This book relies on resources that a lot of people have made available, usually for free.
The raw data comes from JSTOR’s Data for Research program. It wouldn’t really be possible without the work they did to make that set available.
I initially transformed the JSTOR Data into something that could be read in R via some scripts from John Bernau. His paper Text Analysis with JSTOR Archives describes some techniques for modeling trends in sociology journals using the JSTOR archives.
Once I had the data in R, I analysed it using the topicmodels package by Bettina Grün and Kurt Hornik.
The idea for analysing this data using topicmodels to analyse the data in this way comes from What Is This Thing Called Philosophy of Science? A Computational Topic-Modeling Perspective, 1934–2015, by Christophe Malaterre, Jean-François Chartier, and Davide Pulizzotto (Malaterre, Jean-François Chartier, and Pulizzotto 2019).
As well as those sources, I learned a lot about how to use topicmodels from Text Mining with R: A Tidy Approach by Julia Silge and David Robinson, and from some articles in Towards Data Science, including those by Shashank Kapadia and by Farren tang. (Kapadia 2019; tang 2019).
The citation data I use here are from Google Scholar, and I accessed them via Publish or Perish (Harzing 2007). Happily, a Mac version of Publish or Perish came out just last year; in the past I had set up PC emulators just so I could run it.
The whole book was put together using the bookdown package, primarily built by Yihui Xie (Xie 2020). This in turn is built on the Pandoc language, which was originally built by John MacFarlane. And the code uses tools from the tidyverse package throughout, which was originally built by Hadley Wickham.
And my daughter Nyaya has helped catch a lot of errors, though giving the way I write, this is an endless task.