Categories ▸ Visualization
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.
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.
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.
Every couple of years—usually after one of the inevitable mass shootings—I find myself updating this graph. The originals were done in 2012. You can read America is a Violent Country, and Assault Deaths Within the United States to see those. This morning I pulled the latest figures from the OECD Health Status database. The method and scope are the same as before. Here is the main figure, showing assault death rates for the US and 23 other OECD countries.
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.
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