Categories ▸ Visualization
I’ve gotten a couple of reports from people having trouble installing the socviz library that’s meant to be used with Data Visualization: A Practical Introduction. As best as I can tell, the difficulties are being caused by GitHub’s rate limits. The symptom is that, after installing the tidyverse and devtools libraries, you try install_github("kjhealy/socviz") and get an error something like this:
Error in utils::download.file(url, path, method = download_method(), quiet = quiet(): cannot open URL https://api.
A few years ago I wrote a post about the stickiness of college and university rankings in the United States. It’s been doing the rounds again, so I thought I’d revisit it and redraw a few of the graphs I made then.
In 1911, Kendric Babcock made an effort to rank US Universities and Colleges. In his report, Babcock divided schools into four Classes, beginning with Class I:
The better sort of school.
I was asked for some examples of posters I’ve made using R and ggplot. Here are four. Some of these are done from start to finish in R, others involved some post-processing in Illustrator, usually to adjust some typographical elements or add text in a sidebar. I’ve linked to a PDF of each one, along with a pointer to the original post about the graphic.
If you’re interested in learning more about how to making graphs and charts using R and ggplot, then by a staggering coincidence there’s a new visualization book out that can help you with that.
Chapter 2 of Data Visualization walks you through setting up an R Project, and takes advantage of R Studio’s support for RMarkdown templates. That is, once you’ve created your project in R Studio, can choose File > New File > R Markdown, like this:
Select R Markdown …
And then choose “From Template” on the left side of the dialog box that pops up, and select the “Data Visualization Notes” option on the right:
Based on the heatmaps I drew earlier this month, I made a poster of two centuries of data on mortality rates in France for males and females. It turned out reasonably well, I think. I will probably get it blown up to a nice large size and put it up on the wall. I’ve had very good results with PhD Posters for work like this over the years, by the way.
Data Visualization: A Practical Introduction will begin shipping next week. I’ve written an R package that contains datasets, functions, and a course packet to go along with the book. The socviz package contains about twenty five datasets and a number of utility and convenience functions. The datasets range in size from things with just a few rows (used for purely illustrative purproses) to datasets with over 120,000 observations, for practicing with and exploring.
I taught my Data Visualization seminar in Philadelphia this past Friday and Saturday. It covers most of the content of my book, including a unit on making maps. The examples in the book are from the United States. But what about other places? Two of the participants were from Canada, and so here’s an example that walks through the process of grabbing a shapefile and converting it to a simple-features object for use in R.
As part of the run-up to the release of Data Visualization (out in about ten days! Currently 30% off on Amazon!), I’ve been playing with graphing different kinds of data. One great source of rich time-series data is mortality.org, which hosts a collection of standardized demographic data for a large number of countries. Mortality rates are often interesting to look at as a heatmap, as we get data for a series of ages (e.
Since the U.S. midterm elections I’ve been playing around with some Congressional Quarterly data about the composition of the House and Senate since 1945. Unfortunately I’m not allowed to share the data, but here are two or three things I had to do with it that you might find useful.
The data comes as a set of CSV files, one for each congressional session. You download the data by repeatedly querying CQ’s main database by year.
The American Sociological Association released some data on its special-interest sections, including some demographic breakdowns. Dan Hirschman wrote a post on Scatterplot looking at some of the breakdowns. Here are some more. I was interested in two things: first, the relative prevalence of Student and Retired members across sections, and second the distribution of women across sections. About 53% of all ASA members are women, substantially higher than some other social sciences and many other academic disciplines.
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