecting soon to put as package for quick usage, before that thought releasing it for feedback.Below function provides R code for getting data des...
Regression coefficients are typically presented as tables that are easy to understand. Sometimes, estimates are difficult to interpret. This is especially true for interaction or transformed terms (quadratic or cubic terms, polynomials, splines), in particular for more complex models. In such cases, coefficients are no longer interpretable in a direct way and marginal effects are … Weiterlesen Marginal Effects for Regression Models in R #rstats #dataviz →
A new minor release of the anytime package is now on CRAN. This is the twelveth release, and the first in a little over a year as the package has stabilized.
anytime is a very focused package aiming to do just one thing really well: to convert anything in integer, numeric, character, factor, ordered, … format to either POSIXct or Date objects – and to do so without requiring a format string. See the anytime page, or the GitHub README.md for a few examples.
This release adds a few minor tweaks. For numeric input, the function is now immutable: arguments that are not already cast to a different type (a common use case) are now cloned so that the input is never changed. We also added two assertion helpers for dates and datetimes, a new formatting helper for the (arguably awful, but common) ‘yyyymmdd’ format, and expanded some unit tests.
Changes in anytime version 0.3.1 (2017-06-05)
Numeric input is now preserved rather than silently cast to the return object type (#69 fixing #68).
New assertion function assertDate() and assertTime().
Unit tests were expanded for the new functions, for conversion from integer as well as for yyyymmdd().
Courtesy of CRANberries, there is a comparison to the previous release. More information is on the anytime page.
For questions or comments use the issue tracker off the GitHub repo.
This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. Please report excessive re-aggregation in third-party for-profit settings.
Use R to present findings from likert questions in surveys.
Customer churn is a key predictor of the long term success or failure of a business. But when it comes to all this data, what’s...
Introduction Rattle is a popular free and open source Graphical User Interface (GUI) for the R software, one that focuses on beginners looking to point-and-click their way through data mining tasks. Such tasks are also referred to as machine learning or predictive analytics.  … Continue reading →
We are pleased to announce that seplyr version 0.5.8 is now available on CRAN. seplyr is an R package that provides a thin wrapper around elements of the dplyr package and (now with version 0.5.8) the tidyr package. The intent is to give the part time R user the ability to easily program over functions … Continue reading seplyr 0.5.8 Now Available on CRAN
In this post, I follow up on a previous post describing how last year in July, I spent one month mostly procrastinating on Stack Overflow (SO). We’re already in July so it’s time to get back to one year of activity on Stack Overflow. Am I still as much active as before? What is my strategy for answering questions on SO? My activity on Stack Overflow Again, we’ll use David Robinson’s package {stackr} to get data from Stack Overflow API in R. # devtools::install_github(&quot;dgrtwo/stackr&quot;) suppressMessages({ library(stackr) library(tidyverse) library(lubridate) }) Evolution of my SO reputation myID
Recently, I attended a presentation where the following graph was shown illustrating the response to stimulation with Thalidomide among a cohort of HIV-1 patients. The biomarker used to measure the response in this case was TNF (tumor necrosis factor) and … Continue reading →
Introduction to Xy() Simulation is mostly about answering particular research questions. Whenever the word simulation appears somewhere in a discussion, everyone knows that this means additional effort. At STATWORX we are using simulations as a first step to proof concepts we are developing. Sometimes such a simulation is simple, in other cases a simulation is plenty of work. Though, research ... Read More
Der Beitrag Pushing Ordinary Least Squares to the limit with Xy() erschien zuerst auf STATWORX.
I’ve mentioned @stiles before on the blog but for those new to my blatherings, Matt is a top-notch data journalist with the @latimes and currently stationed in South Korea. I can only imagine how much busier his life has gotten since that fateful, awful November 2016 Tuesday, but I’m truly glad his eyes, pen and... Continue reading →
There was quite a reaction to our paper, “Polynomial Regression as an Alternative to Neural Nets” (by Cheng, Khomtchouk, Matloff and Mohanty), leading to discussions/debates on Twitter, Reddit, Hacker News and so on. Accordingly, we have posted a revised version of the paper. Some of the new features: Though originally we had made the disclaimer … Continue reading Update on Polynomial Regression in Lieu of Neural Nets →
I just finished teaching Computer-Assisted Content Analysis at the IQMR summer school at Syracuse. With three lecture and three labs the problem every year is getting the right R packages onto people’s machines. In particular, anything that involves compilation – and when you’re using quanteda, readtext, and stm, that’s lots of things – is going […]
A new minor version of the nanotime package for working with nanosecond timestamps just arrived on CRAN.
nanotime uses the RcppCCTZ package for (efficient) high(er) resolution time parsing and formatting up to nanosecond resolution, and the bit64 pac...
All that noise, and all that sound, all those places I have found (Speed of Sound, Coldplay) Some days ago, my friend Jorge showed me one of the coolest datasets I’ve ever seen: the Google quick draw dataset. In its Github website you can see a detailed description of the data. Briefly, it contains  around … Continue reading Exploring The Quick, Draw! Dataset With R: The Mona Lisa →
This morning I was scrolling through Twitter and noticed Alberto Cairo share this lovely data visualization piece by Adam J. Calhoun about the varying prevalence of punctuation in literature. I thought, “I want to do that!” It also offers me the opportunity to chat about a few of the new options available for tokenizing in tidytext via updates to the tokenizers package.
Adam’s original piece explores how punctuation is used in nine novels, including my favorite Pride and Prejudice.
Getting set up
If there is one realisation in life, it is the fact that you will never have enough CPU or RAM available for your analytics. Luckily for us, cloud computing is becoming cheaper and cheaper each year. One of the more established providers of cloud services is AWS. If you don’t know yet, they provide a free, yes free, option. Their t2.micro instance is a 1 CPU, 500MB machine, which doesn’t sound like much, but I am running a Rstudio and Docker instance on one of these for a small project.
The management console has the following interface:
So, how cool would it be if you could start up one of these instances from R? Well, with the cloudyr project it makes R a lot better at interacting with cloud based computing infrastructure. With this in mind, I have been playing with the aws.ec2 package which is a simple client package for the Amazon Web Services (‘AWS’) Elastic Cloud Compute EC2API. There is some irritating setup that has to be done, so if you want to use this package, you need to follow the instructions on the github page to create AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY and AWS_DEFAULT_REGION parameters in the ENV. But once you have figured out this step, the fun starts.
I always enjoy getting the development version of a package, so I am going to install the package straight from github:
devtools::install_github(&quot;cloudyr/aws.ec2&quot;)
Next we are going to use a Amazon Machine Images (AMI) which is a pre-build image that already contains all the necessary installations such as R and RStudio. You can build your own AMI and I suggest you build your own if you comfortable with Linux CLI.
Release the beast
library(aws.ec2)
# Describe the AMI (from: http://www.louisaslett.com/RStudio_AMI/)
aws.signature::locate_credentials()
image
The animation below, by Shanghai University professor Guy Abel, shows migration within and between regions of the world from 1960 to 2015. The data and the methodology behind the chart is described in this paper. The curved bars around the outside represent the peak migrant flows for each region; globally, migration peaked during the 2005-2010 period and the declined in 2010-2015, the latest data available. This animated chord chart was created entirely using the R language. The chord plot showing the flows between regions was created using the circlize package; the tweenr package created the smooth transitions between time periods,...
- I recently bought a new computer for home and it came with two drives,
one HDD and other SSD. The later is used for the OS and the former for
all of my files. From all computers I had, both home and work, this is
definitely the fastest. While some of the merits are due to the newer
CPUS and RAM, the SSD drive can make all the difference in file
operations.
My research usually deals with large files from financial markets. Being
efficient in reading those files is key to my productivity. Given that,
I was very curious in understanding how much I would benefit in speed
when reading/writing files in my SSD drive instead of the HDD. For that,
I wrote a simple function that will time a particular operation. The
function will take as input the number of rows in the data (1..Inf), the
type of function used to save the file (rds, csv, fst) and the
type of drive (HDD or SSD). See next.
bench.fct
After posting my short blog post about Text-to-speech with R, I got two very useful tips. One was to use the googleLanguageR package, which uses the Google Cloud Text-to-Speech API.
And indeed, it was very easy to use and the resulting audio sounded much better than what I tried before!
Here’s a short example of how to use the package for TTS:
Set up Google Cloud and authentification
You first need to set up a Google Cloud Account and provide credit card information (the first year is free to use, though). If you haven’t used Google Cloud before, you will need to wait until you activated your account; in my case, I had to wait two days until they sent a small amount of money to my bank account which I then needed to enter in order to verify the information.
Then, you create a project, activate the API(s) you want to use and create the authentication information as a JSON file. More information on how to do this is described here.
Install and load the library and give the path to the saved authentication JSON file.
library(googleLanguageR)
gl_auth(&quot;path_to_authentication.json&quot;)
Text-to-Speech with googleLanguageR
Now, we can use the Google Cloud Text-to-Speech API from R.
“Google Cloud Text-to-Speech enables developers to synthesize natural-sounding speech with 30 voices, available in multiple languages and variants. It applies DeepMind’s groundbreaking research in WaveNet and Google’s powerful neural networks to deliver the highest fidelity possible. With this easy-to-use API, you can create lifelike interactions with your users, across many applications and devices.” http://code.markedmondson.me/googleLanguageR/articles/text-to-speech.html
content
I love the pkgdown package. With a single line of code you can create a complete website with examples, vignettes and documentation for your package. Brilliant! So what about a website generator for predictive models? Imagine that you can take a set of predictive models (generated with caret, mlr, glm, xgboost or randomForest, anything) and … Czytaj dalej modelDown: a website generator for your predictive models
As many others too, me and some colleges at STATWORX took part in a little betting game for the World Cup 2018. Since the group stage is over, I was wondering how well – or better – how worse my prediction was. I am comparing my result with other predictions by using the point system of the betting game. All ... Read More
Der Beitrag Comparing predictions: World Cup scores erschien zuerst auf STATWORX.
Histograms are good, density plots are also good. Violin and bean plots too. Recently I had someone ask for a plot where you could see each individual point along a continuum, give the points specific colours based on a second variable (similar to the figure), which deviates somewhat from the typical density type plots. Apparently, […]
While graphics guru Edward Tufte recently claimed that &quot;R coders and users just can't do words on graphics and typography&quot; and need additonal tools to make graphics that aren't &quot;clunky&quot;, data journalists at major publications beg to differ. The BBC has been creating graphics &quot;purely in R&quot; for some time, with a typography style matching that of the BBC website. Senior BBC Data Journalist Christine Jeavans offers several examples, including this chart of life expectancy differences between men and women: ... and this chart on gender pay gaps at large British banks: Meanwhile, the chart below was made for the...
In the introduction of my course next week, I will (briefly) mention networks, and I wanted to provide some illustration of the Friendship Paradox. On network of thrones (discussed in Beveridge and Shan (2016)), there is a dataset with the network of characters in Game of Thrones. The word “friend” might be abusive here, but let’s continue to call connected nodes “friends”. The friendship paradox states that People on average have fewer friends than their friends This was discussed in Feld (1991) for instance, … Continue reading Game of Friendship Paradox →
Do a correlation
APA formatted output
Dataframe of Values
Bayesian Correlation
Contribute
Credits
Previous blogposts
One of the most time-consuming part of data analysis in psychology is the copy-pasting of specific values of some R output to a manuscript or a report. This task is frustrating, prone to errors, and increase de variability of statistical reporting. At the sime time, standardizing practices of what and how to report is crucial for reproducibility and clarity. The psycho package was designed specifically to do this job, at first for complex Bayesian mixed models, but is now also compatible with basic methods, such as correlation.
Do a correlation
df
A new version of Choroplethr is now on CRAN! You can get it by typing the following from the R Console: [crayon-5b341e0528211012241032/] I encourage all...
The post Choroplethr v3.6.2 is now on CRAN appeared first on AriLamstein.com.
Your favourite package for getting model outputs directly into publication ready tables is now available on CRAN. They make you work for it! Thank you to all that helped. The development version will continue to be available from github.
27.06.2018 In Bio7 R and Java code can be easily combined in the Bio7 platform. For instance to create Graphical User Interfaces in Java (with SWT or JavaFX), transfer image pixel/selection data from ImageJ and finally doing the analysis parts in R code/scripts which can be called from within Java (an easy to use API […]
Course Description
Here is a link to our new R course.
This is your chance to dive into the worlds of marketing and business analytics using R. Day by day, there are a multitude of decisions that companies have to face. With the help of statistical...
Project Description
The Nobel Prize is perhaps the worlds most well known scientific award. Every year it is given to scientists and scholars in chemistry, literature, physics, medicine, economics, and peace. The first Nobel Prize was handed out in ...
As an illustration of the lack of convergence of the Gibbs sampler applied to the two “conditionals” defined in the Bayesian GANs paper discussed yesterday, I took the simplest possible example of a Normal mean generative model (one parameter) with a logistic discriminator (one parameter) and implemented the scheme (during an ISBA 2018 session). With […]
The world is flat The new R package flatxml provides functions to easily deal with XML files. When parsing an XML document fxml_importXMLFlat produces a special dataframe that is 'flat' by its very nature but contains all necessary informatio...
Following on from last week’s post on Principled Bayesian Workflow I want to reflect on how to motivate a model.
The purpose of most models is to understand change, and yet, considering what doesn’t change and should be kept constant can be equally important.
I will go through a couple of models in this post to illustrate this idea. The purpose of the model I want to build today is to predict how much ice cream is sold for different temperatures \((t)\).
Maybe this should be filed under topics that are so obvious that it is not worth writing about. But, I hate to let a good simulation just sit on my computer. I was recently working on a paper investigating the relationship of emotion knowledge (EK) in very young kids with academic performance a year or two later. The idea is that kids who are more emotionally intelligent might be better prepared to learn. My collaborator suspected that the relationship between EK and academics would be different for immigrant and non-immigrant children, so we agreed that this would be a key focus of the analysis.
In model terms, we would describe the relationship for each student \(i\) as;
\[ T_i = \beta_0 + \beta_1 I_i + \beta_2 EK_i + \beta_3 I_i \times EK_i + \epsilon_i,\]
where \(T\) is the academic outcome, \(I\) is an indicator for immigrant status (either 0 or 1), and \(EK\) is a continuous measure of emotion knowledge. By including the \(I \times EK\) interaction term, we allow for the possibility that the effect of emotion knowledge will be different for immigrants. In particular, if we code \(I=0\) for non-immigrant kids and \(I=1\) for immigrant kids, \(\beta_2\) represents the relationship of EK and academic performance for non-immigrant kids, and \(\beta_2 + \beta_3\) is the relationship for immigrant kids. In this case, non-immigrant kids are the reference category.
Here’s the data generation:
library(simstudy)
library(broom)
set.seed(87265145)
def
The problem Some time ago, I started a blog. I actually did not post a whole lot of stuff though. I was using Jekyll, but my set-up was rather brittle and there were a few problems:
I could not use R Markdown directly. I always had to knitr manually t...
A great many R users will have to run some python code from time to time, and this short video from our Head of Data Engineering, Mark Sellors outlines one approach you can take that doesn't mean leavin...
Welcome to the (very brief) nineteenth post in the ruefully recalcitrant R reflections series of posts, or R4 for short.
About two months ago, in the most recent post in the series, #18, we provided a short tutorial about how to add the Intel Math Kernel Library to a Debian or Ubuntu system thanks to the wonderful apt tool -- and the prepackaged binaries by Intel. This made for a simple, reproducible, scriptable, and even reversible (!!) solution---which a few people seem to have appreciated. Good.
In the meantime, more good things happened. Debian maintainer Mo Zhou had posted this 'intent-to-package' bug report leading to this git repo on salsa and this set of packages currently in the 'NEW' package queue.
So stay tuned, &quot;soon&quot; (for various definitions of &quot;soon&quot;) we should be able to directly get the MKL onto Debian systems via apt without needing Intel's repo. And in a release or two, Ubuntu should catch up. The fastest multithreaded BLAS and LAPACK for everybody, well-integrated and package. That said, it is still a monstrously large package so I mostly stick with the (truly open source rather than just 'gratis') OpenBLAS but hey, choice is good. And yes, technically these packages are 'outside' of Debian in the non-free section but they will be visible by almost all default configurations.
This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. Please report excessive re-aggregation in third-party for-profit settings.
How we did it
The winners
So it’s here… After lots of entries (147 to be precise), we can finally announce the winner of the WhyR 2018 Competition! But first, we have to tell you quickly about how we picked the winner.
How we did it
So it really wasn’t that hard. We held the questionnaire on typeform. Conveniently, my colleague has created a package rtypeform which is an interface to the typeform API. You can install this from CRAN in the usual way
install.packages(&quot;rtypeform&quot;)
library(&quot;rtypeform&quot;)
The following code will give you a data frame called q containing a column for each question contained in the WhyR questionnaire.
typeforms = get_all_typeforms()$content
uid = typeforms[typeforms$name == &quot;WhyR&quot;, &quot;uid&quot;]
q = get_questionnaire(uid)$completed
Now, obviously, we can’t give you access to this so I’ve hidden the Jumping Rivers API key. But, given you have your own API key assigned to the variable typeform_api this code will work (remember to change the questionnaire name!). With that, a bit of dplyr and the classic sample(), we can generate a winner for the competition
The winners
Drum roll please… the winner is…
Agnieszka Fronczyk!
Congratulations Agnieszka! We’ll see you in Wroclaw!
Commiserations to all our unlucky participants. However, we are sponsoring more data science events to come so keep an eye out here for more competitions!
Sometimes, for visualization purposes, we want to extract a reference grid of our dataset. This reference grid often contains equally spaced values of a “target” variable, and all other variables “fixed” by their mean, median or reference level. The refdata of the psycho package was built to do just that.
The Model
Let’s build a complex machine learning model (a neural network) predicting the Sex (the probability of being a man, as women are here the reference level) of our participants with all the variables of the dataframe.
# devtools::install_github(&quot;neuropsychology/psycho.R&quot;) # Install the latest psycho version if needed
# Load packages
library(tidyverse)
library(caret)
library(psycho)
# Import data
df % standardize() %__% # Standardize
na.omit(df) # Remove missing values
# Fit the model
model % # We remove the sex as it is our variable &quot;to predict&quot;
refdata(&quot;Salary&quot;)
newdata
knitr::kable(newdata, digits=2)
Salary
Age
Birth_Season
Life_Satisfaction
Concealing
Adjusting
Tolerating
Forecasting benchmarks are very important when testing new forecasting methods, to see how well they perform against some simple alternatives. Every week I get sent papers proposing new forecasting methods that fail to do better than even the simplest benchmark. They are rejected without review.
Typical benchmarks include the naïve method (especially for finance and economic data), the seasonal naïve method (for seasonal data), an automatically selected ETS model, and an automatically selected ARIMA model.
As Data Scientists, we often train complex algorithms in order to tackle certain business problems and generate value. These algorithms, however, can take a while to train. Sometimes they take a couple of hours, hours which I’m not going to spend just sitting and waiting. But regularly checking whether the training is done, is also […]
Got compute?
future.apply 1.0.0 - Apply Function to Elements in Parallel using Futures - is on CRAN. With this milestone release, all* base R apply functions now have corresponding futurized implementations. This makes it easier than ever before to parallelize your existing apply(), lapply(), mapply(), … code - just prepend future_ to an apply call that takes a long time to complete. That’s it! The default is sequential processing but by using plan(multiprocess) it’ll run in parallel.
Table: All future_nnn() functions in the future.apply package. Each function takes the same arguments as the corresponding base function does.
Function
Description
future_apply()
Apply Functions Over Array Margins
future_lapply()
Apply a Function over a List or Vector
future_sapply()
- “ -
future_vapply()
- “ -
future_replicate()
- “ -
future_mapply()
Apply a Function to Multiple List or Vector Arguments
future_Map()
- “ -
future_eapply()
Apply a Function Over Values in an Environment
future_tapply()
Apply a Function Over a Ragged Array
*future_rapply() - Recursively Apply a Function to a List - is yet to be implemented.
A Motivating Example
In the parallel package there is an example - in ?clusterApply - showing how to perform bootstrap simulations in parallel. After some small modifications to clarify the steps, it looks like the following:
library(parallel)
library(boot)
run1
As I've been blogging more about statistics, R, and research in general, I've been trying to increase my online presence, sharing my blog posts in groups of like-minded people. Those efforts seem to have paid off, based on my view counts over the past ...
Presenting data results in the most informative and compelling manner is part of the role of the data scientist. It's all well and good to master the arcana of some algorithm, to manipulate and master the numbers and bend them to your will to produce a “solution” that is both accurate and useful. But, those […]Related PostHow to use paletteR to automagically build palettes from picturesVisualize Market Basket analysis in RLattice-Like Forest Plot using ggplot2 in RHow does visualization in Plotly differ from SeabornTwitter Analysis with Python
R is primarily a language for working with numbers, but we often need to work with text as well. Whether it's formatting text for reports, or analyzing natural language data, R provides a number of facilities for working with character data. Handling Strings with R, a free (CC-BY-NC-SA) e-book by UC Berkeley's Gaston Sanchez, provides an overview of the ways you can manipulate characters and strings with R. There are many useful sections in the book, but a few selections include: C-style formatting — very useful for preparing tabular data for reports String manipulation with the stringr package — which...
My previous post was explaining how mathematically it was possible to parallelize computation to estimate the parameters of a linear regression. More speficially, we have a matrix which is matrix and a -dimensional vector, and we want to compute by spliting the job. Instead of using the observations, we’ve seen that it was to possible to compute “something” using the first rows, then the next rows, etc. Then, finally, we “aggregate” the objects created to get our overall estimate. Parallelizing on multiple cores Let … Continue reading Parallelizing Linear Regression or Using Multiple Sources →
  Datasets often arrive to us in a form that is different from what we need for our modelling or visualisations functions who in turn don’t necessary require the same format. Reshaping data.frames is a step that all analysts need but many struggle with. Practicing this meta-skill will in the long-run result in more time […]
Related exercise sets:Spatial Data Analysis: Introduction to Raster Processing (Part 1) Advanced Techniques With Raster Data – Part 3: Exercises Advanced Techniques With Raster Data: Part 1 – Unsupervised Classification Explore all our (__1000) R exercisesFind an R course using our R Course Finder directory
Tap into Twitter data and create custom word clouds that will visualize the conversation in an impactful way. Analysis performed with R.
“Some R functions have an awful lot of arguments”, you think to yourself. “I wonder which has the most?” It’s not an original thought: the same question as applied to the R base package is an exercise in the Functions chapter of the excellent Advanced R. Much of the information in this post came from … Continue reading Idle thoughts lead to R internals: how to count function arguments
Introduction BlueSky Statistics’ desktop version is a free and open source graphical user interface for the R software that focuses on beginners looking to point-and-click their way through analyses.  A commercial version is also available which includes technical support and a … Continue reading →
Last week I published a blog post about how easy it is to train image classification models with Keras.
What I did not show in that post was how to use the model for making predictions. This, I will do here. But predictions alone are boring, so I’m adding explanations for the predictions using the lime package.
I have already written a few blog posts (here, here and here) about LIME and have given talks (here and here) about it, too.
Neither of them applies LIME to image classification models, though. And with the new(ish) release from March of Thomas Lin Pedersen’s lime package, lime is now not only on CRAN but it natively supports Keras and image classification models.
Thomas wrote a very nice article about how to use keras and lime in R! Here, I am following this article to use Imagenet (VGG16) to make and explain predictions of fruit images and then I am extending the analysis to last week’s model and compare it with the pretrained net.
Loading libraries and models
library(keras) # for working with neural nets
library(lime) # for explaining models
library(magick) # for preprocessing images
library(ggplot2) # for additional plotting
Loading the pretrained Imagenet model
model
A mechanistic model for the relationship between x and y sometimes needs parameter estimation. When model linearisation does not work,we need to use non-linear modelling. There are three main differences between non-linear and linear modelling in R: 1. specify the exact nature of the equation 2. replace the lm() with nls() which means nonlinear least […]
Related exercise sets:Spatial Data Analysis: Introduction to Raster Processing (Part 1) Spatial Data Analysis: Introduction to Raster Processing: Part-3 Density-Based Clustering Exercises Explore all our (__1000) R exercisesFind an R course using our R Course Finder directory
You may be interested in my new arXiv paper, joint work with Xi Cheng, an undergraduate at UC Davis (now heading to Cornell for grad school); Bohdan Khomtchouk, a post doc in biology at Stanford; and Pete Mohanty,  a Science, Engineering &amp; Education Fellow in statistics at Stanford. The paper is of a provocative nature, … Continue reading Neural Networks Are Essentially Polynomial Regression →
We here at Win-Vector LLC have some really big news we would please like the R-community’s help sharing. vtreat version 1.2.0 is now available on CRAN, and this version of vtreat can now implement its data cleaning and preparation steps on databases and big data systems such as Apache Spark. vtreat is a very complete … Continue reading Big News: vtreat 1.2.0 is Available on CRAN, and it is now Big Data Capable
An easy arithmetic Le Monde mathematical puzzle again: If coins come in units of 1, x, and y, what is the optimal value of (x,y) that minimises the number of coins representing an arbitrary price between 1 and 149?  If the number of units is now four, what is the optimal choice? The first question […]
The new PYPL Popularity of Programming Languages (June 2018) index ranks Python at #1 and R at #7. Like the similar TIOBE language index, the PYPL index uses Google search activity to rank language popularity. PYPL, however, fcouses on people searching for tutorials in the respective languages as a proxy for popularity. By that measure, Python has always been more popular than R (as you'd expect from a more general-purpose language), but both have been growing at similar rates. The chart below includes the three data-oriented languages tracked by the index (and note the vertical scale is logarithmic). Another language...
In the exercises below, we will explore more in Time Series analysis.The previous exercise is here,Please follow this in sequence Answers to these exercises are available here. Exercise 1 load the AirPassangers data,check its class and see the start and end of the series . Exercise 2 check the cycle of the TimeSeries AirPassangers . […]
Related exercise sets:3D plotting exercises Vector exercises Bayesian Inference – MCMC Diagnostics using coda : Exercises Explore all our (__1000) R exercisesFind an R course using our R Course Finder directory
A quick riddler resolution during a committee meeting (!) of a short riddle: 36 houses stand in a row and collapse at times t=1,2,..,36. In addition, once a house collapses, the neighbours if still standing collapse at the next time unit. What are the shortest and longest lifespans of this row? Since a house with […]
Sometimes, with big data, matrices are too big to handle, and it is possible to use tricks to numerically still do the map. Map-Reduce is one of those. With several cores, it is possible to split the problem, to map on each machine, and then to agregate it back at the end. Consider the case of the linear regression, (with classical matrix notations). The OLS estimate of is . To illustrate, consider a not too big dataset, and run some regression. lm(dist~speed,data=cars)$coefficients (Intercept) speed … Continue reading Linear Regression, with Map-Reduce →
A visual exploration of the 2017-2018 NBA landscape The modern NBA landscape is rapidly changing. Steph Curry has redefined the lead guard prototype with jaw-dropping shooting range coupled with unprecedented scoring efficiency for a guard. The likes of Marc Gasol, Al Horford and Kristaps Porzingis are paving the way for a younger generation of modern […]
You have got the best job of the 21st century. Like a treasure hunter, looking for a data treasure chest while sailing through data lakes. In many companies you are a digital maker – armed with skills that turn you to a modern-day polymath and a toolset which is so holistic and complex at once, … „Dear data scientists, how to ease your job“ weiterlesen
You can find members of the rOpenSci team at various meetings and workshops around the world. Come say ‘hi’, learn about how our software packages can enable your research, or about our process for open peer software review and onboarding, how you can get connected with the community or tell us how we can help you do open and reproducible research.
Where’s rOpenSci?
When
Who
Where
What
June 23, 2018
Maëlle Salmon
Cardiff, UK
satRday Cardiff
June 27-28, 2018
Scott Chamberlain
Portland, OR
Bioinformatics Open Source Conference 2018 (BOSC)
July 4-6, 2018
Maëlle Salmon
Rennes, FR
French R conference
July 10-13, 2018
Jenny Bryan
Brisbane, AU
UseR!
July 28-Aug 2, 2018
Jenny Bryan
Vancouver, CA
Joint Statistical Meetings (JSM)
Aug 6-10, 2018
Carl Boettiger, Dan Sholler
New Orleans, LA
Ecological Society of America (ESA)
Aug 15-16, 2018
Stefanie Butland
Cambridge, MA
R / Pharma
Aug 27-30, 2018
Scott Chamberlain
Dunedin, NZ
Biodiversity Information Standards (TDWG)
Sep 3-7, 2018
Jenny Bryan
Buenos Aires, AR
LatinR
Sep 11, 2018
Maëlle Salmon
Radolfzell, GE
AniMove
Sep 12-14, 2018
Jeroen Ooms
The Hague, NL
Use of R in Official Statistics (uRos2018)
Oct 26, 2018
Nick Tierney (representing rOpenSci)
Seoul, KR
R User Conference in Korea 2018
Even though the libraries for R from Python, or Python from R code execution existed since years and despite of a recent announcement of Ursa Labs foundation by Wes McKinney who is aiming to join forces with RStudio foundation, Hadley Wickham in particularly, (find more here) to improve data scientists workflow and unify libraries to […]Related PostUpdate: Can we predict flu outcome with Machine Learning in R?Evaluation of Topic Modeling: Topic CoherenceNatural Language Generation with Markovify in PythonAnomaly Detection in R – The Tidy WayForecasting with ARIMA – Part I
To post your R job on the next post Just visit  this link and post a new R job  to the R community. You can post a job for  free  (and there are also “featured job” options available for extra exposure). Current R jobs Job seekers:  please follow the links below to learn more and apply for your R job of interest: Featured Jobs Full-Time Research Fellow UC Hastings Institute for Innovation Law – Posted by feldmanr San Francisco California, United States 19 Jun 2018 Full-Time Technical Support Engineer at RStudio RStudio – Posted by agadrow Anywhere 19 Jun 2018 Full-Time Lead Quantitative Developer The Millburn Corporation – Posted by The Millburn Corporation New York New York, United States 15 Jun 2018 Full-Time Data Scientist / Senior Strategic Analyst (Communications &amp; Marketing) Memorial Sloan Kettering Cancer Center – Posted by MSKCC New York New York, United States 30 May 2018 Full-Time Customer Success Rep RStudio – Posted by jclemens1 Anywhere 2 May 2018   All New R Jobs   Full-Time Research Fellow UC Hastings Institute for Innovation Law – Posted by feldmanr San Francisco California, United States 19 Jun 2018 Full-Time Technical Support Engineer at RStudio RStudio – Posted by agadrow Anywhere 19 Jun 2018 Full-Time postdoc in psychiatry: machine learning in human genomics University of Iowa – Posted by michaelsonlab […]
I haven't had a lot of time to play with this but yesterday, I discovered the tuber R package, which allows you to interact with the YouTube API.To use the tuber package, not only do you need to install it in R - you'll need a Google account and will h...
It seems like all the best R packages proudly use GitHub and have a README adorned with badges across the top. The recent Microsoft acquisition of GitHub got me wondering: What proportion of current R packages use GitHub? Or at least refer to it in the URL of the package description. Also, what is the relationship between the number of CRAN downloads and the number of stars on a repository? My curiosity got the best of me so I hastily wrote a script to pull the data. Click here to go straight to the full script and data included at the bottom of this post. I acknowledge there are more elegant ways to have coded this, but let’s press on.
Pulling List of Packages &amp; their Details
CRAN provides a list and links to all current packages at https://cran.rstudio.com/src/contrib. By scraping this page I found 12,675 current R packages (non-archived). For each package I pinged its detail page using the canonical form link (e.g. https://cran.r-project.org/package=ggplot2). From there I looked at the package description fields &quot;URL&quot; and &quot;BugReports&quot; to see if either contained “github.com”. It turns out that 3,718 of the packages (29.3% of the total) referenced GitHub. Below is the code snippet for retrieving the list of packages that was taken from Gergely Daróczi’s gist.
# getting list of the packages
pkgs
James Blair, RStudio
Scalability is a hot word these days, and for good reason. As data continues to grow in volume and importance, the ability to reliably access and r...
These are my sketchnotes for Sam Charrington’s podcast This Week in Machine Learning and AI about Practical Deep Learning with Rachel Thomas:
Sketchnotes from TWiMLAI talk: Practical Deep Learning with Rachel Thomas
You can listen to the podcast here.
In this episode, i’m joined by Rachel Thomas, founder and researcher at Fast AI. If you’re not familiar with Fast AI, the company offers a series of courses including Practical Deep Learning for Coders, Cutting Edge Deep Learning for Coders and Rachel’s Computational Linear Algebra course. The courses are designed to make deep learning more accessible to those without the extensive math backgrounds some other courses assume. Rachel and I cover a lot of ground in this conversation, starting with the philosophy and goals behind the Fast AI courses. We also cover Fast AI’s recent decision to switch to their courses from Tensorflow to Pytorch, the reasons for this, and the lessons they’ve learned in the process. We discuss the role of the Fast AI deep learning library as well, and how it was recently used to held their team achieve top results on a popular industry benchmark of training time and training cost by a factor of more than ten. https://twimlai.com/twiml-talk-138-practical-deep-learning-with-rachel-thomas/
New Features As promised, we kept on working on our bounceR package. For once, we changed the interface: users now do not have to choose a number of tuning parameters, that – thanks to my somewhat cryptic documentation – sound more complicated than they are. Inspired by H2o.ai feature to let the user set the time he or she wants ... Read More
Der Beitrag bounceR 0.1.2: Automated Feature Selection erschien zuerst auf STATWORX.
I’ve heard about a number of consulting companies, that decided to use simple linear model instead of a black box model with higher performance, because ,,client wants to understand factors that drive the prediction’’. And usually the discussion goes as following: ,,We have tried LIME for our black-box model, it is great, but it is … Czytaj dalej Not only LIME
Every inch of sky’s got a star Every inch of skin’s got a scar (Everything Now, Arcade Fire) I think that a very good way to start with R is doing an interactive visualization of some open data because you will train many important skills of a data scientist: loading, cleaning, transforming and combinig data … Continue reading How Much Money Should Machines Earn? →
We’ve released the newest version of NIMBLE on CRAN and on our website. Version 0.6-11 has important new features, notably support for Bayesian nonparametric mixture modeling, and more are on the way in the next few months. New features include: support for Bayesian nonparametric mixture modeling using Dirichlet process mixtures, with specialized MCMC samplers automatically […]
In this post, I will illustrate the use of prediction intervals for the comparison of measurement methods. In the example, a new spectral method for measuring whole blood hemoglobin is compared with a reference method. But first, let's start with discussing the large difference between a confidence interval and a prediction interval. Prediction interval versus […]Related PostSix Sigma DMAIC Series in R – Part 2Six Sigma DMAIC Series in R – Part 1Implementation and Interpretation of Control Charts in RTime series model of forecasting future power demandTennis Grand Slam Tournaments Champions Basic Analysis
Many types of machine learning classifiers, not least commonly-used techniques like ensemble models and neural networks, are notoriously difficult to interpret. If the model produces a surprising label for any given case, it's difficult to answer the question, &quot;why that label, and not one of the others?&quot;. One approach to this dilemma is the technique known as LIME (Local Interpretable Model-Agnostic Explanations). The basic idea is that while for highly non-linear models it's impossible to give a simple explanation of the relationship between any one variable and the predicted classes at a global level, it might be possible to asses...
Throughout the last years I noticed the following happening with a number of people. One of those people was actually yours truely a few years back. Person is aware of S3 methods in R through regular use of print, plot and summary functions and decides to give it a go in own work. Creates a function that assigns a class to its output and then implements a bunch of methods to work on the class. Strangely, some of these methods appear to be working as expected, while others throw an error. After a confusing and painful debugging session, person throws hands in the air and continues working without S3 methods. Which was working fine in the first place. This is a real pity, because all the person is overlooking is a very small step in the S3 chain: the generic function.
R Tip: be wary of “...“. The following code example contains an easy error in using the R function unique(). vec1
You can catch a bit of the @rOpenSci 2018 Unconference experience at home w with this short-ish ‘splainer video on how to use the new middlechild package (https://github.com/ropenscilabs/middlechild) &amp; mitmproxy to automagically create reusable httr verb functions from manual browser form interactions.
INTRODUCTION With flexdashboard, you can easily create interactive dashboards for R. What is amazing about it is that with R Markdown, you can publish a group of related data visualizations as a dashboard. Additionally, it supports a wide variety of components, including htmlwidgets; base, lattice, and grid graphics; tabular data; gauges and value boxes and […]
Related exercise sets:Building Shiny App Exercises (part-8) Data visualization with googleVis exercises part 10 Data Visualization with googleVis exercises part 2 Explore all our (__1000) R exercisesFind an R course using our R Course Finder directory
Today I decided to create a new repository on GitHub where I am sharing code to do spreadsheet data manipulation in R.The first version of the repository and R script is available here: SpreadsheetManipulation_inRAs an example I am using a csv fre...
I’ve been using keras and TensorFlow for a while now - and love its simplicity and straight-forward way to modeling. As part of the latest update to my Workshop about deep learning with R and keras I’ve added a new example analysis:
Building an image classifier to differentiate different types of fruits
And I was (again) suprised how fast and easy it was to build the model; it took not even half an hour and only around 100 lines of code (counting only the main code; for this post I added comments and line breaks to make it easier to read)!
via GIPHY
That’s why I wanted to share it here and spread the keras love.
After my series of post on classification algorithms, it’s time to get back to R codes, this time for quantile regression. Yes, I still want to get a better understanding of optimization routines, in R. Before looking at the quantile regression, let us compute the median, or the quantile, from a sample. Median Consider a sample . To compute the median, solvewhich can be solved using linear programming techniques. More precisely, this problem is equivalent towith and , . To illustrate, consider a sample … Continue reading Quantile Regression (home made) →
OverviewIn this post, I would like to introduce my new R package GLMMadaptive for fitting mixed-effects models for non-Gaussian grouped/clustered outcomes using marginal maximum likelihood.Admittedly, there is a number of packages available for fitting...
Updates
I’ve made small updates to two of my R packages on CRAN: ggseas (seasonal adjustment on the fly for ggplot2 graphics) and Tcomp (tourism forecasting competition data). Neither of the packages changes in a noticeable way for most users.
The ggseas update is to get it ready for the coming release of ggplot2 2.3.0 scheduled for the end of June 2018. It makes changes under the hood to the mapping of variables to support “tidy evaluation” but does not change anything for users. It will still work with older versions of ggplot2.
The Tcomp update fixes a problem where the reported length of time series was incorrect.
I’ve written more about ggseas and Tcomp elsewhere. Given they’re both being updated at once, here’s a brief demo of them in action together.
There are 1,311 series in the list tourism in Tcomp. Each element of tourism contains some limited metadata about the series (for example its length, and how long the forecast is to be for), the original training data, and the “answer” in the form of the actual observations over the forecast period. These objects are of class Mdata, introduced in Rob Hyndmans’ Mcomp package, which comes with a convenient plotting method.
library(tidyverse)
library(scales)
library(ggseas)
library(Tcomp)
# default plot method for forecasting competition datasets of class Mdata
par(bty = &quot;l&quot;, font.main = 1)
plot(tourism[[&quot;M4&quot;]], main = &quot;Series M4 from the tourism forecasting competition&quot;)
ggseas makes it easy to take a seasonal time series object (ie an object of class ts with frequency __ 1), convert it into a data frame, and produce exploratory graphics with it. For example, here’s code to take the training data from that same forecasting competition object and compare the original with a seasonally adjusted version (using X13-SEATS-ARIMA for the seasonal adjustment), and a 12 month rolling average:
# convert a time series to a data frame
the_data
There's growing awareness that the data we collect, and in particular the variables we include as factors in our predictive models, can lead to unwanted bias in outcomes: from loan applications, to law enforcement, and in many other areas. In some instances, such bias is even directly regulated by laws like the Fair Housing Act in the US. But even if we explicitly remove &quot;obvious&quot; variables like sex, age or ethnicity from predictive models, unconscious bias might still be a factor in our predictions as a result of highly-correlated proxy variables that are included in our model. As a result,...
A couple of weeks ago I wrote a blog post about slopegraphs. There was some polite interest and it was a good chance to practice my functional programming skills so I decided to see if I could make a decent R function from what I had learned. It’s in pretty good shape so I just pushed an update to CRAN (it will take awhile to process). You can also get the latest version from GitHub.
R, Julia, Python – todays data scientists have the choice between numerous different programming languages, each with their own strengths and weaknesses. Would it not be convenient to bring those languages together and use the individual strength of each language? The package “reticulate” for R takes a step in this direction. The reticulate package creates … „reticulate – another step towards a multilingual and collaborative way of working“ weiterlesen
“The way to change the world is through individual responsibility and taking local action in your own community.” – Jeff Bridges Over the past few years, R’s adoption has grown rapidly throughout the world. Much of it can be attributed to the growth of ‘data science’ as a domain. But R’s popularity primarily exists because of its amazing community and their contributions. Be it through open source development, Twitter (#rstats), discussion forums or programs such as Google Summer of Code (GSoC), there are numerous channels for beginners and practitioners to interact with each other. I’ve had the opportunity to interact …
Tuesday, we got our conference “Insurance, Actuarial Science, Data &amp; Models” and Dylan Possamaï gave a very interesting concluding talk. In the introduction, he came back briefly on a nice discussion we usually have in economics on the kind of model we should consider. It was about optimal control. In many applications, we start with a one period economy, then a two period economy, and pretend that we can extend it to period economy. And then, the continuous case can also be considered. A … Continue reading Discrete or continuous modeling ? →
Introduction Deducer is a free and open source Graphical User Interface for the R software, one that provides beginners a way to point-and-click their way through analyses. It also integrates into an environment designed to help programmers be more productive. … Continue reading →
Emacs is the swiss-army chainsaw of software, the ultimate killer app. This article explains the basics of using R with Emacs and ESS. Continue reading →
The post Using R with Emacs and ESS appeared first on The Devil is in the Data.
The R package wrapr 1.5.0 is now available on CRAN. wrapr includes a lot of tools for writing better R code: let() (let block) %.__% (dot arrow pipe) build_frame() / draw_frame() ( data.frame builders and formatters ) qc() (quoting concatenate) := (named map builder) %?% (coalesce) NEW! %.|% (reduce/expand args) NEW! uniques() (safe unique() replacement) … Continue reading wrapr 1.5.0 available on CRAN
The larger and more complex the business the more metrics and dimensions. One day you understand that it is impossible to track them with only your eyes.The larger and more complex the business the more metrics and dimensions. One day you understand that it is impossible to track them with only your eyes. Reducing the […]
The post Anomaly Detection for Business Metrics with R appeared first on AnalyzeCore by Sergey Bryl' - data is beautiful, data is a story.
On Monday, we learned about a serious issue with the installer for Microsoft R Open on Linux-based systems. (Thanks to Norbert Preining for reporting the problem.) The issue was that the installation and de-installation scripts would modify the system shell, and did not use the standard practices to create and restore symlinks for system applications. The Microsoft R team developed a solution the problem with the help of some Debian experts at Microsoft, and last night issued a hotfix for Microsoft R Open 3.5.0 which is now available for download. With this fix, the MRO installer no longer relinks /bin/sh...
Movie Browser is here to help you discover new movies to watch, in a lot of different ways. You’ll be able to browse everything from new movies, Documentaries and TV shows, to the best movies in the 1970's. The app lets you see the Critics, IMDB and Audience score of all feature films, TV shows […]
An introduction to the Data + Art STEAM project with Forest Hill Junior and Senior Public School in Toronto!
This report describes an application of the ‘rasterize’ package for R. We rasterize (just) the line segments of two chromatograms in order to render one ...
- It is with great pleasure that I announce the second edition of the portuguese version of my book, Processing and Analyzing Financial Data with R. This edition updates the material significantly. The portuguese version is now not only in par with the international version of the book, but much more!
Here are the main changes:
The structure of chapters changed towards the stages of a research, from obtaining the raw data, cleaning it, manipulating it and, finally, reporting tables and figures.
Many new additions of packages for obtaining data, including my own, GetDFPData and others such as rbcb.
Added new chapter for reporting results, exporting tables and also including a whole section about using Rmarkdown.
Alignment with the tidyverse. I have no doubt that the packages from the tidyverse are here to stay. While the native function are presented, there is an emphasis in using the tidyverse, specially in reading local data, manipulating dataframes and functional programming.
Exercises are available at the end of each chapter, including hard questions that will challenge your programming ability.
You can find the new edition of the book in Amazon. The TOC (table of contents) is available here. As usual, an online (and free) version of the book is available at http://www.msperlin.com/padfeR/.
It was a lot of work (and fun) to write the new edition. I’m very happy with the result. I hope you enjoy it!
Best,
I recently discovered the R Graph Gallery, where users can share the beautiful visualizations they've created using R and its various libraries (especially ggplot2). One of my favorite parts about this gallery is a section called Art From Data, in whic...
  Point-and-click graphical user interfaces (GUIs) for R allow people to analyze data using the R software, without having to learn how to program in the R language. This is a brief look at how popular each one is. Knowing … Continue reading →
The Fantasy Football Analytics API allows developers to programmatically access our data in JSON format. Users must pay for a standalone subscription to obtain a unique API key granting access.[...]
The post API Instructions appeared first on Fantasy Football Analytics.
Have you ever needed to connect to a remote server over SSH to transfer files via SCP or to setup a secure tunnel, and wished you could do so from R itself? The new rOpenSci ssh package provides a native ssh client in R allows you to do that and even more, like running a command or script on the host while streaming stdout and stderr directly to the client. The package is based on libssh, a powerful C library implementing the SSH protocol.
install.packages(&quot;ssh&quot;)
Because the ssh package is based on libssh it does not need to shell out. Therefore it works natively on all platforms without any runtime dependencies. Even on Windows.
The package is still work in progress, but the core functionality should work. Below some examples to get you started from the intro vignette.
Connecting to an SSH server
First create an ssh session by connecting to an SSH server.
session
The tiler package provides a map tile-generator function for creating map tile sets for use with packages such as leaflet.
In addition to generating map tiles based on a common raster layer source, it also handles the non-geographic edge case, producing map tiles from arbitrary images. These map tiles, which have a “simple CRS”, a non-geographic simple Cartesian coordinate reference system, can also be used with leaflet when applying the simple CRS option.
A workshop about Machine Learning in R with H2O and LIME, from experts to R lovers. Very few seats available, register soon! The 25th of June at Mikamai.
The post Machine Learning in R with H2O and LIME: A free workshop! appeared first on MilanoR.
I’m sure there’s a better way out there, but I struggled to find a way to dissolve polygons that touched/overlapped each other (the special case being buffers). For example,  using the osmdata package, we can download the polygons representing hospital buildings in Bern, Switzerland. library(osmdata) library(rgdal) ; library(maptools) ; library(rgeos) q0
R Tip: use isTRUE(). A lot of R functions are type unstable, which means they return different types or classes depending on details of their values. For example consider all.equal(), it returns the logical value TRUE when the items being compared are equal: all.equal(1:3, c(1, 2, 3)) # [1] TRUE However, when the items being … Continue reading R Tip: use isTRUE()
We all know that Shiny is great for interactive data visualisations. But, sometimes even the best attempts to fit all your graphs just in one Shiny page are not enough. From our experience, almost every project with growing amount of KPIs struggles with a problem of messy and not readable final reports. Here is where dashboards appear to be handy. Dashboards allow you to intuitively structure your reports by breaking them down into the sections, panels and tabs. Thanks to that it is much easier for a final user to navigate through your work.
shinydashboard does a decent job here. However, when you create a bunch of apps using it, you quickly realize that they all look the same and are simply boring. In this tutorial, I will show you how to take advantage of semantic.dashboard package. This is an alternative to shinydashboard which makes use of Semantic UI. Thanks to that you can introduce beautiful Semantic components into your app and select from many available themes.
Before we start: if you don’t have semantic.dashboard installed yet, visit this GitHub page for detailed instructions.
How to start?
Let’s begin with creating an empty dashboard:
library(shiny)
library(semantic.dashboard)
ui
We are happy to report that a new version of MODIStsp (1.3.4) is on CRAN as of today !
The new version introduces a strongly improved GUI (thanks mainly to @lwasser
comments in her review for MODIStsp onboarding on ropensci). The new GUI facilitates
the selection of layers to be processed, and allows interactive selection of
the processing spatial extent over a map (thanks to @timsalabim and @timelyportfolio
for implementing some changes on mapview to allow this!). The main changes introduced in the GUI are
highlighted in the image and animation below:
Besides this, the main changes with respect to version 1.3.3 are related to:
Inclusion of some new MODIS products;
Removal of almost all v005 products, which were recently deprecated by NASA;
Removal of FTP download functionality again due to deprecation from NASA;
Improvement in functionality for dealing with NoData for products with multiple
fill-values. If “Change NoData” is set to “Yes”, then in case a layer
has multiple Nodata values all those values are set to NA in the output;
Miscellaneous bug fixing and code refactoring.
The full changelog for v1.3.4 can be found HERE
We hope you will find the new version useful and that we didn’t introduce too
many bugs ! Please report any problems in our
issues GitHub page.
Mark Sellors, Head of Data Engineering
If you use RStudio Connect to publish your Shiny app (and even if you don't) take care with how your arrange your projects. If you...
In the last post of this series we dealt with axis systems. In this post we are also dealing with axes but this time we are taking a look at the position scales of dates, time and datetimes. Since we at STATWORX are often forecasting – and thus plotting – time series, this is an important issue for us. The ... Read More
Der Beitrag Customizing time and date scales in ggplot2 erschien zuerst auf STATWORX.
Traditional Correlation
Bayesian APA formatted Correlation
Indices
Posterior
Credits
The Bayesian framework is the right way to go for psychological science. To facilitate its use for newcommers, we implemented the bayes_cor.test function in the psycho package, a user-friendly wrapper for the correlationBF function of the great BayesFactor package by Richard D. Morey.
Traditional Correlation
Let’s first perform a traditional correlation.
# devtools::install_github(&quot;neuropsychology/psycho.R&quot;) # Install the latest psycho version
# Load packages
library(tidyverse)
library(psycho)
# Import data
df
The World of Anomalies Imagine you are a credit card selling company and you know about a particular customer who makes a purchase of 25$ every week. You guessed this purchase is his fixed weekly rations but one day, this customer makes a different purchase of 700$. This development will not just startle you but … Continue reading Anomaly Detection in R
The probabilistic forecast from the bookmaker consensus model for the 2018 FIFA World Cup is visualized in an interactive Sankey diagram, highlighting the teams' most likely progress through the tournament.
Bookmaker consensus model
Two weeks ago we published our Probabilistic Forecast for the 2018 FIFA World Cup: By adjusting quoted bookmakers’ odds for the profit margins of the bookmakers (also known as overrounds), transforming and averaging them, a predicted winning probability for each team was obtained. By employing millions of tournament simulations in combination with a model for pairwise comparisons (or matches) we could also obtain forecasted probabilities for each team to progress through the tournament. In our original study, we visualized these by “survival” curves. See the working paper for more details and references.
Sankey diagram
Here, we present another display that highlights the likely flow of all teams through the tournament simultaneously. Click on the image to obtain an interactive full-width version of this Sankey diagram produced by Plotly.
Compared to the survival curves shown in our original study this visualization brings out more clearly at which stages of the tournament the strong teams are most likely to meet. Its usage was inspired by the nice working paper On Elo based prediction models for the FIFA Worldcup 2018 by Lorenz A. Gilch and Sebastian Müller.
In a few days we will start learning which of these paths will actually come true. Enjoy the 2018 FIFA World Cup!
A maintenance release 0.1.5 of RcppZiggurat is now on the CRAN network for R.
The RcppZiggurat package updates the code for the Ziggurat generator which provides very fast draws from a Normal distribution. The package provides a simple C++ wrapper class for the generator improving on the very basic macros, and permits comparison among several existing Ziggurat implementations. This can be seen in the figure where Ziggurat from this package dominates accessing the implementations from the GSL, QuantLib and Gretl—all of which are still way faster than the default Normal generator in R (which is of course of higher code complexity).
Per a request from CRAN, we changed the vignette to accomodate pandoc 2.* just as we did with the most recent pinp release two days ago. No other changes were made. Other changes that have been pending are a minor rewrite of DOIs in DESCRIPTION, a corrected state setter thanks to a PR by Ralf Stubner, and a tweak for function registration to have user_norm_rand() visible.
The NEWS file entry below lists all changes.
Changes in version 0.1.5 (2018-06-10)
Description rewritten using doi for references.
Re-setting the Ziggurat generator seed now correctly re-sets state (Ralf Stubner in #7 fixing #3)
Dynamic registration reverts to manual mode so that user_norm_rand() is visible as well (#7).
The vignette was updated to accomodate pandoc 2* [CRAN request].
Courtesy of CRANberries, there is also a diffstat report for the most recent release. More information is on the RcppZiggurat page.
This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. Please report excessive re-aggregation in third-party for-profit settings.
A maintenance update 0.3.6 of RcppGSL is now on CRAN. The RcppGSL package provides an interface from R to the GNU GSL using the Rcpp package.
Per a request from CRAN, we changed the vignette to accomodate pandoc 2.* just as we did with the most recen...
A maintenance release RcppClassic 0.9.9 is now at CRAN. This package provides a maintained version of the otherwise deprecated first Rcpp API; no new projects should use it.
Per a request from CRAN, we changed the vignette to accomodate pandoc 2.* ju...
It is often the case that data is trapped inside pdfs, but thankfully there are ways to extract
it from the pdfs. A very nice package for this task is
pdftools (Github link)
and this blog post will describe some basic functionality from that package.
First, let’s find some pdfs that contain interesting data. For this post, I’m using the diabetes
country profiles from the World Health Organization. You can find them here.
If you open one of these pdfs, you are going to see this:
I’m interested in this table here in the middle:
I want to get the data from different countries, put it all into a nice data frame and make a
simple plot.
Let’s first start by loading the needed packages:
library(&quot;pdftools&quot;)
library(&quot;glue&quot;)
library(&quot;tidyverse&quot;)
## ── Attaching packages ────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
## ✔ ggplot2 2.2.1 ✔ purrr 0.2.5
## ✔ tibble 1.4.2 ✔ dplyr 0.7.5
## ✔ tidyr 0.8.1 ✔ stringr 1.3.1
## ✔ readr 1.1.1 ✔ forcats 0.3.0
## ── Conflicts ───────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::collapse() masks glue::collapse()
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
library(&quot;ggthemes&quot;)
country
Eleventh post of our series on classification from scratch. Today, that should be the last one… unless I forgot something important. So today, we discuss boosting. An econometrician perspective I might start with a non-conventional introduction. But that’s actually how I understood what boosting was about. And I am quite sure it has to do with my background in econometrics. The goal here is to solve something which looks likefor some loss function , and for some set of predictors . This is an … Continue reading Classification from scratch, boosting 11/8 →
The forthcoming RStudio 1.2 release has a new “Jobs” feature for running and managing background R tasks. I did a series of threaded screencaps on Twitter but that doesn’t do the feature justice. So I threw together a quick ‘splainer on how to run and Python (despite RStudio not natively supporting Python) code in the... Continue reading →
Introduction
In the previous articles of the R:case4base series we discussed and learned
how to reshape data with base R to a form that is practical for our use and
how to subset data to get the relevant parts of it with base R.
In this one, we will look at aggregation techniques using base R’s stats::aggregate generic function, focusing on the method for data frames. This will allow us to easily and safely create simple aggregations, but also provide a framework for completely custom aggregation functionality defined as separate functions that can be properly documented and unit tested.
Contents
How to use this article
Simple aggregations
Grouping by more variables and small tweaks
Using aggregate as a framework with custom aggregation functions
Advanced details of aggregate use
Aggregate’s methods for other object classes
Alternatives to base R
TL;DR - Just want the code
Exercises
References
Exercise answers
How to use this article
This article is best used with an R session opened in a window next to it - you can test and play with the code yourself instantly while reading. Assuming the author did not fail miserably, the code will work as-is even with vanilla R, no packages or setup needed - it is a case4base after all!
If you have no time for reading, you can click here to get just the code with commentary
First, let’s read in yearly data on gross disposable income of household in the EU countries into R (click here to download) and reshape them to get a nice, long format data to work with:
gdi
Another maintenance release, now at version 0.1.6, of our RcppDE package is now on CRAN. It follows the most recent (unblogged, my bad) 0.1.5 release in January 2016 and the 0.1.4 release in September 2015.
RcppDE is a &quot;port&quot; of DEoptim, a popular package for derivative-free optimisation using differential evolution optimization, to C++. By using RcppArmadillo, the code becomes a lot shorter and more legible. Our other main contribution is to leverage some of the excellence we get for free from using Rcpp, in particular the ability to optimise user-supplied compiled objective functions which can make things a lot faster than repeatedly evaluating interpreted objective functions as DEoptim (and, in fairness, just like most other optimisers) does.
That is also what lead to this upload: Kyle Baron noticed an issue when nesting a user-supplied compiled function inside a user-supplied compiled objective function -- and when using the newest Rcpp. This has to do with some cleanups we made for how RNG state is, or is not, set and preserved. Kevin Ushey was (once again) a real trooper here and added a simple class to Rcpp (in what is now the development version 0.12.17.2 available on the Rcpp drat repo) and used that here to (selectively) restore behaviour similarly to what we had in Rcpp (but which created another issue for another project). So all that is good now in all use cases. We also have some other changes contributed by Yi Kang some time ago for both JADE style randomization and some internal tweaks. Some packaging details were updated, and that sums up release 0.1.6.
Courtesy of CRANberries, there is also a diffstat report for the most recent release.
This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. Please report excessive re-aggregation in third-party for-profit settings.
Tenth post of our series on classification from scratch. Today, we’ll see the heuristics of the algorithm inside bagging techniques. Often, bagging is associated with trees, to generate forests. But actually, it is possible using bagging for any kind of model. Recall that bagging means “boostrap aggregation”. So, consider a model . Let denote the estimator of obtained from sample with . Consider now some boostrap sample, with is randomly drawn from (with replacement). Based on that sample, estimate . Then draw many samples, … Continue reading Classification from scratch, bagging and forests 10/8 →
For the fourth and last day of project recaps from this year’s unconf, here is an overview of the next five projects.
In the spirit of exploration and experimentation at rOpenSci unconferences, these projects are not necessar...
Microsoft R Open 3.5.0 is now available for download for Windows, Mac and Linux. This update includes the open-source R 3.5.0 engine, which is a major update with many new capabilities and improvements to R. In particular, it includes a major new framework for handling data in R, with some major behind-the-scenes performance and memory-use benefits (and with further improvements expected in the future). Microsoft R Open 3.5.0 points to a fixed CRAN snapshot taken on June 1 2018. This provides a reproducible experience when installing CRAN packages by default, but you always change the default CRAN repository or the...
Another maintenance release of our pinp package for snazzier one or two column vignettes is getting onto CRAN right now.
Everybody's favourite Swiss Army Knife of text processing and document conversion, pandoc, decided in all its wisdow for version ...
Last week I gave one of the keynote addresses at R/Finance 2018 in Chicago. I considered it an honor and a pleasure to be there, both because of the stimulating intellectual exchange and the fine level of camaraderie and hospitality that prevailed. I mentioned at the start of my talk that the success of this … Continue reading Women in R →
A colleague asked if I would blog about how I crafted the grid of world tile grids in this post and I accepted the challenge. The technique isn’t too hard as it just builds on the initial work by Jon Schwabish and a handy file made by Maarten Lambrechts. The Premise For this particular use-case,... Continue reading →
In case you missed them, here are some articles from April of particular interest to R users. The R Consortium has announced a new round of grants for projects proposed by the R community. A look back at the ROpenSci unconference held in Seattle. Video of my European R Users Meeting talk, &quot;Speeding up R with Parallel Programming in the Cloud&quot;. Slides from my talk at the Microsoft Build conference, &quot;Open-Source Machine Learning in Azure&quot;. Discussions on Twitter: R packages by stage of data analysis; thinking differently about AI development; and, why is package management harder in Python than R?...
The proposed solution of the riddle from the Riddler discussed here a few weeks ago is rather approximative, in that the distribution of when the n-sample is made of iid Normal variates is (a) replaced with the distribution of one arbitrary minimum and (b) the distribution of the minimum is based on an assumption of […]
Back-calculating lengths of fish at previous ages from measurements made on calcified structures (scales, otoliths, etc.) is fairly common practice within some fisheries agencies and institutions. The FishBC software distributed by the Amer...
Eighth post of our series on classification from scratch. The latest one was on the SVM, and today, I want to get back on very old stuff, with here also a linear separation of the space, using Fisher’s linear discriminent analysis. Bayes (naive) classifier Consider the follwing naive classification ruleor(where is the density in the continuous case). In the case where takes two values, that will be standard here, one can rewrite the later asand the setis called the decision boundary. Assume thatandthen explicit … Continue reading Classification from scratch, linear discrimination 8/8 →
Ava Yang, Data Scientist
There are times when it costs more than it should to leverage javascript, database, html, models and algorithms in one language. Now maybe is time for connecting some dots, without stretching too much.
If you have been developing shiny apps, consider letting it sit on one live database instead of manipulating data I/O by hand?
If you use DT to display tables in shiny apps, care to unleash the power of interactivity to its full?
If you struggle with constructing SQL queries in R, so did we.
Inspired (mainly) by the exciting new inline editing feature of DT, we created a minimal shiny app demo to show how you can update multiple values from DT and send the edits to database at a time.
As seen in the screenshot, after double clicking on a cell and editing the value, Save and Cancel buttons will show up. Continue editing, the updates are stored in a temporary (reactiveValue) object. Click on Save if you want to send bulk updates to database; click on Cancel to reset.
Global
On the global level, we use pool to manage database connections. A database connection pool object is constructed. With the onStop() function, the pool object gets closed after a session ends. It massively saves you from worrying about when to open or close a connection.
# Define pool handler by pool on global level
pool
It’s happening – and this time in Swabia. eoda is bringing you the popular trainings for R to the South of Germany. Over more than 1500 satisfied participants have trained their practical data science skills with R trainings by eoda. Offering an Introduction to R and an Introduction to Data Mining with R, professional training … „R trainings &amp; data science talks are coming to Stuttgart“ weiterlesen
As part of our series summarizing all projects from this year’s unconf I’m excited to dive into all the security related offerings from this year. In the spirit of exploration and experimentation at rOpenSci unconference...
In an earlier post I focused on an in depth visit with CHAID (Chi-square automatic interaction detection). There are lots of tools that can help you predict an outcome, or classify, but CHAID is especially good at helping you explain to any audience how the model arrives at it’s prediction or classification. It’s also incredibly robust from a statistical perspective, making almost no assumptions about your data for distribution or normality. This post I’ll focus on marrying CHAID with the awesome caret package to make our predicting easier and hopefully more accurate. Although not strictly necessary you’re probably best served by reading the original post first.
In the exercises below, we will work with Time Series analysis and see how R can make your life easier when working with Time Series. doing Time Series Analysis.This will be a series of Exercises and I urge you to take it in series Please install the package and load the library before starting Answers […]
Related exercise sets:Spatial Data Analysis: Introduction to Raster Processing (Part 1) Spatial Data Analysis: Introduction to Raster Processing: Part-3 Advanced Techniques With Raster Data: Part 1 – Unsupervised Classification Explore all our (__1000) R exercisesFind an R course using our R Course Finder directory
A guest post by @MaxMaPichler, MSc student in the Group for Theoretical Ecology / UR Artificial neural networks, especially deep neural networks and (deep) convolutions neural networks, have become increasingly popular in recent years, dominating most machine learning competitions since the early 2010’s (for reviews about DNN and (D)CNNs see LeCun, Bengio, &amp; Hinton, 2015). In ecology,…
A new update of my sjstats-package just arrived at CRAN. This blog post demontrates those functions of the sjstats-package that deal especially with Bayesian models. The update contains some new and some revised functions to compute summary statistics of Bayesian models, which are now described in more detail. hdi() rope() mcse() n_eff() tidy_stan() equi_test() mediation() … Weiterlesen R functions for Bayesian Model Statistics and Summaries #rstats #stan #brms →
Some of the R code I have previously posted here for working with NOAA’s optimal interpolated sea surface temperature (OISST) datasets made its way into a recent piece in a Nature news and commentary piece on open-source map-making tools by Jeffrey Perkel. The article details the expansion of open-source tools for visualizing spatial data that […]
The epubr package provides functions supporting the reading and parsing of internal e-book content from EPUB files. E-book metadata and text content are parsed separately and joined together in a tidy, nested tibble data frame.
E-book formatting is non...
I've just returned from a business trip - lots of long days, working sometimes from 8 am to 9 pm or 10 pm. I didn't get a chance to write this week's Statistics Sunday post, in part because I wasn't entirely certain what to write about. But as I starte...
Sixth post of our series on classification from scratch. The latest one was on the lasso regression, which was still based on a logistic regression model, assuming that the variable of interest has a Bernoulli distribution. From now on, we will discuss technique that did not originate from those probabilistic models, even if they might still have a probabilistic interpretation. Somehow. Today, we will start with neural nets. Maybe I should start with a disclaimer. The goal is not to replicate well designed R … Continue reading Classification from scratch, neural nets 6/8 →
Fifth post of our series on classification from scratch, following the previous post on penalization using the norm (so-called Ridge regression), this time, we will discuss penalization based on the norm (the so-called Lasso regression). First of all, one should admit that if the name stands for least absolute shrinkage and selection operator, that’s actually a very cool name… Funny story, a few years before, Leo Breiman introduce a concept of garrote technique… “The garrote eliminates some variables, shrinks others, and is relatively stable”. … Continue reading Classification from scratch, penalized Lasso logistic 5/8 →
Preambule
NOTE: There is some iframed content in this post and you can bust out of it if you want to see the document in a full browser window. Also, apologies for some lingering GitHub links. I’m waiting for all the repos to import into to other services and haven’t had time to setup my own... Continue reading →
The package xplain is designed to help users interpret the results of their statistical analyses. It does so not in an abstract way as textbooks do. Textbooks do not help the user of a statistical method understand his findings directly. What does...
By Yuri Fonseca Demand models In the previous post about pricing optimization (link here), we discussed a little about linear demand and how to estimate optimal prices in that case. In this post we are going to compare three different … Continue reading →
rquery is an R package for specifying data transforms using piped Codd-style operators. It has already shown great performance on PostgreSQL and Apache Spark. rqdatatable is a new package that supplies a screaming fast implementation of the rquery system in-memory using the data.table package. rquery is already one of the fastest and most teachable (due … Continue reading rqdatatable: rquery Powered by data.table
The Model
Plot
Credits
As Bayesian models usually generate a lot of samples (iterations), one could want to plot them as well, instead (or along) the posterior “summary” (with indices like the 90% HDI). This can be done quite easily by extracting all the iterations in get_predicted from the psycho package.
The Model
# devtools::install_github(&quot;neuropsychology/psycho.R&quot;) # Install the last psycho version if needed
# Load packages
library(tidyverse)
library(psycho)
# Import data
df
I just set up a new newsletter to provide you with updates on my R packages. Don't worry: I will not spam you all the time but only reach out to you when there is really important news! Stay in the loop and click here to subscribe.
If you like magical incantations in Data Science, please welcome the Ceteris Paribus Plots. Otherwise feel free to call them What-If Plots. Ceteris Paribus (latin for all else unchanged) Plots explain complex Machine Learning models around a single observation. They supplement tools like breakDown, Shapley values, LIME or LIVE. In addition to feature importance/feature attribution, … Czytaj dalej Ceteris Paribus Plots – a new DALEX companion
If you don't get enough joy from publishing scientific papers in your day job, or simply want to experience what it's like to be in a publish-or-perish environment where the P-value is the only important part of a paper, you might want to try StatCheck: the board game where the object is to publish two papers before any of your opponents. As the game progresses, players combine &quot;Test&quot;, &quot;Statistic&quot; and &quot;P-value&quot; cards to form the statistical test featured in the paper (and of course, significant tests are worth more than non-significant ones). Opponents may then have the opportunity to play...
The second edition of my book ‘Practical Machine Learning with R and Python – Machine Learning in stereo’ is now available in both paperback ($10.99) and kindle ($7.99/Rs449) versions.  This second edition includes more content,  extensive comments and formatting for better readability. In this book I implement some of the most common, but important Machine … Continue reading My book ‘Practical Machine Learning in R and Python: Second edition’ on Amazon
Someday you will find me caught beneath the landslide (Champagne Supernova, Oasis) I recently read a book called Snowflake Seashell Star: Colouring Adventures in Numberland by Alex Bellos and Edmund Harris which is full of mathematical patterns to be coloured. All images are truly appealing and cause attraction to anyone who look at them, independently of … Continue reading Coloring Sudokus →
The R Consortium has just announced its latest round of project grants. After reviewing the proposals submitted by the R community, the Infrastructure Steering Committee has elected to fund the following projects for the Spring 2018 season: Further updates to the DBI package, to provide a consistent interface between R and databases Updating the infrastructure in R for building binary packages on Windows and Mac A collaboration with Statisticians in the Pharmaceutical Industry to validate R packages to regulatory standards A project to validate and maintain some of the historic numerical algorithms used by R A working group to facilitate...
Geocomputation with R: brief history, vector, raster, mapping, R-GIS bridges
When visualizing a network with nodes that refer to a geographic place, it is often useful to put these nodes … Read More →
I am preparing to facilitate another session of the marketing course for the La Trobe University MBA. The first lecture delves into the definition of marketing. Like most other social phenomena, marketing is tough to define. Definitions of social constructs... Continue Reading →
The post Defining Marketing with the Rvest and Tidytext Packages appeared first on The Devil is in the Data.
I just received a note from Hadley Wickham that a new version of ggplot2 is scheduled to be submitted to CRAN on June 25. Here’s...
The post New Version of ggplot2 appeared first on AriLamstein.com.
Most modern operating systems keep secrets from you in many ways. One of these ways is by associating extended file attributes with files. These attributes can serve useful purposes. For instance, macOS uses them to identify when files have passed through the Gatekeeper or to store the URLs of files that were downloaded via Safari... Continue reading →
Note: Cross-posted with the Stack Overflow blog.
Starting today, you can access the public data release for Stack Overflow’s 2018 Developer Survey. Over 100,000 developers from around the world shared their opinions about everything from their fa...
Converting to NIMBLE from JAGS, OpenBUGS or WinBUGS Converting to NIMBLE from JAGS, OpenBUGS or WinBUGS NIMBLE is a hierarchical modeling package that uses nearly the same modeling language as the popular MCMC packages WinBUGS, OpenBUGS and JAGS. NIMBLE makes the modeling language extensible — you can add distributions and functions — and also allows […]
30.05.2018 A new and improved release of Bio7 is available. The new Bio7 2.8 release comes with a plethora of new R features and bugfixes. Release Notes: General: Updated Eclipse RCP to 4.7.3 Windows R update to version 3.5.0 ImageJ plugin updated to version 1.52d7 Added the ImageJ Edit perspective to the default opened perspectives […]
I’m happy to once again announce the release of a package. This time it happens to be a rather unplanned and quick new package, which is fun for a change. The package in question is scico which provides access to the colour palettes developed by Fabio Crameri as well as scale functions for ggplot2 so they can be used there. As there is not a lot to talk about in such a simple package I’ll also spend some time discussing why choice of colour is important beyond aesthtic considerations, and discuss how the quality of a palette might be assesed.
An overview of the package
scico provides a total of 17 different continuous palettes, all of which are available with the scico() function. For anyone having used viridis() the scico() API is very familiar:
library(scico)
scico(15, palette = 'oslo')
## [1] &quot;#000000&quot; &quot;#09131E&quot; &quot;#0C2236&quot; &quot;#133352&quot; &quot;#19456F&quot; &quot;#24588E&quot; &quot;#3569AC&quot;
## [8] &quot;#4D7CC6&quot; &quot;#668CCB&quot; &quot;#7C99CA&quot; &quot;#94A8C9&quot; &quot;#ABB6C7&quot; &quot;#C4C7CC&quot; &quot;#E1E1E1&quot;
## [15] &quot;#FFFFFF&quot;
In order to get a quick overview of all the available palettes use the scico_palette_show() function:
scico_palette_show()
As can be seen, the collection consists of both sequential and diverging palettes, both of which have their own uses depending on the data you want to show. A special mention goes to the oleron palette which is intended for topographical height data in order to produce the well known atlas look. Be sure to center this palette around 0 or else you will end up with very misleading maps.
ggplot2 support is provided with the scale_[colour|color|fill]_scico() functions, which works as expected:
library(ggplot2)
volcano
At Appsilon we frequently build advanced R/Shiny dashboards that need user authentication. I would like to share with you how we implement user management - user accounts, the authorization process and gather usage statistics to have a better understanding of how the app is used.
For user authentication, we created the shiny.users package. For user administration and usage statistics, we created the shiny.admin package. We will show you a sneak peek of how these packages work. They are not released yet - but we opened an Early Adopter Waitlist - sign up and we will contact you as soon as these packages will be ready for production testing!
Shiny.users - convenient authentication on the application level
Here is an example of how shiny.users works when you add it to your shiny application:
Below you can view the code of this example:
library(shiny)
library(shiny.users)
demo_users
A fun summertime blog creating LaCroix themed R graphs using LaCroixColoR &amp; Magick animations
Mango Solutions are delighted to announce our Keynote Speaker for the EARL Boston Conference: Bob Rudis, [Master] Chief Data Scientist at Rapid7.
Bob has more than 20 ye...
If you build statistical or machine learning models, the recipes package can be useful for data preparation. A recipe object is a container that holds all the steps that should be performed to go from the raw data set to the set that is fed into model a algorithm. Once your recipe is ready it can be executed on a data set at once, to perform all the steps. Not only on the train set on which the recipe was created, but also on new data, such as test sets and data that should be scored by the model. This assures that new data gets the exact same preparation as the train set, and thus can be validly scored by the learned model. The author of recipes, Max Kuhn, has provided abundant material to familiarize yourself with the richness of the package, see here, here, and here. I will not dwell on how to use the package. Rather, I’d like to share what in my opinion is a good way to create new steps and checks to the package1. Use of the package is probably intuitive. Developing new steps and checks, however, does require a little more understanding of the package inner workings. With this procedure, or recipe if you like, I hope you will find adding (and maybe even contributing) your own steps and checks becomes easier and more organized. Together with Max I added the checks framework to the package. Where steps are transformations of variables, checks are assertions of them. If a check passes nothing happens. If it fails, it will break the bake method of the recipe and throws an informative error. ↩
Regular readers will recall the “utility belt” post from back in April of this year. This is a follow-up to a request made asking for a list of all the % infix functions in those files. We’re going to: collect up all of the sources parse them find all the definitions of % infix functions... Continue reading →
Native scoring is a much overlooked feature in SQL Server 2017 (available only under Windows and only on-prem), that provides scoring and predicting in pre-build and stored machine learning models in near real-time.                                                                                              Icons made by Smashicons from www.flaticon.com is licensed by CC 3.0 BY   Depending on the definition of real-time, and what … Continue reading Native scoring in SQL Server 2017 using R
ShinyProxy is a novel, open source platform to deploy Shiny apps for the enterprise
or larger organizations.
Theming
ShinyProxy 1.1.1 is in essence a maintenance release, but there is one new feature that has been on the wish list of our users for ...
A few days ago a friend asked me the following question: how to efficiently extract some specific lines from a large text file, possibily compressed by Gzip? He mentioned that he tried some R functions such as read.table(skip = ...), but found that rea...
I'm currently out of town and not spending as much time on my computer as I have over the last couple months. (It's what happens when you're the only one in your department at work and also most of your hobbies involve a computer.) But I wanted to write up something for Statistics Sunday and I recently discovered two R packages I need to check out in the near future.The first is called echor, which allows you to search and download data directly from the US Environmental Protection Agency (EPA) Environmental Compliance and History Online (ECHO), using the ECHO-API. According to the vignette, linked above, &quot;ECHO provides data for:Stationary sources permitted under the Clean Air Act, including data from the National Emissions Inventory, Greenhouse Gas Reporting Program, Toxics Release Inventory, and Clean Air Markets Division Acid Rain Program and Clean Air Interstate Rule. Public drinking water systems permitted under the Safe Drinking Water Act, including data from the Safe Drinking Water Information System. Hazardous Waste Handlers permitted under the Resource Conservation and Recovery Act, with data drawn from the RCRAInfo data system. Facilities permitted under the Clean Water Act and the National Pollutant Discharge Elimination Systems (NPDES) program, including data from EPA’s ICIS-NPDES system and possibly waterbody information from EPA’s ATTAINS data system.&quot;The second package is papaja, or Preparing APA Journal Articles, which uses RStudio and R Markdown to create APA-formatted papers. Back when I wrote APA style papers regularly, I had Word styles set up to automatically format headers and subheaders, but properly formatting tables and charts was another story. This package promises to do all of that. It's still in development, but you can find out more about it here and here.I have some fun analysis projects in the works! Stay tuned.
I’ve been doing some spatial stuff of late and the next little step will involve intersecting points with possibly many overlapping polygons. The sp package has a function called over which returns the polygons that points intersects with. The catch though, is that it only returns the last (highest numerical value) polygon a point overlaps […]
I’m going to (eventually) write a full post on the package I’m mentioning in this one : osqueryr🔗. The TLDR on osqueryr is that it is an R DBI wrapper (that has just enough glue to also be plugged into dbplyr) for osquery🔗. The TLDR on osquery is that it “exposes an operating system as... Continue reading →
Introduction
In this post in the RStudio:addins series we will try to make our work more efficient with an addin for better inspection of objects, functions and files within RStudio. RStudio already has a very useful View function and a Go To Function / File feature with F2 as the default keyboard shortcut and yes, I know I promised automatic generation of @importFrom roxygen tags in the previous post, unfortunately we will have to wait a bit longer for that one but I believe this one more than makes up for it in usefulness.
The addin we will create in this article will let us use RStudio to View and inspect a wide range of objects, functions and files with 1 keypress.
The addins in action
Contents
Retrieving objects from sys.frames
Viewing files, objects and functions and more efficiently
The addin function, updating the .dcf file and key binding
The addin in action
TL;DR - Just give me the package
References
Retrieving objects from sys.frames
As a first step, we need to be able to retrieve the value of the object we are looking for based on a character string from a frame within the currently present sys.frames() for our session. This may get tricky, as it is not sufficient to only look at parent frames, because we may easily have multiple sets of “parallel” call stacks, especially when executing addins.
An example can be seen in the following screenshot, where we have a browser() call executed during the Addin execution itself. We can see that our current frame is 18 and browsing through its parent would get us to frames 17 -__ 16 -__ 15 -__ 14 -__ 0 (0 being the .GlobalEnv). The object we are looking for is however most likely in one of the other frames (9 in this particular case):
Example of sys.frames
getFromSysframes 0)) {
warning(&quot;Expecting a non-empty character of length 1. Returning NULL.&quot;)
return(invisible(NULL))
}
validframes
R is a great program for generating top-notch graphics. But to get the best out of it, you need to put in a little more work. Here are a few tips for adapting your R graphics to make them look a little better. 1) Dont use the “File/Save as…/” menu. If you set up your […]
Nina Zumel and I have been working on packaging our favorite graphing techniques in a more reusable way that emphasizes the analysis task at hand over the steps needed to produce a good visualization. We are excited to announce the WVPlots is now at version 1.0.0 on CRAN! The idea is: we sacrifice some of … Continue reading WVPlots now at version 1.0.0 on CRAN!
I had an amazing time this week participating in the 2018 ROpenSci Unconference, the sixth annual ROpenSci hackathon bringing together people to advance the tools and community for scientific computing with R. It was so inspiring to be among such a talented and dedicated group of people — special kudos goes to the organizing committee for curating such a great crowd. (I heard there were over 200 hundred nominations from which the 65 or so attendees were selected.) The idea behind the unconference is to spend two full days hacking on projects of interest to the community. Before the conference...
I try to at least scan the R-bloggers feed everyday. Not every article is of interest to me, but I often have one of two different reactions to at least one article. Sometimes it is an “ah ha” moment because the article is right on point for a problem I have now or have had in the past and the article provides a (better) solution. Other times my reaction is more of an “oh yeah”, because it is something I have been meaning to investigate, or something I once knew, but the article brings a different perspective to it.
INTRODUCTION The goal of patchwork is to make it simple to combine separate ggplots into the same graphic. As such it tries to solve the same problem as gridExtra::grid.arrange() and cowplot::plot_grid but using an API that incites exploration and iteration. Installation You can install patchwork from github with: # install.packages(&quot;devtools&quot;) devtools::install_github(&quot;thomasp85/patchwork&quot;) The usage of patchwork […]
Related exercise sets:How to Display Multivariate Relationship Graphs With Lattice Lattice Exercises Explore all our (__1000) R exercisesFind an R course using our R Course Finder directory
In the age of Rmarkdown and Shiny, or when making any custom output from your data you want your output to look consistent and neat. Also, when writing your output you often want it to obtain a specific (decorative) format defined by the html or LaTeX engine. These exercises are an opportunity to refresh our […]
Related exercise sets:Parallel Computing Exercises: Foreach and DoParallel (Part-2) Parallel Computing Exercises: Snow and Rmpi (Part-3) Building Shiny App exercises part 4 Explore all our (__1000) R exercisesFind an R course using our R Course Finder directory
This report explores ways to render specific components of an R plot in raster format, when the overall format of the plot is vector. For ...
Introduction RKWard is a free and open source Graphical User Interface for the R software, one that supports beginners looking to point-and-click their way through analyses, as well as advanced programmers. You can think of it as a blend of … Continue reading →
The method agreement procedure
Tweaking
Credits
The method agreement procedure
When running a factor analysis, one often needs to know how many components / latent variables to retain. Fortunately, many methods exist to statistically answer this question. Unfortunately, there is no consensus on which method to use. Therefore, the n_factors() function, available in the psycho package, performs the method agreement procedure: it runs all the routines and returns the number of factors with the highest consensus.
# devtools::install_github(&quot;neuropsychology/psycho.R&quot;) # Install the last psycho version if needed
library(tidyverse)
library(psycho)
results %
psycho::n_factors()
print(results)
## The choice of 1 factor is supported by 5 (out of 9; 55.56%) methods (Optimal Coordinates, Acceleration Factor, Parallel Analysis, Velicer MAP, VSS Complexity 1).
We can have an overview of all values by using the summary method.
n.Factors
n.Methods
Eigenvalues
Cum.Variance
1
5
3.72
0.53
2
3
1.14
0.69
3
1
0.85
0.81
4
0
0.61
0.90
5
0
0.32
0.95
6
0
0.22
0.98
7
0
0.14
1.00
And, of course, plot it :)
plot(results)
The plot shows the number of methods (in yellow), the Eigenvalues (red line) and the cumulative proportion of explained variance (blue line).
For more details, we can also extract the final result (the optimal number of factors) for each method:
Method
n_optimal
Optimal Coordinates
1
Acceleration Factor
1
Parallel Analysis
1
Eigenvalues (Kaiser Criterion)
2
Velicer MAP
1
BIC
2
Sample Size Adjusted BIC
3
VSS Complexity 1
1
VSS Complexity 2
2
Tweaking
We can also provide a correlation matrix, as well as changing the rotation and the factoring method.
df
So far, we have learned how to count spatial variability in our model. Please look at these two previous exercises here and here if you haven’t tried it yet. However, it only represents 1-Dimension model. On this exercise, we will try to expand our spatial consideration into 2-Dimension model. Have a look at this plan […]
Related exercise sets:Simple Spatial Modeling – Part 1: Exercises Descriptive Analytics-Part 6: Interactive dashboard ( 2/2) Practice Your ggplot Skills: Exercises Explore all our (__1000) R exercisesFind an R course using our R Course Finder directory
This is this second post of the “Create your Machine Learning library from scratch with R !” series. Today, we will see how you can implement K nearest neighbors (KNN) using only the linear algebra available in R. Previously, we managed to implement PCA and next time we will deal with SVM and decision trees. […]
The post Create your Machine Learning library from scratch with R ! (3/5) – KNN appeared first on Enhance Data Science.
Join Andrew Collier and Hanjo Odendaal for a workshop on using R for Web Scraping.
Who should attend?
This workshop is aimed at beginner and intermediate R users who want to learn more about using R for data acquisition and management, with a speci...
In the exercises below, we will work with FFTree pacakge which lets us use fast and frugal decision tree to model the data Please install the package and load the library before starting Answers to these exercises are available here. If you obtained a different (correct) answer than those listed on the solutions page, please […]
Related exercise sets:Spatial Data Analysis: Introduction to Raster Processing: Part-3 Spatial Data Analysis: Introduction to Raster Processing (Part 1) Advanced Techniques With Raster Data: Part 1 – Unsupervised Classification Explore all our (__1000) R exercisesFind an R course using our R Course Finder directory
A brief message for anyone who uses my PubMed retractions report. It’s no longer available at RPubs; instead, you will find it here at Github. Github pages hosting is great, once you figure out that docs/ corresponds to your web root :) Now I really must update the code and try to make it more … Continue reading PubMed retractions report has moved
Tiny things can separate life into “before” and “after”. Here is one. For almost a year I’ve been daily sending mental “thank you” to Ugo (@ugobas) who showed me how to re-organize panes in RStudio. Since then I’ve been spreading this tiny improvement so many times that I thought the tiny advise deserved a separate tiny post. Please note, below is an opinionated view of a comfortable UI improvement; feel free to ignore it if you don’t like. This advise is highly subjective, though, I really believe it is useful.
Thank you for the many requests to provide some extra info on how best to get finalfit results out of RStudio, and particularly into Microsoft Word. Here is how. Make sure you are on the most up-to-date version of finalfit. [crayon-5b04b4e219aae502138484/] What follows is for demonstration purposes and is not meant to illustrate model building. … Continue reading &quot;Finalfit, knitr and R Markdown for quick results&quot;
On September 12-14 the 6th international conference on the use of R in official statistics (#uRos2018) will take place at the Dutch National Statistical Office in Den Haag, the Netherlands. The conference is aimed at producers and users of official … Continue reading →
First, let me say I would love to have an “easy button” for predictive analytics. Unfortunately, predictive analytics is hard work that requires deep subject matter expertise in both the business problem and data science.  I’m not trying to be provocative with this post.  I just want to share my viewpoint over many year of … Continue reading &quot;The myth that AI or Cognitive Analytics will replace data scientists: There is no easy button&quot;
We are pleased to announance upcoming Why R? 2018 conference that is going to happen in central-eastern Europe (Poland, Wroclaw) this July (2-5th). It is the last week for the call for papers! Submit your talk here.
About
More about the conference one can find on the conference website whyr2018.pl and in the previous blog post we’ve prepared Why R? 2018 Conference - Registration and Call for Papers Opened. The general overview
Pre-meetings
We are organizing pre-meetings in many European cities to cultivate the R experience of knowledge sharing. You are more than welcome to visit upcoming events and check photos and presentations from previous ones. If you are interested in co-organizing a Why R? pre-meeting in your city, let us know (under kontakt_at_whyr.pl) and the Why R? Foundation can provide speakers for the venue!
Past event
Why R? 2017 edition, organized in Warsaw, gathered 200 participants. The Facebook reach of the conference page exceeds 15 000 users, with almost 800 subscribers. Our oﬃcial web page had over 8000 unique visitors and over 12 000 visits in general. To learn more about Why R? 2017 see the conference after movie (https://vimeo.com/239259242).
Why R? 2017 Conference from Kinema Indigo on Vimeo.
The competition
It’s competition time! We’re sponsoring Why R? 2018 Conference. The conference runs from the 2-5th of July in Wroclaw, Poland and us nice folks here at Jumping Rivers are giving away a free ticket to the conference!
The competition
To enter your name into the prize draw all you have to do is follow one step:
Step 1) Fill out this questionnaire.
That’s it! Don’t worry, there are no intrusive personal questions. The winner will be decided by drawing one entrant at random. The closing time for entries is midnight on the 2nd June (we’ll take the most extreme time zone).
That’s all, cheers!
I had a great time in Budapest last week for the eRum 2018 conference. The organizers have already made all of the videos available online. Here's my presentation: Speeding up R with Parallel Programming in the cloud. You can find (and download) my presentation slides here. And if you just want the references from the last slide, here are the links: Find foreach on CRAN: CRAN.R-project.org/package=foreach Code for birthday problem simulation: cda.ms/7d Docs for the foreach package: cda.ms/tc Get doAzureParallel: cda.ms/7w Get Azure subscription with $200 free credits: cda.ms/td
What I love in conferences are the people, that come after your talk and say: It would be cool to add XYZ to your package/method/theorem. After the eRum (great conference by the way) I was lucky to hear from Tal Galili: It would be cool to use DALEX for teaching, to show how different ML … Czytaj dalej ML models: What they can’t learn?
Another bi-monthly update and the seventeenth release in the 0.12.* series of Rcpp landed on CRAN late on Friday following nine (!!) days in gestation in the incoming/ directory of CRAN. And no complaints: we just wish CRAN were a little more forthco...
The openrouteservice provides a new method to get geodata into R. It has an API (or a set of them) and an R package has been written to communicate with said API(s) and is available from GitHub. I’ve just been playing around with the examples on this page, in the thought of using it for […]
With tensions heightened recently at the United Nations, one might wonder whether we’ve drawn closer, or farther apart, over the decades since the UN was established in 1945. We'll see if cluster analysis can provide some clues.
The post An East-West less divided? appeared first on thinkr.
A maintenance update of RcppGSL just brought version 0.3.5 to CRAN, a mere twelve days after the RcppGSL 0.3.4. release. Just like yesterday's upload of inline 0.3.15 it was prompted by a CRAN request to update the per-package manual page; see the in...
The cloc Perl script (yes, Perl!) by Al Danial (https://github.com/AlDanial/cloc) has been one of the go-to tools for generating code metrics. Given a single file, directory tree, archive, or git repo, cloc can speedily give you metrics on the count of blank lines, comment lines, and physical lines of source code in a vast array... Continue reading →
Problem Image that someone in a bank want to find out whether some of bank’s credit card holders are acctually the same person, so according to his experience， he set a rule: the people share either the same address or the same phone number can be reasonably regarded as the same person. Just as the … Continue reading Do Clustering by “Dimensional Collapse”
The Decision Analysis in R for Technologies in Health (DARTH) workgroup is hosting a two-day workshop on decision analysis in R in Leiden, The Netherlands from June 7-8, 2018. A one-day introduction to R course will also be offered the day before the workshop, on June 6th. Decision models are mathematical simulation models that are … Continue reading Decision Modelling in R Workshop in The Netherlands!
wrapr 1.4.1 is now available on CRAN. wrapr is a really neat R package both organizing, meta-programming, and debugging R code. This update generalizes the dot-pipe feature’s dot S3 features. Please give it a try! wrapr, is an R package that supplies powerful tools for writing and debugging R code. Introduction Primary wrapr services include: … Continue reading wrapr 1.4.1 now up on CRAN
A maintenance release of the inline package arrived on CRAN today. inline facilitates writing code in-line in simple string expressions or short files. The package is mature and in maintenance mode: Rcpp used it greatly for several years but then mov...
  If you ever pass outside a restaurant in New York City, you’ll notice a prominently displayed letter grade. Since July 2010, the Health Department has required restaurants to post letter grades showing sanitary inspection results. An A grade attests to top marks for health and safety, so you can feel secure about eating there. […]
The D.C. Universe magic hero Zatanna used spells (i.e. incantations) to battle foes and said spells were just sentences said backwards, hence the mixed up jumble in the title. But, now I’m regretting not naming the package zatanna and reversing the function names to help ensure they’re only used deliberately &amp; carefully. You’ll see why... Continue reading →
This year, I have given some talks about understanding principal component analysis using what I spend day in and day out with, Stack Overflow data. You can see a recording of one of these talks from rstudio::conf 2018. When I have given these talks, I’ve focused a lot on understanding PCA. This blog post walks through how I implemented PCA and how I made the plots I used in my talk.
INTRODUCTION The dygraphs package is an R interface to the dygraphs JavaScript charting library. It provides rich facilities for charting time-series data in R, including: 1. Automatically plots xts time-series objects (or any object convertible to xts.) 2. Highly configurable axis and series display (including optional second Y-axis.) 3. Rich interactive features, including zoom/pan and […]
Related exercise sets:Spatial Data Analysis: Introduction to Raster Processing (Part 1) Advanced Techniques With Raster Data: Part 1 – Unsupervised Classification Spatial Data Analysis: Introduction to Raster Processing: Part-3 Explore all our (__1000) R exercisesFind an R course using our R Course Finder directory
18.05.2018 The next release of Bio7 adds a lot of new R features and improvements. One minor change is that the default perspective after the startup of Bio7 now is the R perspective to emphazise the importance of R within this software. The R-Shell view has been simplified and the R templates have been moved […]
Automated Data Science From a very philosophical point of view, as humans evolve we tend to automate repetitive tasks in order to waste our time with more pleasant matters. The same holds true for the field of data science as a whole, as much as for many tasks at STATWORX. What started as a super fancy and fun profession quickly ... Read More
Der Beitrag Automated Feature Selection using bounceR erschien zuerst auf STATWORX.
A combinatoric Le Monde mathematical puzzle of limited size: When the only allowed move is to switch two balls from adjacent boxes, what is the minimal number of moves to return all balls in the above picture to their respective boxes? Same question with six boxes and 12 balls. The question is rather interesting to […]
Nicolas Attalides, Data Scientist
library(purrr)
library(magrittr)
I first started using R long before the RStudio and tidyverse days... I remember writing chunks of cod...
My 4th book(self-published), “Deep Learning from first principles – In vectorized Python, R and Octave” (557 pages), is now available on Amazon in both paperback ($16.99) and kindle ($6.65/Rs449). The book starts with the most primitive 2-layer Neural Network and works  its way to a generic L-layer Deep Learning Network, with all the bells and … Continue reading My book “Deep Learning from first principles” now on Amazon
The riddle from the Riddler for the coming weeks is extremely simple to express in mathematical terms, as it summarises into characterising the distribution of when the n-sample is made of iid Normal variates. I however had a hard time finding a result connected with this quantity since most available characterisations are for either Uniform […]
This article provides a solution in the R language for Euler Problem 144: How many times does a laser bounce inside an elliptical mirror before the light beam exits it? We can use the solution to this problem to simulate elliptical billiard tables. Continue reading →
The post Laser Beams and Elliptical Billiards: Euler Problem 144 appeared first on The Devil is in the Data.
By Gabriel Vasconcelos Before we begin, I would like to thank Anuj for kindly including our blog in his list of the top40 R blogs! Check out the full list at his page, FeedSpot! Introduction Tuning a Boosting algorithm for … Continue reading →
The Delaware River Basin Commission's Delaware Estuary water quality monitoring program, which was initiated in 1967, is one of the longest running monitoring programs in the world.  One advantage of such a long data set is that we can see the cha...
This post is re-published from my blog Context One of things I have to do quite often is create tables for papers and presentations. Often the “Table 1” of a paper has descriptives about the study, broken down by subgroups. For presentation purposes, it doesn’t look good (to me, at least) that the name of […]
A while back Simon Jackson and Kara Woo shared some great ideas and graphs on grouped bar charts and density plots (link). Win-Vector LLC‘s Nina Zumel just added a graph of this type to the development version of WVPlots. Nina has, as usual, some great documentation here. More and more I am finding when you … Continue reading Ready Made Plots make Work Easier
The finafit package brings together the day-to-day functions we use to generate final results tables and plots when modelling. I spent many years repeatedly manually copying results from R analyses and built these functions to automate our standard healthcare data workflow. It is particularly useful when undertaking a large study involving multiple different regression analyses. … Continue reading &quot;Elegant regression results tables and plots in R: the finalfit package&quot;
The problem
The tidyverse api is one the easiest APIs in R to follow - thanks the concept of grammer based code. But every now and again you come across an error that boggles your mind, my personal one is the error spread delivers when I have no ind...
I often have to perform meta-analysis of past experiments for which the only info I have is the fold change and the p-value (of any measure you may imagine: species richness, gene expression, depth of coverage, plates of carbonara eaten in 5 minutes, everything). The hardest thing to find out for me was how to […]
DALEX invasion has started with the workshop and talk @ eRum 2018. Find workshop materials at DALEX: Descriptive mAchine Learning EXplanations. Tools for exploration, validation and explanation of complex machine learning models (thanks Mateusz Staniak for having the second part of the workshop). And my presentation Show my your model 2.0! (thanks go to the … Czytaj dalej DALEX @ eRum 2018
These two functions are unbelievably useful for positioning graphical elements (text, axes, labels, …) in R. They allow one to convert coordinates between various different formats. For instance, you can convert your user coordinate (say 5 where x ranges from 0 to 200) to normalized device coordinates (proportional distance across the device) and vice versa. […]
This is this second post of the “Create your Machine Learning library from scratch with R !” series. Today, we will see how you can implement Principal components analysis (PCA) using only the linear algebra available in R. Previously, we managed to implement linear regression and logistic regression from scratch and next time we will […]
The post Create your Machine Learning library from scratch with R ! (2/5) – PCA appeared first on Enhance Data Science.
A modern data scientist using R has access to an almost bewildering number of tools, libraries and algorithms to analyze the data. In my next two posts I’m going to focus on an in depth visit with CHAID (Chi-square automatic interaction detection). The title should give you a hint for why I think CHAID is a good “tool” for your analytical toolbox. There are lots of tools that can help you predict or classify but CHAID is especially good at helping you explain to any audience how the model arrives at it’s prediction or classification. It’s also incredibly robust from a statistical perspective, making almost no assumptions about your data for distribution or normality. I’ll try and elaborate on that as we work the example.
Laura Swales, Marketing and Events Assistant Another month, another sweepstake to raise money for the Bath Cats &amp; Dogs home!
This time, we picked the Eurovision so...
The R ecosystem knows a ridiculous number of time series classes. So, I decided to create a new universal standard that finally covers everyone’s use case… Ok, just kidding! tsbox, now freshly on CRAN, provides a set of tools that are agnostic towards existing time series classes. It is built around a set of converters, […]
Music is a world within itself, with a language we all understand (Sir Duke, Stevie Wonder) This serious man on the left is Gustav Theodor Fechner, a German philosopher, physicist and experimental psychologist who lived between 1801 and 1887. To be honest, I don’t know almost anything of his life or work exepct one thing: … Continue reading The Pleasing Ratio Project →
Exercise template for solving a system of three linear equations (numeric answer) with a problem description based on shuffled images.
Name:
fruit
Type:
num
Related: fruit2 Description:
A system of three linear equations has to be solved and the solution has to be entered into a fourth equation. However, the system is not defined through a verbal description or mathermatical notation but through images (clip art of tropical fruits). The problem can be interpreted as prices of three fruits (banana, orange, pineapple) and corresponding fruit baskets with different combinations of fruits. Images are stored in Base64 encoding within the exercise files and embedded dynamically into the output. PDFs are best generated from the Rnw version, HTML is best generated with pandoc from either the Rmd version (where pandoc is used by default) or the Rnw version (where ttm is used by default, but pandoc can be easily used as well.)
Solution feedback:
Yes
Randomization:
Random numbers, shuffled graphics
Mathematical notation:
Yes
Verbatim R input/output:
No
Images:
Yes
Other supplements:
No
Template:
fruit.Rnw
fruit.Rmd
Raw: (1 random version)
fruit.tex
fruit.md
PDF:
HTML:
(Note that the HTML output contains mathematical equations in MathML. It is displayed by browsers with MathML support like Firefox or Safari - but not Chrome.)
Demo code:
library(&quot;exams&quot;)
set.seed(1090)
exams2html(&quot;fruit.Rnw&quot;)
set.seed(1090)
exams2pdf(&quot;fruit.Rnw&quot;)
set.seed(1090)
exams2html(&quot;fruit.Rmd&quot;)
set.seed(1090)
exams2pdf(&quot;fruit.Rmd&quot;)
Exercise template for solving a system of three linear equations (single-choice) with a problem description based on shuffled images.
Name:
fruit2
Type:
schoice
Related: fruit Description:
A system of three linear equations has to be solved and the solution has to be entered into a fourth equation. However, the system is not defined through a verbal description or mathermatical notation but through images (clip art of tropical fruits). The problem can be interpreted as prices of three fruits (banana, orange, pineapple) and corresponding fruit baskets with different combinations of fruits. Images are stored in Base64 encoding within the exercise files and embedded dynamically into the output. A set of five answer alternatives is generated based on two potential mistakes and two random solutions from a suitable range. PDFs are best generated from the Rnw version, HTML is best generated with pandoc from either the Rmd version (where pandoc is used by default) or the Rnw version (where ttm is used by default, but pandoc can be easily used as well.)
Solution feedback:
Yes
Randomization:
Random numbers, shuffled graphics
Mathematical notation:
Yes
Verbatim R input/output:
No
Images:
Yes
Other supplements:
No
Template:
fruit2.Rnw
fruit2.Rmd
Raw: (1 random version)
fruit2.tex
fruit2.md
PDF:
HTML:
(Note that the HTML output contains mathematical equations in MathML. It is displayed by browsers with MathML support like Firefox or Safari - but not Chrome.)
Demo code:
library(&quot;exams&quot;)
set.seed(1090)
exams2html(&quot;fruit2.Rnw&quot;)
set.seed(1090)
exams2pdf(&quot;fruit2.Rnw&quot;)
set.seed(1090)
exams2html(&quot;fruit2.Rmd&quot;)
set.seed(1090)
exams2pdf(&quot;fruit2.Rmd&quot;)
We’re pleased to announce that sparklyr 0.8 is now available on CRAN! Sparklyr provides an R interface to Apache Spark. It supports dplyr syntax for working with Spark DataFrames and exposes the full range of machine learning algorithms available in Spark ML. You can also learn more about Apache Spark and sparklyr at spark.rstudio.com and the sparklyr webinar series. In this version, we added support for Spark 2.3, Livy 0.5, and various enhancements and bugfixes. For this post, we’d like to highlight a new feature from Spark 2.3 and introduce the mleap and graphframes extensions.
Parallel Cross-Validation
Spark 2.3 supports parallelism in hyperparameter tuning. In other words, instead of training each model specification serially, you can now train them in parallel. This can be enabled by setting the parallelism parameter in ml_cross_validator() or ml_train_split_validation(). Here’s an example:
library(sparklyr)
sc %
rename(from = src, to = dst) %__%
collect()
vertices_local %
gf_vertices() %__%
filter(id %in% top_packages) %__%
mutate(
group = case_when(
id %in% !!rlib_repos ~ &quot;r-lib&quot;,
id %in% !!tidyverse_repos ~ &quot;tidyverse&quot;,
id %in% !!base_packages ~ &quot;base&quot;,
TRUE ~ &quot;other&quot;
),
title = id) %__%
collect()
visNetwork(vertices_local, edges_local, width = &quot;100%&quot;) %__%
visEdges(arrows = &quot;to&quot;)
spark_disconnect(sc)
Notice that GraphFrames currently supports Spark 2.0 and 2.1. You can find additional details in the graph analysis guide.
Kjell and I are writing another book on predictive modeling, this time focused on all the things that you can do with predictors. It's about 60% done and we'd love to get feedback....
It’s coming round to that time of year where we have to create the assessment material for courses with an October start date. In many cases, we reuse question forms from previous presentations but change the specific details. If a question is suitably defined, then large parts of this process could be automated. In the OU, automated […]
These are my sketchnotes for Sam Charrington’s podcast This Week in Machine Learning and AI about Adversarial Attacks Against Reinforcement Learning Agents with Ian Goodfellow &amp; Sandy Huang:
Sketchnotes from TWiMLAI talk: Adversarial Attacks Against Reinforcement Learning Agents with Ian Goodfellow &amp; Sandy Huang
You can listen to the podcast here.
In this episode, I’m joined by Ian Goodfellow, Staff Research Scientist at Google Brain and Sandy Huang, Phd Student in the EECS department at UC Berkeley, to discuss their work on the paper Adversarial Attacks on Neural Network Policies. If you’re a regular listener here you’ve probably heard of adversarial attacks, and have seen examples of deep learning based object detectors that can be fooled into thinking that, for example, a giraffe is actually a school bus, by injecting some imperceptible noise into the image. Well, Sandy and Ian’s paper sits at the intersection of adversarial attacks and reinforcement learning, another area we’ve discussed quite a bit on the podcast. In their paper, they describe how adversarial attacks can also be effective at targeting neural network policies in reinforcement learning. Sandy gives us an overview of the paper, including how changing a single pixel value can throw off performance of a model trained to play Atari games. We also cover a lot of interesting topics relating to adversarial attacks and RL individually, and some related areas such as hierarchical reward functions and transfer learning. This was a great conversation that I’m really excited to bring to you! https://twimlai.com/twiml-talk-119-adversarial-attacks-reinforcement-learning-agents-ian-goodfellow-sandy-huang/
Exercise template for assessing the interpretation of an automaton diagram (drawn with TikZ) based on randomly generated input sequences.
Name:
automaton
Type:
mchoice
Related: logic Description:
An automaton diagram with four states A-D is drawn with TikZ and is to be interpreted, where A is always the initial state and one state is randomly picked as the accepting state. Five binary 0/1 input sequences acceptance have to be assessed with approximately a quarter of all sequences being accepted. Depending on the exams2xyz() interface the TikZ graphic can be rendered in PNG, SVG, or directly by LaTeX.
Solution feedback:
Yes
Randomization:
Random numbers, text blocks, and graphics
Mathematical notation:
No
Verbatim R input/output:
No
Images:
Yes
Other supplements:
No
Template:
automaton.Rnw
automaton.Rmd
Raw: (1 random version)
automaton.tex
automaton.md
PDF:
HTML:
Demo code:
library(&quot;exams&quot;)
set.seed(1090)
exams2html(&quot;automaton.Rnw&quot;)
set.seed(1090)
exams2pdf(&quot;automaton.Rnw&quot;)
set.seed(1090)
exams2html(&quot;automaton.Rmd&quot;)
set.seed(1090)
exams2pdf(&quot;automaton.Rmd&quot;)
Exercise template for matching logic gate diagrams (drawn with TikZ) to the corresponding truth table.
Name:
logic
Type:
schoice
Related: automaton Description:
Gate diagrams for three logical operators (sampled from: and, or, xor, nand, nor) are drawn with TikZ and have to be matched to a truth table for another randomly drawn logical operator. Depending on the exams2xyz() interface the TikZ graphic can be rendered in PNG, SVG, or directly by LaTeX.
Solution feedback:
Yes
Randomization:
Shuffling, text blocks, and graphics
Mathematical notation:
No
Verbatim R input/output:
No
Images:
Yes
Other supplements:
No
Template:
logic.Rnw
logic.Rmd
Raw: (1 random version)
logic.tex
logic.md
PDF:
HTML:
Demo code:
library(&quot;exams&quot;)
set.seed(1090)
exams2html(&quot;logic.Rnw&quot;)
set.seed(1090)
exams2pdf(&quot;logic.Rnw&quot;)
set.seed(1090)
exams2html(&quot;logic.Rmd&quot;)
set.seed(1090)
exams2pdf(&quot;logic.Rmd&quot;)
I privately had some questions and reply here, because it may also help others including me. 1. How to specify size With plot axis parameters. __ ellipseplot(iris[,c(‘Species’, ‘Sepal.Length’)], iris[,c(‘Species’, ‘Sepal.Width’)], … Continue reading →
Madison Shake I had heard that the lines to get some food at Shake Shack are long. So when I saw a new location opening in downtown LA, I wondered how many locations does it have and how fast are they spreading across the US. The answers surprised me. Using R and previous code, I […]
The post Do know Shake Shack’s locations outside of the US? You’d be surprised appeared first on nandeshwar.info.
Calculation of the propagated uncertainty using (1), where is the gradient and the covariance matrix of the coefficients , is called the “Delta Method” and is widely applied in nonlinear least-squares (NLS) fitting. However, this method is based on first-order Taylor expansion and thus assummes linearity around . The second-order approach can partially correct for […]
We are looking for our next Enterprise Advocate to join the RStudio team. See what Pete Knast, Global Director of New Business, has to say about working at RStudio and the Enterprise Advocate role.
When did you join RStudio and what made yo...
Last Wednesday, a small presentation was given at the RBelgium meetup in Brussels on Basic R Automation. For those of you who could not attend, here are the slides of that presentation which showed the use of the cronR and taskscheduleR R packages for ...
This week at the BUILD conference, Microsoft announced that Power BI custom visuals will soon be available as charts with Excel. You'll be able to choose a range of data within an Excel workbook, and pass those data to one of the built-in Power BI custom visuals, or one you've created yourself using the API. Since you can create Power BI custom visuals using R, that means you'll be able to design a custom R-based chart, and make it available to people using Excel — even if they don't know how to use R themselves. There also many pre-defined custom...
I recently came across some data about multilateral agreements, which needed to be visualized as network plots. This data had … Read More →
My BARUG rquery talk went very well, thank you very much to the attendees for being an attentive and generous audience. (John teaching rquery at BARUG, photo credit: Timothy Liu) I am now looking for invitations to give a streamlined version of this talk privately to groups using R who want to work with SQL … Continue reading rquery: SQL from R
The simple riddle of last week on The Riddler, about the minimum number of urinals needed for n men to pee if the occupation rule is to stay as far as possible from anyone there and never to stand next to another man,  is quickly solved by an R code: ocupee=function(M){ ok=rep(0,M) ok[1]=ok[M]=1 ok[trunc((1+M/2))]=1 while […]
Changing Your Viewpoint for Factors In real life, data tends to follow some patterns but the reasons are not apparent right from the start of the data analysis. Taking a common example of a demographics based survey, many people will answer questions in a particular ‘way’. For example, all married men will have higher expenses … Continue reading Exploratory Factor Analysis in R
The data
Fit the model
The analyze function
Summary
Print
Credits
You find it time-consuming to manually format, copy and paste output values to your report or manuscript? That time is over: the psycho package is here for you!
The data
Let’s take the example dataset included in the psycho package.
library(psycho)
library(tidyverse)
df % select(Participant_ID, Emotion_Condition, Subjective_Valence,
Autobiographical_Link)
summary(df)
Participant_ID Emotion_Condition Subjective_Valence Autobiographical_Link
10S : 48 Negative:456 Min. :-100.000 Min. : 0.00 11S : 48 Neutral :456 1st Qu.: -65.104 1st Qu.: 0.00 12S : 48 Median : -2.604 Median : 16.15 13S : 48 Mean : -18.900 Mean : 28.99 14S : 48 3rd Qu.: 7.000 3rd Qu.: 59.90 15S : 48 Max. : 100.000 Max. :100.00 (Other):624 NA's :1 Our dataframe (called df) contains data from several participants, exposed to neutral and negative pictures (the Emotion_Condition column). Each row corresponds to a single trial. During each trial, the participant had to rate its emotional valence (Subjective_Valence: positive - negative) experienced during the picture presentation and the amount of personal memories associated with the picture (Autobiographical_Link).
Our dataframe contains, for each of the 48 trials, 4 variables: the name of the participant (Participant_ID), the emotion condition (Emotion_Condition), the valence rating (Subjective_Valence) and the Autobiographical Link (Autobiographical_Link).
Fit the model
Let’s fit a linear mixed model to predict the autobiographical link with the condition and the subjective valence.
library(lmerTest)
fit
Like many useRs, I am also a big fan of the sqldf package developed by Grothendieck, which uses SQL statement for data frame manipulations with SQLite embedded database as the default back-end. In examples below, I drafted a couple R utility functions with the MonetDBLite back-end by mimicking the sqldf function. There are several interesting […]
Flow charts are an important part of a clinical trial report. Making them can be a pain though. One good way to do it seems to be with the grid and Gmisc packages in R. X and Y coordinates can be designated based on the center of the boxes in normalized device coordinates (proportions of […]
In case you missed them, here are some articles from April of particular interest to R users. Microsoft R Open 3.4.4, based on R 3.4.4, is now available. An R script by Ryan Timpe converts a photo into instructions for rendering it as LEGO bricks. R functions to build a random maze in Minecraft, and have your avatar solve the maze automatically. A dive into some of the internal changes bringing performance improvements to the new R 3.5.0. AI, Machine Learning and Data Science Roundup, April 2018. An analysis with R shows that Uber has overtaken taxis for trips in...
Introduction to R - 13th
Mastering the Tidyverse - 14th
Next Steps in the Tidyverse - 15th
Automated Reporting (First Steps Towards Shiny) - 20th
Interactive Graphics with Shiny - 21st
Predictive Analytics - 27/28th
Big news, from the 13th til the...
The topic for my talk at the Microsoft Build conference yesterday was &quot;Migrating Existing Open Source Machine Learning to Azure&quot;. The idea behind the talk was to show how you can take the open-source tools and workflows you already use for machine learning and data science, and easily transition them to the Azure cloud to take advantage of its capacity and scale. The theme for the talk was &quot;no surprises&quot;, and other than the Azure-specific elements I tried to stick to standard OSS tools rather than Microsoft-specific things, to make the process as familiar as possible. In the talk I...
Reposted from Abhijit’s blog. Some
I’m excited to announce a new package for accessing official statistics from the UK. nomisr is the R client for the Nomis database. Nomis is run by Durham University on behalf of the UK’s Office for National Statistics (ONS), and contains over a thousand datasets, primarily on the UK labour market, census data, benefit spending and general economic activity. Registration is optional, although registration and the use of an API key allows for larger queries without the risk of being timed out or rate limited by the API.
Most data in Nomis is arranged in statistical geographies – ranging from the whole of the UK to small output areas, making nomisr a useful tool for map-making and social geography. The example below shows how to retrieve the latest data on the number of Job Seekers Allowance claimants in each parliamentary constitutency, and maps them onto a cartogram from the parlitools package, using the development version of ggplot2
library(nomisr)
library(dplyr)
library(parlitools)
library(viridis)
#devtools::install_github(&quot;tidyverse/ggplot2/&quot;)
library(ggplot2)
library(sf)
jsa_constits
The U.S. FBI Internet Crime Complaint Center was established in 2000 to receive complaints of Internet crime. They produce an annual report, just released 2017’s edition, and I need the data from it. Since I have to wrangle it out, I thought some folks might like to play long at home, especially since it turns... Continue reading →
I recently wrote a 80-page guide to how to get a programming job without a degree, curated from my experience helping students do just that at Springboard, a leading data science bootcamp. This excerpt is a part where I focus on an overview of the R programming language. Description: R is an open-source programming language … Continue reading An overview of R with a curated learning path
This is a repost of the original article that was posted as an embedded PDF file.   Douglas M. Wiig April 8, 2018 Abstract This article is part of my series of articles exploring the use of R packages that allow for visualization of complex relationships among variables. Other articles have examined visual representations produced … Continue reading An R Tutorial: Visual Representation of Complex Multivariate Relationships Using the R qgraph Package, Part Two Repost →
[Re-posted from Abhijit’s blog] Personal expressiveness, or how data is stored in a spreadsheet When you get data from a broad research community, the variability in how that data is formatted and stored is truly astonishing. Of course there are the standardized formats that are output from machines, like Next Generation Sequencing and other automated […]
A brief introduction to machine learning using the intuition behind decision trees and random forest for non-developers.
How would you go about describing your favorite new song? What makes it catchy to you? According to the people at Spotify, characteristics like like energy or mood of a song can actually be quantified, and have many algorithms to describe music in an amazing amount of ways. Now, why would you care about what the […]
The 3.8.0 release of simmer, the Discrete-Event Simulator for R, hit CRAN almost a week ago, and Windows binaries are already available. This version includes two highly requested new features that justify this second consecutive minor release. Attachment of precomputed data Until v3.7.0, the generator was the only means to attach data to trajectories, and it was primarily… Continuar leyendo simmer 3.8.0 →
Today in Machine Learning Explained, we will tackle a central (yet under-looked) aspect of Machine Learning: vectorization. Let’s say you want to compute the sum of the values of an array. The naive way to do so is to loop over the elements and to sequentially sum them. This naive way is slow and tends […]
The post Machine Learning Explained: Vectorization and matrix operations appeared first on Enhance Data Science.
A minor update version 0.3.4 of RcppGSL is now on CRAN. It contains an improved Windows build system (thanks, Jeroen!) and updates the C++ headers by removing dynamic exception specifications which C++11 frowns upon, and the compilers lets us know th...
All plots have coordinate systems. Perhaps because they are such an integral element of plots, they are easily overlooked. However, in ggplot2, there are several very useful options to customize the coordinate systems of plots, which we will not overlook but explore in this blog post. Since it is spring, we will use a random subset of the famous iris ... Read More
Der Beitrag Coordinate systems in ggplot2: easily overlooked and rather underrated erschien zuerst auf STATWORX.
Am maintenance release of RcppMsgPack got onto CRAN this afternoon. It contains a single static_cast fix to address a warning which g++-8.1 shows whereas older compilers remained silent---and CRAN asked us to address this.
MessagePack itself is an ef...
There are a few reasons why you might want to send tweets from R. You might want to write a Twitter bot or - as in my case - you want to send yourself a tweet when a very long computation finishes.So, here I will run you through all the steps you have ...
Most data scientists wished that all data lived neatly managed in some DB. However, in reality, Excel files are ubiquitous and often a common way to disseminate results or data within many companies. Every now and then I found myself in the situation where I wanted to protect Excel sheets against users accidentally changing them. […]
1. Introduction You don’t understand anything until you learn it more than one way. Marvin Minsky No computer has ever been designed that is ever aware of what it’s doing; but most of the time, we aren’t either. Marvin Minsky A wealth of information creates a poverty of attention. Herbert Simon This post, Deep Learning from … Continue reading Deep Learning from first principles in Python, R and Octave – Part 8
I’ve had few enquires about how to install the summarizer package on a server without internet access, such as the NHS Safe Havens. Upload summarizer-master.zip from here to server. Unzip. Run this: library(devtools) source = devtools:::source_pkg(&quot;summarizer-master&quot;) install(source)
Integrals are so easy only math teachers could make them difficult.When I was in high school I really disliked math and, with hindsight, I would say it was just because of the the prehistoric teaching tools (when I saw this video I thought I’m not alone). I strongly believe that interaction CAUSES learning (I’m using “causes” here … Continue reading R as learning tool: solving integrals
I hate to have to read a long blog post before I get to the payoff.  So here is mine: Social media fosters a short attention span by triggering a dopamine (reward) response in our brains.  Innovation in Data Science (and many other technical fields) requires deep thought.  Mindless tasks like mowing the grass allows … Continue reading &quot;Confessions of a Data Scientist: Why I quit social media and still cut my own grass&quot;
This post will discuss the idea of the geometric Calmar ratio — a way to modify the Calmar ratio to … Continue reading →
tidyposterior is an R package for comparing models based on their resampling statistics. There are a few case studies on the webpage to illustrate the process. I gave a talk at th...
I am delighted to announce a new package on CRAN. It is called “greybox”. I know, what my American friends will say, as soon as they see the name – they will claim that there is a typo, and that it should be “a” instead of “e”. But in fact no mistake was made – […]
In this post I will show some of the new simulation features that will be available in powerlmm 0.4.0. You can already install the dev version from GitHub.
# GitHub
devtools::install_github(&quot;rpsychologist/powerlmm&quot;)
The revamped simulation functions offer 3 major new features:
Compare multiple model formulas, including OLS models (no random effects).
Evaluate a &quot;forward&quot; or &quot;backward&quot; model selection strategy using LRT.
Apply a data transformation during the simulation, this makes it possible to compare e.g. an ANCOVA versus a 2-level LMM, or write your own custom MNAR or MAR missing data function.
I will cover these new function in two examples.
library(powerlmm)
nsim
Capture errors, warnings and messages, but keep your list operations going In a recent post about text mining, I discussed some solutions to webscraping the contents of our STATWORX blog using the purrr-package. However, while preparing the next the episode of my series on text mining, I remembered a little gimmick that I found quite helpful along the way. Thus, ... Read More
Der Beitrag About Risks and Side-Effects… Consult your Purrr-Macist erschien zuerst auf STATWORX.
In my previous post (https://statcompute.wordpress.com/2018/02/25/mle-in-r/), it is shown how to estimate the MLE based on the log likelihood function with the general-purpose optimization algorithm, e.g. optim(), and that the optimizer is more flexible and efficient than wrappers in statistical packages. A benchmark comparison are given below showing the use case of other general optimizers commonly […]
Earlier today, @noamross posted to Twitter: #rstats #lazyweb What's the R/httr/curl equivalent of curl -F &quot;file=@somefile.html&quot; https://t.co/abbugLz9ZW — Noam Ross (@noamross) May 3, 2018 The answer was a 1:1 “file upload” curl to httr translation: but I wanted to do more than that since Noam took 20 minutes out his day this week (with no... Continue reading →
Our onboarding process, that
ensures that packages contributed by the community undergo a
transparent, constructive, non adversarial and open review process,
involves a lot of work from many actors: authors, reviewers and editors;
but how much work? Managing the effort involved in the peer-review
process is a major part of ensuring its sustainability and quality. In
this post, we’ll take a look at the effort put in by participants in the
review process, and also learn something about exploring GitHub data
along the way.
Work done by authors
We can try to quantify the development work of authors by looking at the
number of lines deleted and added in the git repo of the package before,
during, and after review. By number of lines we mean number of lines in
any file in the repo so it can be lines of actual code and lines of docs
(in the README for instance). To do so, we link information from the
package git repos (information about sizes and dates of commits) and
information from the onboarding issue threads (start and end dates of
the review process, as measured by open/close dates).
library(&quot;ggplot2&quot;)
library(&quot;magrittr&quot;)
commits
I recently described why we might want to conduct a multi-factorial experiment, and I alluded to the fact that this approach can be quite efficient. It is efficient in the sense that it is possible to test simultaneously the impact of multiple interventions using an overall sample size that would be required to test a single intervention in a more traditional RCT. I demonstrate that here, first with a continuous outcome and then with a binary outcome.
In all of the examples that follow, I am assuming we are in an exploratory phase of research, so our alpha levels are relaxed a bit to \(\alpha = 0.10\). In addition, we make no adjustments for multiple testing. This might be justifiable, since we are not as concerned about making a Type 1 error (concluding an effect is real when there isn’t actually one). Because this is a screening exercise, the selected interventions will be re-evaluated. At the same time, we are setting desired power to be 90%. This way, if an effect really exists, we are more likely to select it for further review.
Two scenarios with a continuous outcome
To start, I have created two sets of underlying assumptions. In the first, the effects of the four interventions (labeled fac1, fac2, fac3, and fac4) are additive. (The factor variables are parameterized using effect-style notation, where the value -1 represents no intervention and 1 represents the intervention.) So, with no interventions the outcome is 0, and each successive intervention adds 0.8 to the observed outcome (on average), so that individuals exposed to all four factors will have an average outcome \(4 \times 0.8 = 3.2\).
cNoX
I've been heads-down this week preparing for some upcoming talks, so not as much blogging as usual this week. But there have been some interesting conversations on Twitter this week that you may be interested to check out if you're not on the platform. Steph Lock shares her go-to R packages for every stage of the data science process: My #rstats #datascience goto 📦 IO: odbc readxl httr EDA: DataExplorer Prep: tidyverse Sampling: rsample modelr Feature Engineering: recipes Modelling: glmnet h2o FFTrees Evaluation: broom yardstick Deployment: sqlrutils AzureML opencpu Monitoring: flexdashboard Docs: rmarkdown — Steph Locke (@SteffLocke) April 28, 2018...
Previous posts in this series on MCMC samplers for Bayesian inference (in order of publication): Bayesian Simple Linear Regression with Gibbs Sampling in R Blocked Gibbs Sampling in R for Bayesian Multiple Linear Regression Metropolis-in-Gibbs Sampling...
How can R be used to conduct qualitative data science? An example analyses interviews with customer advocacy groups and regulators of water utilities using the RQDA package. Continue reading →
The post Qualitative Data Science: Using RQDA to analyse interviews appeared first on The Devil is in the Data.
I really should make a minimal effort to resist opening a data analysis blog post with Beach Boys’ lyrics, but this time the combination is too apt. We use the purrr package to show how to let your pipes roar in R. The tidyverse GitHub site contains a simple example illustrating how well pipes and … Continue reading &quot;purrr Like a Kitten till the Lake Pipes RoaR&quot;
Prediction with Binomial RegressionApril A to Z is complete! We now return to your regularly scheduled statistics blog posts! Today, I want to talk about an issue I touched on during A to Z: using regression to predict values and see how well your model is doing.Specifically, I talked a couple of times about binomial regression (here and here), which is used to predict (read: recreate with a set of variables significantly related to) a binary outcome. The data example I used involved my dissertation data and the binary outcome was verdict: guilty or not guilty. A regression model returns the linear correction applied to the predictor variables to reproduce the outcome, and will highlight whether a predictor was significantly related to the outcome or not. But a big question you may be asking of your binomial model is: how well does it predict the outcome? Specifically, how can you examine whether your regression model is correctly classifying cases? We'll start by loading/setting up the data and rerunning the binomial regression with interactions.dissertation
We're delighted to announce RStudio's Garrett Grolemund as one of our Keynote Speakers at this year's EARL London.
He will join Starcount's Edwina Dunn and a whole host ...
I’ve been in the process of transferring my blog (along with creating a personal website) to blogdown, which is hosted on Github Pages. The new blog, or rather, the continuation of this blog, will be at webbedfeet.github.io/posts, and it went live today. I’ll be cross-posting here for a while, at least until Tal gets my […]
 Hi there! I was training some ways to simulate animal (or other organisms) movements having into account habitat suitability. To do this, I used my previous eWalk model as the underlying process to simulate random or directional walks. This model is based on Brownian / Ornstein–Uhlenbeck process. You can find more about eWalk model here! Today, I will add one more element to this movement simulations. In this case, we will have into account the habitat or environmental preferences of the simulated species, to perform a simulation like this: First, we will create a raster layer as a random environmental variable, for example tree cover. library (raster) library (dismo) tc
Here is some updated R code from my previous post. It doesn't throw any warnings when importing tracks with and without heart rate information. Also, it is easier to distinguish types of tracks now (e.g., when you want to plot runs and rides separately...
RcppArmadillo release 0.8.500.0, originally prepared and uploaded on April 21, has hit CRAN today (after having already been available via the RcppCore drat repo). A corresponding Debian release will be prepared as well. This RcppArmadillo release c...
An update to Microsoft R Open (MRO) is now available for download on Windows, Mac and Linux. This release upgrades the R language engine to version 3.4.4, which addresses some minor issues with timezone detection and some edge cases in some statistics functions. As a maintenance release, it's backwards-compatible with scripts and packages from the prior release of MRO. MRO 3.4.4 points to a fixed CRAN snapshot taken on April 1 2018, and you can see some highlights of new packages released since the prior version of MRO on the Spotlights page. As always, you can use the built-in checkpoint...
The entrance to our office in Redmond in is adorned with this sculpture of our department logo, rendered in LEGO: We had fun with LEGO bricks at work this week. APEX is our internal team name, this was fun. Oh and we're hiring for all roles in Azure! pic.twitter.com/VlNNaTexA5 — Jeff Sandquist (@jeffsand) March 30, 2017 Our team, the Cloud Developer Advocates, has a logo as well, created by the multitalented Ashley Macnamara. (The mascot's name is Bit: he's a raccoon because, like developers, he's into everything.) It would be nice to have a LEGO rendition of Bit for the...
To post your R job on the next post Just visit  this link and post a new R job  to the R community. You can post a job for  free  (and there are also “featured job” options available for extra exposure). Current R jobs Job seekers:  please follow the links below to learn more and apply for your R job of interest: Featured Jobs Freelance Freelance Project: R Libraries Install on Amazon AWS EC2 WS Anywhere 27 Apr 2018 Full-Time Associate, Data Analytics National Network for Safe Communities (NNSC>) – Posted by nnsc New York New York, United States 18 Apr 2018 Full-Time Senior Research Associate Kellogg Research Support – Posted by lorbos Evanston Illinois, United States 17 Apr 2018 Full-Time Empirical Research Fellow Kellogg School of Management (Northwestern University)– Posted by lorbos Evanston Illinois, United States 17 Apr 2018 Part-Time Data Scientist Alchemy – Posted by Andreas Voniatis Anywhere 7 Apr 2018 Full-Time Solutions Engineer RStudio – Posted by nwstephens Anywhere 3 Apr 2018 All New R Jobs Full-Time Lead Data Scientist @ Washington, District of Columbia, U.S. AFL-CIO – Posted by carterkalchik Washington District of Columbia, United States 27 Apr 2018 Full-Time Data Scientist R @ Medellín, Antioquia, Colombia IDATA S.A.S – Posted by vmhoyos Medellín Antioquia, Colombia 27 Apr 2018 Full-Time Data Scientist @ […]
Machine learning models repeatedly outperform interpretable, parametric models like the linear regression model. The gains in performance have a price: The models operate as black boxes which are not interpretable.
Fortunately, there are many methods that can make machine learning models interpretable. The R package iml provides tools for analysing any black box machine learning model:
This report describes a complex R graphics customisation example using functions from the ‘grid’ and ‘gridGraphics’ packages and introduces two new functions in ‘grid’: deviceLoc ...
For some time now, I have been trading traditional assets—mostly U.S. equities. About a year ago, I jumped into the cryptocurrency markets to try my hand there as well. In my time in investor Telegram chats and subreddits, I often saw people arguing over which investments had performed better over time, but the reality was … Continue reading Using Shiny Dashboards for Financial Analysis
Here it is, everyone! The promised 4th post on meta-analysis, and my second video for Deeply Trivial! In this video, I walk through conducting a basic meta-analysis, both fixed and random effects, in the metafor package:See these previous posts and lin...
Artificial Intelligence is the new electricity. – Prof Andrew Ng Most of human and animal learning is unsupervised learning. If intelligence was a cake, unsupervised learning would be the cake, supervised learning would be the icing on the cake, and reinforcement learning would be the cherry on the cake. We know how to make the icing … Continue reading Deep Learning from first principles in Python, R and Octave – Part 7
Y is for Ys, Y-hats, and Residuals When working with a prediction model, like a linear regression, there are a few Ys you need to concern yourself with: the ys (observed outcome variable), the y-hats (predicted outcome variables based on the equation), and the residuals (y minus y-hat). Today, I'll dig into the different flavors of y and how you might work with them when conducting linear regression.As my data example, I'll use my dissertation dataset and the linear model I introduced in the GLM post.dissertation
X is for ByToday's post will be rather short, demonstrating a set of functions from the psych package, which allows you to conduct analysis by group. These commands add &quot;By&quot; to the end of existing functions. But first, a word of caution: With great power comes great responsibility. This function could very easily turn into a fishing expedition (also known as p-hacking). Conducting planned group comparisons is fine. Conducting all possible group comparisons and cherry-picking any differences is problematic. So use these group by functions with care.Let's pull up the Facebook dataset for this.Facebook
… or how I stoped worrying and wrote a blog post to remember it ad infinitum.
Magrittr’s pipe operator is one of those newish R-universe features that I really want to have around whenever I put some lines into an R-console. This is even TRUE when writing a package.
So the first thing I do is put magrittr into the DESCRIPTION file and add an __imports.R file to the packages R/-directory with the following lines:
#' re-export magrittr pipe operator
#'
#' @importFrom magrittr %__%
#' @name %__%
#' @rdname pipe
#' @export
NULL
These lines import and re-export the pipe operator (%__%) therewith allowing to
use it within my package but also beeing able to use it interactively whenever
the package is loaded.
Best of all these lines will also ensure passing all package checks (CRAN complient) and preventing any “The following objects are masked from …” messages.
Last but not least the file name “__imports.R” serves two purposes (1) making the it appear at the very beginning of an alphabetical sorted lists of file names and (2) second giving it a speaking name to inform - however reads the file name - that some R “Imports” are most likely happening inside.
Happy coding!
PS.: Those lines above require the usage of roxygen2 as documentation framework.
Given R data frames stored in the memory, sometimes it is beneficial to sample and examine the data in a large-size csv file before importing into the data frame. To the best of my knowledge, there is no off-shelf R function performing such data sampling with a relatively low computing cost. Therefore, I drafted two […]
Introduction RStudio is a powerful IDE that helped me so many times with conveniently debugging large and complex R programs as well as shiny apps, but there is one thing that bugs me about it: there is no easy option or interface in RStudio that lets you customize your theme, just as you can do in more developed text editors ... Read More
Der Beitrag Make RStudio Look the Way You Want — Because Beauty Matters erschien zuerst auf STATWORX.
Last week at the New York R Conference, I gave a presentation on using R in Minecraft. (I've embedded the slides below.) The demo gods were not kind to me, and while I was able to show building a randomly-generated maze in the Minecraft world, my attempt to have the player solve it automatically was stymied by some server issues. In this blog post, I'll show you how you can write an R function to build a maze, and use the left-hand rule to solve it automatically. If you want to play along, you'll need to launch a Spigot server...
Here is another announcement for a post-doctoral position in London (UK) to work with Sarah Filippi. In the Department of Mathematics at Imperial College London. (More details on the site or in this document. Hopefully, the salary is sufficient for staying in London, if not in South Kensington!) The post holder will work on developing […]
Here I am sharing the slides for a talk that my colleague Uwe Friedrichsen and I gave about Deep Learning - a Primer at the JAX conference on Tuesday, April 24th 2018 in Mainz, Germany.
Slides can be found here: https://www.slideshare.net/ShirinGlander/deep-learning-a-primer-95197733
Deep Learning is one of the “hot” topics in the AI area – a lot of hype, a lot of inflated expectation, but also quite some impressive success stories. As some AI experts already predict that Deep Learning will become “Software 2.0”, it might be a good time to have a closer look at the topic. In this session I will try to give a comprehensive overview of Deep Learning. We will start with a bit of history and some theoretical foundations that we will use to create a little Deep Learning taxonomy. Then we will have a look at current and upcoming application areas: Where can we apply Deep Learning successfully and what does it differentiate from other approaches? Afterwards we will examine the ecosystem: Which tools and libraries are available? What are their strengths and weaknesses? And to complete the session, we will look into some practical code examples and the typical pitfalls of Deep Learning. After this session you will have a much better idea of the why, what and how of Deep Learning, including if and how you might want to apply it to your own work. https://jax.de/big-data-machine-learning/deep-learning-a-primer/
https://twitter.com/jaxcon/status/957990506331557890
Why I’m going to eRum this year instead of useR! I have attended the useR! conferences every year now for the past 9 years, and loved it! However, this year I’m saddened that I won’t be able to go. This is because this year the conference will be held in Australia, and going there would require … Continue reading &quot;Registration for eRum 2018 closes in two days!&quot;
shiny.semantic helps you introduce semantic elements to all kinds of shiny apps. It can make your Shiny app look awesome!
If you’re new to shiny semantic, check out demos and How to use it.
What you can find in this version?
New features
Support for Semantic UI Themes
Slider input based on this great add-on Search field Multiple selection search dropdown Syntactic sugar for semantic UI: form, message, menu, field and dropdown
Semantic UI switched to CDN to achieve great speed ups!
Add more examples
Bugs fixes
Fix input registry by adding keyup event
Fixed rendering hidden tabset content
We also introduced continuous integration in our development via CircleCI, see here for more details.
How to install shiny.semantic?
From CRAN:
install.packages(&quot;shiny.semantic&quot;)From github:
devtools::install_github(&quot;Appsilon/shiny.semantic&quot;)Thanks to the great leadership from Dominik, our opensource is growing dynamically. Track new changes in the opensource packages on our GitHub. Don’t miss latest release of great semantic.dashboard package, that allows you to create beatiful Shiny app dashboards!
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A quick note for displaying R htmlwidgets in Jupyter notebooks without requiring pandoc – there may be a more native way but this acts as a workaround in the meantime if not: PS and from the other side, using reticulate for Python powered Shiny apps.
On March the 17th I had the honor to give a keynote talk about rOpenSci’s package onboarding system at the satRday conference in Cape Town, entitled “Our package reviews in review: introducing and analyzing rOpenSci onbo...
[Disclaimer: I received this book of Coryn Bailer-Jones for a review in the International Statistical Review and intend to submit a revised version of this post as my review. As usual, book reviews on the ‘Og are reflecting my own definitely personal and highly subjective views on the topic!] It is always a bit of […]
The municipal services of Amsterdam (The Netherlands) is providing open panorama images. See here and here. A camera car has driven around in the city, and now you can download these images. Per neighborhood of Amsterdam  I randomly sampled 20 … Continue reading →
A major update to R is now available. The R Core group has announced the release of R 3.5.0, and binary versions for Windows and Linux are now available from the primary CRAN mirror. (The Mac release is forthcoming.) Probably the biggest change in R 3.5.0 will be invisible to most users — except by the performance improvements it brings. The ALTREP project has now been rolled into R to use more efficient representations of many vectors, resulting in less memory usage and faster computations in many common situations. For example, the sequence vector 1:1000000 is now represented just by...
This post is by Jonah. Last week I posted here about the release of version 2.0.0 of the loo R package, but there have been a few other recent releases and updates worth mentioning. At the end of the post I also include some general thoughts on R package development with Stan and the growing number of […]
The post The current state of the Stan ecosystem in R appeared first on Statistical Modeling, Causal Inference, and Social Science.
EARL Houston has just got even better! Not only do we have Hadley Wickham delivering a keynote address, we also have one of R’s originators, Dr. Robert Gentleman.
Robert Gentleman is the vice president of computational biology at 23andMe. His work focuses on the exploration of how human genetic and trait data in the 23andMe database can be used to identify new therapies for disease. Robert Gentleman has also been awarded the Benjamin Franklin Award, a recognition for Open Access in the Life Sciences presented by the Bioinformatics Organisation and is Fellow of the International Society for Computational Biology (ISCB). We can’t wait to hear Robert Gentleman’s keynote, which will surely be one of the many highlights at EARL Houston this year.
Early bird tickets available
Make sure you don’t miss out on our early bird ticket prices. Tickets for all EARL Conferences are now available:
London: 11-13 SeptemberSeattle: 7 NovemberHouston: 9 NovemberBoston: 13 November
R 3.5.0 (codename “Joy in Playing”) was released yesterday. You can get the latest binaries version from here. (or the .tar.gz source code from here). This is a major release with many new features and bug fixes, the full list is provided below. Upgrading R on Windows and Mac If you are using Windows you can easily upgrade to the … Continue reading &quot;R 3.5.0 is released! (major release with many new features)&quot;
I was surprised by greta. I had assumed that the tensorflow and reticulate packages would eventually enable R developers to look beyond deep learning applications and exploit the TensorFlow platform to create all manner of production-grade statistical applications. But I wasn’t thinking Bayesian. After all, Stan is probably everything a Bayesian modeler could want. Stan is a powerful, production-level probability distribution modeling engine with a slick R interface, deep documentation, and a dedicated development team.
But greta lets users write TensorFlow-based Bayesian models directly in R! What could be more charming? greta removes the barrier of learning an intermediate modeling language while still promising to deliver high-performance MCMC models that run anywhere TensorFlow can go.
In this post, I’ll introduce you to greta with a simple model used by Richard McElreath in section 8.3 of his iconoclastic book: Statistical Rethinking: A Bayesian Course with Examples in R and Stan. This model seeks to explain the log of a country’s GDP based on a measure of terrain ruggedness while controlling for whether or not the country is in Africa. I am going to use it just to illustrate MCMC sampling with greta. The extended example in McElreath’s book, however, is a meditation on the subtleties of modeling interactions, and is well worth studying.
First, we load the required packages and fetch the data. DiagrammeR is for plotting the TensorFlow flow diagram of the model, and bayesplot is used to plot trace diagrams of the Markov chains. The rugged data set which provides 52 variables for 234 is fairly interesting, but we will use a trimmed-down data set with only 170 counties and three variables.
library(rethinking)
library(greta)
library(DiagrammeR)
library(bayesplot)
library(ggplot2)
# Example from section 8.3 Statistical Rethinking
data(rugged)
d
Semantic dashboard is on!
Are you fed up with ordinary shinydashboard look?
Give your app a new life with Semantic UI support. It cannot be any easier! Install semantic.dashboard and load it instead to your app. It’s compatible with classical shinydashboard! You don’t have to start from scratch:
#########################
library(shinydashboard) #
In late June, I will be in New York to teach my 3-day workshop on Forecasting using R. Tickets are available at Eventbrite.
This is the first time I’ve taught this workshop in the US, having previously run it in the Netherlands and Australia. It ...
These are my sketchnotes for Sam Charrington’s podcast This Week in Machine Learning and AI about Reproducibility and the Philosophy of Data with Clare Gollnick:
Sketchnotes from TWiMLAI talk #121: Reproducibility and the Philosophy of Data with Clare Gollnick
You can listen to the podcast here.
In this episode, i’m joined by Clare Gollnick, CTO of Terbium Labs, to discuss her thoughts on the “reproducibility crisis” currently haunting the scientific landscape. For a little background, a “Nature” survey in 2016 showed that more than 70% of researchers have tried and failed to reproduce another scientist’s experiments, and more than half have failed to reproduce their own experiments. Clare gives us her take on the situation, and how it applies to data science, along with some great nuggets about the philosophy of data and a few interesting use cases as well. We also cover her thoughts on Bayesian vs Frequentist techniques and while we’re at it, the Vim vs Emacs debate. No, actually I’m just kidding on that last one. But this was indeed a very fun conversation that I think you’ll enjoy! https://twimlai.com/twiml-talk-121-reproducibility-philosophy-data-clare-gollnick/
Ireland is a typically stable region from a seismic activity perspective as it is distant from major plate boundaries where subduction and sea-floor spreading occur. However, in reading the following article, I was surprised to discover that earthquakes occur quite frequently both off the Irish coast and within the country itself. Most of these events… Continue reading Mapping earthquakes off the Irish Coast using the leaflet package in R →
- My paper about the penetration of predatory journals in Brazil, Is predatory publishing a real threat? Evidence from a large database study, just got published in Scientometrics!. The working paper version is available in SSRN.
This is a nice example of a data-intensive scientific work cycle, from gathering data to reporting results. Everything was done in R, using web scrapping algorithms, parallel processing, tidyverse packages and more. This was a special project for me, given its implications in science making in Brazil. It took me nearly one year to produce and execute the whole code. It is also a nice case of the capabilities of package ggplot2 in producing publication-ready figures. As a side output, our database of predatory journals is available as a shiny app.
More details about the study itself is available in the paper. Our abstract is as follows:
Using a database of potential, possible, or probable predatory scholarly open-access journals, the objective of this research is to study the penetration of predatory publications in the Brazilian academic system and the profile of authors in a cross-section empirical study. Based on a massive amount of publications from Brazilian researchers of all disciplines during the 2000–2015 period, we were able to analyze the extent of predatory publications using an econometric modeling. Descriptive statistics indicate that predatory publications represent a small overall proportion, but grew exponentially in the last 5 years. Departing from prior studies, our analysis shows that experienced researchers with a high number of non-indexed publications and PhD obtained locally are more likely to publish in predatory journals. Further analysis shows that once a journal regarded as predatory is listed in the local ranking system, the Qualis, it starts to receive more publications than non-predatory ones.
Regular readers will know that I have a somewhat unhealthy relationship with GAMs and the mgcv package. I use these models all the time in my research but recently we’ve been hitting the limits of the range of models that mgcv can fit. So I’ve been looking into alternative ways to fit the GAMs I want to fit but which can handle the kinds of data or distributions that have been cropping up in our work. The brms package (Bürkner, 2017) is an excellent resource for modellers, providing a high-level R front end to a vast array of model types, all fitted using Stan. brms is the perfect package to go beyond the limits of mgcv because brms even uses the smooth functions provided by mgcv, making the transition easier. In this post I take a look at how to fit a simple GAM in brms and compare it with the same model fitted using mgcv.
Bürkner, P.-C. (2017). brms: An R package for bayesian multilevel models using Stan. Journal of Statistical Software 80, 1–28. doi:10.18637/jss.v080.i01.
As I continue to learn and grow in using R I have been trying to develop the habit of being more formal in documenting and maintaining the various functions and pieces of code I write. It’s not that I think they are major inventions but they are useful and I like having them stored in one place that I can keep track of. So I started building them as a package and even publishing them to CRAN. For any of you who might find them of interest as well.
This year marks the 10th anniversary of the R/Finance Conference!  As in prior years, we expect more than 250 attendees from around the world. R users from industry, academia, and government will joining 50+ presenters covering all areas of f...
I spent some time this morning upgrading the JDBC driver (and changing up some supporting code to account for changes to it) for my metis package🔗 which connects R up to Amazon Athena via RJDBC. I’m used to JDBC and have to deal with Java separately from R so I’m also comfortable with Java, JDBC... Continue reading →
We’ll be giving a two day workshop at the 43rd Annual Summer Institute of Applied Statistics at Brigham Young University (BYU) in Utah, June 19-20, 2018. Abstract is below, and registration and logistics information can be found here. This workshop provides a hands-on introduction to using, programming, and sharing Bayesian and hierarchical modeling algorithms using […]
Hello, R-Users! Have you ever joined an hackathon? If so, you surely know how fun and stimulating these events are. If not… now’s your chance! Quantide is collaborating with Hack the City 2018, the 4th edition of the Southern Switzerland’s hackathon. Aside from being partners, we’re actively part of it: we have members in the mentors’ […]
The post Hackathon – Hack the city 2018 appeared first on Quantide - R training &amp; consulting.
IntroYesterday I gave a workshop on applied predictive modelling with caret at the 1st LSE Computational Social Science hackathon. Organiser privileges. I put together some introductory code and started a simple GitHub repo for the participants, so I thought I’d share it here as well. This is not supposed to cover all aspects of caret (plus there is already this), but more of a starter-pack for those who might be migrating from Python or another machine learning library like mlr. I have also saved the environment as caret.rdata, so that the participants can load it up during the workshop (insert harrowing experience about live coding) and follow through—that’s included in the repo too if you rather have a test run first.The DataLet’s start by creating some synthetic data using caret. The twoClassSim generates a dataset suitable for binary-outcomes:dat
I have a couple of public appearances coming up soon. The East Bay R Language Beginners Group: Preparing Datasets – The Ugly Truth &amp; Some Solutions, Tuesday, May 1, 2018 at Robert Half Technologies, 1999 Harrison Street, Oakland, CA, 94612. Official May 2018 BARUG Meeting: rquery: a Query Generator for Working With SQL Data, Tuesday, … Continue reading Upcoming speaking engagments
I recently saw a Reddit thread in r/PoliticalDiscussion asking the question “If the economy is still booming 2020, how should the Democratic address this?” This gets to an issue that’s been on my mind since at least 2016, maybe even 2014: when will the current period of economic growth end? For some context, the Great…Read more A Recession Before 2020 Is Likely; On the Distribution of Time Between Recessions
Laura Swales, Marketing and Events Assistant
This year at Mango we're proudly sponsoring the Bath Cats &amp; Dogs
Home. To start our fundraising
for them, we decided to run a sweepstake on the Grand National. We asked
for £2 per horse, which would go to the cats and dogs home and the
winner was promised a bottle of wine for their charitable efforts.
Working in a Data Science company I knew that I couldn't simply pick
names out of a hat for the sweepstake, 'That's not truly random!' they
would cry. So in my hour of need, I turned to our two university
placement students Owen and Caroline to help me randomise the names in
R.
Non-starter
To use an appropriate horse-based metaphor, I would class myself as a
'non-starter' in R - I'm not even near the actual race! My knowledge is
practically non-existent ('Do you just type alot of random letters?')
and up until this blog I didn't even have RStudio on my laptop.
The first hurdle
We began by creating a list of the people who had entered the
sweepstake. With some people betting on more than one horse their name
was entered as many times as needed to correlate to how many bets they
had laid down.
people_list
In this post, I talk about performance through an efficient algorithm I developed for finding closest points on a map. This algorithm uses both concepts from mathematics and algorithmics. Problem to solve This problem comes from a recent question on StackOverflow. I have two matrices, one is 200K rows long, the other is 20K. For each row (which is a point) in the first matrix, I am trying to find which row (also a point) in the second matrix is closest to the point in the first matrix. This is the first method that I tried on a sample dataset: # Test dataset: longitude and latitude pixels.latlon
To post your R job on the next post Just visit  this link and post a new R job  to the R community. You can post a job for  free  (and there are also “featured job” options available for extra exposure). Current R jobs Job seekers:  please follow the links below to learn more and apply for your R job of interest: Featured Jobs Full-Time Associate, Data Analytics National Network for Safe Communities (NNSC>) – Posted by nnsc New York New York, United States 18 Apr 2018 Full-Time Senior Research Associate Kellogg Research Support – Posted by lorbos Evanston Illinois, United States 17 Apr 2018 Full-Time Empirical Research Fellow Kellogg School of Management (Northwestern University)– Posted by lorbos Evanston Illinois, United States 17 Apr 2018 Freelance Dashboard/Visualization developer in R/Shiny International Finance Corporation – Posted by Oleksiy Anywhere 9 Apr 2018 Part-Time Data Scientist Alchemy – Posted by Andreas Voniatis Anywhere 7 Apr 2018 Full-Time Product Owner (Data Science) (m/f) Civey GmbH – Posted by Civey Berlin Berlin, Germany 6 Apr 2018 Full-Time R Engineer (Data Science) (m/f) Civey GmbH – Posted by Civey Berlin Berlin, Germany 6 Apr 2018 Full-Time Solutions Engineer RStudio – Posted by nwstephens Anywhere 3 Apr 2018 More New R Jobs Full-Time Associate, Data Analytics National Network for Safe Communities (NNSC>) – Posted by nnsc New […]
I don’t know whether ‘rename taxa’ is a common task or not. It seems not a good idea to rename taxa in Newick tree text, since it may introduce problems when mapping the original sequence alignment to the tree.
If you just want to show different or additional information when plotting the tree, it is fine and easy to do it using ggtree:
require(treeio)
## Loading required package: treeio
require(ggtree)
## Loading required package: ggtree
## ggtree v1.11.6 For help: https://guangchuangyu.github.io/software/ggtree
## ## If you use ggtree in published research, please cite:
## Guangchuang Yu, David Smith, Huachen Zhu, Yi Guan, Tommy Tsan-Yuk Lam. ggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods in Ecology and Evolution 2017, 8(1):28-36, doi:10.1111/2041-210X.12628
tr
Various methods to approximate and visualise the digits of pi using the R computing language for statistics. The apparent randomness of the decimal expression of Pi is a source for beautiful visualisations. Continue reading →
The post Approximations of Pi: A Random Walk though the Beauty of Pi appeared first on The Devil is in the Data.
We were thrilled by the response to our summer internship program. After carefully reviewing over 250 applications, we have made our final selections. Here is a brief description of each intern and the projects they will be working on this sum...
In an update to his analysis of taxi and ride-share trips, Todd Schnieder reports that the number of daily Uber rides exceeds the number of taxi rides in New York City, as of November 2017. The data is provided by the New York Taxi and Limousine Commission, and the analysis and graphics were created using the open-source R language. (The code and data for the analysis are available on Github.) You can find a wealth of detailed analysis of rides in New York, including some gorgeous maps of pick-up and drop-off locations, at the link below. Todd W. Schneider: Analyzing...
If you want to find the structure hidden behind your data, this is right class for you: you will learn how to group similar observations using Clustering; how to “naturally” aggregate your variables using Dimensionality Reduction; how to predict outcomes using Regression and Classification (LMs, GLMs, Trees, Random forests, Neural networks). In other words, you will get a full-immersion in the Data Mining and the Machine Learning world, using R. Our third live class in Milan, on October 25-26
The post R live classes Spring Program – Machine Learning with R &amp; Data viz with R appeared first on Quantide - R training &amp; consulting.
This week’s edition of Data is Plural had two really fun data sets. One is serious fun (the first comprehensive data set on U.S. evictions, and the other I knew about but had forgotten: The Federal Register Executive Order (EO) data set(s). The EO data is also comprehensive as the summary JSON (or CSV) files... Continue reading →
ggimage 0.1.4 is available on CRAN.
This release introduces a new function called ggbackground for setting image background as ggplot canvas.
require(ggplot2)
p
These are my sketchnotes for Sam Charrington’s podcast This Week in Machine Learning and AI about Systems and Software for Machine Learning at Scale with Jeff Dean:
Sketchnotes from TWiMLAI talk #124: Systems and Software for Machine Learning at Scale with Jeff Dean
You can listen to the podcast here.
n this episode I’m joined by Jeff Dean, Google Senior Fellow and head of the company’s deep learning research team Google Brain, who I had a chance to sit down with last week at the Googleplex in Mountain View. As you’ll hear, I was very excited for this interview, because so many of Jeff’s contributions since he started at Google in ‘99 have touched my life and work. In our conversation, Jeff and I dig into a bunch of the core machine learning innovations we’ve seen from Google. Of course we discuss TensorFlow, and its origins and evolution at Google. We also explore AI acceleration hardware, including TPU v1, v2 and future directions from Google and the broader market in this area. We talk through the machine learning toolchain, including some things that Googlers might take for granted, and where the recently announced Cloud AutoML fits in. We also discuss Google’s process for mapping problems across a variety of domains to deep learning, and much, much more. This was definitely one of my favorite conversations, and I’m pumped to be able to share it with you. https://twimlai.com/twiml-talk-124-systems-software-machine-learning-scale-jeff-dean/
On April 12th, 2018 I gave a talk about Explaining complex machine learning models with LIME at the Hamburg Data Science Meetup - so if you’re intersted: the slides can be found here: https://www.slideshare.net/ShirinGlander/hh-data-science-meetup-explaining-complex-machine-learning-models-with-lime-94218890
Traditional machine learning workflows focus heavily on model training and optimization; the best model is usually chosen via performance measures like accuracy or error and we tend to assume that a model is good enough for deployment if it passes certain thresholds of these performance criteria. Why a model makes the predictions it makes, however, is generally neglected. But being able to understand and interpret such models can be immensely important for improving model quality, increasing trust and transparency and for reducing bias. Because complex machine learning models are essentially black boxes and too complicated to understand, we need to use approximations to get a better sense of how they work. One such approach is LIME, which stands for Local Interpretable Model-agnostic Explanations and is a tool that helps understand and explain the decisions made by complex machine learning models.
HH Data Science Meetup: Explaining complex machine learning models with LIME from Shirin Glander
– slide deck was produced with beautiful.ai –
My R package powerlmm 0.3.0 is now out on CRAN. It can be installed from CRAN https://cran.r-project.org/package=powerlmm or GitHub https://github.com/rpsychologist/powerlmm.
New features
This version adds support for raw effect sizes, and new standardized effect sizes using the function cohend(...). Here's an example that use the different types. p
An algorithmic Le Monde mathematical puzzle with a direct Alice and Bob play a game by picking alternatively one of the remaining digits between 1 and 10 and putting it in either one of two available stacks, 1 or 2. Their respective gains are the products of the piles (1 for Alice and 2 for […]
Con las bombas que tiran los fanfarrones, se hacen las gaditanas tirabuzones (Palma y corona, Carmen Linares) This time I draw Franky again using an algorithm to solve the Travelling Salesman Problem as I did in my last post. On this occasion, instead of doing just one single line drawing, I overlap many of them … Continue reading Pencil Scribbles →
The Data Science Live Book is now available at Amazon! Kindle &amp; Paperback versions! 🚀 Learn Data Science from a practical and intuitive approach using R!
In my last four posts I have been working at automating a process, that I am likely to repeat many times, by turning it into a proper R function. In my last post I overcame some real performance problems, combined two sub-functions into one and generally had a workable piece of code. In the final post in this series today I’ll accomplish two more important tasks. I’ll once again refactor the code to streamline it, and I’ll give the user a lot more flexibility on how they input their request.
I am going to be giving this course on 10 August and 6 December this year. It is organised by … More
R FOR HYDROLOGISTS SEASONALITY AND TREND DECOMPOSITION If you don’t have the data, please first get it from the first tutorial here. Also, you need to install and load the ggplot2 package. if(!require(ggplot2)){install.packages(ggplot2, dep=T)} Answers to these exercises are available here. Time series decomposition is a mathematical procedure which transforms a time series into multiple different […]
Related exercise sets:Forecasting: Exponential Smoothing Exercises (Part-3) Forecasting for small business Exercises (Part-3) Parallel Computing Exercises: Foreach and DoParallel (Part-2) Explore all our (__1000) R exercisesFind an R course using our R Course Finder directory
The R Consortium has been quite busy lately, so I thought I'd take a moment to bring you up to speed on some recent news. (Disclosure: Microsoft is a member of the R Consortium, and I am a member of its board of directors.) Many R Consortium funded projects are underway. The &quot;Quantities for R&quot; project has released its first working prototype, the DBI project for database interfaces to R is near completion, and the second SatRDays conference was recently held in Cape Town. A survey of R package developers on licensing, revealed several insights on motivations behind choices on...
R tip: use slices. R has a very powerful array slicing ability that allows for some very slick data processing. Suppose we have a data.frame “d“, and for every row where d$n_observations __ 5 we wish to “NA-out” some other columns (mark them as not yet reliably available). Using slicing techniques this can be done … Continue reading R Tip: Use Slices
A simple riddle express this week on The Riddler, about finding the years between 2001 and 2099 with the most cases when day x month = year [all entries with two digits]. For instance, this works for 1 January, 2001 since 01=01 x 01. The only difficulty in writing an R code for this question […]
This post is by Jonah and Aki. We’re happy to announce the release of v2.0.0 of the loo R package for efficient approximate leave-one-out cross-validation (and more). For anyone unfamiliar with the package, the original motivation for its development is in our paper: Vehtari, A., Gelman, A., and Gabry, J. (2017). Practical Bayesian model evaluation […]
The post loo 2.0 is loose appeared first on Statistical Modeling, Causal Inference, and Social Science.
@mkjcktzn asked if one can access Feedly “Saved for Later” items via the API. The answer is “Yes!”, and it builds off of that previous post. You’ll need to read it and get your authentication key (still no package 😞) before continuing. We’ll use most (I think “all”) of the code from the previous post,... Continue reading →
I’ve written previously about Ordnance Survey’s Terrain 50 data and how you can merge it together. You can download the dataset from the OS’s opendata repo. I’d like to update that post with… Continue reading →
“Today you are You, that is truer than true. There is no one alive who is Youer than You.” Dr. Seuss “Explanations exist; they have existed for all time; there is always a well-known solution to every human problem — neat, plausible, and wrong.” H L Mencken Introduction In this 6th instalment of ‘Deep Learning … Continue reading Deep Learning from first principles in Python, R and Octave – Part 6
The compositional data are proportionals of mutually exclusive groups that would be summed up to the unity. Statistical models for compositional data have been applicable in a number of areas, e.g. the product or channel mix in the marketing research and asset allocations of a investment portfolio. In the example below, I will show how […]
Yesterday I wrote this blog post which showed how one could use {furrr} and {mice} to impute missing data in parallel, thus speeding up the process tremendously.
To make using this snippet of code easier, I quickly cobbled together an experimental package called {pmice} that you can install from Github:
devtools::install_github(&quot;b-rodrigues/pmice&quot;)
For now, it returns a list of mids objects and not a mids object like mice::mice() does, but I’ll be working on it. Contributions welcome!
If you found this blog post useful, you might want to follow me on twitter for blog post updates.
R has great potential in Economic data analysis, as well as econometrics. In this set of exercises, we will go through a wide variety of topics in econometrics, including wage equations, growth regressions, dynamic regressions and time series models, hedonic regressions, the demand for health care, and labor force participation. In the first set of […]
Related exercise sets:Spatial Data Analysis: Introduction to Raster Processing (Part 1) Density-Based Clustering Exercises Parallel Computing Exercises: Snow and Rmpi (Part-3) Explore all our (__1000) R exercisesFind an R course using our R Course Finder directory
Today I saw this tweet on my timeline:
For those of us that just can't wait until RStudio officially supports parallel purrr in #rstats, boy have I got something for you. Introducing `furrr`, parallel purrr through the use of futures. Go ahead, break things, you know you want to:https://t.co/l9z1UC2Tew— Davis Vaughan (@dvaughan32) April 13, 2018
and as a heavy {purrr} user, as well as the happy owner of a 6-core AMD Ryzen 5 1600X cpu, I was very excited to try out {furrr}. For those unfamiliar with {purrr}, you can read some of my previous blog posts on it here, here or here.
To summarize very quickly: {purrr} contains so-called higher order functions, which are functions that take other functions as argument. One such function is map(). Consider the following simple example:
numbers
Introduction to Metricsgraphics Metricsgraphics is an htmlwidget interface to the MetricsGraphics.js JavaScript/D3 chart library. Installation devtools::install_github(“hrbrmstr/metricsgraphics”) Usage Building metricsgraphics charts follows the “piping” idiom, made popular through the magrittr, ggvis and dplyr packages. This makes it possible to avoid one giant function with a ton of parameters and facilitates, breaking out the chart building into […]
Related exercise sets:Data visualization with googleVis exercises part 10 Data Visualization with googleVis exercises part 4 Data Visualization with googleVis exercises part 8 Explore all our (__1000) R exercisesFind an R course using our R Course Finder directory
The latest version of the forecast package for R is now on CRAN. This is the version used in the 2nd edition of my forecasting textbook with George Athanasopoulos. So readers should now be able to replicate all examples in the book using only CRAN pack...
In my last post I worked on two functions that took pairs of variables from a dataset and produced some nice useful ggplot plots from them. We started with the simplest case, plotting counts of how two variables cross-tabulate. Then we worked our way up to being able to automate the process of plotting lots of pairings of variables from the same dataframe. We added a feature to change the plot type and tested for general functionality. To be honest I thought I was in great shape until I went and started trying the function on a much larger dataset. Performance was terrible I had made a couple of mistakes at least. Today I’ll fix those problems and combine our two functions into one function.
I apologize up-front for using bad words in this post. Said bad words include “Facebook”, “Mark Zuckerberg” and many referrals to entities within the U.S. Government. Given the topic, it cannot be helped. I’ve also left the R tag on this despite only showing some ggplot2 plots and Markdown tables. See the end of the... Continue reading →
How do I interpret the AIC?
My student asked today how to interpret the AIC (Akaike’s Information
Criteria) statistic for model selection. We ended up bashing out some R
code to demonstrate how to calculate the AIC for a simple GLM (general
linear model). I always think if you can understand the derivation of a
statistic, it is much easier to remember how to use it.
Now if you google derivation of the AIC, you are likely to run into a
lot of math. But the principles are really not that complex. So here
we will fit some simple GLMs, then derive a means to choose the ‘best’
one.
Skip to the end if you just want to go over the basic principles.
Before we can understand the AIC though, we need to understand the
statistical methodology of likelihoods.
Explaining likelihoods
Say you have some data that are normally distributed with a mean of 5
and an sd of 3:
set.seed(126)
n
K-means is efficient, and perhaps, the most popular clustering method. It is a way for finding natural groups in otherwise unlabeled data. You specify the number of clusters you want defined and the algorithm minimizes the total within-cluster variance. In this exercise, we will play around with the base R inbuilt k-means function on some labeled […]
Related exercise sets:Advanced Techniques With Raster Data: Part 1 – Unsupervised Classification Density-Based Clustering Exercises Spatial Data Analysis: Introduction to Raster Processing (Part 1) Explore all our (__1000) R exercisesFind an R course using our R Course Finder directory
Introduction My son is 8 years old and he has shown a lot of interest in cars, which is strange because I have zero interest in cars. But he is driving me crazy when we have a car ride: “dad … Continue reading →
This Monday we had a machine learning oriented meeting of Warsaw R Users. Prof Bernd Bischl from LMU gave an excellent overview of mlr package (machine learning in R), then I introduced DALEX (Descriptive mAchine Learning EXplanations) and Mateusz Staniak introduced live and breakDown packages. The meeting pushed me to write down a gentle introduction … Czytaj dalej DALEX Stories – Warsaw apartments
future 1.8.0 is available on CRAN.
This release lays the foundation for being able to capture outputs from futures, perform automated timing and memory benchmarking (profiling) on futures, and more. These features are not yet available out of the b...
I had a great time this week at the Qcon.ai conference in San Francisco, where I had the pleasure of presenting to an audience of mostly Java and Python developers. It's unfortunate that videos won't be available for a while, because there were some amazing presentations: those by Matt Ranney, Mike Williams and Rachel Thomas were particular standouts. My goal for the presentation I gave was to encourage developers to take a look at R (and its community) for developing AI applications, and in particular to bring a statistical perspective to data, inference and prediction as used by AI applications:...
The R package cdata now has version 0.7.0 available from CRAN. cdata is a data manipulation package that subsumes many higher order data manipulation operations including pivot/un-pivot, spread/gather, or cast/melt. The record to record transforms are specified by drawing a table that expresses the record structure (called the “control table” and also the link between … Continue reading cdata Update
[…]
@RMHoge asked the following on Twitter: Hello #rstats hyve mind! Is there a package that reads epub into R? I can not find any, I now convert to text and parse the text but you sort of lose the structure of the text. Pinging @dataandme @hrbrmstr — Roel (@RMHoge) April 12, 2018 Here’s one way... Continue reading →
(require '[tnoda.rashinban :as rr]
'[tnoda.rashinban.core :as rc]
'[clojure.core.matrix.dataset :as dt]
'[clojure.core.matrix.impl.dataset :as id])
;; CREATE A TOY DATA
(def ds [{:id 1.0 :name &quot;name1&quot;}
{:id 2.0 :n...
Shiny Server 1.5.7.907 and Shiny Server Pro 1.5.7.954 are now available.
Highlights for this release are a major-version Node upgrade, support for HTTP gzip/deflate compression and (optionally) secure cookies, and numerous bug fixes. We&amp;rsq...
Here's the slides from a talk I gave recently at Stockholm University: &quot;Power Analysis for Longitudinal 2- and 3-Level Models: Challenges and Some Solutions Using the R Package powerlmm&quot;.
The slides gives several code examples for a lot of powerlmm's f...
R has a lot of under-appreciated super powerful functions. I list a few of our favorites below. Atlas, carrying the sky. Royal Palace (Paleis op de Dam), Amsterdam. Photo: Dominik Bartsch, CC some rights reserved. stats::approx(): approximate a curve/function. base::cumsum(): cumulative ordered sum. stats::ecdf(): estimate the cumulative distribution function. base::findInterval(): assign values to bins. base::match(): … Continue reading Neglected R Super Functions
On April 4th, 2018 I gave a talk about Deep Learning with Keras at the Ruhr.Py Meetup in Essen, Germany. The talk was not specific to Python, though - so if you’re intersted: the slides can be found here: https://www.slideshare.net/ShirinGlander/ruhrpy-introducing-deep-learning-with-keras-and-python
Ruhr.PY - Introducing Deep Learning with Keras and Python von Shirin Glander
There is also a video recording of my talk, which you can see here: https://youtu.be/Q8hVXnpEPmc
I am so lucky to work with so many generous, knowledgeable, and amazing people at Stack Overflow, including Ian Allen and Kirti Thorat. Both Ian and Kirti are part of biweekly sessions we have at Stack Overflow where several software developers join me in practicing R, data science, and modeling skills. This morning, the two of them went to a high school outreach event in NYC for students who have been studying computer science, equipped with Stack Overflow ✨ SWAG ✨, some coding activities based on Stack Overflow internal tools and packages, and a Shiny app that I developed to share a bit about who we are and what we do.
In the exercises below, we will continue working with lubridate and examine more features of the library. Answers to these exercises are available here. Please check the previous exercise here. If you obtained a different (correct) answer than those listed on the solutions page, please feel free to post your answer as a comment on […]
Related exercise sets:Parsing Dates and Time – Part 1: Exercises Dates and Times – Simple and Easy with lubridate exercises (part 1) Dates and Times – Simple and Easy with lubridate exercises (part 2) Explore all our (__1000) R exercisesFind an R course using our R Course Finder directory
Prediction competitions are now so widespread that it is often forgotten how controversial they were when first held, and how influential they have been over the years.
To keep this exercise manageable, I will restrict attention to time series forecast...
I'll be conducting a workshop called &quot;Applied Machine Learning&quot; in Washington DC on August 15 and 16. The last one, at the RStudio conference, sold out quickly. The 2 day course i...
Tal Galili's R-bloggers.com has been syndicating blog posts about R for quite a while — from memory I'd say about 8 years, but I couldn't find the exact date it started aggregating. Anyway, it contains a wealth of information about activity in the R ecosystem, but without any easy way to access that information other than the blog post feed. Bob Rudis figured it out though — by using the Feedly API and the RSS feeds of R-bloggers, you can extract quite a bit of data about the posts of the 750+ bloggers syndicated on the site. Among the insights:...
R tip. Use wrapr::match_order() to align data. Suppose we have data in two data frames, and both of these data frames have common row-identifying columns called “idx“. library(&quot;wrapr&quot;) d1
Now that software ate the world, what’s for dessert? Those in the know know that it’s AI. It seems everyone …Continue reading →
In my last post I started to build two functions that took pairs of variables from a dataset and produced some nice useful ggplot plots from them. We started with the simplest case, plotting counts of how two variables cross-tabulate. Then we worked our way up to being able to automate the process of plotting lots of pairings of variables from the same dataframe. Today we’ll improve our functions and add a feature. For now, we’re going to continue to make use of the built in dataframe known as mtcars. We’re doing that to make sure that whatever we do in this post today, it works on a known start point so we can compare and contrast.
  Data science gives you tools to find opportunities A few months ago, I wrote a blog post about Amazon’s search for a location for a second headquarters. For those of you who don’t know about it, the technology giant – which currently has its headquarters in Seattle Washington – announced last September that it […]
The post Mapping the best states for business appeared first on SHARP SIGHT.
Udemy is offering readers of R-bloggers access to its global online learning marketplace for $15 per course! This deal (offering over 50%-90% discount) is for hundreds of their courses – including many R-Programming, data science, machine learning etc. Click here to browse ALL (R and non-R) courses Advanced R courses:  Regression modelling Comprehensive Linear Modeling with R (15 Hours of video) Linear regression in R for Data Scientists (7 hours) Linear Mixed-Effects Models with R (11 Hours of video) Linear Regression, GLMs and GAMs with R (8 hours) Forecasting Models with R (5 hours) Time Series Analysis and Forecasting in R (3 Hours of video) Regression, Data Mining, Text Mining, Forecasting using R (32 hours) Statistical/machine learning Bayesian Computational Analyses with R (12 Hours of video) Data Science and Machine Learning Bootcamp with R (17 hours) Decision Tree – Theory, Application and Modeling using R (5 hours) Data Mining with R: Go from Beginner to Advanced! (12 hours) Machine Learning A-Z™: Hands-On Python &amp; R In Data Science (40 hours) Applied Multivariate Analysis with R (13 Hours of video)  Visualization Graphs in R (ggplot2, plotrix, base R) – Data Visualization with R Programming Language (5 Hours of video) Multivariate Data Visualization with R (7 Hours of video) Other topics Create Interactive Web […]
INTRODUCTION Highcharter is a R wrapper for Highcharts javascript libray and its modules. Highcharts is very mature and flexible javascript charting library and it has a great and powerful API. Before proceeding, please follow our short tutorial. Look at the examples given and try to understand the logic behind them. Then, try to solve the […]
Related exercise sets:Data Visualization with googleVis exercises part 2 ggvis Exercises (Part-1) Data visualization with googleVis exercises part 10 Explore all our (__1000) R exercisesFind an R course using our R Course Finder directory
The Herfindahl-Hirschman Index (HHI) is a widely used measure of concentration in a variety of fields including, business, economics, political science, finance, and many others. Though simple to calculate (summed squared market shares of firms/actors in a single market/space), calculation of the HHI can get onerous, especially as the number of firms/actors increases and the … Continue reading A New Package (hhi) for Quick Calculation of Herfindahl-Hirschman Index scores
TL;DR: Use the Vectorize() function!
If you’re reading this, you’ve either encountered this problem before, or you just got to this article out of curiousity (in which case you probably don’t know what problem I’m talking about).
A few days ago I was given code by a client for a function that, given a path to a patient’s file, generates a useful ID for the patient. I won’t post the actual function, but it was something along the lines of this:
library(stringr)
library(dplyr)
patient_name
This post is to announce a second occurrence of the exciting “masterclass in Bayesian Statistics” that we organised in 2016, near Marseilles. It will take place on 22-26 October 2018 once more at CIRM (Centre International de Recherches Mathématiques, Luminy, Marseilles, France). The targeted audience includes all scientists interested in learning how Bayesian inference may […]
A new release of the tint package just arrived on CRAN. Its name expands from tint is not tufte as the package offers a fresher take on the Tufte-style for html and pdf presentations.
This version adds support for the tufte-book latex style. The pack...
Many R package authors (including myself) lump a collection of small, useful functions into some type of utils.R file and usually do not export the functions since they are (generally) designed to work on package internals rather than expose their functionality via the exported package API. Just like Batman’s utility belt, which can be customized... Continue reading →
This exercise set will continue to present the STAN platform, but with another useful tool: the bayesplot package. This package is very useful to construct diagnostics that can be used to have insights on the convergence of the MCMC sampling since the convergence of the generated chains is the main issue in most STAN models. […]
Related exercise sets:Spatial Data Analysis: Introduction to Raster Processing (Part 1) Parallel Computing Exercises: Foreach and DoParallel (Part-2) Parallel Computing Exercises: Snow and Rmpi (Part-3) Explore all our (__1000) R exercisesFind an R course using our R Course Finder directory
In a couple of recent posts (Textualisation With Tracery and Database Reporting 2.0 and More Tinkering With PyTracery) I’ve started exploring various ways of using the pytracery port of the tracery story generation tool to generate variety of texts from Python pandas data frames. For my F1DataJunkie tinkerings I’ve been using R + SQL as […]
A bit more tinkering with F1 data from the ergast db, this time trying to generating trivia / facts around races. The facts are identified using SQL queries: Some of the queries also embed query fragments, which I intend to develop further… I'm using knitr to generate Github flavoured markdown (gfm) from my Rmd docs […]
Yesterday I came across Aaron Penne’s collection of very nice data visualizations, one of which was of monthly births in the United States since 1933. He made a tiled heatmap of the data, taking care when calculating the average rate to correct f...
Some time ago we discussed how you can access data that are stored in Amazon Redshift and PostgreSQL with Python and R. Let’s say you did find an easy way to store a pile of data in your BigQuery data warehouse and keep them in sync. Now you want to start messing with it using statistical techniques, maybe build a model of your customers’ behavior, or try to predict your churn rate.
The post Access your data in Google BigQuery with Python and R appeared first on Blendo.
In this exercise, we will continue to build our model from our previous exercise here, specifically to revise the errors that may be generated from the model, including rounding and truncating errors. Answers to these exercises are available here. If you obtained a different (correct) answer than those listed on the solutions page, please feel […]
Related exercise sets:3D plotting exercises Descriptive Analytics-Part 6: Interactive dashboard ( 2/2) Bayesian Inference – MCMC Diagnostics using coda : Exercises Explore all our (__1000) R exercisesFind an R course using our R Course Finder directory
[Thanks to Scott Glover for correcting me.] Someone recently said to me that the lower the p-value, the higher the likelihood ratio under the alternative vs the null. The arXiv paper by Michael Lew makes analogous points (thanks to Titus von der Malsb...
One of the nicest things about working with R is that with very little effort you can customize and automate activities to produce the output you want – just the way you want it. You can contrast that with more monolithic packages that may allow you to do a bit of scripting, but for the most part, the price of a GUI or packaging everything in one package is that you lose the ability to have things just your way. Since everything in R is pretty much a function already, you may as well invest a little time and energy in making functions… your way, and to exactly your tastes and needs. This post is not meant to be an exhaustive or complete treatment of writing a function. For that you probably want a book, or at least a Chapter like the one Hadley has in Advanced R. This post will focus on a very practical, and hopefully useful, single example.
Let’s consider piping in R both using the magrittr package and using the wrapr package. magrittr pipelines The magittr pipe glyph “%__%” is the most popular piping symbol in R. magrittr documentation describes %__% as follow. Basic piping: x %__% f is equivalent to f(x) x %__% f(y) is equivalent to f(x, y) x %__% … Continue reading magrittr and wrapr Pipes in R, an Examination
On Twitter the other day, Philip Cohen put up some data on changes in Bachelor’s degrees awarded between 1995 and 2015. The data come from the National Center for Education Statistics. It seemed like a good candidate for drawing as a figure, so I had a go at it:
Changes in the number of Bachelor's degrees awarded over the past twenty years. Afterwards, I was messing around with the data and wanted to draw some time-series plots for the various subject areas the NCES tracks.
I recently met up with someone who does geospatial stuff but uses the more traditional GIS software to do it. I showed him a few things in R but not being a person who does a lot of geospatial analysis I thought I’d ask the lovely #rspatial crowd...
Modern MacBook Pros have a fairly useless (c’mon, admit it!) “Touch Bar” that did little more than cause severe ire in the developer community after turning a full-fledged, tactile Escape key into a hollow version if its former self. Having said, that, some apps do make OK use of it, with Fantastical and Omnigraffle being... Continue reading →
I’m back on the R thang with F1 data from ergast.com, and started having a look at how drivers and teams compare at a circuit. One metric I came across for comparing teams over a season is the supertime, typically calculated for each manufacturer as the average of their fastest single lap recorded by the team […]
After avoiding the entire medium for years, I've been rather getting into listening to podcasts lately. As a worker-from-home I don't have a commute (the natural use case of podcasts, I guess), but I have been travelling a lot more recently and it's been great to listen to during long flights. It turns out there are a lot of great podcasts out there about R, data science, and AI, so if you're looking for something to listen to here's what's currently in rotation for me: Not So Standard Deviations. Roger Peng and Hilary Parker chat about data science, real-world data...
Preface I’ve realized that all my previous posts were quite substantial in length and took quite a long time to create them. From this point forward I’ll be generating posts of shorter length (partially for my sanity and more for my impulsivity with ideas). A few of these posts won’t be public health related (like... Continue Reading →
For some reason, I am unable to load this post to google blogger. I have linked the post to an html file on my home page. Please comment here on this blog. Here is the post: Posterior probability of the null hypothesis being true, given a significant...
If you're a fan of the HBO show Silicon Valley, you probably remember the episode where Jian Yang creates an application to identify food using a smartphone phone camera: Surprisingly, the app in that scene isn't just a pre-recorded special effect: the producers actually developed a smartphone application using Tensorflow (and you can even download the app for your phone to play with). It was an impressive feat, especially give the relative infancy of deep learning tools back in 2016. Things have advanced since then, though, and I wanted to see how easy it would be to build an equivalent...
I have noticed even people who claim everything is predestined, and that we can do nothing to change it, look before they cross the road (Stephen Hawking) Imagine a salesman and a set of cities. The salesman has to visit each one of the cities starting from a certain one and returning to the same … Continue reading The Travelling Salesman Portrait →
There’s a yuge chance you’re reading this post (at least initially) on R-Bloggers right now (though you should also check out R Weekly and add their live feed to your RSS reader pronto!). It’s a central “watering hole” for R folks and is read by many (IIRC over 20,000 Feedly users have it in their... Continue reading →
Four years ago today authors Nina Zumel and John Mount received our author’s copies of Practical Data Science with R! It has its imitators, but it remains the best “I have R, now what do I do with it?” book (as it works the user through non-trivial projects, analyses, presentations, predictive analytic, data science, and … Continue reading Four Years of Practical Data Science with R
I’ve been using the reticulate package occasionally for a while now, so I was surprised to see that it had only just been officially released. reticulate: R interface to Python https://t.co/qVWmwoMQAP. Comprehensive set of interoperability tools including R Markdown Python...Continue Reading →
Meet Dominik, our Open Source Tech Lead
Before Dominik took a leadership role he used to be a Data Scientist at Appsilon.
He has five years of experience in Python and R programming, mostly from data science and machine learning related project...
Nic Crane, Data Scientist
This article was first published on Nic Crane's Blog and kindly contributed to the Mango Blog.
I’m going to begin this post somewhat backwards, and start with the conclusion: tidy eval is important to anyone who writes R functions and uses dplyr and/or tidyr.
I’m going to load a couple of packages, and then show you exactly why.
library(dplyr)
library(rlang)
Data wrangling with base R
Here’s an example function I have written in base R. Its purpose is to take a data set, and extract values from a single column that match a specific value, with both input and output both being in data frame format.
wrangle_data %
head()
## Species
## 51 versicolor
## 52 versicolor
## 53 versicolor
## 54 versicolor
## 55 versicolor
## 56 versicolor
It works, but it’s not great; the code is clunky and hard to decipher at a quick glance. This is where using dplyr can help.
Data wrangling with dplyr
If I was to run the same code outside of the context of a function, I might do something like this:
one_col %
head()
## Species
## 1 versicolor
## 2 versicolor
## 3 versicolor
## 4 versicolor
## 5 versicolor
## 6 versicolor
This has worked, but how can we turn this into a function?
I may naively attempt the solution below
wrangle_data
If you still follow my Twitter feed – I pity you, as it’s been rather boring of late. Consisting largely of Github commit messages, many including the words “knit to github document”. Here’s why. RPubs, an early offering from RStudio, has been a great platform for easy and free publishing of HTML documents generated from … Continue reading Moving from RPubs to Github documents
At the end of March, Jonah Gabry and I released
bayesplot 1.5.0. The major additions
to the package were visualizations using ridgelines and a new plot for PIT
diagnostics from LOO validation. I don’t know what that LOO PIT thing is yet, so
I’ll ta...
Who says there's no art in mathematics? I've long admired the generative art that Thomas Lin Peterson occasionally posts (and that you can see on Instagram), and though he's a prolific R user I'm not quite sure how he makes his art. Marcus Volz has another beautiful portfolio of generative art, and has also created an R package you can use to create your own designs: the mathart package. Generative art uses mathematical equations and standard graphical rendering tools (point and lines, color and transparency) to create designs. The mathart package provides a number of R functions to create some...
In the last months, I started increasingly using Rmd documents for preparing scientific reports, blog posts, etcetera. While I really like the flexibility offered by the system, one thing that I thought could be improved is the support for easily inserting tables. So, “inspired” also by the recent addition of the excellent insert image addin in blogdown, I decided to give it a go and try to implement some kind of addin to facilitate table insertion in Rmd documents.
After struggling a bit due to my rather nonexistent shiny skills, in the end I managed to obtain a “basic but useful” (IMO) addin.
See full post on my new blogdown page here: https://lbusett.netlify.com/post/a-new-rstudio-addin-to-facilitate-inserting-tables-in-rmarkdown-documents/
This post describes the tools I currently use for working with data.
People often ask me to recommend specific tools, and I always
hesitate, because so much boils down to personal preference. I
recently added a workshop to the DSS lineup providing an ...
I’ve often said that the foundation of data science is data analysis. That’s not to say that data analysis and data science are 100% synonymous (they’re not), but rather that you need to master the basic tools of data analysis before you can get into the more advanced skills that make up data science. Said […]
The post Why you should master the small multiple chart appeared first on SHARP SIGHT.
In this post we’re going to explore the Chi Squared Goodness of Fit test using M&amp;M’s as our subject material. From there we’ll take a look at simultaneous confidence intervals a.k.a. multiple comparisons. On the R side of things we’ll make use of some old friends like ggplot2 and dplyr but we’ll also make use of two packages that were new to me scimp and ggimage. We’ll also make heavy use of the kable package to make our output tables look nicer.
In this blogpost, we will show 6 keyword extraction techniques which allow to find keywords in plain text. Keywords are frequently occuring words which occur somehow together in plain text. Common examples are New York, Monte Carlo, Mixed Models, Brussels Hoofdstedelijk Gewest, Public Transport, Central Station, p-values, ...
If you master these techniques, it will allow you to easily step away from doing simple word frequency statistics to more business-relevant text summarisation. For this, we will use the udpipe R package (docs at https://CRAN.R-project.org/package=udpipe or https://bnosac.github.io/udpipe/en) which is the core R package you need for doing this type of t ext processing.We'll basically show how to easily extract keywords as follows:
1. Find keywords by doing Parts of Speech tagging in order to identify nouns2. Find keywords based on Collocations and Co-occurrences3. Find keywords based on the Textrank algorithm4. Find keywords based on RAKE (rapid automatic keyword extraction)5. Find keywords by looking for Phrases (noun phrases / verb phrases)6. Find keywords based on results of dependency parsing (getting the subject of the text)
These techniques will allow you to move away from showing silly word graphs to more relevant graphs containing keywords.
Example
As an example we are going to use feedback in Spanish of customers going to an AirBnB appartment in Brussels. This data is part of the udpipe R package. We extract the Spanish text and annotate it using the udpipe R package. Annotation performs tokenisation, parts of speech tagging, lemmatisation and dependency parsing.
library(udpipe)library(textrank)## First step: Take the Spanish udpipe model and annotate the text. Note: this takes about 3 minutesdata(brussels_reviews)comments
The Reliability chapter of my Empirical software engineering book has been added to the draft pdf (download here). I have been working on this draft for four months and it still needs lots of work; time to move on and let it stew for a while. Part of the problem is lack of public data; […]
Need help with R, data visualization, and/or statistics? Hire me or attend my 2 day workshop! As the maintainer of the R package plotly, I’m certainly aware that ggplotly() is not perfect.1 And, even when conversion from ggplot2 to plotly ‘works’ it can leave some things to be desired. For example, it might take a […]
x[idxs + 1] or x[idxs + 1L]? That is the question.
Assume that we have a vector $x$ of $n = 100,000$ random values, e.g.
__ n x idxs y y typeof(idxs)
[1] &quot;integer&quot;
__ typeof(idxs + 1)
[1] &quot;double&quot;
__ typeof(1)
[1] &quot;double&quot;
Note also that doubles (aka “numerics” in R) take up twice the amount of memory:
__ object.size(idxs)
400040 bytes
__ object.size(idxs + 1)
800032 bytes
which is because integers are stored as 4 bytes and doubles as 8 bytes.
By using 1L instead, we can avoid this coercion from integers to doubles:
__ typeof(idxs)
[1] &quot;integer&quot;
__ typeof(idxs + 1L)
[1] &quot;integer&quot;
__ typeof(1L)
[1] &quot;integer&quot;
and we save some, otherwise wasted, memory;
__ object.size(idxs + 1L)
400040 bytes
Does it really matter for the overall performance? It should because less memory is allocated which always comes with some overhead. Possibly more importantly, by using objects that are smaller in memory, the more likely it is that elements can be found in the memory cache rather than in the RAM itself, i.e. the chance for cache hits increases. Accessing data in the cache is orders of magnitute faster than in RAM. Furthermore, we also avoid coercion/casting of doubles to integers when R adds one to each element, which may add some extra CPU overhead.
The performance gain is confirmed by running microbenchmark on the two alternatives:
__ microbenchmark::microbenchmark(
+ y
We are very pleased to announce the availability of an RStudio TensorFlow template for the Paperspace cloud desktop service.
If you don’t have local access to a modern NVIDIA GPU, your best bet is typically to run GPU intensive training jobs...
Six months ago we considered the question: Is the Government realising its ambition for SMEs on G-Cloud? This article reviews progress six months later, additionally considering the Digital Outcomes &amp; Specialists (DOS) framework.
The post Digital Marketplace. Six months later. appeared first on thinkr.
Need help with R, data viz, and/or stats? Work with me or attend my 2 day workshop! In my last post, we explored interactive visualizations of simple features (i.e., interactive maps) via ggplot2’s geom_sf() and plotly’s ggplotly(). This time we’ll make similar visualizations using plotly’s “non-ggplot2” mapping interfaces (namely plot_ly(), plot_geo(), and plot_mapbox()). Here’s a […]
R tip: first organize your tasks in terms of data, values, and desired transformation of values, not initially in terms of concrete functions or code. I know I write a lot about coding in R. But it is in the service of supporting statistics, analysis, predictive analytics, and data science. R without data is like … Continue reading R Tip: Think in Terms of Values
In my last two posts I have been writing about the task of using R to “drive” MS Excel. The first post focused on just the basic mechanics of getting my colleague what she needed. The second post picked up with some ugly inefficient code and made it better using lapply and a for loop, just good old fashioned automation (the thing that computers excel at). Today I’ll take it another step and show how to produce the same graphs in R using ggplot2 as well as how to write some simple functions to make your programming life easier.
You have to have been living under a rock to not know about Cloudflare’s new 1.1.1.1 DNS offering. I won’t go into “privacy”, “security” or “speed” concepts in this post since that’s a pretty huge topic to distill for folks given the, now, plethora of confusing (and pretty technical) options that exist to support one... Continue reading →
I am happy to introduce oneliner, a package that implements the one-line-style as a third-party style guide ready to use with styler. Hence, after the tidyverse style guide, this is the first third-party style guide for styler I am aware of - and a particularly useful one.
The fs package makes it super quick and easy to find out just how much “package hoarding” you’ve been doing: While you can modify the above and peruse the list of packages/directories in tabular format or programmatically, you can also do a bit more work to get a visual overview of package size (click/tap the... Continue reading →
Track performance over time - A Bump Chart is a special form of a line plot. This kind of plot is designed for exploring changes in rank over time. The focus here is usually on comparing the position or performance of multiple observation...
An arithmetic Le Monde mathematical puzzle: A magical integer m is such that the remainder of the division of any prime number p by m is either a prime number or 1. What is the unique magical integer between 25 and 100? And is there any less than 25? The question is dead easy to […]
Caution - this blog post discusses homicide and suicide statistics and could be upsetting. If you feel disturbed or unhappy then here is a list of ways to get some emergency counselling help around the world.
A grim research question
In my last post...
Recently, while working on the Azure Data Lake R extension, I had to figure out a good way to create a zip file containing a package together with all its dependencies. This came down to understanding where does R store and search for packages. Despite the documentation, it did require additional reading and experimentation. First, […]
The post Package Paths in R appeared first on Quintuitive.
 It’s the ten crack commandments, what? homie can’t tell me nothing about this code Can’t tell me nothing about these #rstats Number 1, make a function from a script. Everyone knows we’re to busy to be copy/pasting shit http://adv-r.had.co.nz/Functions.html Number 2, never let ’em know your data manipulation moves. Don’t you know Bad Boys move in silence and […]
Discover who is coming to eRum 2018 in Budapest while playing with an interactive data visualization in D3.js. Vist visualization’s page.
You can meet me (Olga) at the conference where I’ll be speaking about cool and open source packages ...
Since its inception over 40 years ago, when S (R's predecessor) was just a sketch on John Chambers' wall at Bell Labs, R has always been a language for providing interfaces. I was reminded of this during Dirk Eddelbuettel's presentation at the Chicago R User Group meetup last night, where he enumerated Chambers' three principles behind its design (from his 2016 book, Extending R): Object: Everything that exists in R is an object Function: Everything that happens in R is a function call Interface: Interfaces to other software are a part of R The third principle &quot;Interface&quot; is demonstrated by...
Remember my blog post about automatic tools for improving R packages? One of these tools is Jim Hester’s lintr, a package that performs static code analysis. In my experience it mostly helps identifying too long code lines and missing space, although...
Introduction
I have received a few queries recently that can be categorized as “How do I collapse a list of categories or values into a shorter list of category or values?” For example, one user wanted to collapse species of fish into ...
Opening day is on the way Time to set up a persistent database to collect every pitch thrown in this year’s baseball season.
The mlbgameday package is designed to facilitate extract, transform and load for MLBAM “Gameday” data. The package is op...
Excited to see our new Hangul/Korean edition of “Practical Data Science with R” by Nina Zumel, John Mount, translated by Daekyoung Lim. Thank you for producing a handsome edition, Manning and JPub.kr!
Twitter's bot problem is well documented, influencing discourse on divisive topics like politics and civil rights. But it's getting harder and harder to spot such nefarious bots, who often borrow biographies and tweets from real (and often stolen) profiles to evade detection. (The New York Times recently published an outstanding feature on bots and follower factories.) Can we distinguish bots from real users using data science? Journalism professor Mike Kearney has developed an R package and Shiny web application that analyzes the tweets of a given user to calculate the probability that user is a bot. For example, it correctly...
In this post, I come back to my first experience teaching an advanced R course over the past month.
Content
This course was programmed for 10 sessions (3 hours each) and I initially wanted to talk about the following subjects:
R programming and g...
We’ve released the newest version of NIMBLE on CRAN and on our website. Version 0.6-10 primarily contains updates to the NIMBLE internals that may speed up building and compilation of models and algorithms, as well as a few bug fixes. Changes include: some steps of model and algorithm building and compilation are faster; compiled execution […]
After a lot of slacking around, I finally got to finishing the upgraded version of the inegiR package on CRAN. This version combines quite a few changes that I will explain further in this post.
New language
The biggest change upfront is the migration to english in both function names and documentation. The rationale behind this is to make it more accessible to developers around the world (I have recieved a few emails asking for translations). Also, the non-ASCII characters were not helpful. For the Mexican users, I assume that if you know R, you can probably find yourself around an english document.
To avoid crashing workflows, I left the legacy functions intact except for a warning to use the english version instead. An example of this is the commercial growth rate functions, which are:
# english
rate_commerce()
# spanish (old version)
tasa_comercio()Route API
With some help from Arturo Cárdenas and a revamp of the Sákbe API in INEGI, I was able to add functions to access route information.
The two main ones are:
# to search for a destiny id
inegi_destiny()
# to get route information
inegi_route()The first thing to understand is that INEGI has categorized sites in Mexico according to a “destiny id”. For example, the International Airport in Mexico city is destiny id #57. The inegi_destiny() function will help you find a destiny id based on a text criteria, sort of like googling the place and getting an address. Here is an example with a plaza in Monterrey:
# download on CRAN or newest dev version (if not accepted yet)
# install.packages(&quot;inegiR&quot;)
# or... # devtools::install_github(&quot;eflores89/inegiR&quot;)
library(inegiR)
library(knitr)
# to search for Macroplaza destiny id
token
(R⁶ == brief, low-expository posts) @yoniceedee suggested I look at the Cambridge Analytics “whistleblower” testimony proceedings: @hrbrmstr giving the term &quot;improving r&amp;d&quot; a whole new meaning … https://t.co/f1KA8U3htT — yoni sidi (@yoniceedee) March 29, 2018 I value the resources @yoniceedee tosses my way (they often end me down twisted paths like this one, though :-)... Continue reading →
You’ve heard me say it a thousand times: to master data science, you need to practice. You need to “practice small” by practicing individual techniques and functions. But you also need to “practice big” by working on larger projects. To get some practice, my recommendation is to find reasonably sized datasets online and plot them. […]
The post How to map public debt data with ggplot2 appeared first on SHARP SIGHT.
Hello everyone, welcome back! This post marks the beginning, hopefully, of your foray into the wonderful world of R and RStudio…and my delve into the odd vlog to go with the blog! I’ve brushed my hair for you, and I don’t do that for just anyone, so you’d better watch it at least once! So without further ado.
R R is an open source language released in 2001 that’s ideal for data wrangling and data science.
A Simple Method for Purging Mediation Effects among Independent Variables Mediation can occur when one independent variable swamps the effect of another, suggesting high correlation between the two variables. Though there are some great packages for mediation analysis out there, the simple intuition of its need is often ambiguous, especially for younger graduate students. Thus, … Continue reading Introducing purging: An R package for addressing mediation effects
In yesterday’s post I focused on the task of using R to “drive” MS Excel. I deliberately ended the post with a fully functioning (pun intended) but very ugly set of code. Why “ugly”? Well, because the last set of code wound up repeating 4 lines of code 12 times!
Beth Ashlee, Data Scientist
After the success of rstudio::conf 2017 this year the conference was back and bigger and better than ever with 1000+ attendees in sunny San D...
I’ll be talking about Deep Learning with Keras in R and Python at the following upcoming meetup:
Ruhr.Py 2018 on Wednesday, April 4th
Introducing Deep Learning with Keras and Python Keras is a high-level API written in Python for building and prot...
Here is an R tip. Want to re-map a column of values? Use a named vector as the mapping. Example: library(&quot;dplyr&quot;) library(&quot;wrapr&quot;) head(starwars[, qc(name, gender)]) # # A tibble: 6 x 2 # name gender # # 1 Luke Skywalker male # 2 C-3PO NA # 3 R2-D2 NA # 4 Darth Vader … Continue reading R Tip: Use Named Vectors to Re-Map Values
Newly published postcode open data!
Time is running out for you to send in your abstract submissions for this year’s EARL Conferences. If you have an R story you’d like to share then make sure you send...
In our next MünsteR R-user group meetup on Tuesday, April 17th, 2018 Kai Lichtenberg will talk about deep learning with Keras. You can RSVP here: http://meetu.ps/e/DDY1B/w54bW/f
Although neural networks have been around for quite a while now, dee...
Cats are great. Perhaps Hadley Wickham and Lionel Henry think so too given the wonderful choice of name for their purrr package. Hadley Wickham has also created a superb package called forcats, likely  an abbreviation of “for categoricals” but wittingly cat-themed, which is very, very useful to the data scientist. In the data science profession,… Continue reading Cats are great and so is the forcats R package →
This article shows you how to enter data so that you can easily open in statistics packages such as R, SAS, SPSS, or jamovi (code or GUI steps below). Excel has some statistical analysis capabilities, but they often provide incorrect answers. For … Continue reading →
Discriminant analysis is used when the variable to be predicted is categorical in nature. This analysis requires that the way to define data points to the respective categories is known which makes it different from cluster analysis where the classification criteria is not know. It works by calculating a score based on all the predictor … Continue reading Discriminant Analysis: Statistics All The Way
I have until recently made it a habit to draw a clear distinction about using R for data analysis and Microsoft Excel for other office productivity tasks. I know there are people who use Excel to process data and even (gasp) to teach statistics with it. But I’m a bit snobbish that way and to date all my efforts have been in getting data out of Excel and into R, either by simple methods like read.csv or if the task was more meaty by using Hadley Wickham’s marvelous readxl package.
The Azure Computer Vision API can extract all sorts of interesting information from images — tags describing objects found in the images, locations of detected faces, and more — but today I want to play around with just one: caption generation. I was inspired by @picdescbot on Twitter, which selects random images from Wikimedia Commons and generates a caption using the API. The results are sometimes impressive, sometimes funny, and sometimes bizarre. On the bizarre side, recurring motifs include &quot;lush green field&quot;, and &quot;a clock tower on a building&quot;, but the bot doesn't discriminate when the confidence score returned by...
When checking the Python t distribution random generator, np.random.standard_t(), I came upon this manual page, which actually does not explain how the random generator works but spends instead the whole page to recall Gosset’t test, illustrating its use on an energy intake of 11 women, but ending up misleading the readers by interpreting a .009 […]
This article explains how to call or run python from R. Both the tools have its own advantages and disadvantages. It's always a good idea to use the best packages and functions from both the tools and combine it. In data science world, these tools have a good market share in terms of usage. R is mainly known for data analysis, statistical modeling and visualization. While python is popular for deep learning and natural language processing.In recent KDnuggets Analytics software survey poll, Python and R were ranked top 2 tools for data science and machine learning. If you really want to boost your career in data science world, these are the languages you need to focus on.
Combine Python and R
RStudio developed a package called reticulate which provides a medium to run Python packages and functions from R.Install and Load Reticulate PackageRun the command below to get this package installed and imported to your system.# Install reticulate packageinstall.packages(&quot;reticulate&quot;)# Load reticulate packagelibrary(reticulate)
Check whether Python is available on your system
py_available()
It returns TRUE/FALSE. If it is TRUE, it means python is installed on your system.Import a python module within RYou can use the function import( ) to import a particular package or module.os
The abstract submission deadline for the Insurance Data Science conference at Cass Business School on 16 July 2018 is closing soon. You have until the 9th of April to submit your abstract.
Please send your abstract to info@insurancedatascience.org.
...
To post your R job on the next post Just visit  this link and post a new R job  to the R community. You can post a job for  free  (and there are also “featured job” options available for extra exposure). Current R jobs Job seekers:  please follow the links below to learn more and apply for your R job of interest: Featured Jobs Full-Time Director, Data Labs Pew Research Center – Posted by oneclickorders@jobtarget.com Washington District of Columbia, United States 12 Mar 2018 Full-Time Customer Success Rep RStudio Inc. – Posted by jclemens1 Anywhere 26 Feb 2018 More New R Jobs Full-Time Post-Doctoral Fellow – Data Scientist @ CIMMYT (eastern Africa) International Maize and Wheat Improvement Center – Posted by cimmyt-jobs Marsabit County Kenya 27 Mar 2018 Freelance Statistician / R Developer – for Academic Statistical Research Academic Research – Posted by empiricus Anywhere 27 Mar 2018 Full-Time Graduate Data Scientist JamieAi – Posted by JamieAi Anywhere 27 Mar 2018 Freelance Backtesting stock-trading strategy termod Anywhere 14 Mar 2018 Full-Time Director, Data Labs Pew Research Center – Posted by oneclickorders@jobtarget.com Washington District of Columbia, United States 12 Mar 2018 Freelance R Report Builder DDI – Posted by survey.support@ddiworld.com Anywhere 10 Mar 2018 Freelance Statistician/Econometrician – R Programmer for Academic Statistical Research (for 2-3 weeks) Academic […]
Another way of analysis? When there are so many tools and techniques of prediction modelling, why do we have another field known as survival analysis? As one of the most popular branch of statistics, Survival analysis is a way of prediction at various points in time. This is to say, while other prediction models make … Continue reading Steps to Perform Survival Analysis in R
Another R tip. Need to replace a name in some R code or make R code re-usable? Use wrapr::let(). Here is an example involving dplyr. Let’s look at some example data: library(&quot;dplyr&quot;) library(&quot;wrapr&quot;) starwars %__% select(., name, homeworld, species) %__% head(.) # # A tibble: 6 x 3 # name homeworld species # … Continue reading R Tip: Use let() to Re-Map Names
Quantide has been a sponsor for MilanoR for a long time. Let's have a look at their collaborations and Milano's amazing contributions and events of 2017.
The post Quantide and MilanoR: a year-long experience appeared first on Quantide - R training &amp; consulting.
https://twitter.com/PennCausal/status/977329657610584064 I wanted to share the news that registration is open for this summer institute (see tweet above for link to registration/schedule). Along with lectures on theory/methods, we will be having interactive R sessions. Attendees will bring their laptops and we'll go through live R examples of things like Estimating treatment effects (matching, propensity score estimation, … Continue reading Registration Open: Causal Inference and Big Data Summer Institute at UPenn →
I’m pleased to announce an update of my sjstats-package. New features are specifically implemented for the Anova and Bayesian statistic and summary functions. Here’s a short overview of what’s new… Anova statistics Beside the already implemented functions to calculate eta-squared, partial eta-squared and omega-squared, it is now also possible to calculate partial omega-squared statistics for … Weiterlesen Anova-Freak and Bayesian Hipster #rstats →
Third post from the short series about black-box explainers implemented in the DALEX package. Learn more about DALEX at SER (Warsaw, April 2018), eRum (Budapest, May 2018), WhyR (Wroclaw, June 2018) or UseR (Brisbane, July 2018). Two weeks ago I wrote about single variable conditional responses and last week I wrote about decompositions of a … Czytaj dalej DALEX: which variables are really important? Ask your black box model!
The M4 forecasting competition is well under-way, and a few of my PhD students have been working on submissions.
Pablo Montero-Manso, Carla Netto, and Thiyanga Talagala have made an R package containing all of the data (100,000 time series), which shou...
There are only a few well-known signals which I consider reliable. One of them is the Dow Theory. According to it, or at least to some interpretations of it, the bull market cycle almost ended this Friday. This time I am adding something new. I recorded myself while doing some of the analysis: Different people […]
The post The Bull Survived on Friday, but Barely appeared first on Quintuitive.
My second Shiny app is dedicated to data visualization.Here users can simply upload any csv or txt file and create several plots:Histograms (with option for faceting)
Barchart (with error bars, and option for color with dodging and faceting)
BoxPlots (with option for faceting)
Scatterplots (with options for color, size and faceting)
TimeSeries
Error bars in barcharts are computed with the mean_se function in ggplot2, which computes error bars as mean ± standard error. When the color option is set, barcharts are plotted one next to the other for each color (option dodging).
For scatterplots, if the option for faceting is provided each plot will include a linear regression lines.
Some examples are below:
For the time being there is no option for saving plots, apart from saving the image from the screen. However, I would like to implement an option to have plots in tiff at 300dpi, but all the code I tried so far did not work. I will keep trying.
The app can be accessed here: https://fveronesi.shinyapps.io/DataViz/
The R code is available here: https://github.com/fveronesi/Shiny_DataViz
One week ago I was in Cape Town for the local satRday conference, where I had the honor to be one of the two keynote speakers, the other one being sports analytics extraordinaire Stephanie Kovalchik (You can read Stephanie Kovalchik’s account of the conference in this blog post). It was a fantastic experience! The event was very well organized, and 100% corresponds to its description as a “one day conference packed with R goodness”. You can watch all talks on Youtube. In my talk, I presented rOpenSci onboarding system of packages and… wore a hard hat!
It’d be a bit hard for me to really write a good recap of satRday that’d do it justice! Instead, I’ll use rtweet and a bit of html hacking to storrrify it (like Storify, but in R) using my live tweets!
Getting my satRday tweets
To get my own satRday Cape Town tweets, I’ll first get my timeline and then filter tweets containing the official conference hashtag, “#SatRdayCapeTown”. I shall only keep my tweets.
my_tweets
It’s Happening, Folks!Whew! Less than a month left and I still haven’t publicised this on my blog. People, we are hosting the 1st Computational Social Science Hackathon at the London School of Economics and you are invited!It’s completely free an...
A quick Friday post to let folks know about three in-development R packages that you’re encouraged to poke the tyres o[fn] and also jump in and file issues or PRs for. Alleviating aversion to versions I introduced a “version chart” in a recent post and one key element of tagging years (which are really helpful... Continue reading →
Should I learn sf or sp for spatial R programming?
I recently received an email about some of the short-courses on my webpage. The correspondant was asked whether my course on Maps and GIS in R was still relevant, it being written in 2014.
The R env...
It’s clear that there are some economic shifts happening in the world, if not the US itself. In light of this, I decided to do some simple investigation into the economic performance of US cities. This is, by the way, one of the critical reasons to master data science. One you know a few critical […]
The post Plotting US Metro Area GDP with ggplot appeared first on SHARP SIGHT.
Introduction a. A robot may not injure a human being or, through inaction, allow a human being to come to harm. b. A robot must obey orders given it by human beings except where such orders would conflict with the First Law. c. A robot must protect its own existence as long as such protection … Continue reading Deep Learning from first principles in Python, R and Octave – Part 5
Another minor maintenance release of the RcppCNPy package arrived on CRAN this evening.
RcppCNPy provides R with read and write access to NumPy files thanks to the cnpy library by Carl Rogers.
There is only small code change: a path is now checked be...
I came across this excellent article lately “Machine learning at central banks” which I decided to use as a basis for a new cheat sheet called Machine Learning Modelling in R. The cheat sheet can be downloaded from RStudio cheat sheets repository. As the R ecosystem is now far too rich to present all available packages and […]
During a discussion with some other members of the R Consortium, the question came up: who maintains the most packages on CRAN? DataCamp maintains a list of most active maintainers by downloads, but in this case we were interested in the total number of packages by maintainer. Fortunately, this is pretty easy to figure thanks to the CRAN repository tools now included in R, and a little dplyr (see the code below) gives the answer quickly[*]. And the answer? The most prolific maintainer is Scott Chamberlain from ROpenSci, who is currently the maintainer of 77 packages. Here's a list of...
One of the basic things the students are taught in statistics classes is that the comparison of models using information criteria can only be done when the models have the same response variable. This means, for example, that when you have \(\log(y_t)\) and calculate AIC, then this value is not comparable with AIC from a […]
While in Brussels last week I noticed an interesting question on X validated that I considered in the train back home and then more over the weekend. This is a question about spacings, namely how long on average does it take to cover an interval of length L when drawing unit intervals at random (with […]
There are a number of easy ways to avoid illegible code nesting problems in R. In this R tip we will expand upon the above statement with a simple example. At some point it becomes illegible and undesirable to compose operations by nesting them, such as in the following code. head(mtcars[with(mtcars, cyl == 8), c(&quot;mpg&quot;, … Continue reading R Tip: Break up Function Nesting for Legibility
Last week we updated the cronR R package and released it to CRAN allowing you to schedule any R code on whichever timepoint you like. The package was updated in order to comply to more stricter CRAN policies regarding writing to folders. Along the lines, the RStudio add-in of the package was also updated. It now looks as shown below and is tailored to Data Scientists that want to automate basic R scripts.
 
The cronR (https://github.com/bnosac/cronR) and taskscheduleR (https://github.com/bnosac/taskscheduleR) R packages are distributed on CRAN and provide the basic functionalities to schedule R code at your specific timepoints of interest. The taskscheduleR R package is designed to schedule processes on Windows, the cronR R package allows to schedule your jobs on Linux or Mac. Hope you enjoy the packages.
If you need support in automating and integrating more complex R flows in your current architecture, feel free to get into contact here.
In this post I have a few goals: 1. Become (re-)familiar with available geoms 2. Become (re-)familiar with aesthetic mappings in geoms (stroke who knew?) 3. Answer these questions: How often do various geoms appear and how often do they … Continue reading →
My R packge powerlmm 0.2.0 is now out on CRAN. It can be installed from CRAN https://cran.r-project.org/package=powerlmm or GitHub https://github.com/rpsychologist/powerlmm.
Changes in version 0.2.0
New features
Analytical power calculations now suppo...
If you regularly have to deal with specific versions of R, or different package combinations, or getting R set up to work with other databases or applications then, well, it can be a pain. You could dedicate a special machine for each configuration you need, I guess, but that's expensive and impractical. You could set up virtual machines in the cloud which works well for one-off situations, but gets tedious having to re-configure a new VM each time. Or, you could use Docker containers, which were expressly designed to make it quick easy to configure and launch an independent and...
We’ll be presenting a webinar on NIMBLE, hosted by the Eastern North America Region of the International Biometric Society. Details are as follows. Programming with hierarchical statistical models: An introduction to the BUGS-compatible NIMBLE system for MCMC and more Friday, April 13, 2018 11:00 a.m. – 1:00 p.m. EST Must register before April 12. You […]
Last week I did an analysis of gold diggers at the Olympics. Here I take you along how I scraped the data from wikepedia and subsequently used the tools of the tidyverse to get the data in a format in which they can be analyzed. You are invited to copy-paste the code and see how the data is gradually getting into a shape in which we can analyze it. I will often not show the data in the intermediate steps, because this will clog the blog. Copy the code to find out for yourself.
This post introduces a new R package I am working on called tabr for creating guitar tablature (“tabs”) from R code. The tabr package provides programmatic music notation and a wrapper around LilyPond for creating quality guitar tablature. [Click here for original post] (in process of switching where I post to R-Bloggers from) tabr offers […]
I am happy to say that the latest issue of The R Journal includes a paper
describing rentrez,
the rOpenSci package for retrieving data from the National Center for Biotechnology Information
(NCBI).
The NCBI is one of the most important sources of biological data. The centre
provides access to information on 28 million scholarly articles through PubMed and 250
million DNA sequences through GenBank. More importantly, records in the 50 public
databases maintained by the NCBI are strongly cross-referenced. As a result, it is
possible to pinpoint searches using almost 2 million taxonomic names or a
controlled vocabulary with 270,000 terms.
rentrez has been designed to make it easy to search for and download NCBI
records and download them from within an R session.
The paper and the package vignette
both describe typical usages of rentrez. I though it might be fun to use this
post to find out where papers describing R packages are published these days.
Although PubMed only covers journals in the biological sciences, searching that
database will at least give us an idea of which journals like to publish these
sorts of papers. Here we use the entrez_search and entrez_summary functions
to get some information on all of the papers published in 2017 with the term
‘R package’ in their title:
library(rentrez)
pkg_search
Data that’s been only partially observed
I’ve been updating my skills in fitting models to truncated data and was pleased to find that, like so much else in statistics, it’s easier than it used to be.
First, some definitions:
censored data ...
Kids today are so sophisticated (at least they are in New York City, where I live). While I didn’t hear about the chi-square test of independence until my first stint in graduate school, they’re already talking about it in high school. When my kids came home and started talking about it, I did what I usually do when they come home asking about a new statistical concept. I opened up R and started generating some data. Of course, they rolled their eyes, but when the evening was done, I had something that might illuminate some of what underlies the theory of this ubiquitous test.
Actually, I created enough simulations to justify two posts - so this is just part 1, focusing on the \(\chi^2\) distribution and its relationship to the Poisson distribution. Part 2 will consider contingency tables, where we are often interested in understanding the nature of the relationship between two categorical variables. More on that the next time.
The chi-square distribution
The chi-square (or \(\chi^2\)) distribution can be described in many ways (for example as a special case of the Gamma distribution), but it is most intuitively characterized in relation to the standard normal distribution, \(N(0,1)\). The \(\chi^2_k\) distribution has a single parameter \(k\) which represents the degrees of freedom. If \(U\) is standard normal, (i.e \(U \sim N(0,1)\)), then \(U^2\) has a \(\chi^2_1\) distribution. If \(V\) is also standard normal, then \((U^2 + V^2) \sim \chi^2_2\). That is, if we add two squared standard normal random variables, the distribution of the sum is chi-squared with 2 degrees of freedom. More generally, \[\sum_{j=1}^k X^2_j \sim \chi^2_k,\]
where each \(X_j \sim N(0,1)\).
The following code defines a data set with two standard normal random variables and their sum:
library(simstudy)
def
This xts release contains mostly bugfixes, but there are a few noteworthy features. Some of these features were added in version 0.10-1, but I forgot to blog about it. Anyway, in no particular order:
endpoints() gained sub-second accuracy on Windows (#202)!
na.locf.xts() now honors 'x' and 'xout' arguments by dispatching to the next method (#215). Thanks to Morten Grum for the report.
na.locf.xts() and na.omit.xts() now support character xts objects. Thanks to Ken Williams and Samo Pahor for the reports (#42).
Many of the bug fixes were related to the new plot.xts() introduced in 0.10-0. And a handful of bugfixes were to make xts more consistent with zoo in some edge cases.As always, I'm looking forward to your questions and feedback!  If you have a question, please ask on Stack Overflow and use the [r] and [xts] tags.  Or you can send an email to the R-SIG-Finance mailing list (you must subscribe to post).  Open an issue on GitHub if you find a bug or want to request a feature, but please read the contributing guide first!
A new release, now at 0.2.1, of the RcppSMC package arrived on CRAN earlier this afternoon (and once again as a very quick pretest-publish within minutes of submission).
RcppSMC provides Rcpp-based bindings to R for the Sequential Monte Carlo Templat...
R tip: use stringsAsFactors = FALSE. R often uses a concept of factors to re-encode strings. This can be too early and too aggressive. Sometimes a string is just a string. Sigmund Freud, it is often claimed, said: “Sometimes a cigar is just a cigar.” To avoid problems delay re-encoding of strings by using stringsAsFactors … Continue reading R Tip: Use stringsAsFactors = FALSE
Per a CRAN email sent to 300+ maintainers, this package (just like many others) was asked to please register its S3 method. So we did, and also overhauled a few other packagaging standards which changed since the last upload in 2014.
No NEWS.Rd file ...
Previous posts in this series on MCMC samplers for Bayesian inference (in order of publication): Bayesian Simple Linear Regression with Gibbs Sampling in R Blocked Gibbs Sampling in R for Bayesian Multiple Linear Regression Metropolis-in-Gibbs Sampling and Runtime Analysis with Profviz The code for all of these posts can be found in my BayesianTutorials GitHub … Continue reading Speeding up Metropolis-Hastings with Rcpp →
Per a CRAN email sent to 300+ maintainers, this package (just like many others) was asked to please register its S3 method. So we did, and also overhauled a few other packagaging standards which changed since the previous uploads in December of 2012 ...
Take care if trying the new RPostgres database connection package. By default it returns some non-standard types that code developed against other database drivers may not expect, and may not be ready to defend against. Danger, Will Robinson! Trying the new package One can try the newer RPostgres as a drop-in replacement for the usual … Continue reading Take Care If Trying the RPostgres Package
Some advices from a referee - Back in 2007 I wrote a Matlab package for estimating regime switching models. I was just starting to learn to code and this project was my way of doing it. After publishing it in FEX (Matlab file exch...
This will be my last post for a while (I promise!).  After today I’ll be taking a rest from all this, until at least the start of April.  Hopefully all this USS stuff will be resolved by then, though! In yesterday’s post I showed a graph, followed by some comments to suggest that future USS […]
R 3.4.4 has been released, and binaries for Windows, Mac, Linux and now available for download on CRAN. This update (codenamed &quot;Someone to Lean On&quot; — likely a Peanuts reference, though I couldn't find which one with a quick search) is a minor bugfix release, and shouldn't cause any compatibility issues with scripts or packages written for prior versions of R in the 3.4.x series. This update improves automatic timezone detection on some systems, and adds fixes for a some unusual corner cases in the statistics library. For a complete list of the changes, check the NEWS file for R...
In response to my previous post, “Latest USS proposal: Who would lose most?“, someone asked me about doing the same calculation for the USS JNC-supported proposals from January.  For a summary of those January proposals and my comments about their fairness, please see my earlier post “USS pension scheme and fairness“. Anyway, the calculation is […]
Simulation studies are used in a wide range of areas from risk management, to epidemiology, and of course in statistics. The MonteCarlo package provides tools to automatize the design of these kind of simulation studies in R. The user only has to specify the random experiment he or she wants to conduct and to specify … Continue reading Visualizing MonteCarlo Simulation Results: Mean vs Median
In this post you will learn: How to create expressions that have mixed (1) strings, (2) expressions, &amp; (3) numbers How to pass in values as variables to an expression I wanted to name this post “Ahhhhhhhhhhh #$@%&amp;!!!!” but SEO isn’t … Continue reading →
PubMed Commons, the NCBI’s experiment in comments for PubMed articles, has been discontinued. Thoroughly too, with all traces of it expunged from the NCBI website. Last time I wrote about the service, I concluded “all it needs now is more active users, more comments per user and a real API.” None of those things happened. … Continue reading Farewell then, PubMed Commons
   
   Last March 8, millions of women in more than 170 countries around the world joined street protests calling for &quot;a society free of sexist oppression, exploitation and violence&quot;. Spanish strike was one of the most numerous, where around 5.3 million women joined the strike. Today, I will use some data (download) regarding the scientific career in Spain (2016) to make a plot using ggplot2 and discuss about it. Here you have the graph and the code:
library(ggplot2) library(scales) dat
Some days I see R as an eclectic programming language preferred by scientists. “Programming languages as people.” From Leftover Salad (David Marino). Other days I see it more like the following. “Statistical tools as cars.” Shared previously by Darren L. Dhaly and brought to my attention by post by Dimitri Liakhovitski, not sure of original … Continue reading The Many Faces of R
In case you missed them, here are some articles from February of particular interest to R users. The R Consortium opens a new round of grant applications for R-related user groups and projects, and has issued US$0.5M in grants to date for R-related projects and events. Microsoft R Client 3.4.3 and Microsoft ML Server 9.3, both built with R 3.4.3, have been released. An 8-step, 5-minute tutorial for setting up a cluster in Azure for use with the sparklyr package. A smartphone app uses R and the keras package to identify &quot;spells&quot; using accelerometer data. &quot;Machine Learning with R and...
“Darwin Core (DwC) is a standard maintained by the Darwin Core maintenance group. It includes a glossary of terms (in other contexts these might be called properties, elements, fields, columns, attributes, or concepts) intended to facilitate the sharing of information about biological diversity by providing identifiers, labels, and definitions. Darwin Core is primarily based on […]
With pretty regular frequency I get emails asking if RSiteCatalyst can be used with Microsoft Power BI. While admittedly I’m not a frequent user of the Windows operating system (nor dashboarding tools like Tableau or Power BI), I am pleased to report...
The problem
I am in the process of migrating my (rather ugly) small blog from “Bloggers” to `blogdown` and, as several others, I choose to use the hugo-academic theme due to its good looks, simplicity, and “focus” towards researchers.
One nice feature of hugo-academic is that it includes out-of-the-box a “Publications” section, allowing researchers to easily create a list of their publication as a section of the website.
Unfortunately, in order to populate that list, users have to manually create one different .md file for each publication, by cutting and pasting several different info (e.g., title, authors, etc.) in a &quot;simple&quot; template like this one.
THIS IS BORING!
Since I was not in the mood of doing that, and no automatic solutions could be found (well, there appears to be a python one, but we are speaking R, here…), I decided to try and develop some script to automatically create the required md files starting from a BibTex list of my publications. Here are the results of that effort.
A possible solution
Preparing the BibTex file
To automatically create the publications md files, all you need is a (properly formatted) BibTex file. Since I did not have one ready, I created mine by exporting my publications list from Scopus, but you could use any valid BibTeX file.
One important thing, though, is that you have to be sure that the file is saved with UTF-8 encoding. If you are not sure, you can open it in RStudio (or any decent text editor), and then re-save it with a forced encoding (in RStudio, you can use File--__Save with Encoding)
Creating an import script
Now, you need a script that reads the BibTex entries and use the data to populate one different mdfile for each publication. Below you can find my attempt at this.
bibtex_2academic
The sixteenth update the 0.12.* series of Rcpp landed on CRAN earlier this evening after a few days of gestation in incoming/ at CRAN.
Once again, this release follows the 0.12.0 release from July 2016, the 0.12.1 release in September 2016, the 0.12....
Being a self learner and explorative by nature, I love to try out new technology and keep learning something all the time. Especially, working in a Biotech organization as a data scientist, and being the only one in the region, &quot;technical&quot; talks and discussions are very limited from the programming world perspective. So I spend my evenings experimenting with technology and reading tech blogs, websites and papers.One visualization tool I am in love with is Tableau. I find it very cool to be able to quickly build a dashboard of graphs, with all the interactivity, in few minutes. Of course data needs to be &quot;prepared&quot; first like any other data science.I installed the Tableau app from Tableau public website. Now the data, there is a plethora of data for budding data scientists to play with. I got this data set from https://data.gov.in/ which provides data to the public as a part of the open data initiative from the Govt.I wanted to see how much the various minerals were produced and what were their values were. Also see the states on the map of India and in parallel visualize the numbers. Another interesting bit is to know what type of mineral each is - fuel, metallic or non metallic. After a few iterations, this is how my dashboard finally looks.
R project has been participating in Google Summer of Code (GSoC) since 2008. We have numerous packages which have received code contributions from GSoC participants. This year too, R is participating and there is already an impressive list of potential projects listed at [https://github.com/rstats-gsoc/gsoc2018/wiki/table-of-proposed-coding-projects] and the project mentors are seeking students to work on these […]
Many companies are still largely managed with spreadsheets. While working with data we have seen the full spectrum of use cases of Excel, including some truly scary ones. There is, however, a clear trend of companies switching to alternatives af...
In the previous blog post, I showed you usage of my TSrepr package. There was shown what kind of time series representations are implemented and what are they good for.
In this tutorial, I will show you one use case how to use time series representati...
In the latest Redmonk language rankings, R has risen to the #12 position, up from #14 in the June 2017 rankings. (Python remains steady in the #3 position.) The Redmonk rankings are based on activity in StackOverflow (as a proxy for user engagement) and Github (as a proxy for developer engagement). Here's the chart from January 2018 of Github popularity ranking versus StackOverflow popularity ranking. Here's what Redmonk analyst Stephen O'Grady had to say about Powershell, R and Typescript. (R isn't a Microsoft property, but Microsoft is a founding member of the R Consortium and incorporates R into several products...
Yesterday the UUK/UCU negotiations at ACAS concluded with an agreement document. In this post I’ll look at the numbers in those proposed interim changes to the Universities Superannuation Scheme, to work out how much money would effectively be lost by USS members at each salary level. This is inevitably a fairly rough calculation, but its […]
(No statistical graphs in this one. This is what my dog Artemis looks like when she wants my attention during work hours.)
Mindy L. Mallory (@ace_prof) wrote a blog post on Machine...
Often overlooked, testing is a critical process that saves time over the long term and enables building complex systems. Unit …Continue reading →
The R-Lab#6 is ready to go, and on the occasion, we're also discussing the outcomes and contents of the precedent R-Lab, about personalized medicine.
The post R-Lab#5: the report &amp; R-Lab#6 ready to go! appeared first on MilanoR.
(FWIW I think I even caused myself pain due to the title of this blog post). Kaiser Fung (@junkcharts) did a makeover post on this chart about U.S. steel tariffs: Kaiser’s makeover is good (Note: just because I said “good” does not mean I’m endorsing the use of pie charts): But, I’m curious as to... Continue reading →
Not all data analysis tools are created equal. Recently, I started looking into data sets to compete in Go Code Colorado (check it out if you live in CO). The problem with such diversity in data sets is finding a way to quickly visualize the data and do exploratory analysis. While tools like Tableau make data […]
In the digital age of today, data comes in many forms. Many of the more common file types like CSV, XLSX, and plain text (TXT) are easy to access and manage. Yet, sometimes, the data we need is locked away in a file format that is less accessible such ...
Prologue In 2013, I had worked on developing a Music Recommendation System as a pilot project, which had not even completed. There were lots of reasons, but the critical one was the choice and concentration strategy of my division. Since then, I keep these passion in my deep mind for a long time, but could […]
tldr: I created an open dataset for the explicit practice of data munging.
Feel free to use it in assignments,
but do mention where you got it from (CC-by-4.0). Also unicorns are awesome.
Find the dataset at: https://github.com/RMHogervorst/unico...
It came to a close two weeks ago. Over two weeks of obsessive watching everything that moves on skis, skates or boards. My tiny country of the Netherlands won about half of all the medals at the speed skating events. Since there are many medals to be won here this made it to end up high in the medal table. This makes it look as winter sports nation, where all we skating buffs. (Sadly, we can’t hardly skate anymore last years. Stop global warming everyone, the Dutch need their ice!) To me, this begged the question, are other top nations also in the high ranks because they dominate a single sport. Or are they all round snow and ice eating badasses?
Apriétame bien la mano, que un lucero se me escapa entre los dedos (Coda Flamenca, Extremoduro) I have the privilege of being teacher at ESTALMAT, a project run by Spanish Royal Academy of Sciences that tries to detect, guide and stimulate in a continuous way, along two courses, the exceptional mathematical talent of students of … Continue reading Mandalas Colored →
If you are working with predictive modeling or machine learning in R this is the R tip that is going to save you the most time and deliver the biggest improvement in your results. R Tip: Use the vtreat package for data preparation in predictive analytics and machine learning projects. When attempting predictive modeling with … Continue reading R Tip: Use the vtreat Package For Data Preparation
When I first encountered R, I learned to use the levels() function to find the possible values of a categorical variable.  However, I recently noticed something very strange about this function. Consider the built-in data set “iris” and its character variable “Species”.  Here are the possible values of “Species”, as shown by the levels() function. __ levels(iris$Species) […]
March Madness is officially upon us as College Basketball teams across the US try to get a few more signature wins on their resume during conference tournaments before Selection Sunday. With only 30 or so games in a given season and around 350 teams in...
styler 1.0.1 is now awailable on CRAN. It’s mainly a maintenance release and contains bug fixes and speed improvements. The highlights are:
This is guest post contributed by Thomas Lin Pedersen, creator of the lime package. The post originally appeared on Thomas’ blog at https://www.data-imaginist.com/2018/lime-v0-4-the-kitten-picture-edition/.
I’m happy to report a new maj...
Last semester I taught an R programming at MIMUW. My lectures are project oriented, the second project was related to package development. The idea was straightforward: each team of students shall create a package that produces IFS fractals (based on iterated function systems). Each package shall have two generic functions: create() and plot(), documentation and … Czytaj dalej How fractals helped my students to master package development in R
Here is an R tip. Use loop indices to avoid for()-loops damaging classes. Below is an R annoyance that occurs again and again: vectors lose class attributes when you iterate over them in a for()-loop. d [1] &quot;2018-02-18 10:16:16 PST&quot; &quot;2018-02-18 10:16:16 PST&quot; for(di in d) { print(di) } #__ … Continue reading R Tip: Introduce Indices to Avoid for() Class Loss Issues
I have been searching for various ways to find information about R packages for some time now, but I only recently learned about the CRAN_package_db() function in the base tools package. If a colleague hadn’t pointed it out to me, I am sure I would never have found it on my own.
pdb = 3.2.0)&quot;, &quot;R (...
## $ Imp NA, &quot;httr, XML, curl, readr, plyr, progress&quot;, NA, NA, ...
## $ Aut &quot;Scott Fortmann-Roe&quot;, &quot;Gaurav Sood [aut, cre]&quot;, &quot;Csill...
## $ RD NA, NA, &quot;abctools, EasyABC&quot;, &quot;abc&quot;, NA, NA, NA, NA, NA...
## $ RI NA, NA, &quot;ecolottery&quot;, NA, NA, NA, NA, NA, NA, NA, NA, ...
Note that in this post, I am using data from a file I pulled from CRAN just before my talk, but you should not have any trouble working with CRAN_package_db() to get an updated data set.
The next thing I did was to add two new variables to the data set: DepImp, the union of the dependent packages and imported packages, and RDRI, the union of the reverse depends and reverse imports packages.
library(stringr)
# A helper function to unlist and string split.
fcn %
mutate(Package = meta_data$Package[.x])
}) %__%
select(Package,Aut, Dep, Imp, RD, RI)
#head(c_dat1)
# Process the fields DepImp RDRI
c_dat2 %
map_df(~{
meta_data[.x, ] %__%
select(-Package, -Aut, -Dep, -Imp, -RD, -RI) %__%
map_df(~ifelse(is.na(.x), 0, length(rm_na(.x)[[1]])))
}) %__%
select(DepImp, RDRI)
#head(c_dat2)
c_dat
by Antony Unwin, University of Augsburg, Germany There are many different methods for identifying outliers and a lot of them are available in R. But are outliers a matter of opinion? Do all methods give the same results? Articles on outlier methods use a mixture of theory and practice. Theory is all very well, but outliers are outliers because they don’t follow theory. Practice involves testing methods on data, sometimes with data simulated based on theory, better with `real’ datasets. A method can be considered successful if it finds the outliers we all agree on, but do we all agree...
Getting out of the office and making time to share our R stories and ideas can often slip down our to-do lists when we’re busy, but taking the opportunity to meet othe...
Recently, I have written and hosted several multi-user Shiny apps. One example, is Taddle that runs on my server at http://taddle.mathematik.uni-ulm.de. Taddle is a multi-user shiny app that offers a free and simple way to allocate seminar topics. (Nex...
In a recent blog post I wrote about having a template for blogdown posts. I wanted to know if it was possible to do this and make my life (and others hopefully) easier for writing new blog posts that are ready to go with the features I frequently re-us...
As emailed to me by Aki Vehtari, the next StanCon will take place this summer in the wonderful city of Helsinki, at the end of August. On Aalto University Töölö Campus precisely. The list of speakers and tutorial teachers is available on the webpage. (The only “negative point” is that the conference does not include […]
The intrval R package is lightweight (~11K), standalone (apart from importing from graphics, has exactly 0 non-base dependency), and it has a very narrow scope: it implements relational operators for intervals — very well aligned with the tiny manifesto. In this post we will explore the use of the package in two shiny apps with sliders.
While looking at X validated questions, I came upon this comment that simulating a uniform distribution on a d-dimensional unit sphere does not proceed from generating angles at random on (0,2π) and computing spherical coordinates… Which I must confess would have been my initial suggestion! This is obvious, nonetheless, when computing the Jacobian of the […]
In an effort to streamline product development, maintenance, and support to ensure the best experience for our users, we have created a strategy for operating system and browser deprecation. This will allow us to focus our work on modern platf...
In R, we often need to get values or perform calculations from information not on the same row. We need to either retrieve specific values or we need to produce some sort of aggregation. This post explores some of the options and explains the weird (to...
Excel users starting to use R likely have some established concepts about data: where it's stored, how functions apply to data, etc. In general, R does things differently to Excel (or any spreadsheet, in fact). In a useful guide, Steph de Silva from Rex Analytics explains the concepts of data management in R and how they differ from Excel, which provides a useful mental model for those making the transition or working in both. Click the image above for the complete guide, but in summary the differences are: Excel stores data in the grid structure of the worksheet, whereas R...
I work with internet-scale data and do my fair share of macro-analyses on vulnerabilities. I use the R semver package for most of my work and wanted to blather on a bit about it since it’s super-helpful for this work and doesn’t get the attention it deserves. semver makes it possible to create charts like... Continue reading →
by Subhadeep (Deep) Mukhopadhyay and Douglas Fletcher, Department of Statistical Science, Temple University Bayesians and Frequentists have long been ambivalent toward each other. The concept of “Prior” remains the center of this 250 years old tug-of-war: frequentists view prior as a weakness that can cloud the final inference, whereas Bayesians view it as a strength to incorporate expert knowledge into the data analysis. So, the question naturally arises, how can we develop a Bayes-frequentist consolidated data analysis workflow that enjoys the best of both worlds? To develop a “defendable and defensible” Bayesian learning model, we have to go beyond blindly...
Another R tip. Use vector(mode = &quot;list&quot;) to pre-allocate lists. result [[1]] #__ NULL #__ #__ [[2]] #__ NULL #__ #__ [[3]] #__ NULL The above used to be critical for writing performant R code (R seems to have greatly improved incremental list growth over the years). It … Continue reading R Tip: Use vector(mode = &quot;list&quot;) to Pre-Allocate Lists
I recently submitted a Proposal to the R Consortium. I decided to share this proposal on my blog for two reasons: To raise awareness of...
The post My Proposal to the R Consortium appeared first on AriLamstein.com.
If that’s not a great 1960’s band name then I don’t know what is (hint: I don’t know what is). At the start of the year I set out my ‘goals for 2018’ just like many of us do; an...Continue Reading →
This afternoon, I took a quick look at the current Riddler puzzle, which sums up as, given three points A, B, C, arbitrarily moving on a plane with a one-shot view of their respective locations, find a moving rule to bring the three together at the same point at the same time. And could not […]
SQL Server Reporting services (SSRS) is an outstanding tool for creating, deploying and managing paginated, mobile, KPI reports as well as Power BI reports. Tool provides simple way to share and get data insights in your corporate environment. (Photo source: Microsoft docs) Using the privileges of R language to enrich your data, your statistical analysis … Continue reading Using R in SQL Server Reporting Services (SSRS)
A. INTRODUCTION When building statistical models, the goal is to define a compact and parsimonious mathematical representation of some data generating process. Many of these techniques require that one make assumptions about the data or how the analysis is specified. For example, Auto Regressive Integrated Moving Average (ARIMA) models require that the time series is … Continue reading Semiparametric Regression in R →
I posted a while back about using joy plots for heart rate data. Over the past couple of months, I grew tired of opening RStudio every time I wanted to look through my fitness tracker data. I decided to create a shiny dashboard that I can load via web browser. This involved setting up a shiny server in a dockerized container that runs an Rmd file. I’ve had issues getting plots to size themselves appropriately using base shiny, so I used the flexdashboard package to created a dashboard that will automatically resize. I wanted to be able to look at individual workouts as …
a faceting function with bare variable names in ggplot2 - I’m working on a package of dplyr and ggplot2 functions that I can use on a day to day basis to plot data over time. I’ve done some basic experimenting with my preferred ggplot2 th...
We've released our videos of the talks at the 2018 RStudio conference. My talk was Modeling in the Tidyverse (video) and I was also in the Tidyverse fireside chat (video). There ar...
Exploratory multivariate data analysis is studied and has been taught in a “French-way” for a long time in France. You can enroll in a MOOC (completely free) on Exploratory Multivariate Data Analysis. The MOOC will start the 5th of March 2018. This MOOC focuses on 5 essential and basic methods, those with the largest potential in terms of […]
R tip: get out of the habit of calling View() directly. View() only works correctly in interactive environments, not currently in RMarkdown contexts. It is better to call something else that safely dispatches to View(), or to something else depending if you are in an interactive or non-interactive session. The following code will work interactively, … Continue reading R Tip: Get Out of the Habit of Calling View() Directly
A codebook is a technical document that provides an
overview of and information about the variables in a dataset. The
codebook ensures that the statistician has the complete background
information necessary to undertake the analysis, and a codebook
do...
I would like to thank LinkedIn for letting me speak with some of their data scientists and analysts. John Mount discussing rquery SQL generation at LinkedIn. If you have a group using R at database or Spark scale, please reach out ( jmount at win-vector.com ). We (Win-Vector LLC) have some great new tools I’d … Continue reading Speaking on New Tools for R at Big Data Scale
The 3.7.0 release of simmer, the Discrete-Event Simulator for R, is on CRAN. It includes several API improvements and bug fixes. Among the former, the new timeout_from_attribute() activity makes it easier and more efficient the common task of placing a timeout based on a previously set attribute. Another common task is to increment or decrement a given attribute.… Continuar leyendo simmer 3.7.0 →
The R programming language is a powerful tool used in data science for business (DS4B), but R can be unnecessarily challenging to learn. We believe you can learn R quickly by taking an 80/20 approach to learning the most in-demand functions and package...
Tis the season for finding out how well Maine fisherfolk did last year; specifically, Maine lobsterfolk. Most of the news sites in Maine do a feature on the annual landings (here’s one from Bangor Daily News). There was a marked decline — the largest ever — in both poundage and revenue in 2017 and many... Continue reading →
A while back I got interested in (baby) surnames. It is interesting to follow how friends name their newly born. There are various naming “strategies”, a) pick a name that has been used in previous generations but now uncommon (e.g. in Germany tha...
-- title: &quot;2015 US Fatal Vehicle Accidents Analysis&quot; author: &quot;Matthew Sun&quot; date: &quot;2/01/2018&quot; output: html_document: default --- #### Introduction   There are over 5 millions of vehicle accidents reported in the US in 2015, according to data from National Highway Traffic Safety Administration (NHTSA). About 7% of them are fatal accidents. As the […]
RcppArmadillo release 0.8.400.0.0, originally prepared and uploaded on February 19, finally hit CRAN today (after having been available via the RcppCore drat repo for a number of days). A corresponding Debian release was prepared and uploaded as wel...
We told you EARL 2018 was going to be awesome!
We're excited to announce that Hadley Wickham will be the Keynote Speaker at our EARL Houston event on 9 November 2018. T...
I think this is the R Tip that is going to be the most controversial yet. Its potential pitfalls include: it is a style prescription (which makes it different than and less immediately useful than something of the nature of R Tip: Force Named Arguments), and it is heterodox (this is not how magrittr/dplyr is … Continue reading R Tip: Make Arguments Explicit in magrittr/dplyr Pipelines
There's a pay-off from an exploration of multiple supervised machine learning models. We'll explore five approaches whilst predicting criminal goings-on in London.
The post Criminal goings-on in a random forest appeared first on thinkr.
With ggimage, we are able to plot images using grammar of graphics. The layers defined in ggimage can be directly applied to ggtree to annotate phylogenetic tree using local/online image files.
ggtree seamlessly work with ggimage. The geom_tiplab and geom_nodelab can accept parameter of geom=&quot;image&quot; to parse taxa labels as image files and use them to “label” the taxa using images instead of text strings. Here are some examples for demonstration.
Annotate tips
library(ggimage)
library(ggtree)
url
This is a very short post with example code
Over time when you move your shiny app from your computer to a server, you
want to add some logging. Generally logging is defined in levels : INFO (everything you want to print),
WARNING (it does not sto...
The R Consortium passed a significant milestone this month: since its inception, the non-profit body has provided more than US$500,000 in grant funding to project proposed by the R Community. The R Consortium uses the dues from its member organizations to fund grant proposals, which are reviewed twice a year by its Infrastructure Steering Committee. (If you'd like to propose a project, proposals for the next round are being accepted through April 1.) New projects funded in this round include: Creating a new data type for R to unify the &quot;units&quot; and &quot;errors&quot; packages Updating the R module for the...
The mlr: Machine Learning in R package provides a generic, object-oriented and extensible framework for classification, regression, survival analysis and clustering for the statistical programming language R. The package targets practitioners who want to quickly apply machine learning algorithms, as well as researchers who want to implement, benchmark, and compare their new methods in a structured environment.
We are happy to announce that we now offer training courses specialized on mlr:
The Munich R Courses already offer the three-day course “Machine Learning and Data Mining in R”.
Introduction
In the quest for ever faster code, one generally begins exploring ways to integrate C++
with R using Rcpp. This post provides an example of multiple
implementations of a European Put Option pricer. The implementations are done in pure R,
pure Rcpp using some Rcpp sugar functions,
and then in Rcpp using
RcppArmadillo, which exposes the
incredibly powerful linear algebra library, Armadillo.
Under the Black-Scholes model The value of a European put option has the closed form solution:
where
and
Armed with the formulas, we can create the pricer using just R.
put_option_pricer
This report describes different methods for correctly rendering macrons in Māori text within R plots. The topics covered will also have relevance to rendering other special characters in R graphics and possibly to rendering macrons in other software. ...
I’m running a one day course with Timberlake, Stata’s UK distributors on this topic. We’ll run it next Friday the … More
In this latest blog, I’m responding to a cry for help. Someone got in touch with us recently asking for some advice on image detection algorithms, so let’s see what we can do!
They already know what algorithms they want to use, so let’s start with those. Hang on no, for the uninitiated, let’s start with what even is an image detection algorithm?
“An image detection algorithm takes an image, or piece of an image as an input, and outputs what it thinks the image contains.
This past week I was preparing slides for a reading assessment class with a lecture focus on the Visual Word Form Area [VWFA] (Cohen, et al., 2000). This is an area of the brain that is hypothesized to be able … Continue reading →
Experiences with using R/exams for multiple automated and randomized exams in an applied statistics course for environmental scientists at Universität Koblenz-Landau.
Guest post by Ralf B. Schäfer (Universität Koble...
The 2013 force majeur is a big news for Cartier: they introduced their first internal timing movement, the 1904-CH MC, on the Cartier chronograph. Self-winding vertical clutches Column wheel motors are brand-based Cartier campaigns. We got it today and it impressed me in a few ways. The vertical clutch cylinder rollers are known for their … Continue reading Introducing The Calibre De Cartier Chronograph: An Impressive New Interior Movement
An update to Microsoft R Client, Microsoft's distribution of open source R with additional proprietary packages — including RevoScaleR (for data analysis at scale) and MicrosoftML (for machine learning) — is now available. Microsoft R Client 3.4.3 updates the R engine to R 3.4.3, and (on Linux) now supports deploying computations to a remote SQL Server with the sqlrutils package. Microsoft R Client 3.4.3 is free to download and use, and as the video above shows is designed for developing analytical applications that will be deployed to production servers. It works with Microsoft Machine Learning Server 9.3, also released today....
This may be Cartier Replica, many of us are waiting. Earlier today, in fact, when CNN asked every hodinkee team member to pick and see, they really liked the performance of the day, and this is the new form of men’s wristwatches Cartier-driven – I chose. why? Because I’ve been eager to find the right … Continue reading Cartire Drive, A New Man’s Wear Watch With Past Eyes
Just In Time For Baseball
The mlbgameday package has just reached the milestone of version 0.1.0.
Designed to facilitate extract, transform and load for MLBAM “Gameday” data. The package is optimized for parallel processing of data that may be la...
The nmslibR package is a wrapper of NMSLIB, which according to the authors “… is a similarity search library and a toolkit for evaluation of similarity search methods. The goal of the project is to create an effective and comprehensive to...
Another R tip. Get in the habit of using drop = FALSE when indexing (using [ , ] on) data.frames. Prince Rupert’s drops (img: Wikimedia Commons) In R, single column data.frames are often converted to vectors when manipulated. For example: d x #__ 1 1 #__ 2 2 #__ … Continue reading R Tip: Use drop = FALSE with data.frames
Data analytics is rapidly changing the face of manufacturing as we know it. At Mango, we’re seeing companies using their data effectively to gain an advantage over com...
In this 4th post of my series on Deep Learning from first principles in Python, R and Octave – Part 4, I explore the details of creating a multi-class classifier using the Softmax activation unit in a neural network. The earlier posts in this series were 1. Deep Learning from first principles in Python, R … Continue reading Deep Learning from first principles in Python, R and Octave – Part 4
I’ve just updated The Popularity of Data Science Software to reflect my take on Gartner’s 2018 report, Magic Quadrant for Data Science and Machine Learning Platforms. To save you the trouble of digging though all 40+ pages of my report, … Continue reading →
Last week, my brand new DataCamp course Shiny Case Studies was finally released after being in the works for a full year!
Check out the course
This is an online course consisting of a series of short videos, mulitple choice exercises, a...
Last week I wrote about single variable explainers implemented in the DALEX package. They are useful to plot relation between a model output and a single variable. But sometimes we are more focused on a single model prediction. If our model predicts possible drug response for a patient, we really need to know which factors … Czytaj dalej DALEX: how would you explain this prediction?
Consider you have a server that is running behind a firewall and, for security
reasons, cannot make external http(s) requests. Further, you have R running on
this server and you need to install a set of packages. The simple approach of
install.packa...
An updated tutorial to set up a blogdown blog    Git &amp; GitHub repos RStudio and blogdown setup Customising paths and styles Modify config.toml file Custom css Custom javascript Site build with blogdown Deploying the site Updating the local git repository Pushing to GitHub Add R code A new … Sigue leyendo →
To post your R job on the next post Just visit  this link and post a new R job  to the R community. You can post a job for  free  (and there are also “featured job” options available for extra exposure). Current R jobs Job seekers:  please follow the links below to learn more and apply for your R job of interest: Featured Jobs Freelance Short-term consultancy – Biostatistics Ausvet Pty Ltd – Posted by Ausvet Bruce Australian Capital Territory, Australia 11 Feb 2018 Full-Time Customer Success Representative RStudio, Inc. – Posted by jclemens1 Anywhere 8 Jan 2018   More New R Jobs Freelance R + SQL + Google Cloud Platform + Microsoft PowerBi + Multiplatform APIs Automated Reporting Project @ London, U.K. Greenlight Digital – Posted by Hend Erigat London England, United Kingdom 21 Feb 2018 Full-Time Data Scientist and R Programmer @ Cambridge, Massachusetts, U.S. Metrum Research Group, LLC – Posted by michellej Cambridge Massachusetts, United States 18 Feb 2018 Full-Time Data Scientist and R Programmer @ Simsbury, Connecticut, U.S. Metrum Research Group, LLC – Posted by michellej Simsbury Connecticut, United States 18 Feb 2018 Freelance R Programmer for Statistical Research Data Processing (for $350) Academic Research – Posted by empiricus Anywhere 18 Feb 2018 Part-Time Garch(1,1) forecasting – fast job – Quick money […]
I need a few volunteers to please “test pilot” the development version of the R package cdata, please. Jacqueline Cochran: at the time of her death, no other pilot held more speed, distance, or altitude records in aviation history than Cochran. Our cdata package has an upcoming new feature called “build_frame()” that allows for the … Continue reading Wanted: cdata Test Pilots
When I learned and experimented a new model, I always like to start with its likelihood function in order to gain a better understanding about the statistical nature. That’s why I extensively used the SAS/NLMIXED procedure that gives me more flexibility. Today, I spent a couple hours playing the optim() function and its wrappers, e.g. […]
If you've ever wanted to play around with big data sets in a Spark cluster from R with the sparklyr package, but haven't gotten started because setting up a Spark cluster is too hard, well ... rest easy. You can get up and running in about 5 minutes using the guide SparklyR on Azure with AZTK, and you don't even have to install anything yourself. I'll summarize the steps below, but basically you'll run a command-line utility to launch a cluster in Azure with everything you need already installed, and then connect to RStudio Server using your browser to analyze...
Branko Milanovic asked on Twitter:
Idea for a paper: “Homogamy” on Twitter. Do people with more followers follow people with more followers?
I don’t have time to write a paper but I was sufficiently interested to want to blog about it. Th...
Just like your mom's chippity chipper recipe - In Data Science, there are numerous instances where different techniques call for the use of different tools. For me, this means hopping between R and python on a weekly basis. I’ve been fortun...
Is R base::subset() really that bad? Notes discussing subset() often refer to the following text (from help(subset), referred to in examples: 1, 2): Warning This is a convenience function intended for use interactively. For programming it is better to use the standard sub-setting functions like [, and in particular the non-standard evaluation of argument subset … Continue reading Is R base::subset() really that bad?
Yesterday a friend told me, “Yihui has written the most remarkably open blog post, and you’ve got to read it.” I did and it was. Though my post here is not about R per se, it is about a great contributor to R, our Yihui, Dr. of Statistics and (according to him) Master of Procrastination. … Continue reading Xie Yihui, R Superstar and Mensch →
By J Espasandin, O Lado, A Bouzas, A Baluja. You can also check this post, written in #blogdown, here: intro-joint-modeling-r. These days, between the 19th and 21st of February, has taken place the learning activity titled “An Introduction to the … Sigue leyendo →
Using your smartphone (any modern phone with a built-in accelerometer should work), visit the Cast Your Spell page created by Nick Strayer. (If you need to type it to your phone browser directly, here's a shortlink: bit.ly/castspell .) Scroll down and click the &quot;Press To Cast!&quot; button, and then wave your phone like a wand using one of the shapes shown. The app will attempt to detect which of the four &quot;spells&quot; you gestured. It was pretty confident in its detection when I cast &quot;Incendio&quot;, but your mileage may vary depending on your wizarding ability and the underlying categorization model....
Yesterday I wrote that I’ve received a number of choroplethr bug reports recently that I simply cannot reproduce. Due to a large number of people...
The post Update: “Difficult to Reproduce Choroplethr Bug” Isolated and Fixed appeared first on AriLamstein.com.
Hex stickers remind me of Pogs, except they’re cooler because you can combine them together! Some people do that very smartly.
Now when I forget how to do an #rstats analysis I can just check the back of my laptop #runconf16 pic.twitter.com/DY7UgeBzYV— David Robinson (@drob) 1 d’abril de 2016
I’ve got a pretty random hex stickers combination on my laptop, but after all it could be worse…
My boyfriend decided he is post-hex / post-#tidyverse pic.twitter.com/ADtyORMxHL— Hilary Parker (@hspter) 13 de setembre de 2017
Now since I’m a magick/collage fan, you can bet I’ve wondered how to use R in order to combine stickers automatically! Say I have a bunch of sticker PNGs, how could I produce a map to design my laptop style? Read to find out more…
Getting some hex stickers to play with
I do agree that in real life you’ll most often not have PNGs of the stickers but hey I can dream… especially since I’m sure that one will soon be able to identify stickers on a conference haul pic in the blink of an eye with the Rvision package. I got stickers from hex.bin whose existence I learnt from this tweet:
A plea to my favorite #rstats 📦 developers: please submit your BIG hex sticker image files (with vector versions 🙏) to https://t.co/7KWg3d2fZA! @old_man_chester @nj_tierney @samfirke they will be right at home 👇 pic.twitter.com/AIqgnBIx1X— Alison Hill (@apreshill) 21 de febrer de 2018
And well if more R packages are added to it my Rvision dream could come true.
Note: When I say I got them from hex.bin, I mean I lazily downloaded the “hexagons/” folder of this GitHub repository by using a link as explained in this SO answer.
Combining a few of them at random
I’ll sample 42 stickers from my nice little internet haul.
library(&quot;magrittr&quot;)
set.seed(42)
ultimate_sample %
sample(size = 42)
After that I’ll create rows which is pretty straightforward.
no_row
R tip: force the use of named arguments when designing function signatures. R’s named function argument binding is a great aid in writing correct programs. It is a good idea, if practical, to force optional arguments to only be usable by name. To do this declare the additional arguments after “...” and enforce that none … Continue reading R Tip: Force Named Arguments
Introduction to Looping system Imagine you were to perform a simple task, let’s say calculating sum of columns for 3X3 matrix, what do you think is the best way? Calculating it directly using traditional methods such as calculator or even pen and paper doesn’t sound like a bad approach. A lot of us may prefer … Continue reading Whys and Hows of Apply Family of Functions in R
R-Ladies Remote is kicking off and we want YOU! Do you want to be part of the R community but can’t attend meetups? There are many R-Ladies across the globe who love the idea of the organisation, but aren’t able to connect with it easily due to their distance, their work or their caring responsibilities. … Continue reading Introducing R-Ladies Remote Chapter
About a year and a half back I was working in Python a bit and became accustomed to the explicit importing of modules (akin to R packages) and functions. Python imports packages like this: import tidyr import dplyr as dp … Continue reading →
Topology is the silly putty of mathematics. One of the strangest shapes in topology is the Möbius strip. This strip is a surface with only one side. This article shows how the visualise this shape in R using the RGL package. Continue reading →
The post Topological Tomfoolery in R: Plotting a Möbius Strip appeared first on The Devil is in the Data.
Modern machine learning platforms like Tensorflow have to date been used mainly by the computer science crowd, for applications like computer vision and language understanding. But as JJ Allaire pointed out in his keynote at the RStudio conference earlier this month (embedded below), there's a wealth of applications in the data science domain that have yet to be widely explored using these techniques. This includes things like time series forecasting, logistic regression, latent variable models, and censored data analysis (including survival analysis and failure data analysis). The keras package for R provides a flexible, high-level interface for specifying machine learning...
R tip: use [[ ]] wherever you can. In R the [[ ]] is the operator that (when supplied a scalar argument) pulls a single element out of lists (and the [ ] operator pulls out sub-lists). For vectors [[ ]] and [ ] appear to be synonyms. However, when writing reusable code you may … Continue reading R Tip: Use [[ ]] Wherever You Can
Locke Data’s great blog is Markdown-based. What this means is that all blog posts exist as Markdown files: you can see all of them here. They then get rendered to html by some sort of magic cough blogdown cough we don’t need to fully understand here. For marketing efforts, I needed a census of existing blog posts along with some precious information. Here is how I got it, in other words here is how I rectangled the website GitHub repo and live version to serve our needs.
Since November 2017 I have received three bug reports from users who see this error when running any command in the choroplethr package: Theme element panel.border missing   Error in if (theme$panel.ontop)...
The post Difficult to Reproduce Choroplethr Bug: Can You Help? appeared first on AriLamstein.com.
I have to admit my initial thoughts of deep learning were pessimistic and in order to not succumb to impostor syndrome, I put off learning any new techniques in the growing sub field of machine learning, until recently. After attending &amp; ...
Great effort has recently been made to encourage also the not-so-experienced to jump into the water and blog about data science. Some of the community’s hot shots gracefully draw attention to blogs of first-times on twitter to give them extra exposure. Most of the blogger newbies write about an analysis they did on a topic they care about. That sounds like the obvious thing to do. However, I think there is a second option that is not considered by many. That is writing about a topic you just learned. This might seem strange, why would you want to tell the world about something you are by no means an expert on? Won’t you just make a fool of yourself by pretending you know stuff you just picked up? I don’t think so, let me give you four reasons why I think this is actually a good idea.
This post will cover two aspects: the first will be a function to convert daily returns into a table of … Continue reading →
The problem
Last week, I replied to this interesting question posted by @Tim_K over stackoverflow. He was seeking efficient solutions to identify all points falling within a maximum distance of xx meters with respect to each single point in a spatial points dataset.
If you have a look at the thread, you will see that a simple solution based on creating a “buffered” polygon dataset beforehand and then intersecting it with the original points is quite fast for “reasonably sized” datasets, thanks to sf spatial indexing capabilities which reduce the number of the required comparisons to be done (See http://r-spatial.org/r/2017/06/22/spatial-index.html). In practice, something like this:
# create test data: 50000 uniformly distributed points on a &quot;square&quot; of 100000# metresmaxdist
Hi everyone,
In this blog post, I will be short and I will introduce our shiny application on bike self-service stations. Your browser does not support the video tag. The code is in 2 parts, the ui.R file for the interface and the server.R file for the backend. You can check the code on GitHub if you want to download the code and run it on your computer. JCDecaux provides an API that gives us real time information on each bike self-service station. This infomation is: •Station id •Station name •Address •Position latitude/longitude •Presence of a payment terminal •Presence of a bonus station •If the station is open •How many bike stands are in the station •how many bike stands are available in the station (no bikes on the stand) •How many bikes are available •Time of the last api update The JCDecaux API gives the data under the following format:
{
&quot;number&quot;: 123,
&quot;contract_name&quot; : &quot;Paris&quot;,
&quot;name&quot;: &quot;stations name&quot;,
&quot;address&quot;: &quot;address of the station&quot;,
&quot;position&quot;: {
&quot;lat&quot;: 48.862993,
&quot;lng&quot;: 2.344294
},
&quot;banking&quot;: true,
&quot;bonus&quot;: false,
&quot;status&quot;: &quot;OPEN&quot;,
&quot;bike_stands&quot;: 20,
&quot;available_bike_stands&quot;: 15,
&quot;available_bikes&quot;: 5,
&quot;last_update&quot;: }
Hence, our shiny application gets real time information on bike stations in 27 cities.
We also used the modern open-source library leaflet to display city map (open street map). This application works better on computer than on smartphone because shiny is not fully smartphone friendly. However, Shiny has a user-friendly interface.
If you want to try your own Shiny app, I advice you to check this gallery. It contains a lot of examples that will serve as a good introduction.
Don’t hesitate to follow us on twitter @rdata_lu and to subscribe to our youtube channel. You can also contact us if you have any comments or suggestions. See you for the next post!
Getting our feet wet with dplyr programming - I’ve been putting off trying to suss out dplyr’s tidy evaluation for a long time, but I’ve been experimenting a bit with it so just making some notes here. It’s hard to talk about tidy e...
Another R tip. Use seq_len() to avoid The backwards seqeunce bug. Many R users use the “colon sequence” notation to build sequences. For example: for(i in 1:5) { print(paste(i, i*i)) } #__ [1] &quot;1 1&quot; #__ [1] &quot;2 4&quot; #__ [1] &quot;3 9&quot; #__ [1] &quot;4 16&quot; However, the colon notation can be unsafe as … Continue reading R Tip: Use seq_len() to Avoid The Backwards Sequence Bug
Black-box models, like random forest model or gradient boosting model, are commonly used in predictive modelling due to their elasticity and high accuracy. The problem is, that it is hard to understand how a single variable affects model predictions. As a remedy one can use excellent tools like pdp package (Brandon Greenwell, pdp: An R … Czytaj dalej DALEX: understand a black box model – conditional responses for a single variable
These are my sketchnotes for Sam Charrington’s podcast This Week in Machine Learning and AI about Learning “Common Sense” and Physical Concepts with Roland Memisevic:
Sketchnotes from TWiMLAI talk #111: Learning “Common Sense” and Physical ...
1. Introduction and the Business Problem In the sneaker world, the two big names &quot;Nike&quot; and &quot;Adidas&quot; are cutthroat rivals. The business problem for both brands is to figure out how to sell more products and add to the bottom line. Based on the trends in sneaker resales, we can infer preferences of sneaker buyers. […]
A few people have recently asked me about the SwissID, as SwissPost has just been sending spam emails out to people telling them &quot;Link your Swiss Post user account to SwissID&quot;.
SwissID is not the only digital identity solution in Switzerland but as it ...
Want to know whether lapply is quicker than a for-loop? Is there a faster solution? This is the place!
A. Motivation During the recent RStudio Conference, an attendee asked the panel about the lack of support provided by the tidyverse in relation to time series data. As someone who has spent the majority of their career on time series problems, this was somewhat surprising because R already has a great suite of tools for … Continue reading Packages for Getting Started with Time Series Analysis in R →
Here is an R tip. Need to quote a lot of names at once? Use qc(). This is particularly useful in selecting columns from data.frames: library(&quot;wrapr&quot;) # get qc() definition head(mtcars[, qc(mpg, cyl, wt)]) # mpg cyl wt # Mazda RX4 21.0 6 2.620 # Mazda RX4 Wag 21.0 6 2.875 # Datsun 710 22.8 … Continue reading R Tip: Use qc() For Fast Legible Quoting
This blog post is mostly for myself but maybe it’s useful to others. It contains my current R markdown blog template. I initially posted this as a question at StackOverflow. Then I read how much a burden we put in Yihui Xie and decided that my curren...
I last blogged about books in September 2016 and it’s still in my 10 most popular posts. I thought I’d update it with some of the data-relevant books I read in 2017. Mostly these are books that were published before 2017 - it’s just that it was...
Like the last several updates, this blog post will be fairly short, given only a single bug fix was added.
Thanks again to GitHub user leocwlau who reported that the GetReportSuiteGroups function added an additional field AND provided the solution. No...
What’s Up? The NPR Visuals Team created and maintains a javascript library that makes it super easy to embed iframes on web pages and have said documents still be responsive. The widgetframe R htmlwidget uses pym.js to bring this (much needed) functionality into widgets and (eventually) shiny apps. NPR reported a critical vulnerability in this... Continue reading →
Summary
Running an application on Spark with external dependencies, such as R and python packages, requires the installation of these dependencies on all the workers. To automate this tedious process, a continuous deployment workflow has been developed...
Make memorable plots with memery. memery is an R package that generates internet memes including superimposed inset graphs and other atypical features, combining the visual impact of an attention-grabbing meme with graphic results of data analysis. Version 0.4.2 of memery is now on CRAN. The latest development version and a package vignette are available on […]
While ggplot2 (and its various extensions) is often the go-to package for graphics in R these days, if you need to step outside the boundaries of what ggplot2 can do, you can always step back to base R graphics (and the built-in lattice package) and customize to your hearts content. The problem is that (unlike for ggplot2) the default look for base graphics is kinda ... meh. That being said, the base graphics system offers almost unlimited flexibility, both via function options and via the par system for modifying layouts and graphic defaults. As Colin Gallespie explains in a recent...
Many data scientists (and even statisticians) often suffer under one of the following misapprehensions: They believe a technique doesn’t work in their current situation (when in fact it does), leading to useless precautions and missed opportunities. They believe a technique does work in their current situation (when in fact it does not), leading to failed … Continue reading We Want to be Playing with a Moderate Number of Powerful Blocks
In November I announced a new collaboration with the Census Bureau around data science training. Today I’d like to provide an update on this project....
The post Update on My Collaboration with the Census Bureau appeared first on AriLamstein.com.
MEDIA RELEASE
14 February 2018
Mango Solutions are delighted to announce that loyalty programme pioneer and data science innovator, Edwina Dunn, will keynote at the 2018...
This blog post is about my newly released RGF package (the blog post consists mainly of the package Vignette). The RGF package is a wrapper of the Regularized Greedy Forest python package, which also includes a Multi-core implementation (Fast...
Somebody recently shared this with me, this is what happens when you attempt to access Parship, an online dating site, from the anonymous Tor Browser.
Experian is basically a private spy agency. Their website boasts about how they can:
Know who your ...
This is a quick script showing how to make multi-tabbed .xlsx files. I recently had the need to do this and used the flexible openxlsx package maintained by : Alexander Walker. The package is described by the author like this: … Continue reading →
Mathematics is a place where you can do things which you can’t do in the real world (Marcus Du Sautoy, mathematician) From time to time I have a look to some of my previous posts: it’s like seeing them through another’s eyes. One of my first posts was this one, where I draw fractals using the … Continue reading Mandalas →
Outcome Supervision
Yesterday I was part of an introductory session on machine learning and unsurprisingly, the issue of supervised vs. unsupervised learning came up. In social sciences, there is a definite tendency for the former; there is more or less always a target outcome or measure that we want to optimise the performance of our models for. This reminded me of a draft that I had written the code a couple of months ago but for some reason never converted into a blog post until now. This will also allow me to take a break from conflict forecasting for a bit and go back to my usual topic of UK. My frequent usage of all things UK is at such a level that my Google Search Console insights lists r/CasualUK as the top referrer. Cheers, mates!
UK General Elections and Brexit
I think I have exploited rvest enough recently, so I will rely on a couple of good old csv’s this time. The Electoral Commission website provides the EU referendum voting totals by region. I want to combine electoral data with other socio-economic indicators so I will subset the data to London only (this is how privilege perpetuates itself). Within that subset, I will only keep the area code (for matching purposes later), and the raw number of votes for each option in the referendum. I will also create a variable indicating ‘the outcome’ based on which side got more votes:
##EU Referendum data for London
eu
A new release of the BH package arrived on CRAN a little earlier: now at release 1.66.0-1. BH provides a sizeable portion of the Boost C++ libraries as a set of template headers for use by R, possibly with Rcpp as well as other packages.
This release...
jamovi is software that aims to simplify two aspects of using R. It offers a point-and-click graphical user interface (GUI). It also provides functions that combines the capabilities of many others, bringing a more SPSS- or SAS-like method of programming … Continue reading →
How we taught a computer to understand Luxembourguish
Today we reveal a project that Kevin and myself have been working on for the past 2 months, Liss. Liss is a sentiment analysis artificial intelligence; you can let Liss read single words or who...
Our paper, weakly informative reparameterisations of location-scale mixtures, with Kaniav Kamary and Kate Lee, got accepted by JCGS! Great news, which comes in perfect timing for Kaniav as she is currently applying for positions. The paper proposes a unidimensional mixture Bayesian modelling based on the first and second moment constraints, since these turn the remainder […]
I was genuinely chuffed to get a shout-out in the most recent episode of Not So Standard Deviations, the awesome statistics-and-R themed podcast hosted by Hilary Parker and Roger Peng. In that episode, Roger recounts his recent discovery of the Microsoft ecosystem of tools for R, which he (jokingly) dubbed the &quot;Microsoft-verse&quot;. While we're flattered by the allusion to the tidyverse, in general Microsoft's developments with R are designed to work with the entire R ecosystem rather than be distinct from it. Here's a quick overview of what Microsoft has developed around R. It's in three sections: the first two...
Earlier this month we released a new version of the tesseract package to CRAN. This package provides R bindings to Google’s open source optical character recognition (OCR) engine Tesseract.
Two major new features are support for HOCR and support for the upcoming Tesseract 4.
hOCR output
Support for HOCR output was requested by one of our users on Github. The ocr() function gains a parameter HOCR which allows for returning results in hOCR format:
library(tesseract)
# Text output
text
A brand new shiny package has entered the world yesterday: shinyalert. It does only one thing, but does it well: show a message to the user in a modal (aka popup, dialog, or alert box). Actually, it can do one more thing: shinyalert can also ...
One of the greatest things about the R community is its diversity. This is largely thanks to organizations like Forwards and R-Ladies, who have been instrumental in welcoming women and other under-represented groups to the world of R. Likewise, conferences in the R community encourage diversity, with open codes of conduct, facilitations like on-site child-care, and by offering scholarships for travel and lodging to encourage attendees from diverse backgrounds. Here are three upcoming R community events that are offering diversity scholarships: The rOpenSci Unconference (May 21-22, Seattle) is now accepting self-nominations to participate. You can request travel support as part...
Trick question: is a 10,000 cell numeric data.frame big or small? In the era of &quot;big data&quot; 10,000 cells is minuscule. Such data could be fit on fewer than 1,000 punched cards (or less than half a box). The joking answer is: it is small when they are selling you the system, but can be … Continue reading Is 10,000 Cells Big?
In my last posts (here and here) I explored how to use embeddings to represent categorical variables. Furthermore, I showed how to represent categorical variables with embeddings and add other variable to create a more complex model. Both posts focused...
Udemy is offering readers of R-bloggers access to its global online learning marketplace for $15 per course! This deal (offering over 50%-90% discount) is for hundreds of their courses – including many R-Programming, data science, machine learning etc. Click here to browse ALL (R and non-R) courses Advanced R courses:  Regression modelling Comprehensive Linear Modeling with R (15 Hours of video) Linear regression in R for Data Scientists (7 hours) Linear Mixed-Effects Models with R (11 Hours of video) Linear Regression, GLMs and GAMs with R (8 hours) Forecasting Models with R (5 hours) Time Series Analysis and Forecasting in R (3 Hours of video) Regression, Data Mining, Text Mining, Forecasting using R (32 hours) Statistical/machine learning Bayesian Computational Analyses with R (12 Hours of video) Data Science and Machine Learning Bootcamp with R (17 hours) Decision Tree – Theory, Application and Modeling using R (5 hours) Data Mining with R: Go from Beginner to Advanced! (12 hours) Machine Learning A-Z™: Hands-On Python &amp; R In Data Science (40 hours) Applied Multivariate Analysis with R (13 Hours of video)  Visualization Graphs in R (ggplot2, plotrix, base R) – Data Visualization with R Programming Language (5 Hours of video) Multivariate Data Visualization with R (7 Hours of video) Other topics Create Interactive Web […]
Have you spent hours, pulling your hair out trying to figure out how to access datasets in R? Once imported to a variable, columns from a dataset (eg: CSV) can be very tricky to access. Sometimes columns contain spaces, funky characters or other incosistencies. Here are some examples on how to access the data from CSV and JSON datasets.
CSV
We’ll use this CSV dataset since it has *very* long column names.Read File: csvDataset
Owen Jones, Placement Student
Songwriting is a very mysterious process. It feels like creating something from nothing. It’s something I don’t feel like I really con...
Steph is currently out of the office, teaching people cool Data Science stuff on a cruise at Tech Outbound. She counts on her team to keep the company’s Twitter account afloat in the meantime, so I had to think of a way to contribute. What about advertising existing content from her blog in the style of her Twitter role model Mara Averick, i.e. an informative tweet accompanied by appealing screenshots?
By Gabriel Vasconcelos Overview There are several ways to do portfolio optimization out there, each with its advantages and disadvantages. We already discussed some techniques here. Today I am going to show another method to perform portfolio optimization that works … Continue reading →
Keras is a popular Python package to do the prototyping for deep neural networks with multiple backends, including TensorFlow, CNTK, and Theano. Currently, there are two R interfaces that allow us to use Keras from R through the reticulate package. While the keras R package is able to provide a flexible and feature-rich API, the […]
Previously we’ve covered the basics of exogenous variables in smooth functions. Today we will go slightly crazy and discuss automatic variables selection. But before we do that, we need to look at a Santa’s little helper function implemented in [crayon-5a7f874ccc437104037960-i/]. It is called [crayon-5a7f874ccc443987759959-i/]. It is useful in cases when you think that your exogenous […]
Official statistical classifications can be big, complex things. For example, the complete version of the “International Standard Industrial Classification of All Economic Activities, Rev.4” comes as either a 300 page PDF or a 1.3GB Microsoft Acce...
It all started with this paper in Methods in Ecol. Evol. where we looked at
detectability of many species. So we wanted to use life history
traits to validate our results. But we had to cut the manuscript,
and there was this leftover with some neat patterns, but without much focus.
It took a few years, and the most positive peer-review experience ever,
and the paper is now early view in Ecography. This post is a quick summary of the goodies stuffed inside the lhreg R package that makes the whole analysis reproducible, and provides some functions for similar PGLMM models.
We’ve released the newest version of NIMBLE on CRAN and on our website. Version 0.6-9 is primarily a maintenance release with various bug fixes and fixes for CRAN packaging issues. New features include: dimensions in a model will now be determined from either ‘inits’ or ‘data’ if not otherwise available; one can now specify “nBootReps […]
For a fifth year running, we are excited to announce the rOpenSci unconference, our annual event loosely modeled on Foo Camp. rOpenSci unconferences have a rich history. You can get a feel for them by reading collected stories about people and projects from unconf17.
We’re organizing unconf18 to bring together scientists, developers, and open data enthusiasts from academia, industry, government, and non-profits to get together for a couple of days to hack on various projects and generally enrich our community. The agenda is mostly decided during the unconference itself. Past projects have related to open data, data visualization, data publication and open science using R. This event is unlike many other unconferences in that it is primarily invite-only, with a few spots set aside for self-nominations from the community at large. That’s you!
rOpenSci is committed to providing a welcoming community for all participants. One of our priorities is to increase and retain the diversity of contributors to open research by helping people “see what they can be”. Therefore, we strongly encourage applications from women and other underrepresented genders, people of color, people who are LGBTQ, people with disabilities or any other underrepresented minorities in research. To ensure a safe, enjoyable, and friendly experience for everyone who participates, we follow a code of conduct.
Where: Downtown Seattle, WA
When: May 21-22, 2018
Apply: If you’d like to attend, it only takes a few minutes to nominate yourself. Submissions close Thursday, March 8, 2018 at midnight Pacific Time.
We’re grateful to our sponsors for making #runconf18 possible.
Questions? Contact us
by Boxuan Cui, Data Scientist at Smarter Travel Once upon a time, there was a joke: In Data Science, 80% of time spent prepare data, 20% of time spent complain about need for prepare data. — Big Data Borat (@BigDataBorat) February 27, 2013 According to a Forbes article, cleaning and organizing data is the most time-consuming and least enjoyable data science task. Of all the resources out there, DataExplorer is one of them, with its sole mission to minimize the 80%, and make it enjoyable. As a result, one fundamental design principle is to be extremely user-friendly. Most of the...
We get a lot of questions about the usage of R libraries. The most common question is “can I use all the R libraries in your notebooks/consoles?”
Yes! You can use any of the libraries that have been published to the R package repository (CRAN).
Open up your notebook/console:
1. Install
install.packages(&quot;ggplot2&quot;) this will install the package if it hasn’t already been installed.
2. Include
library(ggplot2) this will call/include the library into your session.
3. Use
ggplot(data=iris,aes(x=Sepal.Length,y=Petal.Length))+geom_point()
Remember, when installing packages, you have to use quote marks inside the install.packages(&quot;&quot;) function but not when including them using the library().
Once libraries are installed, you don’t have to install them again. You can just call them with library() and then use them as you wish.
Link to notebook: https://www.datazar.com/file/f105230b5-0a2f-4976-af08-db1b00f793a4
How to Install and Include an R Package was originally published in Datazar Blog on Medium, where people are continuing the conversation by highlighting and responding to this story.
Many water utilities are implementing or considering digital metering. This article describes analysing digital water meter data using the data science Tidyverse library. Continue reading →
The post Analysing Digital Water Meter Data using the Tidyverse appeared first on The Devil is in the Data.
A new minor release 0.3.3.4.0 of RcppEigen hit CRAN earlier today, and just went to Debian as well. It brings Eigen 3.3.4 to R.
Yixuan once again did the leg-work of bringing the most recent Eigen release in along with the small set of patches we hav...
This report describes the development of a colour list and colour metric for the ‘roloc’ package that is based on the ISCC-NBS System of Color Designation. Paul Murrell Download
Data science tells a story through visualisation. Both story and visualisation rely on a good plot. And an abundance of those has evolved over time. Many have their own dedicated Wikipedia page! Which generate the most interest? How is the interest in each trending over time?
The post The plots thicken appeared first on thinkr.
In case you missed them, here are some articles from January of particular interest to R users. Josh Katz and Peter Aldhous used R to analyze the content and presentation of the most recent State of the Union speech from the US president. Slides for my presentation &quot;Speeding up R with Parallel Processing in the Cloud&quot;, with applications of the doAzureParallel and sparklyr packages. An example of using the doAzureParallel package to speed up a statistical simulation. 5 lines of R code to create a list of US Representatives from a Wikipedia table. A package to visualize routes from activities...
If you have ever developed or used an open-source R package, you’re likely familiar with continuous integration. By automating the process of testing each proposed change in the source code, you can reduce the risk of errors, avoid unnecessary...
In my previous post (https://statcompute.wordpress.com/2018/01/28/modeling-lgd-with-proportional-odds-model), I’ve discussed how to use Proportional Odds Models in the LGD model development. In particular, I specifically mentioned that we would estimate a sub-model, which can be Gamma or Simplex regression, to project the conditional mean for LGD values in the (0, 1) range. However, it is worth pointing out […]
I’m pleased to announce an update for the sjmisc-package, which was just released on CRAN. Here I want to point out two important changes in the package. New default option for recoding and transformation functions First, a small change in the code with major impact on the workflow, as it affects argument defaults and is … Weiterlesen Data transformation in #tidyverse style: package sjmisc updated #rstats →
The Data Science Virtual Machine was featured on a recent episode of the AI Show with Seth Juarez and Gopi Kumar. If you want a quick and easy way to spin up a virtual machine with all of the data science tools you'll ever need — including R and RStudio — already installed and ready to go, this video explains what the Data Science Virtual Machine is used for and (at 21:00) how to launch one in the Azure portal. The Data Science Virtual Machine is available in both Windows and Linux flavors. Even if you don't yet have an...
Leeds (Predictive Analytics in R)
London (Tensorflow)
Birmingham (Python &amp; Machine Learning)
Hi there!
We’re running some courses on R, Python and Tensorflow around the UK that you might be interested in! All courses are spearheaded with lec...
Much of what I need to do for work-work involves using tools that are (for the moment) not in R. Today, I needed to test the validity of (and other processing on) DMARC records and I’m loathe to either reinvent the wheel or reticulate bits from a fragmented programming language ecosystem unless absolutely necessary. Thankfully,... Continue reading →
Adding a file to your R notebook is a simple 2 step process.1. “Import” file to notebook session.This makes sure your dataset is isolated in one state so that it doesn’t change as you analyze it. Remember, you can always load the latest content...
Last week I set up a shiny server, it was relatively easy! But I wanted something more, a way to make changes on my local computer and push it to the server.
Shiny server (I used the open source version) has multiple installers provided by RStudi...
We’re delighted to announce that Julia Silge will be joining us on 7 November in Seattle as our Keynote speaker.
Julia is a Data Scientist at Stack Overflow, has a PhD...
A friend and colleague of mine, Péter Batáry
has circulated news from Nature
magazine about the EU freezing innovation funds to Bulgaria.
The article had a figure about publication trends for
Bulgaria, compared with Romania and Hungary.
As I have blogged about such trends in ecology before
(here and here), I felt the need to update my PVA models
with two years worth of data from WoS.
The 2018 edition of the long-running Statistical Practice in Epidemiology using R (SPE) course will take place in Lyon, Fance on 20-24 June 2018 at the International Agency for Research on Cancer. The deadline for applications is 1 April.
Project Objective
Untappd has some usage restrictions for their API namely not allowing any exploratory of analytics uses, so I’m going to explore tweets of beer and brewery check-ins from the Untappd app to find some implicit trends in how users share their activity.
Exploratory Analysis
library(tidyverse)
library(rtweet)
library(stringr)
library(wesanderson)
library(maps)
library(tidytext)
library(dumas) # http://jasdumas.github.io/dumas/
library(wordcloud)
All social media shares from the Untappd app include their own short URL ‘untp.beer’, which makes the search query criteria identifiable using the search_tweets() function.
untp
Registration is now open for my 1.5-day workshop on deep learning with Keras and TensorFlow using R.
It will take place on April 12th and 13th in Hamburg, Germany.
In my workshop, you will learn
the basics of deep learning
what cross-entropy and loss ...
This is my Shiny Dashboard for stocks listed on S&amp;P500. Using R shiny to give a visualized general insight of market and using ARIMA model to analyze risk for a single stock. Background When I worked as a trader. I found that it was really inconvenient to do the daily market research because the information on […]
The Charges Data Set The NYC Open Data project provides the Department of Consumer Affairs data from 2016-2017.  The Department of Consumer Affairs was started in 1969 with the goal to &quot;ensure compliance with local consumer protection and licensing laws, and State and federal regulations.&quot;  They oversee almost every industry in NYC.  This data set describes the charges […]
Abstract Background: This blog post is going to show you how to write a reproducible article in the field of clinical chemistry using R Mardkown. The only thing that will change for journal to journal will be the reference fomating and perhaps section numbering. The source code itself will be provided so that you can … Continue reading Reproducible Research: Write your Clinical Chemistry paper using R Markdown →
My new office introduced me to a betting game I wasn’t previously familiar with: Super Bowl squares. It’s played with a ten-by-ten grid, like this one from printyourbrackets.com:
Each row and column gets an assortment of digits from 0-9 represen...
Background Do you like earning money? And, at the same time contribute to saving the planet? What if I prove to you Solar Business is the next booming field having plenty of opportunities in coming decades, with plus point of reducing our carbon footprint. You'd argue...Really? But solar installations are so expensive! They are not […]
In my last post, I explored how to use embeddings to represent categorical variables. Furthermore, I showed how to extract the embeddings weights to use them in another model.
While the concept of embedding representation has been used in NLP for quit...
Yet more Metalliplots
I’m just going to tidy up some issues from my last post where I did some tidytext analysis on the legendary rockers - Metallica.
It was a fun way to pass some time and keep my hand in with text mining, something I st...
 In this tutorial, we will explore the characterisitcs of projects on Kickstarter and try to understand what separates the winners from the projects that failed to reach their funding goals. ( This post is mirrored from our main blog site blog.journeyofanalytics.com . The code can be downloaded or run “LIVE” from Kaggle using this link.) Qs … Continue reading How to raise money on Kickstarter – tutorial with EDA and predictions
As the reader may know, R objects are documented in files written in “R documentation” (Rd) format, a simple markup language much of which closely resembles (La)TeX, which can be processed into a variety of formats, including LaTeX, HTML and plain text. This LaTeX-like syntax, combined with the fact that the actual R objects live… Continuar leyendo Documenting R packages: roxygen2 vs. direct Rd input →
And yet another boring little RVowpalWabbit package update, now to version 0.0.12, and still in response to the CRAN request of not writing files where we should not (as caught by new tests added by Kurt). I had misinterpreted one flag and actually i...
A puzzle
In my last blog post, I gave a hypothetical test exercise for candidates in a recruitment process, involving the International Visitor Survey (IVS) from the Ministry of Business, Innovation and Employment (MBIE). I once managed the team resp...
Date conversion in R can be a real pain. However, it is a very important initial step when you first get your data into R...
The Main Competition
The Secondary Competition
So… big news.
Jumping Rivers is sponsoring eRum 2018 and in light of this news we are giving away a free place at the conference!
(Not to mention our very own lead consultant, Colin Gillespie, i...
Here at Win-Vector LLC we like permutation tests. Our team has written on them (for example: How Do You Know if Your Data Has Signal?) and they are used to estimate significances in our sigr and WVPlots R packages. For example permutation methods are used to estimate the significance reported in the following ROC plot. … Continue reading Why No Exact Permutation Tests at Scale?
For those of you out there who speak German:
I was interviewed for a tech podcast where I talked about machine learning, neural nets, why I love R and Rstudio and how I became a Data Scientist.
You can download and listen to the podcast here: https://m...
On Wednesday, April 25th 2018 I am going to talk about explainability of machine learning models at the Minds Mastering Machines conference in Cologne. The conference will be in German, though.
ERKLÄRBARKEIT VON MACHINE LEARNING: WIE KÖNNEN WIR ...
This article simulates water consumption to assist with developing leak detection algorithms. Simulating water consumption helps to develop business tools. Continue reading →
The post Simulating Water Consumption to Develop Analysis and Reporting appeared first on The Devil is in the Data.
President Trump's State of the Union speech was last night, and it seemed to me it dragged on a bit. That's because it was apparently the slowest SOTU speech in history, based on average number of words spoken per minute. The chart below, based on these data, was created by Josh Katz. (The R code for the chart is here. Note the use of his needs package to load the required R packages.) Peter Aldhous at Buzzfeed analyzed last night's State of the Union speech as well. Not only was the speech delivered slowly, the script was at a 9-year-old's...
In another continuation of the summer of Bayesian conferences in Europe, the Universidat de Valencià is organising a summer school on Bayesian statistics, from 16 July till 20 July, 2018. Which thus comes right after our summer school on computational statistics at Warwick. With a basic course on Bayesian learning (2 days). And a more […]
If you use SQL Server (or Azure SQL DB) as your data store and you need to connect to the databasse from shinyapps.io, you’re presently stuck with FreeTDS. If you have any control over infrastructure I cannot recommend highly enough the actual ODBC Driver on Linux for ease. Alas, shinyapps.io does not let you control the infrastructure. We have to make do with with FreeTDS and it can be pretty painful to get right.
Introduction Not long ago I was reading on t-Distributed Stochastic Neighbor Embedding (t-sne), a very interesting dimension reduction technique, and on Mel frequency cepstrum a sound processing technique. Details of both techniques can be found here and here. Can we combine … Continue reading →
Last call for the course on Advanced R programming scheduled in Leuven, Belgium on Febuary 20-21 2018. Register at: https://lstat.kuleuven.be/training/coursedescriptions/AdvancedprogramminginR.html You'll learn during that course:
The apply family of functions, basic parallel programming for these functions and commonly needed data manipulation skills
Making a basic reproducible report using Sweave and knitr including tables, graphs and literate programming
How to create an R package
Understand how S3 programming works, generics, environments, namespaces.
Basic tips on how to organise and develop R code and test it.
Need other training: visit http://bnosac.be/index.php/training
 
So, Strava's heatmap made quite a stir the last few weeks. I decided to give it a try myself. I wanted to create some kind of &quot;personal heatmap&quot; of my runs, using Strava's API. Also, combining the data with Leaflet maps allows us to make use of the beautiful map tiles supported by Leaflet and to zoom and move the maps around - with the runs on it, of course.So, let's get started. First, you will need an access token for Strava's API. I found all the necessary information for this in this helpful &quot;Getting started&quot; post. As soon as you have the token, you have access to your own data.Now, let's load some packages and define functions for getting and handling the data. For the get.activities() function, I adapted code from here.library(httr)library(rjson)library(OpenStreetMap)library(leaflet)library(scales)library(dplyr)token %
  fitBounds(lng1 = min(lons.range), lat1 = max(lats.range), lng2
When dealing with Machine Learning problems in R, most of the time you rely on already existing libraries. This fastens the analysis process, but do you really understand what is behind the algorithms? Could you implement a logistic regression from scratch with R? The goal of this post is to create our own basic machine […]
The post Create your Machine Learning library from scratch with R ! (1/3) appeared first on Enhance Data Science.
[…]
I was pleased to read Shirish Agarwal's blog in reply to the blog I posted last week Do the little things matter?
Given the militaristic theme used in my own post, I was also somewhat amused to see news this week of the Strava app leaking locations and...
“Once upon a time, I, Chuang Tzu, dreamt I was a butterfly, fluttering hither and thither, to all intents and purposes a butterfly. I was conscious only of following my fancies as a butterfly, and was unconscious of my individuality as a man. Suddenly, I awoke, and there I lay, myself again. Now I do … Continue reading Deep Learning from first principles in Python, R and Octave – Part 3
Get WikiTables from Lists
Recently I was asked to submit a short take-home challenge and I thought what better excuse for writing a quick blog post! It was on short notice so initially I stayed within the confines of my comfort zone and went for something safe and bland. However, I alleviated that rather fast; I guess you want to stand out a bit in a competitive setting. Note that it was a visualisation task, so the data scraping was just a necessary evil. On that note.
I resorted to using Wikipedia as I was asked to visualise change in a certain x going back about 500 hundred years. Not many academic datasets go that far, so Wiki will have to do for our purposes. And once you are there, why only visualise half a millennium, let’s go from 1 AD to present day. First, we need a subject. I went with my PhD topic, war duration. So let’s get the list of lists first using rvest:
#Get wikipedia time periods for List of Wars
url %
html_nodes(&quot;ul&quot;) %__%
html_text(trim = TRUE) %__%
strsplit(split = &quot;\n&quot;) %__%
unlist()
periods
I am happy to announce that on Tuesday, April 24th 2018 Uwe Friedrichsen and I will give a talk about Deep Learning - a Primer at the JAX conference in Mainz, Germany.
Deep Learning is one of the “hot” topics in the AI area – a lot of hype, a lo...
Song lyric and sentiment analysis for all
So - a while back I did a tidy text analysis on Faith No More lyrics. I had thought about doing this with Metallica album lyrics, as they have had a long career, spanning thier late teens/twenties to...
We are super pumped to be launching a weekly data science podcast called DataFramed, in which Hugo Bowne-Anderson (me), a data scientist and educator at DataCamp, speaks with industry experts about what data science is, what it's capable of, what it looks like in practice and the direction it is heading over the next decade … Continue reading Introducing DataFramed, a Data Science Podcast
This is a guest post from Leon Eyrich Jessen, a postdoctoral researcher in the Immunoinformatics and Machine Learning Group at the Technical University of Denmark.
Introduction
Simon Caulton, Adoptive T-cell therapy, CC BY-SA 3.0
In my...
This past weekend I attended the R User Day at Data Day Texas in Austin. It was a great event, mainly because so many awesome people from the R community came to give some really interesting talks. Lucy D’Agostino McGowan has kindly provided a list of the talks and links to slides, and I thoroughly recommend checking it out: you're sure to find a talk (or two, or five or ten) that interests you. My own talk was on Speeding up R with Parallel Programming in the Cloud, where I talked about using the doAzureParallel package to launch clusters for...
At Mango, we’re passionate about R and promoting its use in enterprise – it’s why we created the EARL Conferences. We understand the importance of sharing knowledg...
Hey folks, I wanted to give y’all an update about Locke Data one year on from when I started it up.
In the past year, I’ve delivered more than 32 days of training, wrote and published 2 books, worked with 3 clients, and generally whimpered at my schedule. It has been amazing how much support the community has given me, and I’ve tried to give back where possible by giving away books each month, doing the usual presenting, holding free office hours, and offering community workshops.
Another boring little RVowpalWabbit package update to version 0.0.11 came in response to another CRAN request: We were writing temporary output (a cache file for the fit/prediction, to be precise) to a non-temporary directory, which is now being caug...
Unsupervised machine learning looks for hidden structure in &quot;unlabeled&quot; data, i.e. a classification or categorisation that is not included in the observations. Hierarchical clustering will reveal a &quot;cluster of six&quot; Labour MPs as being the most &quot;distant&quot; from the wider party.
The post The “cluster of six” appeared first on thinkr.
I love making maps, I also love making gifs.
In this short post I make an animated gif of parts of a map moving. In this case the parts of the map only move in the xy direction, but you can also turn them, and make them bigger or smaller.
Today I ...
And yet another small maintenance release, now at version 0.6.15, of the digest package arrived on CRAN and in Debian today.
digest creates hash digests of arbitrary R objects (using the 'md5', 'sha-1', 'sha-256', 'sha-512', 'crc32', 'xxhash32', 'xxh...
What do you do when analyzing data is fun, but you don't have any new data? You make it up. This simulation tests the type I error rates of two-sample t-test in R and SAS. It demonstrates efficient methods for simulation, and it reminders the reader not to take the result of any single hypothesis test as gospel truth. That is, there is always a risk of a false positive (or false negative), so determining truth requires more than one research study.
A type I error is a false positive. That is, it happens when a hypothesis test rejects the null hypothesis when in fact it is not true. In this simulation the null hypothesis is true by design, though in the real world we cannot be sure the null hypothesis is true. This is why we write that we &quot;fail to reject the null hypothesis&quot; rather than &quot;we accept it.&quot; If there were no errors in the hypothesis tests in this simulation, we would never reject the null hypothesis, but by design it is normal to reject it according to alpha, the significance level. The de facto standard for alpha is 0.05. R
First, we run a simulation in R by repeatedly comparing randomly-generated sets of normally-distributed values using the two-sample t-test. Notice the simulation is vectorized: there are no &quot;for&quot; loops that clutter the code and slow the simulation. Read more »For more posts like this, see Heuristic Andrew.
If you work on applications for clients or have open sourced some shiny apps, a question that arises is how is your application being used. What you can do in order to find out how your hard work is being consumed is putting your code in logs and then ...
In order to stay up to date, I try to follow Jeremy Howard on a regular basis. In one of his recent videos, he shows how to use embeddings for categorical variables (e.g. weekdays).
First off; what are embeddings?
An embedding is a mapping of a catego...
On every SQL community event, where there could be a cluster of sessions dedicated to BI or analytics, I would have people asking me, “which one would you recommend?” or “which one I  prefer?” So, questions about recommendation and preferences are in my opinion the hardest one. And not that I would know my preferences … Continue reading R or Python? Python or R? The ongoing debate.
These are my sketchnotes for Sam Charrington’s podcast This Week in Machine Learning and AI about Neuroevolution: Evolving Novel Neural Network Architectures with Kenneth Stanley:
Sketchnotes from TWiMLAI talk #94: Neuroevolution: Evolving Novel Ne...
The LGD model is an important component in the expected loss calculation. In https://statcompute.wordpress.com/2015/11/01/quasi-binomial-model-in-sas, I discussed how to model LGD with the quasi-binomial regression that is simple and makes no distributional assumption. In the real-world LGD data, we usually would observe 3 ordered categories of values, including 0, 1, and in-betweens. In cases with a […]
I would like to demonstrate some helpful wrapr R notation tools that really neaten up your R code. Img: Christopher Ziemnowicz. Named Map Builder First I will demonstrate wrapr‘s &quot;named map builder&quot;: :=. The named map builder adds names to vectors and lists by nice &quot;names on the left and values on the right&quot; notation. … Continue reading Supercharge your R code with wrapr
Remember the nascent series of blog posts about Parks and recreation? Well, we’re still at one post, but don’t worry, here is a new one, and I’m sure the series will eventually be a real one. I’m looking at you, my R-Ladies friends. That said, ...
I wrote a quick function for simulating a Gaussian process given only its kernel function and used it to simulate processes with different kernels and very different properties.
I read this tweet thread yesterday, and one of the great things in it was discovering that the Department for Transport release traffic count data for Great Britain. When you download individual observation… Continue reading →
Time for another look at what’s new and interesting in the #rstats world with the help of Peter Meissner’s (@marvin_dpr) crossword.r🔗. The answers to last week’s puzzle have been posted (it seemed to make more sense posting the answers a week later vs the Monday after). There is a dedicated category — puzzler — to... Continue reading →
The prrd package was introduced recently, and made it to CRAN shortly thereafter. The idea of prrd is simple, and described in some more detail on its webpage and its GitHub repo. Reverse dependency checks are an important part of package development...
This blog post series is on machine learning with R. We will use the Caret package in R. In this part, we will first perform exploratory Data Analysis (EDA) on a real-world dataset, and then apply non-regularized linear regression to solve a supervised regression problem on the dataset. We will predict power output given a […]
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The keynotes for eRum 2018 are announced! Check them out and every other information about the international conference taking place in Budapest, this year! ---- The eRum conferences are particularly thought for the many Europeans that can’t manage to take part in the use!R conferences when they are based in USA – or, just like this year, in Australia. The 2018 eRum edition will take place in Budapest, Hungary, from the 14th to the 16th of May. Today we are particularly happy to announce the conference keynotes speakers, and what a list of names!
We will have with us Achim Zeileis, Stefano Iacus, Martin Mächler and Nathalie Villa-Vialaneix.
The post eRum 2018: Keynotes announced! appeared first on Quantide - R training &amp; consulting.
In a widely shared video, US Admiral McRaven addressing University of Texas at Austin's Class of 2014 chooses to deliver a simple message: make your bed every day.
A highlight of this talk is the quote The little things in life matter. If you can't do...
I'm a big fan using R to simulate data. When I'm trying to understand a data set, my first step is sometimes to simulate data from a model and compare the results to the data, before I go down the path of fitting an analytical model directly. Simulations are easy to code in R, but they can sometimes take a while to run — especially if there are a bunch of parameters you want to explore, which in turn requires a bunch of simulations. When your pc only has four core processors and your parallel processing across three of the.#DataScience...
In a recent release of tidytext, we added tidiers and support for building Structural Topic Models from the stm package. This is my current favorite implementation of topic modeling in R, so let’s walk through an example of how to get started with th...
We are pleased to announce the release of XLConnect 0.2-14, now available from CRAN and from our GitHub repository.
This release is mainly a maintenance release and comes with the following changes:
Upgrade to Apache POI 3.17
Upgraded XLCo...
Unfortunately this was not taught in any of my statistics or data analysis classes at university (wtf it so needs to be :scream_cat:).
So it took me some until I learned that the AUC has a nice probabilistic meaning.
What’s AUC anyway?
Consider:
A dataset : , where
is a vector of features collected for the th subject,
is the th subject’s label (binary outcome variable of interest, like a disease status, class membership, or whatever binary label).
A classification algorithm (like logistic regression, SVM, deep neural net, or whatever you like), trained on , that assigns a score (or probability) to any new observation signifying how likely its label is .
Then:
A decision threshold (or operating point) can be chosen to assign a class label ( or ) to based on the value of .
The chosen threshold determines the balance between how many false positives and false negatives will result from this classification.
Plotting the true positive rate (TPR) against the false positive rate (FPR) as the operating point changes from its minimum to its maximum value yields the receiver operating characteristic (ROC) curve. Check the confusion matrix if you are not sure what TPR and FPR refer to.
The area under the ROC curve, or AUC, is used as a measure of classifier performance.
Here is some R code for clarification (not even using tidyverse :stuck_out_tongue:):
# load some data, fit a logistic regression classifier
data(iris)
versicolor_virginica
This report introduces the ‘roloc’ package for R, which provides functions for converting colour specifications into colour names. Paul Murrell Download
In what is rapidly becoming a series — cool things you can do with R in a tweet — Julia Silge demonstrates scraping the list of members of the US house of representatives on Wikipedia in just 5 R statements: library(rvest) library(tidyverse) h % html_node(&quot;#mw-content-text __ div __ table:nth-child(18)&quot;) %__% html_table() reps % as_tibble() pic.twitter.com/25ANm7BHkj — Julia Silge (@juliasilge) January 12, 2018 Since Twitter munges the URL in the third line when you cut-and-paste, here's a plain-text version of Julia's code: library(rvest) library(tidyverse) h % html_node(&quot;#mw-content-text __ div...
This is guest post contributed by Pete Mohanty, creator of the kerasformula package.
Overview
The kerasformula package offers a high-level interface for the R interface to Keras. It’s main interface is the kms function, a regression-style...
Exploratory data analysis, data preparation and model performance using 'funModeling' R package.
This post will be about comparing a volatility signal using three different variations of implied volatility indices to predict when … Continue reading →
There is a new version of the R package vtreat now up on CRAN. vtreat is an essential data preparation system for predictive modeling that helps defend your predictive modeling work against real world data issues including: High cardinality categorical variables Rare levels (including new or novel levels during application) in categorical variables Missing data … Continue reading Latest vtreat up on CRAN
To post your R job on the next post Just visit  this link and post a new R job  to the R community. You can post a job for  free  (and there are also “featured job” options available for extra exposure). Current R jobs Job seekers:  please follow the links below to learn more and apply for your R job of interest: Featured Jobs Freelance R Shiny Developer – Work From Home Data Science Talent – Posted by Data Science Talent Frankfurt am Main Hessen, Germany 16 Jan 2018 Full-Time Customer Success Representative RStudio, Inc. – Posted by jclemens1 Anywhere 8 Jan 2018 Full-Time Core Data Science @Facebook – PhD Intern (London 2018) Tal Galili London England, United Kingdom 21 Dec 2017 Full-Time R Shiny Dashboard Engineer in Health Tech Castor EDC – Posted by Castor EDC Amsterdam-Zuidoost Noord-Holland, Netherlands 12 Dec 2017 Part-Time Data Scientist Alchemy – Posted by Andreas Voniatis Anywhere 8 Dec 2017 More New R Jobs Full-Time Senior Data Scientist @ Manchester, UK Hello Soda – Posted by LeanneFitzpatrick Manchester England, United Kingdom 24 Jan 2018 Full-Time Staff Scientist – Imaging, Statistics, AI @ Maryland, U.S. US FDA, Center for Devices and Radiological Health – Posted by brandon.gallas Colesville Maryland, United States 24 Jan 2018 Freelance R shiny application developer (freelance) World […]
Pretty recently I found a paper with the title “Why Should I Trust You?”: Explaining the Predictions of Any Classifier. The topic of interpretability is very important in the times of complex machine learning models and it’s also related to my PhD topic (reliability of machine learning models). Therefore I wanted to play around with … Continue reading Exploring lime on the house prices dataset
The post Exploring lime on the house prices dataset appeared first on verenahaunschmid.
We’re proud to be part of the Data Science and R communities.
We recognise the importance of knowledge sharing across industries, helping people with their personal an...
In our next MünsteR R-user group meetup on March 5th, 2018 Frank Rühle will talk about bioinformatics and how to analyse genome data.
You can RSVP here: http://meetu.ps/e/DDY1B/w54bW/f
Next-Generation sequencing and array-based technologies prov...
A purely geometric Le Monde mathematical puzzle this (or two independent ones, rather): Find whether or not there are inscribed and circumscribed circles to a convex polygon with 2018 sides of lengths ranging 1,2,…,2018. In the first (or rather second) case, the circle of radius R that is tangential to the polygon and going through […]
Highlights from StanCon 2018
This year we had the privelage of sponsoring StanCon. Unfortunately, we weren’t able to actually attend the conference. Rather than let our ticket go to waste, we ran a small competition, which Ignacio Martinez won ...
The pbapply R package that adds progress bar to vectorized functions has been know to accumulate overhead when calling parallel::mclapply with forking (see this post for more background on the issue). Strangely enough, a GitHub issue held the key to the solution that I am going to outline below. Long story short: forking is no longer expensive with pbapply, and as it turns out, it never was.
Strava is a fitness app that records you activities, including the routes of your walks, rides and runs. The service also provides an API that allows you to extract all of your data for analysis. University of Melbourne research fellow Marcus Volz created an R package to download and visualize Strava data, and created a chart to visualize all of his runs over six years as a small multiple. Inspired by his work (and the availability of the R package he created), others also visualized bike rides, activity calendars, and aggregated route maps with elevation data. (You can see several...
Of course, I’m paraphrasing Dirk’s fifteenth post in the rarely rational R rambling series: #15: Tidyverse and data.table, sitting side by side … (Part 1). I very much liked it, because, although I’m a happy tidyverse user, I’m always trying not to be tied into that verse too much by replicating certain tasks with other tools (and languages) as an… Continuar leyendo Tidyverse and data.table, sitting side by side… and then base R walks in →
Before we start…
We hope you found the first half of this post useful and interesting. Before we dive into the code, I want to explain a few important aspects of data science. Firstly, implementing data science in practice is always a researc...
Introduction Services that deliver the ingredients and recipes you need to make fresh, home-cooked meals, like Instacart have grown in popularity as people are turning away from low-cost, fast food in favor of healthier alternatives. However, those subscription services are still luxury for many so I decided to create a interactive dashboard to help the […]
A management challenge
Between 2011 and 2017 I selected somewhere between 20 and 30 staff and contractors, for New Zealand public sector roles with titles like Analyst, Senior Analyst and Principal Analyst. Alternative names for these roles could h...
Making it even easier to download and organize stock prices from Yahoo Finance - I just released a long due update to package BatchGetSymbols. The
files are under review in CRAN and you should get the update soon.
Meanwhile, you c...
The Best Algorithms are the Simplest The field of data science has progressed from simple linear regression models to complex ensembling techniques but the most preferred models are still the simplest and most interpretable. Among them are regression, logistic, trees and naive bayes techniques. Naive Bayes algorithm, in particular is a logic based technique which … Continue reading Understanding Naïve Bayes Classifier Using R
In any model development exercise, a considerable amount of time is spent in understanding the underlying data, visualizing relationships and validating preliminary hypothesis (broadly categorized as Exploratory data Analysis). A key element of EDA involves visually analyzing the data to glean valuable insights and understand underlying relationships &amp; patterns in the data. While EDA is […]
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Another Rblpapi release, now at version 0.3.8, arrived on CRAN yesterday. Rblpapi provides a direct interface between R and the Bloomberg Terminal via the C++ API provided by Bloomberg Labs (but note that a valid Bloomberg license and installation is...
In war, truth is the first casualty (Aeschylus) I am not a pessimistic person. On the contrary, I always try to look at the bright side of life. I also believe that living conditions are now better than years ago as these plots show. But reducing the complexity of our world to just six graphs … Continue reading Fatal Journeys: Visualizing the Horror →
One of the great features of R is the possibility to quickly access web-services. While some companies have the habit and policy to document their APIs, there is still a large chunk of undocumented but great web-services that help the regular data scie...
I currently advise R dplyr users to take care when using multiple assignment dplyr::mutate() commands on databases. (image: Kingroyos, Creative Commons Attribution-Share Alike 3.0 Unported License) In this note I exhibit a troublesome example, and a systematic solution. First let’s set up dplyr, our database, and some example data. library(&quot;dplyr&quot;) ## ## Attaching package: 'dplyr' … Continue reading Advisory on Multiple Assignment dplyr::mutate() on Databases
I revisited my previous post on creating beautiful time series calendar heatmaps in ggplot, moving the code into the tidyverse. To obtain following example:Simply use the following code: I hope the commented code is self-explanatory - enjoy :-)
In this tutorial, we will explore the open roles at Google, and try to see what common attributes Google is looking for, in future employees. This dataset is a compilation of job descriptions of 1200+ open roles at Google offices across the world. This dataset is available for download from the Kaggle website, and contains … Continue reading Who wants to work at Google?
This week’s R bulletin covers some interesting ways to list functions, to list files and illustrates the use of double colon operator. Hope you like this R weekly bulletin. Enjoy reading!   Shortcut Keys 1. New document – Ctrl+Shift+N 2. Close active document – Ctrl+W 3. Close all open documents – Ctrl+Shift+W Problem Solving Ideas...
The post R Weekly Bulletin Vol – XIV appeared first on .
Statisfix - Which fixing should I buy?
I have a bathroom cabinet to put up.
It needs to go onto a tiled plasterboard (drywall) wall. Because of the tiles, I can’t use the fixings I normally use to keep heavy objects fixed to the wall. A...
ggtern version 2.2.2 has just been submitted to CRAN, and it includes a number of new features. This time around, I have adapted the hexbin geometry (and stat), and additionally, created an almost equivalent geometry which operates on a triangular mesh rather than a hexagonal mesh. There are some subtle differences which give some added […]
The post Version 2.2.2 Released appeared first on ggtern: ternary diagrams in R.
Recently, there has been a lot of attention for the imposter syndrome. Even seasoned programmers admit they suffer from feelings of anxiety and low self-esteem. Some share their personal stories, which can be comforting for those suffering in silence. I focus on a method that helped me grow confidence in recent years. It is a simple, yet very effective way to deal with being overwhelmed by the many things a data scientis can acquaint him or herself with.
General Linear Models: The Basics
General linear models are one of the most widely used statistical tool
in the biological sciences. This may be because they are so flexible and
they can address many different problems, that they provide useful
output...
Peter Meissner (@marvin_dpr) released crossword.r🔗 to CRAN today. It’s a spiffy package that makes it dead simple to generate crossword puzzles. He also made a super spiffy javascript library to pair with it, which can turn crossword model output into an interactive puzzle. I thought I’d combine those two creations with a way to highlight... Continue reading →
This week I had the opportunity to teach R at my workplace, again. This course was the “advanced R” course, and unlike the one I taught at the end of last year, I had one more day (so 3 days in total) where I could show my colleagues the joys of th...
These are my sketchnotes for Sam Charrington’s podcast This Week in Machine Learning and AI about Learning State Representations with Yael Niv: https://twimlai.com/twiml-talk-92-learning-state-representations-yael-niv/
Sketchnotes from TWiMLAI talk...
Registration is now open for useR! 2018, the official R user conference to be held in Brisbane, Australia July 10-13. If you haven't been to a useR! conference before, it's a fantastic opportunity to meet and mingle with other R users from around the world, see talks on R packages and applications, and attend tutorials for deep dives on R-related topics. This year's conference will also feature keynotes from Jenny Bryan, Steph De Silva, Heike Hofmann, Thomas Lin Pedersen, Roger Peng and Bill Venables. It's my favourite conference of the year, and I'm particularly looking forward to this one. This...
What is SatRday?
SatRday in Cape Town
Be in it to win it
Jumping Rivers is proud to be sponsoring the upcoming SatRday conference in Cape Town, South Africa on 17th March 2018.
What is SatRday?
SatRdays are a collection of free/cheap accessible ...
When fitting a Bayesian model using MCMC (often via JAGS/BUGS/Stan), a number of checks are typically performed to make sure your model is worth interpreting without further manipulation (remember: all models are wrong, some are useful!): R-hat (AKA Gelman-Rubin statistic) … Continue reading →
We’re doing some interesting studies (cybersecurity-wise, not finance-wise) on digital currency networks at work-work and — while I’m loathe to create a geo-map from IPv4 geolocation data — we: do get (often, woefully inaccurate) latitude &amp; longitude data from our geolocation service (I won’t name-and-shame here); and, there are definite geo-aspects to the prevalence of... Continue reading →
Slides from January 2018 EdinbR session on spatial data in R.
After last year’s successful premiere, the eoda R-Akademie on the Road will be back in Hamburg this year for four inspiring days of professional R training for data scientists, programmers and R enthusiasts. The program for German-speaking users will start with a two-day introduction to R on 17-18 April. This course provides insights into the … „R-Akademie back in town – eoda R training returns to Hamburg in April“ weiterlesen
caret has been able to utilize parallel processing for some time (before it was on CRAN in October 2007) using slightly different versions of the package. Around September of 2011, caret started using the foreach package was used to &quot;harmonize&quot; the par...
Bioinformatics platform at Berlin Institute for Medical Systems Biology has been organizing computational genomics courses in Berlin since 2015. This year we will focus on bisulfite sequencing analysis and genomics data integration.The course will cover the following:1) bisulfite-seq analysis (alignment, QC, differential methylation, segmentation)2) integrating and visualizing processed genomics data. These methods can be used for any genomics data set not necessarily BS-seq data. There will be theoretical lectures followed by practical sessions where students directly apply what they have learned. The programming will be mainly done in R and unix shell.The deadline for application is March 1st! You can get more info at http://compgen.mdc-berlin.de computational genomics course 2018 berlin from Altuna Akalin
Microsoft R Open (MRO), Microsoft's enhanced distribution of open source R, has been upgraded to version 3.4.3 and is now available for download for Windows, Mac, and Linux. This update upgrades the R language engine to the latest R (version 3.4.3) and updates the bundled packages (specifically: checkpoint, curl, doParallel, foreach, and iterators) to new versions. MRO is 100% compatible with all R packages. MRO 3.4.3 points to a fixed CRAN snapshot taken on January 1 2018, and you can see some highlights of new packages released since the prior version of MRO on the Spotlights page. As always, you...
I’ve just shared a short webcast on data reshaping in R using the cdata package. (link) We also have two really nifty articles on the theory and methods: Fluid data reshaping with cdata Coordinatized Data: A Fluid Data Specification Please give it a try! This is the material I recently presented at the January 2017 … Continue reading Data Reshaping with cdata
Mazama Science has released the first official version (1.0) of the PWFSLSmoke R package for working with PM2.5 monitoring data. A beta version was released last year, along with an accompanying blog post. In this post, we discuss the purpose …   read more ...
I have created a new data package for R to help users obtain national population statistics of Mexico.
The conapomx package contains the mxpopulation data set, which is a tidy data.frame containing population estimates from the CONAPO (National Population Commission) official agency. The estimates are divided by age groups, gender, municipality and year.
To install, just call CRAN (or github if you are reading this before it is accepted).
install.packages(&quot;conapomx&quot;) # or... library(devtools)
install_github(&quot;eflores89/conapomx&quot;)
# explore the dataset
head(mxpopulation)
y
st
id_st
mun
id_mun
geoid
gender
age
population
2010
Aguascalientes
1
Aguascalientes
1
1001
0
0-14
124263.71
2010
Aguascalientes
1
Aguascalientes
1
1001
0
15-29
106695.14
2010
Aguascalientes
1
Aguascalientes
1
1001
0
30-44
81088.65
2010
Aguascalientes
1
Aguascalientes
1
1001
0
45-64
60379.29
2010
Aguascalientes
1
Aguascalientes
1
1001
0
65+
17679.26
2010
Aguascalientes
1
Aguascalientes
1
1001
1
0-14
119535.77
Here is the data for 2018, just out of curiosity:
library(dplyr)
mxpopulation %__% filter(y == &quot;2018&quot;) %__%
group_by(st) %__% summarize(&quot;Population&quot; = sum(population))
st
Population
Aguascalientes
1337792.5
Baja California
3633772.2
Baja California Sur
832827.2
Campeche
948459.3
Chiapas
5445232.7
Chihuahua
3816865.4
Coahuila
3063662.5
Colima
759686.5
Distrito Federal
8788140.6
Durango
1815965.6
Guanajuato
5952086.5
Guerrero
3625040.0
Hidalgo
2980532.2
Jalisco
8197483.1
Mexico
17604619.1
Michoacan
4687210.5
Morelos
1987595.8
Nayarit
1290518.8
Nuevo Leon
5300618.6
Oaxaca
4084674.0
Puebla
6371380.8
Queretaro
2091823.2
Quintana Roo
1709478.7
San Luis Potosi
2824976.0
Sinaloa
3059321.7
Sonora
3050472.7
Tabasco
2454294.5
Tamaulipas
3661161.7
Tlaxcala
1330142.6
Veracruz
8220321.9
Yucatan
2199617.6
Zacatecas
1612014.2
Hopefully this helps to avoid the notoriously bad datos.gob webpage! Happy data wrangling!
The new package simmer.bricks has found its way to CRAN. The simmer package provides a rich and flexible API to build discrete-event simulations. However, there are certain recurring patterns that are typed over and over again, higher-level tasks which can be conceptualised in concrete activity sequences. This new package is intended to capture this kind of patterns into usable bricks,… Continuar leyendo simmer.bricks 0.1.0: new add-on for simmer →
Bob Rudis (@hrbrmstr) is a famed expert, author and developer in Data Security and the Chief Security Data Scientist at Rapid7. Bob also creates the most deliciously vivid images of his meals documented by the #nom hashtag. I’m going to use a similar method used in my previous projects (Hipster Veggies &amp; Machine Learning Flashcards) to wrangle all those images into a nice collection - mostly for me to look at for inspiration in recipe planning.
Yum! Have you ever thought about collecting all these recipes &amp; images into a cookbook?!— Jasmine Dumas (@jasdumas) January 15, 2018
Source Repository: jasdumas/bobs-noms
Analysis
library(rtweet) # devtools::install_github(&quot;mkearney/rtweet&quot;)
library(tidyverse)
library(dplyr)
library(stringr)
library(magick)
library(knitr)
library(kableExtra)
# get all of bob's recent tweets
bobs_tweets
Am update of RcppMsgPack got onto CRAN today. It contains a number of enhancements Travers had been working on, as well as one thing CRAN asked us to do in making a suggested package optional.
MessagePack itself is an efficient binary serialization f...
The SparklyR package from RStudio provides a high-level interface to Spark from R. This means you can create R objects that point to data frames stored in the Spark cluster and apply some familiar R paradigms (like dplyr) to the data, all the while leveraging Spark's distributed architecture without having to worry about memory limitations in R. You can also access the distributed machine-learning algorithms included in Spark directly from R functions. If you don't happen to have a cluster of Spark-enabled machines set up in a nearby well-ventilated closet, you can easily set one up in your favorite cloud...
I have created a new data package for R to help users obtain national population statistics of Mexico.
The conapomx package contains the mxpopulation data set, which is a tidy data.frame containing population estimates from the CONAPO (National Population Commission) official agency. The estimates are divided by age groups, gender, municipality and year.
To install, just call CRAN (or github if you are reading this before it is accepted).
install.packages(&quot;conapomx&quot;) # or... library(devtools)
install_github(&quot;eflores89/conapomx&quot;)
# explore the dataset
head(mxpopulation)
y
st
id_st
mun
id_mun
geoid
gender
age
population
2010
Aguascalientes
1
Aguascalientes
1
1001
0
0-14
124263.71
2010
Aguascalientes
1
Aguascalientes
1
1001
0
15-29
106695.14
2010
Aguascalientes
1
Aguascalientes
1
1001
0
30-44
81088.65
2010
Aguascalientes
1
Aguascalientes
1
1001
0
45-64
60379.29
2010
Aguascalientes
1
Aguascalientes
1
1001
0
65+
17679.26
2010
Aguascalientes
1
Aguascalientes
1
1001
1
0-14
119535.77
Here is the data for 2018, just out of curiosity:
library(dplyr)
mxpopulation %__% filter(y == &quot;2018&quot;) %__%
group_by(st) %__% summarize(&quot;Population&quot; = sum(population))
st
Population
Aguascalientes
1337792.5
Baja California
3633772.2
Baja California Sur
832827.2
Campeche
948459.3
Chiapas
5445232.7
Chihuahua
3816865.4
Coahuila
3063662.5
Colima
759686.5
Distrito Federal
8788140.6
Durango
1815965.6
Guanajuato
5952086.5
Guerrero
3625040.0
Hidalgo
2980532.2
Jalisco
8197483.1
Mexico
17604619.1
Michoacan
4687210.5
Morelos
1987595.8
Nayarit
1290518.8
Nuevo Leon
5300618.6
Oaxaca
4084674.0
Puebla
6371380.8
Queretaro
2091823.2
Quintana Roo
1709478.7
San Luis Potosi
2824976.0
Sinaloa
3059321.7
Sonora
3050472.7
Tabasco
2454294.5
Tamaulipas
3661161.7
Tlaxcala
1330142.6
Veracruz
8220321.9
Yucatan
2199617.6
Zacatecas
1612014.2
Hopefully this helps to avoid the notoriously bad datos.gob webpage! Happy data wrangling!
Problem overview
This article will be divided into a series of posts. In this post, I will explain the basic concepts behind convolutional neural networks and how to build them using Keras. In the next post, I will focus on improving the perfor...
Mark Sellors, Head of Data Engineering
I started working with R around about 5 years ago. Parts of the R world have changed substantially over that time, while other par...
We're very excited to announce a new format for the Enterprise Applications of the R Language Conference (EARL) in the US - a three-stop roadshow! We'll be visiting thr...
NOTE: The likelihood of this recipe being added to the recent practice bookdown book is slim, but I’ll try to keep the same format for the blog post. Problem You want to collect all the tweets in a Twitter tweet thread Solution Use a few key functions in rtweet to piece the thread elements back... Continue reading →
“Base R” (call it “Pure R”, “Good Old R”, just don’t call it “Old R” or late for dinner) can be fast for in-memory tasks. This is despite the commonly repeated claim that: “packages written in C/C++ are faster than R code.” The benchmark results of “rquery: Fast Data Manipulation in R” really called out … Continue reading Base R can be Fast
One of the features of the functions in smooth package is the ability to use exogenous (aka “external”) variables. This potentially leads to the increase in the forecasting accuracy (given that you have a good estimate of the future exogenous variable). For example, in retail this can be a binary variable for promotions and we […]
Owen Jones, Placement Student
Prelude
Maybe you're looking for a change of scene. Maybe you're looking for your first job. Maybe you're stuck in conversation with a rela...
Another small maintenance release, version 0.6.14, of the digest package arrived on CRAN and in Debian today.
digest creates hash digests of arbitrary R objects (using the 'md5', 'sha-1', 'sha-256', 'crc32', 'xxhash' and 'murmurhash' algorithms) perm...
One of the highest pleasures for an academic teacher is to be surprised by an extraordinary student’s project or homework. Something that greatly exceeds expectations. I’ve reoriented my courses in a way to make such surprises frequent. The second project in my Data Visualisation classes was related to interactive graphics. The task was to create … Czytaj dalej Top interactive visualizations of movie scripts
These are my sketchnotes for Sam Charrington’s podcast This Week in Machine Learning and AI about Philosophy of Intelligence with Matthew Crosby:
You can listen to the podcast here.
This week on the podcast we’re featuring a series of conversa...
[crayon-5a5ad6bfdb9ea208679487-i/] is a useful frequentist approach to hierarchical/multilevel linear regression modelling. For good reason, the model output only includes t-values and doesn’t include p-values (partly due to the difficulty in estimating the degrees of freedom, as discussed here). Yes, p-values are evil and we should continue to try and expunge them from our analyses. But I […]
I have recently been working on projects using Amazon EC2 (elastic compute cloud), and RStudio Server. I thought I would share some of my working notes. Amazon EC2 supplies near instant access to on-demand disposable computing in a variety of sizes (billed in hours). RStudio Server supplies an interactive user interface to your remote R … Continue reading Setting up RStudio Server quickly on Amazon EC2
This post will demonstrate how to replicate the volatility ETNs (XIV, VXX, ZIV, VXZ) from CBOE futures, thereby allowing any … Continue reading →
In a recent post, I mentioned three avenues for working with TensorFlow from R:
* The keras package, which uses the Keras API for building scaleable, deep learning models * The tfestimators package, which wraps Google’s Estimators API for fitting models with pre-built estimators
* The tensorflow package, which provides an interface to Google’s low-level TensorFlow API
In this post, Edgar and I use the linear_classifier() function, one of six pre-built models currently in the tfestimators package, to train a linear classifier using data from the titanic package.
library(tfestimators)
library(tensorflow)
library(tidyverse)
library(titanic)
The titanic_train data set contains 12 fields of information on 891 passengers from the Titanic. First, we load the data, split it into training and test sets, and have a look at it.
titanic_set % filter(!is.na(Age))
# Split the data into training and test data sets
indices
by Le Zhang (Data Scientist, Microsoft) and Graham Williams (Director of Data Science, Microsoft) As an in-memory application, R is sometimes thought to be constrained in performance or scalability for enterprise-grade applications. But by deploying R in a high-performance cloud environment, and by leveraging the scale of parallel architectures and dedicated big-data technologies, you can build applications using R that provide the necessary computational efficiency, scale, and cost-effectiveness. We identify four application areas and associated applications and Azure services that you can use to deploy R in enterprise applications. They cover the tasks required to prototype, build, and operationalize an...
Summary: in this post, I implemenent an R function for computing \( P(\theta_1 __ \theta2) \), where \( \theta_1 \) and \( \theta_2 \) are beta-distributed random variables. This is useful for estimating the probability that one binomial proportion is greater than another.
I am working on a project in which I need to compare two binomial proportions to see if one is likely to be greater than the other. I wanted to do this in a Bayesian framework; fortunately, estimating binomial proportions is one of the first subjects taught to introduce Bayesian statistics (and statistics in general, for that matter) so the method is well established. Rasmus Bååth has a very nice article describing the Bayesian binomial test, and an estimation approach using JAGS.
I made the problem even simpler by using the fact that the beta distribution is the conjugate prior to the binomial distribution. That is, if the prior is \( \mathrm{beta}(\alpha, \beta) \) distributed, then the posterior after observing \( k \) successes in \( n \) trials is
If you want to start with a noninformative prior, then you can use the beta distribution with \( \alpha = \beta = 1\), which collapses to the uniform distribution. This makes the posterior
What I am interested is in comparing two binomial proportions, \( \theta_1 \) and \( \theta_2 \). I want to calculate
We observe \( k_1 \) successes out of \( n_1 \) trials and estimate a posterior distribution for \( \theta_1 \), and \( k_2 \) successes out of \( n_2 \) trials for \( \theta_2 \). Then the probability in question is
So far, so textbook. At this point, the simplest way to get an estimate is to draw two sets of random numbers, distributed by the two respective beta distributions, and see how many times the first random numbers is greater than the second:
set.seed(1)
k1
If you've ever wondered how neural networks work behind the scenes, check out this guide to implementing neural networks in scratch with R, by David Selby. You may be surprised how with just a little linear algebra and a few R functions, you can train a function that classifies the red dots from the blue dots in a complex pattern like this: David also includes some elegant R code that implements neural networks using R6 classes. For a similar implementation using base R function, you may also want to check out this guide to implementing neural networks in R by...
“What does the world outside your head really ‘look’ like? Not only is there no color, there’s also no sound: the compression and expansion of air is picked up by the ears, and turned into electrical signals. The brain then presents these signals to us as mellifluous tones and swishes and clatters and jangles. Reality … Continue reading Deep Learning from first principles in Python, R and Octave – Part 2
I use RStudio every day at work. And every day my eyes say thank you to the developers for implementing themes support for the editor, and more recently a complete dark skin for the GUI. Dark themes are not only important for your health and the health of the planet. They are also absolutely essential to look cool in the office and make your colleagues believe you’re working on very complicated projects. 😀 However, one little thing could dispel the...Lire la suite Lire la suite
This is guest post contributed by Matt Dancho, CEO of Business Science. The post was originally published on the Business Science blog.
Customer churn is a problem that all companies need to monitor, especially those that depend on subscript...
The R language surged to 8th place in the 2017 TIOBE language rankings, up 8 places from a year before. Fellow data science language language Python also saw an increase in rankings, taking the 4th spot (one ahead of its January 2016 ranking). (Click the table for the current top 20 rankings.) TIOBE ranks programming languages according to their search engine rankings, and R has been steadily climbing since the rankings began: You can find the current TIOBE language rankings, updated monthly, at the link below. TIOBE: TIOBE Index
Udemy is offering readers of R-bloggers access to its global online learning marketplace for only $10 per course! This deal (offering over 50%-90% discount) is for hundreds of their courses – including many R-Programming, data science, machine learning etc. Click here to browse ALL (R and non-R) courses Advanced R courses:  Regression modelling Comprehensive Linear Modeling with R (15 Hours of video) Linear regression in R for Data Scientists (7 hours) Linear Mixed-Effects Models with R (11 Hours of video) Linear Regression, GLMs and GAMs with R (8 hours) Forecasting Models with R (5 hours) Time Series Analysis and Forecasting in R (3 Hours of video) Regression, Data Mining, Text Mining, Forecasting using R (32 hours) Statistical/machine learning Bayesian Computational Analyses with R (12 Hours of video) Data Science and Machine Learning Bootcamp with R (17 hours) Decision Tree – Theory, Application and Modeling using R (5 hours) Data Mining with R: Go from Beginner to Advanced! (12 hours) Machine Learning A-Z™: Hands-On Python &amp; R In Data Science (40 hours) Applied Multivariate Analysis with R (13 Hours of video)  Visualization Graphs in R (ggplot2, plotrix, base R) – Data Visualization with R Programming Language (5 Hours of video) Multivariate Data Visualization with R (7 Hours of video) Other topics Create Interactive […]
- While I would like to post a success story about the Brazilian post office using R to analyze its internal data, the real story is the use of R’s logo for its Registered mail :). Have a look:
My best guess is that someone...
Overview
We are excited to announce the availability of the cloudml package, which provides an R interface to Google Cloud Machine Learning Engine. CloudML provides a number of services including:
Scalable training of models built with th...
The property market in London SW10 is weak. But planning applications are in the ascendency. Is this how home owners are adding space?
The post SW10 digs deep appeared first on thinkr.
By Gabriel Vasconcelos When dealing with forecasting models there is an issue that generates a lot of confusion, which is the difference between direct and recursive forecasts. I believe most people are more used to recursive forecasts because they are … Continue reading →
These are my sketchnotes taken from the “This week in Machine Learning &amp; AI” podcast number 88 about Using Deep Learning and Google Street View to Estimate Demographics with Timnit Gebru:
Sketchnotes from TWiMLAI talk #88: Using Deep Learning...
LSE IR Gender and Diversity ProjectTwo shinydashboard posts in a row, that’s a first. As I mentioned on Twitter, I’m not really this productive; rather, the apps had been on the proverbial shelf for a while and I’m just releasing them now. In fac...
Let’s admit it. The whole world has been going crazy with Bitcoin. Bitcoin (BTC), the first cryptocurrency (in fact, the first digital currency to solve the double-spend problem) introduced by Satoshi Nakamoto has become bigger than well-established firms (even a few countries). So, a lot of Bitcoin Enthusiasts and Investors are looking to keep a […]
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With the release of the new version of mlrMBO we added some minor fixes and added a practical feature called Human-in-the-loop MBO.
It enables you to sequentially
visualize the state of the surrogate model,
obtain the suggested parameter configuration for the next iteration and
update the surrogate model with arbitrary evaluations.
In the following we will demonstrate this feature on a simple example.
I have written another blogpost about Looking beyond accuracy to improve trust in machine learning at my company codecentric’s blog:
Traditional machine learning workflows focus heavily on model training and optimization; the best model is usually chosen via performance measures like accuracy or error and we tend to assume that a model is good enough for deployment if it passes certain thresholds of these performance criteria. Why a model makes the predictions it makes, however, is generally neglected. But being able to understand and interpret such models can be immensely important for improving model quality, increasing trust and transparency and for reducing bias. Because complex machine learning models are essentially black boxes and too complicated to understand, we need to use approximations to get a better sense of how they work. One such approach is LIME, which stands for Local Interpretable Model-agnostic Explanations and is a tool that helps understand and explain the decisions made by complex machine learning models.
Continue reading at https://blog.codecentric.de/en/2018/01/look-beyond-accuracy-improve-trust-machine-learning/…
Links to the entire example code and more info are given at the end of the blog post.
The blog post is also available in German.
Before beginning work on a new data science project I like to do the following: 1. Get my work area ready by creating an R Project for use with the RStudio IDE. 2. Organize my work area by creating a series of directories to store my project inputs and outputs. I create ‘data’ (raw data), … Continue reading R Work Areas. Standardize and Automate.
Imagine you were to buy a car, would you just go to a store and buy the first one that you see? No, right? You usually consult few people around you, take their opinion, add your research to it and then go for the final decision. Let’s take a simpler scenario: whenever you go for … Continue reading How to implement Random Forests in R
Do you ever have to recode many values at once? It’s a frequent chore when preparing data. For example, suppose we had to replace state abbreviations with the full names:
abbs
One is an accident. Two is a coincidence. Three is a pattern. Perhaps it’s no coincidence that there are three new interfaces that use Stan’s C++ implementation of adaptive Hamiltonian Monte Carlo (currently an updated version of the no-U-turn sampler). ScalaStan embeds a Stan-like language in Scala. It’s a Scala package largely (if not entirely […]
The post Three new domain-specific (embedded) languages with a Stan backend appeared first on Statistical Modeling, Causal Inference, and Social Science.
Win-Vector LLC recently announced the rquery R package, an operator based query generator. In this note I want to share some exciting and favorable initial rquery benchmark timings. Let’s take a look at rquery’s new “ad hoc” mode (made convenient through wrapr‘s new “wrapr_applicable” feature). This is where rquery works on in-memory data.frame data by … Continue reading rquery: Fast Data Manipulation in R
In case you missed them, here are some articles from December of particular interest to R users. Hadley Wickham's Shiny app for making eggnog. Using R to analyze the vocal range of pop singers. A video tour of the data.table package from its creator, Matt Dowle. The European R Users Meeting (eRum) will be held in Budapest, May 14-18. Winners of the ASA Police Data Challenge student visualization contest. An introduction to seplyr, a re-skinning of the dplyr package to a standard R evaluation interface. How to run R in the Windows Subsystem for Linux, along with the rest of...
In this post we will use Keras to classify duplicated questions from Quora. The dataset first appeared in the Kaggle competition Quora Question Pairs and consists of approximately 400,000 pairs of questions along with a column indicating if th...
If you can walk away from a landing, it's a good landing. If you use the airplane the next day, it's an outstanding landing. -Chuck Yeager As the first airplane invented by Wright brothers in 1903, aviation accident became an inevitable tragedy as well as an attractive topic. In this project, I used the data […]
It is quite likely to get address info when scraping data from the web, but not geo-coordinates which may be required for further analysis like clustering. Thus geocoding is often needed to get a location’s coordinates by its address. There are several options, including one of the most popular, google geocoding API. This option can […]
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One of the most powerful things about R is the ecosystem that has emerged around it. In addition to the R language itself and the many packages that extend it, you have a network of users, developers, governance bodies, software vendors and service providers that provide resources in technical information and support, companion applications, training and implementation services, and more. I gave the talk above at the useR! conference last year, but never posted the slides before because they weren't particularly useful without the associated talk track. Mark Sellors from Mango Solutions has thankfully filled the gap with his new...
08.01.2018 I recently discovered on a high dpi display (Retina-Display) enabled 13’’ notebook that the latest release of Bio7 for MacOSX is not displayed in high dpi but in a lower resolution which makes text and icons blurry (scaled) and unnecessary strains the eyes when editing R scripts with the R editor. So I uploaded […]
Knowledge of the way how R evaluates expressions is crucial to avoid hours of staring at the screen or hitting unexpected and difficult bugs.
We’ll start with an example of an issue I came accross a few months ago when using the purrr::map function....
How to internationalize exams2nops() by adding support for new natural languages in written R/exams (that can be automatically scanned and evaluated).
Idea
In addition to completely customizable PDF output, R/exams provides a standardized format called
“NOPS” for written exams with multiple-choice
(and/or single-choice) exercises that can be
automatically generated, scanned, and evaluated.
In order to assure that the automatic scanning works the title page has a fixed layout
that cannot be modified by R/exams users. However, to internationalize the format there
is a possibility for customizing the natural language support. A number of languages
is already available but it is not difficult to add further languages or to tweak
existing languages if desired.
Example
To illustrate how the language support works, once it has been fully incorporated into the
exams package, we set up a short exam with three exercises:
deriv2, tstat2,
swisscapital. All of these are readily available
in the package (and are actually in English).
library(&quot;exams&quot;)
myexam
A new package is now on the ghrr drat. It was uploaded four days ago to CRAN but still lingers in the inspect/ state, along with a growing number of other packages. But as some new packages have come through, I am sure it will get processed eventuall...
This is the considerably belated second part of my blog series on fitting diffusion models (or better, the 4-parameter Wiener model) with brms. The first part discusses how to set up the data and model. This second part is concerned with perhaps the most important steps in each model based data analysis, model diagnostics and […]
I’m starting off 2018 with dropping my involvement with jug. The reason for this being that I simply cannot find the time or interest anymore to maintain the code.
All of the open-source efforts that I started always had to do with a concrete need t...
I’ve always thought that it was ironic that most countries with the word “Democratic” in their name are exceptionally un-democratic in reality. So I was very interested in the following post on Reddit this week showing exactly this (https://www.reddit.com/r/dataisbeautiful/comments/7nkyek/countries_with_the_word_democratic_in_their/). However, there are only 8 countries that have “democratic” in their name: People’s Democratic Republic of […]
I’ve now down a few collages from R using magick: the faces of #rstats Twitter, We R-Ladies with Lucy D’Agostino McGowan, and a holiday card for R-Ladies. The faces of #rstats Twitter and holiday card collages were arranged at random, while the We ...
Data Visualization for Social Science will be published later this year by Princeton University Press. You can read a near-complete draft of the book at socviz.co. If you would like to receive one (1) email when the book is available for pre-order, ple...
A minor maintenance release of the RcppCNPy package arrived on CRAN this week.
RcppCNPy provides R with read and write access to NumPy files thanks to the cnpy library by Carl Rogers.
There is no code change here. But to process the vignette we rely ...
The R package wrapr now has a neat new feature: “wrapr_applicable”. This feature allows objects to declare a surrogate function to stand in for the object in wrapr pipelines. It is a powerful technique and allowed us to quickly implement a convenient new ad hoc query mode for rquery. A small effort in making a … Continue reading New wrapr R pipeline feature: wrapr_applicable
A maintenance release of the tint package arrived on CRAN earlier this week. Its name expands from tint is not tufte as the package offers a fresher take on the Tufte-style for html and pdf presentations.
A screenshot of the pdf variant is below.
...
Classification algorithm defines set of rules to identify a category or group for an observation. There is various classification algorithm available like Logistic Regression, LDA, QDA, Random Forest, SVM etc. Here I am going to discuss Logistic regression, LDA, and QDA. The classification model is evaluated by confusion matrix. This matrix is represented by a […]
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I'm pleased to announce that the RQuantLib Windows binaries are now up to 0.4.4!  The RQuantLib pre-built Windows binaries have been frozen on CRAN since 0.4.2, but now you can get version 0.4.4 binaries on Dirk's ghrr drat repo.
Installation is as simple as:
drat::addRepo(&quot;ghrr&quot;) # maybe use 'install.packages(&quot;drat&quot;)' first 
install.packages(&quot;RQuantLib&quot;, type=&quot;binary&quot;)
I will be able to create Windows binaries for future RQuantLib versions too, now that I have a Windows QuantLib build (version 1.11) to link against.
Dirk and I plan to talk with CRAN about getting the new binaries hosted there.  Regardless, they will always be available via the drat repo.
It looks pretty cool! Wednesday, Jan 10 Invited Talk: Predictive information criteria in hierarchical Bayesian models for clustered data. Sophia Rabe-Hesketh and Daniel Furr (U California, Berkely) 10:40-11:30am Does the New York City Police Department rely on quotas? Jonathan Auerbach (Columbia U) 11:30-11:50am Bayesian estimation of mechanical elastic constants. Ben Bales, Brent Goodlet, Tresa Pollock, […]
The post StanCon is next week, Jan 10-12, 2018 appeared first on Statistical Modeling, Causal Inference, and Social Science.
The new year begins with me being on the hook to crank out a book on advanced web-scraping in R by July (more on that in a future blog post). The bookdown🔗 package seemed to be the best way to go about doing this but I had only played with the toy/default examples of it... Continue reading →
In this post, taken from the book R Data Mining by Andrea Cirillo, we’ll be looking at how to scrape PDF files using R. It’s a relatively straightforward way to look at text mining – but it can be challenging if you don’t know exactly what you’re doing. Until January 15th, every single eBook and … Continue reading How to extract data from a PDF file with R
In diesem Artikel möchten wir Ihnen das Konzept der Markov Kette vorstellen, dessen Grundlagen veranschaulichen und Ihnen mehrere mögliche Anwendungsbereiche aufzeigen, in denen Sie mit einer gezielten statistischen Programmierung von Markov Ketten profitieren können. Eine Markov Kette ist ein stochastischer Prozess mit den vielfältigsten Anwendungsbereichen aus der Natur, Technik und Wirtschaft. Klassische Anwendungsmöglichkeiten aus der […]
Der Beitrag Die Markov Kette: Wie berechne ich die Gleichgewichtsverteilung? erschien zuerst auf Statistik Service.
The objective of this post is to create a method which easily combines loss runs, or listings of insurance claims, into triangles. Using only Excel, the common method is to create links between the excel files which must be updated manually for each new evaluation. This is prone to human error and is time-consuming. Using […]
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I have some big news about our R package cdata. We have greatly improved the calling interface and Nina Zumel has just written the definitive introduction to cdata. cdata is our general coordinatized data tool. It is what powers the deep learning performance graph (here demonstrated with R and Keras) that I announced a while … Continue reading Big cdata News
by [Błażej Moska](https://www.linkedin.com/in/b%C5%82a%C5%BCej-moska-9a316113a/), computer science student and data science intern Got stuck with too large a dataset? R speed drives you mad? Divide, parallelize and go with Rcpp! One of the frustrating moments while working with data is when you need results urgently, but your dataset is large enough to make it impossible. This happens often when we need to use algorithm with high computational complexity. I will demonstrate it on the example I've been working with. Suppose we have large dataset consisting of association rules. For some reasons we want to slim it down. Whenever two rules consequents are...
Do you know the Black Metal Cats Twitter account? As explained in this great introduction, it “combines kitties with heavy metal lyrics”. I know the account because I follow Scott Chamberlain who retweets them a lot, which I enjoy as far as one can...
The fifth update of the 3.6.x release of simmer, the Discrete-Event Simulator for R, is on CRAN. This release extends the attributes API by allowing users to set/get multiple attributes at once (a pretty straightforward change as well as useful; I don’t know why it didn’t occurred to me before…). Vectors as attributes and other data… Continuar leyendo simmer 3.6.5 →
“You don’t perceive objects as they are. You perceive them as you are.” “Your interpretation of physical objects has everything to do with the historical trajectory of your brain – and little to do with the objects themselves.” “The brain generates its own reality, even before it receives information coming in from the eyes and … Continue reading Deep Learning from first principles in Python, R and Octave – Part 1
We’re happy to announce the third release of the tibbletime package. This is a huge update, mainly due to a complete rewrite of the package. It contains a ton of new functionality and a number of breaking changes that existing users need to be aware ...
An arithmetic Le Monde mathematical puzzle to conclude 2017: Find (a¹,…,a¹³), a permutation of (1,…,13) such that a¹/a²+a³=a²+a³/a³+a⁴+a⁵=b¹
There are lots of tricks you can use to make R code run faster: use more efficient data structures; vectorize your R code; offload complex data management tasks to databases. Emily Robinson shares many of these R performance tips in a case study on A/B testing for Etsy. The tips are just as valuable as the process Emily shares for evaluating them — and also the process of asking the R community for help. Check out her post, linked below. Hooked on Data: Making R Code Faster : A Case Study
Here are some of the books that I found interesting and useful in 2017. Scrum: The Art of Doing Twice the Work in Half the Time by Jeff Sutherland Jeff Sutherland, one of the creators of the scrum methodology of project management lays down the rational for adopting scrum over more traditional project management frameworks. … Continue reading Helpful Data Science Reads
An interesting (if vaguely formulated) question on X validated: given two Gaussian variates that are maximally coupled, what is the correlation between these variates? The answer depends on the parameters of both Gaussian, with a correlation of one when both Gaussians are identical. Answering the question by simulation (as I could not figure out the […]
Another year, and lots of exciting reading!
Davidson, R. &amp; V. Zinde-Walsh, 2017. Advances in specification testing. Canadian Journal of Economics, online.
Dias, G. F. &amp; G. Kapetanios, 2018. Estimation and forecasting in vector autoregressive moving average models for rich datasets. Journal of Econometrics, 202, 75-91.  
González-Estrada, E. &amp; J. A. Villaseñor, 2017. An R package for testing goodness of fit: goft. Journal of Statistical Computation and Simulation, online.
Hajria, R. B., S. Khardani, &amp; H. Raïssi, 2017. Testing the lag length of vector autoregressive models:  A power comparison between portmanteau and Lagrange multiplier tests. Working Paper 2017-03, Escuela de Negocios y EconomÍa. Pontificia Universidad Católica de ValaparaÍso.
McNown, R., C. Y. Sam, &amp; S. K. Goh, 2018. Bootstrapping the autoregressive distributed lag test for cointegration. Applied Economics, 50, 1509-1521.
Pesaran, M. H. &amp; R. P. Smith, 2017. Posterior means and precisions of the coefficients in linear models with highly collinear regressors. Working Paper BCAM 1707, Birkbeck, University of London.
Yavuz, F. V. &amp; M. D. Ward, 2017. Fostering undergraduate data science. American Statistician, online. 
© 2018, David E. Giles
Makridakis Competitions (also known as the M Competitions or M-Competitions) are a series of competitions organized by teams led by forecasting researcher Spyros Makridakis and intended to evaluate and compare the accuracy of different forecasting methods. So far three competitions have taken place, named as M1 (1982), M2 (1993) and M3 (2000). The fourth competition […]
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RStudio's Jenny Bryan (whose recent interviews here and here you should definitely check out) has some excellent advice for improving your workflow for R: Use. Projects. — @JennyBryan at #EARLConf2017 #rstats pic.twitter.com/r4a08JhWHT — David Smith (@revodavid) September 13, 2017 If you're you're routinely using setwd to manually change R's working directory, or using rm(list = ls()) to 'reset' your R session, that's a good sign that you're doing all of your R work in the default R workspace. It's a much better idea to set up a separate project (at its simplest, a separate directory) for each distinct piece of...
02.01.2018 A new version of Bio7 is available. The application Bio7 is an integrated development environment for ecological modeling scientific image analysis and statistical analysis. Over the years Bio7 evolved to a platform useful for a broad scientific context. This release includes a plethora of new features and improvements for the R editor, RShell, RMarkdown […]
Over the year 2017 the [crayon-5a4afb5f2a18f739005075-i/] package has grown from v1.6.0 to v2.3.1. Now it is much more mature and has more downloads. It even now has its own hex (thanks to Fotios Petropoulos): A lot of changes happened in 2017, and it is hard to mention all of them, but the major ones are: […]
To post your R job on the next post Just visit  this link and post a new R job  to the R community. You can post a job for  free  (and there are also “featured job” options available for extra exposure). Current R jobs Job seekers:  please follow the links below to learn more and apply for your R job of interest: Featured Jobs Full-Time Core Data Science @Facebook – PhD Intern (London 2018) Tal Galili London England, United Kingdom 21 Dec 2017 Full-Time R Shiny Dashboard Engineer in Health Tech Castor EDC – Posted by Castor EDC Amsterdam-Zuidoost Noord-Holland, Netherlands 12 Dec 2017 Part-Time Data Scientist Alchemy – Posted by Andreas Voniatis Anywhere 8 Dec 2017 Full-Time Data Analyst: Travel Behavior RSG – Posted by patricia.holland@rsginc.com San Diego California, United States 22 Nov 2017 Full-Time Customer Success Representative RStudio, Inc. – Posted by jclemens1 Anywhere 17 Nov 2017 More New R Jobs Full-Time Core Data Science @Facebook – PhD Intern (London 2018) Tal Galili London England, United Kingdom 21 Dec 2017 Full-Time Post-doc on Sensitivity Analysis for Bayesian Inference via… @ Melbourne, Australia Melbourne University – Posted by dicook Melbourne Victoria, Australia 21 Dec 2017 Full-Time R Shiny Dashboard Engineer in Health Tech Castor EDC – Posted by Castor EDC Amsterdam-Zuidoost Noord-Holland, Netherlands 12 […]