In my first semester teaching one of my department’s graduate methods courses in digital history, I realized that there was not a lot good material for teaching computer programming and data analysis in R for historians. So I started writing up a series of tutorials for my students, which they said were helpful. It seemed like those materials could be the nucleus of a textbook, so I started writing one with the title Digital History Methods in R.
It was too soon to start writing, though. Besides needing to spend my time on more pressing projects, I didn’t really have a clear conception of how to teach the material. And in the past few years, the landscape for teaching computational history has been transformed. There are many more books available, some specifically aimed at humanists, such as Graham, Milligan, and Weingart’s Exploring Big Historical Data and Arnold and Tilton’s Humanities Data in R, and others aimed at teaching a modern version of R, such as Hadley Wickham’s Advanced R and R for Data Science. The “tidyverse” of R packages has made a consistent approach to data analysis possible, and the set of packages for text analysis in R is now much better. R markdown and bookdown have made writing a technical book about R much easier, and Shiny has made it much easier to demonstrate concepts interactively.