Let’s imagine a situation. You generate some number(s) in R or Python or whatever and want to include these numbers in a LaTeX document. Most people (I assume) just copy/paste the numbers from the console to the document.1 This means that if you update the analysis, your document may be reporting incorrect information.
There are some solutions to this, of course. One solution is to mix the code together with the document (rmarkdown users will be familiar with this).
Lots of little things happen in grad school that take you by surprise but really shouldn’t. One of those things for me happened after I finished all of my coursework. Without the deadlines of weekly seminars, I didn’t read journal articles or books nearly as often as I should have. I figured this out about this time last year and made it my New Year’s resolution to get better at keeping up with journals.
Update 19-May-2016: broken link removed and link to the github repo added instead
I ran across this post (link broken, github repo) announcing a weekly R/python visualization challenge and decided that this was the perfect excuse I needed to brush up on my mapping skills.
An R script with all the code in one place is available on my github repo.
Here, we’ll graph the lower 48 states and color them depending on how many drone sightings have been reported to the FAA in 2014 and 2015 total.
The Southern Political Science Association’s 2016 Annual Meeting just finished up in San Juan, Puerto Rico. Since I’ve been meaning to learn the twitteR package for a little bit, I figured that this was a perfect time to do a fun little analysis. You can find the R code I used to generate the images on Github.
I started out by grabbing all the tweets containing the official conference tag (#SPSA2016).