Here at OU, there are at least 4 or 5 that teach introductory R. You can take a statistics class, an economics class, a biological stats class, or a class just on R as a programming language. R has become one of the most commonly used languages for computational stats and data visualization, so it’s not surprising to see it pop up in a number of different departments. However, it has not yet made its way into the humanities.
For those of us in the humanities then, I wanted to pull together a few online resources that can help you get started.
My favorite introduction
is was TryR from Code School. This pirate themed introduction is was great for people with little coding experience. It walks walked you through basic expressions, variables, arrays, loops, and graphing in a lightly gamified, campy platform. Unfortunately, it looks like Code School was bought out or rebranded, and this resource is no longer available.
In a recent blog post, Jesse Sadler from UCLA, offered a more targeted ‘Introduction to Network Analysis using R.’ Jesse does a great job of explaining how nodes and edges come together in network graphs and how various R libraries make it relatively easy to produce these graphs. Jesse’s research involves mapping the correspondence of the 16th-century Dutch merchant, Daniel van der Meulen, which serves as a great example of the promise of R for DH research.
Lincoln Mullen is currently composing an open textbook called Computational Historical Thinking which uses and teaches R. The resources he’s already assembled are fantastic, and his book serves as an excellent example of open-writing and review.
Less open but more complete, Matthew Jockers has produced a book and website with Springer called Text Analysis with R for Students of Literature. Taylor Arnold and Lauren Tilton also have a Springer book out called Humanities Data in R.
Last, but certainly not least, are the workshops provided by Software Carpentry. Here at OU and throughout the world. Software Carpentry provides two day workshops that introduce command line programming, versioning (usually with Git and GitHub) and R. These workshops are great because they work from a very introductory level and are meant to ease people into coding and data management. The group on OU’s campus is based out of the library and are particularly eager to help graduate students who are venturing into data analysis for the first time.
If you haven’t tried out R yet, take a minute to poke around at one of the resources above and thinking about how you already use maps, graphs, and tables in your work. Rather than hand-drawing your next map or searching for something to represent a network graph, take the same time to learn a new skill.