This post is meant to be a short intro on how to create visualizations like the following using R and ggplot2:

Update (February 6, 2017): I’ve updated the content of this post to be much more modern, taking advantage of developments in the spatial package ecosystem and in the capabilities of ggplot2.

If you’re doing any kind of empirical work in Economics, you probably have a huge, messy folder containing a mix of

• Data files (.csv, .dta, .xlsx, etc.) in various states of merge-ness and cleanliness.
• Scripts for creating graphs & figures, producing summary statistics, and computing models. Probably written for Stata, R, or the Pandas data stack¹.
• Files containing written work. These are usually .doc(x) files, but I’ve seen lots of LaTeX lately as well, and being a plain-text format, this is a huge boon to reproducible research.

A really simple research workflow (start with data, make some figures, make some summary statistics, and run some models) might look like the following:

But of course that’s not clear when looking at the .zip file you send your coauthor.

I recently moved the site over to Pelican. Although I liked Octopress, it wasn’t working exactly like I wanted it to, and I’m not comfortable enough working with Ruby to modify it. Since Python has become my go-to language for most things these days, it made sense to move to what seems to be the most popular static site generator written in that language. For posteriority’s sake, the following is a guide based on the steps I took to get it working. I’ve also included most of my configuration files and a short program for creating new posts.