Doing New Things, Like Coding, But Not Just Coding

In this class, you will likely be asked to do and to read new things, things with which you may not have much, or any, experience. This may include coding, but it also may include learning about methods in humanities data analysis and/or learning how to design a computational research project. These tasks involve new ways of thinking and will sometimes feel frustrating and hard. There will be times when you get stuck, or when you are confused about what a method is or does, or when you are wrong. There will be times when all of those things will happen to me, as well. All of that is fine and expected. I do not expect anyone to walk into or out of this class an expert in computational criticism. We’re all learning this together. What I do expect is a good-faith effort to achieve literacy in some methods in computational criticism.

As mentioned above, we will be doing a small amount of coding in R in this class. Why R? Because that’s what the majority of the case studies we examine in the second half of the semester use, and I want us to be able to follow along with what these researchers have done. There are lots of other good reasons for learning R, just as there are also lots of good reasons for learning Python, another language popular among those who do data analysis in the humanities. We will be focusing on R because it suits our very limited purposes in this class.

There’s been some debate in the digital humanities about whether people “need” to code in order to do DH. You do not need to code to do DH, at all, and there are also GUI (graphical-user interface) tools designed to accomplish some tasks in data analysis (ArcGIS, Gephi, the GUI topic modeling tool, etc). Generally speaking, these tools require no coding. However, the argument of this class is that achieving some level of literacy with coding is advantageous because it gives you a baseline for understanding – and for potentially being able to implement – new and unfamiliar developments in humanities data analysis (which does not in any way constitute the entirety of “DH”). Furthermore, doing your data analysis in an R Markdown notebook, as we will do in this class, makes your research reproducible. The case studies I have chosen for each of the weeks after spring break feature a reproducible workflow, meaning others can see what the researcher has done with their data and can reproduce their results. Making your research reproducible is one of the most important contributions you can make when doing humanities data analysis.