<p><div><div><div><div data-show-faces="false" data-height="21" data-width="100" data-layout="button_count" data-send="true" data-href="https://www.r-bloggers.com/new-course-visualization-best-practices-in-r/"></div></div><div></div></div><div></div></div>
<div>
(This article was first published on <strong><a href="https://www.datacamp.com/community/blog/course-visualization-best-practices-r"> DataCamp Community - r programming</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p><center><a rel="nofollow" href="https://www.datacamp.com/courses/visualization-best-practices-in-r?tap_a=5644-dce66f&tap_s=10907-287229" target="_blank"><img src="https://i2.wp.com/res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1539955419/Visualization_Best_Practices_orggdw.png?w=456&ssl=1" data-recalc-dims="1"></a></center></p>
<p>Here is the course <a rel="nofollow" href="https://www.datacamp.com/courses/visualization-best-practices-in-r?tap_a=5644-dce66f&tap_s=10907-287229" target="_blank">link</a>.</p>
<h3>Course Description</h3>
<p>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 to cover different types of data (proportions, point-data, single distributions, and multiple distributions) and discuss the pros and cons of the most common visualizations. In addition, we will cover some less common alternatives visualizations for the data types and how to tweak default ggplot settings to most efficiently and effectively get your message across.</p>
<h3>Chapter 1: Proportions of a whole (Free)</h3>
<p>In this chapter, we focus on visualizing proportions of a whole; we see that pie charts really aren’t so bad, along with discussing the waffle chart and stacked bars for comparing multiple proportions.</p>
<h3>Chapter 2: Point data</h3>
<p>We shift our focus now to single-observation or point data and go over when bar charts are appropriate and when they are not, what to use when they are not, and general perception-based enhancements for your charts.</p>
<p><center><a rel="nofollow" href="https://www.datacamp.com/courses/visualization-best-practices-in-r?tap_a=5644-dce66f&tap_s=10907-287229" target="_blank"><img src="https://i2.wp.com/res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1539955550/Bitmap_wnobz9.png?w=456&ssl=1" data-recalc-dims="1"></a></center></p>
<h3>Chapter 3: Single distributions</h3>
<p>We now move on to visualizing distributional data, we expose the fragility of histograms, discuss when it is better to shift to a kernel density plots, and how to make both plots work best for your data.</p>
<h3>Chapter 4: Comparing distributions</h3>
<p>Finishing off we take a look at comparing multiple distributions to each other. We see why the traditional box plots are very dangerous and how to easily improve them, along with investigating when you should use more advanced alternatives like the beeswarm plot and violin plots.</p>
<h3>Prerequisites</h3>
<ul>
<li><a rel="nofollow" href="https://www.datacamp.com/courses/introduction-to-the-tidyverse?tap_a=5644-dce66f&tap_s=10907-287229" target="_blank">Introduction to the Tidyverse</a></li>
<li><a rel="nofollow" href="https://www.datacamp.com/courses/data-visualization-with-ggplot2-1?tap_a=5644-dce66f&tap_s=10907-287229" target="_blank">Data Visualization with ggplot2 (Part 1)</a></li>
</ul>
	<p>
<div>
<div>To <strong>leave a comment</strong> for the author, please follow the link and comment on their blog: <strong><a href="https://www.datacamp.com/community/blog/course-visualization-best-practices-r"> DataCamp Community - r programming</a></strong>.</div>
<hr>
<a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers.com</a> offers <strong><a rel="nofollow" href="https://feedburner.google.com/fb/a/mailverify?uri=RBloggers">daily e-mail updates</a></strong> about <a rel="nofollow" href="https://www.r-project.org/" title="The R Project for Statistical Computing">R</a> news and <a rel="nofollow" href="https://www.r-bloggers.com/search/tutorial" title="R tutorials">tutorials</a> on topics such as: <a rel="nofollow" href="https://www.r-bloggers.com/search/data%20science" title="Data science">Data science</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/Big%20Data" title="Big Data">Big Data, <a rel="nofollow" href="https://www.r-users.com/" title="R jobs">R jobs</a>, visualization (<a rel="nofollow" href="https://www.r-bloggers.com/search/ggplot2" title="ggplot and ggplot2 tutorials">ggplot2</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/boxplot" title="Boxplots using lattice and ggplot2 tutorials">Boxplots</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/map" title="Maps and gis">maps</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/animation" title="Animation in R">animation</a>), programming (<a rel="nofollow" href="https://www.r-bloggers.com/search/RStudio" title="RStudio IDE for R">RStudio</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/sweave" title="Sweave and literate programming">Sweave</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/LaTeX" title="LaTeX in R">LaTeX</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/SQL" title="SQL and databases">SQL</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/eclipse" title="Eclipse IDE for R">Eclipse</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/git" title="git and github, Version Control System">git</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/hadoop" title="Large data in R using Hadoop">hadoop</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/Web+Scraping" title="Web Scraping of google, facebook, yahoo, twitter and more using R">Web Scraping</a>) statistics (<a rel="nofollow" href="https://www.r-bloggers.com/search/regression" title="Regressions and ANOVA analysis tutorials">regression</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/PCA" title="principal component analysis tutorial">PCA</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/time+series" title="Time series">time series</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/trading" title="finance trading">trading</a>) and more...
<img width="1" alt="" src="http://feeds.feedburner.com/~r/RBloggers/~4/631gVYkaRHg" height="1"></a></div>