I was interviewed recently by Brian Southwell for his public radio show, The Measure of Everyday Life. We talked for about half an hour, first about Nuance and then a little about performativity in social research, and the ethical issues associated with it. You can listen directly to the episode, find it on iTunes, or through your favorite podcast app.
Data Visualization for Social Science: A Practical Introduction with R and ggplot2 I’m writing a book on data visualization, provisionally titled Data Visualization for Social Science: A practical introduction with R and ggplot2. As part of that process, largely because I’ve benefited so much myself from the availability of open and widely shared tools for software development, I’m making the draft version of the book available as its own website.
“Fuck Nuance” has just been published in Sociological Theory. The pace of academic publishing being what it is, the paper has been out in the world for a while in draft form, but it’s nice to see the canonical version appear. The issue also contains a symposium on theory in Sociology, with contributions from Ivan Ermakoff, Ashley Mears, and Max Besbris and Shamus Khan. I’ve described the circumstances of the paper’s conception before.
I saw this pie chart via Beth Popp Berman on Twitter yesterday:
Pie charts of student debts by percent of all borrowers and percent of all debt. As you probably know, the perceptual qualities of pie charts are not great. In a single pie chart, it is usually harder than it should be to estimate and compare the values shown, especially when there are more than a few wedges and when there are a number of wedges reasonably close in size.
The Congressional Budget Office released its cost estimate report for the American Health Care Act yesterday. There are a few tables at the back summarizing the various budgetary and coverage effects of the proposed law. Of these, Table 4 is pretty interesting. The CBO “projected the average national premiums for a 21-year-old in the nongroup health insurance market in 2026 both under current law and under the AHCA. On the basis of those amounts, CBO calculated premiums for a 40-year-old and a 64-year-old, assuming that the person lives in a state that uses the federal default age-rating methodology”.
Update: Since writing this post, I’ve repeatedly tried to delete the offending review from my profile, but Google Scholar keeps re-inserting it as part of its automated trawl through its corpus of articles. So it seems that the robots are determined to grant me these citations whether I want them or not.
Google Scholar is one of the most visible and widely-used examples of the rise of “impact measurement” in academia.
I was playing with some county-level data from the U.S. general election, partly out of a spirit of honest inquiry and partly out of a feeling of morbid curiosity. Because I had some county-level census data to hand, I took a look at the results using some extremely basic demographic information—the two variables that structure America’s ur-choropleths, namely population density and percent black. I focused on the counties that flipped from their vote in the 2012 general election.
Yesterday I had a conversation on Twitter with Josh Zumbrun that followed on from this tweet:
This is one of the most horrifying graphics I've ever seen:https://t.co/wM0VJZn0Wg pic.twitter.com/qaUaNFtRPl
— Josh Zumbrun (@JoshZumbrun) September 28, 2016 The striking maps he linked to tracked the rise in deaths due to drug-related overdoses over the past 15 years, caused in large part to the surge in use of heroin and synthetic opiates. The details are in the WSJ report on the problem.
Last year I wrote about vaccination exemptions in California kindergartens, drawing on school-level data provided by the state of California about the number of kindergarteners with “personal belief exemptions” (or PBEs) that allow them not to be vaccinated. Today I came across a ggplot package called ggbeeswarm that’s designed to create a “beeswarm plot”, or a 1-D scatterplot with a bit of information about the density of the distribution. I had used geom_jitter to do something like this for one of my plots last year, but the geoms in ggbeeswarm are better.
Back in January, it snowed in Chapel Hill. When that happens around here, as you can imagine, things tend to shut down fast. The schools were closed, and we were iced in at home for a couple of days. The kids had a lot of quality Playstation time. Meanwhile, my wife and I ended up sitting across from one another at the kitchen table, arguing. In the end we resolved things by doing something we’d never done before—we co-authored a paper.