Categories ▸ Visualization
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”.
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.
The Gravitational Waves paper that was in the news yesterday has almost a thousand authors. (Actually there’s more than one paper—there’s the “discovery” paper and the “implications” paper.) Out of interest, I fed the list of authors in the “implications” paper into R and constructed an affiliation network with ties based on the university or research institute listed. Then I colored the nodes by the country of the primary institutional affiliation.
ASA Section Membership and Revenues. I taught a half-sized introductory seminar on data visualization last semester. It’s an introduction to some principles of data visualization for working social scientists, and is focused mostly on teaching people how to use ggplot effectively. I’ve made the (slightly rough-and-ready) course notes available as a website. The notes include numerous code samples, .Rmd files for every week, and there’s a GitHub repository containing all the material to build the site, including the datasets used to make the plots.
Continuing my nonremunerative career as an IT Analyst, I updated my Apple Sales plots to the most recent (end of 2015) round of quarterly data. These plots were originally inspired by Dr Drang, and the trend for the iPad (shown below) continues to confirm his views. I also took the opportunity to clean up the code a little, and to fix a small problem in the earlier versions. The x-axis of the “Remainder” panel didn’t line up properly with the line plots above and below it.
A few days ago, Matt Yglesias shared this tweet from Liz Ann Sonders, Chief Investment Strategist with Charles Schwab, Inc:
DailyShot: Here is a comparison of the monetary base with the S&P500 ... Coincidence? pic.twitter.com/QsdNhJdbRP
— Liz Ann Sonders (@LizAnnSonders) January 15, 2016 Matt remarked that “Friends don’t let friends use two y-axes”. It’s a good rule. The topic came up a couple of times during the data visualization short course I taught last semester.
While making the maps for yesterday’s post about the extent of US federal landownership, I noticed an odd checker-pattern in one part of it. It flowed through northern Nevada and Utah, and then out a ways into southern Wyoming. I did enough work to make sure it wasn’t a coding error on my part, but didn’t pursue it any further. This morning, JP Lien asked me about it on Twitter, and we both took a closer look.
The current occupation of a federal wildlife refuge building in rural Oregon prompted me to make a map of the land owned or administered by the US government. There are a few such maps floating around, but I wanted to see if I could draw one in R. The US Geological Survey makes a shapefile available containing the boundaries of federal lands, so I grabbed that and simplified the category codings a bit, to make the main classes of land a bit more tractable.
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