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.
Yesterday, Vox ran a story about changes in food consumption patterns in the United States over the past few decades. It featured this graph:
Vox Time Series When I saw it, one of those little bells went off in my head:
As a rule, when you see a sharp change in a long-running time-series, you should always check to see if some aspect of the data-generating process changed—such as the measurement device or the criteria for inclusion in the dataset—before coming up with any substantive stories about what happened and why.
Recently Tyler Cowen asked whether there has been progress in Philosophy. Agnes Callard wrote a thoughtful reply, saying amongst other things:
We don’t demand progress in the fields of fashion or literature, because these things please us. Philosophy, by contrast, is bitter, and we want to know what good it will do us, and when, finally, it will be over. It is not pleasant to be told that maybe you don’t know who you are, or how to treat your friends, or how to be happy.
To close out what has become demography week, I combined the US monthly birth data with data for England and Wales (from the same ONS source as before), so that I could look at the trends together. The monthly England and Wales data I have to hand runs from 1938 to 1991. I thought combining the monthly tiled heatmap and the LOESS decomposition would work well as a poster, so I made one.
Amateur demography week continues around here. Today we are looking at the population of England and Wales since 1961, courtesy of some data from the UK Office of National Statistics. We have data on population counts by age (in nice, detailed, yearly increments) broken down by sex. We’re going to tidy the data, make a pyramid for a year, and then make an animated gif that shows the changing age distribution of the population over more than fifty years.
Yesterday I came across Aaron Penne’s collection of very nice data visualizations, one of which was of monthly births in the United States since 1933. He made a tiled heatmap of the data, taking care when calculating the average rate to correct for the varying number of days in different months. Aaron works in Python, so I took the opportunity to play around with the data and redo the plots in R.
On Twitter the other day, Philip Cohen put up some data on changes in Bachelor’s degrees awarded between 1995 and 2015. The data come from the National Center for Education Statistics. It seemed like a good candidate for drawing as a figure, so I had a go at it:
Changes in the number of Bachelor's degrees awarded over the past twenty years. Afterwards, I was messing around with the data and wanted to draw some time-series plots for the various subject areas the NCES tracks.
I was asked to give a short talk in my Departmental Proseminar yesterday on the topic of giving presentations, and specifically about making slides effective.
There is more than one way to give a good talk, and there is more than one way to make “good slides” or—better—make good use of slides and other material you might want to show people. So the things I’ll talk about and especially the specific techniques I’ll discuss are selected from many good ways to present yourself and your work.
Data Visualization: A Practical Introduction will be published later this year by Princeton University Press. You can read a near-complete draft of the book at socviz.co. If you would like to receive one (1) email when the book is available for pre-order, please fill out this very short form. The goal of the book is to introduce readers to the principles and practice of data visualization in a humane, reproducible, and up-to-date way.