Many academic journals require figures in greyscale. This can be done relatively simply by printing to PDF as greyscale; however, this doesn’t give you a) a way to preview what the resulting figure will look like and b) much control over the process of desaturation. If you’re making figures in R, this process is almost as easy as the more simplistic print to PDF method.
Recently, I was asked by a reviewer to include a large number of regression results in a manuscript I am revising. In my experience, attempting to display more than a few regression results in tabular format is a fool’s errand so I went looking for a more visual means of delivering the content. My attempt at doing this is complicated by the estimation taking place in one piece of software (Stata) and visualization taking place in another ( R ).
Zipf’s law is a constant curiosity for urban observers. According to the law, the size of a city (i.e. population) is inversely related to the city’s population rank. This implies that the largest city is twice as large as the second largest city, three times as large as the third largest city, and so on.
Full color, stylized maps created for my working paper on local political fragmentation and economic growth. These three maps examine long-run (1960-2000) growth in population, employment, and per capita money income among 314 U.S. metropolitan areas.
The Urban Institute has released a new package for mapping state and county data in R, urbnmapr. Their posts can be found here and
here. Since I occasionally write about how to make simple maps in R, I am going to quickly go from start to finish on making a county map.