Did you know that you can completely replace the “knitting” engine in R Markdown documents? Well, you can!
Why would you want to do this? Well, in the case of this post, to commit the unpardonable sin of creating a clunky jupyter notebook from a pristine Rmd file.
I’m definitely not “a fan” of “notebook-style” interactive data science workflows (apologies to RStudio, but I don’t even like their take on the interactive notebook). However, if you work with folks who are more productive in jupyter-style environments, it can be handy to be able to move back and forth between the ipynb and Rmd formats.
The notedown module and command-line tool does just that. I came across that after seeing this notedown example. There’s a script there to do the conversion but it’s very Windows-specific and it’s a pretty manual endeavour if all you want to do is quickly generate both an ipynb file and a notebook preview html file from an Rmd you’re working on.
We can exploit the fact that you can specify a knit: parameter in the Rmd YAML header. Said parameter can be inline code or be a reference to a function in a package. When you use the “Knit” command from RStudio (button or key-cmd-shortcut) this parameter will cause the Rmd file to be passed to that function and bypass all pandoc processing. Your function has to do all the heavy lifting.
To that end, I modified my (github only for now) markdowntemplates package and added a to_jupyter() function. Provided you have jupyter setup correctly (despite what the python folk say said task is not always as easy as they’d like you to believe) and notedown installed properly, adding knit: markdowntemplates::to_jupyter to the YAML header of (in theory) any Rmd document and knitting it via RStudio will result in
- an
ipynbfile being generated - an
htmlfile generated vianbconverting the notebook file, and - said HTML file being rendered in your system’s default browser
You can take this test Rmd:
---
knit: markdowntemplates::to_jupyter
---
## Notedown Test
Let's try a python block
```{r engine="python"}
def test(x):
return x * x
test(2)
```
And a ggplot test
```{r}
suppressPackageStartupMessages(library(ggplot2))
```
We'll use an old friend
```{r}
head(mtcars)
```
and plot it:
```{r}
ggplot(mtcars, aes(wt, mpg)) + geom_point() + ggthemes::theme_fivethirtyeight()
```and, after doing devtools::install_github("hrbrmstr/markdowntemplates") and ensuring you have notedown working, knit it in RStudio to generate the ipynb file and render an HTML file:
Note the python block is a fully functioning notebook cell. I haven’t tried other magic language cells, but they should work according to the notedown docs.
I’ve only performed light testing (on a MacBook Pro with jupyter running under python 3.x) and I’m sure there will be issues (it’s python, it’s jupyter and this is dark alchemy bridging those two universes), so when you run into errors, please file an issue. Also drop any feature requests to the same location.
North Carolina’s Neighborhood
When I saw the bombastic headline “North Carolina is no longer classified as a democracy” pop up in my RSS feeds today (article link: http://www.newsobserver.com/opinion/op-ed/article122593759.html) I knew it’d help feed polarization bear that’s been getting fat on ‘Murica for the past decade. Sure enough, others picked it up and ran with it. I can’t wait to see how the opposite extreme reacts (everybody’s gotta feed the bear).
As of this post, neither site linked to the actual data, so here’s an early Christmas present: The Electoral Integrity Project Data. I’m very happy this is public data since this is the new reality for “news” intake:
Data literacy is even more important than it has been.
Back to the title of the post: where exactly does North Carolina fall on the newly assessed electoral integrity spectrum in the U.S.? Right here (click to zoom in):
Focusing solely on North Carolina is pretty convenient (I know there’s quite a bit of political turmoil going on down there at the moment, but that’s no excuse for cherry picking) since — frankly — there isn’t much to be proud of on that entire chart. Here’s where the ‘States fit on the global rankings (we’re in the gray box):
You can page through the table to see where our ‘States fall (we’re between Guana & Latvia…srsly). We don’t always have the nicest neighbors:
This post isn’t a commentary on North Carolina, it’s a cautionary note to be very wary of scary headlines that talk about data but don’t really show it. It’s worth pointing out that I’m taking the PEI data as it stands. I haven’t validated the efficacy of their process or checked on how “activist-y” the researchers are outside the report. It’s somewhat sad that this is a necessary next step since there’s going to be quite a bit of lying with data and even more lying about-and/or-without data over the next 4+ years on both sides (more than in the past eight combined, probably).
The PEI folks provide methodology information and data. Read/study it. They provide raw and imputed confidence intervals (note how large some of those are in the two graphs) – do the same for your research. If their practices are sound, the ‘States chart is pretty damning. I would hope that all the U.S. states would be well above 75 on the rating scale and the fact that we aren’t is a suggestion that we all have work to do right “here” at home, beginning with ceasing to feed the polarization bear.
If you do download the data, here’s the R code that generated the charts: