N.B. In this course we will be working on U.S. religion in the long nineteenth-century. You are more than welcome to work on your own research project. Ideally your work in this course will advance your dissertation or other research project. Talk to me early in the course and we will figure out together what would work best for you.

Familiarize yourself with R (5%)

By the beginning of week 4 you must complete the Try R exercises at Code School. Students who want more of a challenge or who have come to class with some background in programming or data analysis are welcome to substitute another source of exercises. I recommend the first three chapters in Arnold and Tilton, Humanities Data in R, along with the exercises in the appendix. But the introductory chapters in Jockers, Text Analysis with R for Students of Literature, would also be a good choice if you prefer to work with textual data. Kaplan, Data Computing, will give you a different perspective focused on the “Hadleyverse” of R packages.

Weekly worksheets or assignments (50%)

After class each week you will be given an assignment. In the first several weeks of the course, these will be historical research assignments which might even get you inside of the library. The rest of the semester they will be data analysis worksheets. These are intended to practice what we have gone over in class to cement the ideas. They will also serve as a helpful reference sheet when you need to remember how to perform some kind of analysis. Some of the questions on the worksheet will be easy; most will be difficult; some you may find nearly impossible. The aim is to practice, building on whatever level of skill you bring to the course. We will go over the worksheets in class the next week. Unless otherwise specified, you may work with up to three other people to solve the problems posed, and you are always welcome to ask for help in the Slack channel. But each student must turn in his or her own worksheet. If you attempt a problem and can’t solve it, you should still turn in whatever work you did on it.

Here is how I will grade these worksheets. There will be no maximum level of points. Instead, I will give you points based on the truthfulness and skillfulness of your work. For instance, a correct answer to an obvious problem might garner you one point. A correct but sloppy answer to a more difficult problem might garner you two or three points, where a correct and elegant solution might garner you five or six. An incomplete answer to a very difficult problem which nevertheless reveals deep thought or historical insight might get you eight or nine points. I will total up your scores as we go, and grade them on a (very generous) curve at the end of the semester. Students who complete all the easy and moderate difficulty questions, attempt the very difficult questions, and ask me for help as needed will do just fine.

The worksheets are available here, and you can also get them from this GitHub repository.

Visualization essays (45%)

You will be graded on three brief visualization essays. You must submit three, but you may submit as many as five, of which I will count the three best. This assignment is deliberately open ended. At a minimum these visualization should include the following: (1) explanatory text written for a scholarly but non-technical audience that frames the visualizations in both historical and historiographical terms and argues some worthwhile point; (2) as many well-crafted visualizations or data tables as are necessary to argue that point; (3) access to the code (though not necessarily in the essay itself) so that I can see how you got your results; and (4) citations. The most obvious way to accomplish this is in an knitr report (which we will discuss in class). But you may have better ideas about what you want to accomplish, and I will provide the direction and support you need to accomplish them. Students who wish to attempt some more ambitious project (which must still meet those requirements) are welcome to talk with me about combining two or three of the visualization essays into a single format. I will provide more formal rubrics for evaluation once we have made it further into the course.


Regular, informed participation is expected as a matter of course in a graduate class.