Methods in Digital History I
This course in computational history teaches you to create, manipulate, explore, and visualize historical data with the goal of advancing historical arguments. We move from the command line and reproducible research, through the fundamentals of programming in R, to a survey of computational methods — visualization, text analysis, topic modeling, and mapping.
Throughout, we ask what historical questions these methods make possible. You'll also build a personal website and portfolio with the static site generator Hugo.
- Use GitHub to create repositories, make commits, and manage code following reproducible-research best practices.
- Understand the basic computational patterns shared across programming languages.
- Use documentation for programming languages and libraries to learn skills as they become applicable to your work.
- Create, clean, organize, and document historical data from primary sources.
- Employ methods like text analysis, topic modeling, and data manipulation.
- Create static visualizations to communicate your work and methods to other historians.
Hardin Hall 004
W01Jan 7Introductions & infrastructure setupSyllabus overview and account setup.GitHub setup›
- Create a GitHub account and sign up for GitHub Education.
- Install VS Code and Slack; join the course Slack.
- Complete the Tech Fluency Survey (link on Canvas).
W02Jan 12–14Command line & GitHubNavigating the shell; reproducible research.—›
Mon · Jan 12 — The command line
- Download iTerm2 (Mac) or Powerline (Windows).
- Watch Command Line Overview.
- Watch NixieDoesLinux, The Most Important Thing You’ll Learn in the Command Line.
- Milligan & Baker, “Intro to the Bash Command Line,” Programming Historian 3 (2014).
Wed · Jan 14 — GitHub & reproducible research
- The Turing Way Community et al., The Turing Way: A Handbook for Reproducible Data Science, v0.0.4, Zenodo, 2019.
- Sandve et al., “Ten Simple Rules for Reproducible Computational Research,” PLOS Computational Biology 9(10).
- Watch Free Code Camp, Git and GitHub.
- Free Code Camp, Git and GitHub for Beginners.
W03Jan 19–21MLK Day & Tech TableNo class Mon (MLK Day); Tech Table Wed.Terminal assgmt›
Mon · Jan 19 — No class (MLK Day)
Wed · Jan 21 — Tech Table: GitHub & command line
Due: questions to Slack by 10am · GitHub & Terminal assignment by 4pm.
W04Jan 26–28Getting started in RR in VS Code & Quarto; first Tech Table.—›
Mon · Jan 26 — Introduction to R in VS Code & Quarto
- Wickham & Grolemund, R for Data Science: chs. 2, 6, 7, 28–29.
Wed · Jan 28 — Tech Table
Due: GitHub questions to Slack by 10am · reasonable start on Worksheet 1.
W05Feb 2–4Data structures in RVectors, data frames, lists; Wed Tech Table.Worksheet 1›
Mon · Feb 2 — Data structures in R (vectors, data frames, lists)
Due before class: Worksheet 1 (R Basics), by 4pm to Canvas.
Wed · Feb 4 — Tech Table
Due: GitHub questions to Slack by 10am · reasonable start on Worksheet 2.
W06Feb 9–11Loops, conditionals & functionsCore computational patterns in R; Wed Tech Table.Worksheet 2›
Mon · Feb 9 — Loops, conditionals & functions in R
Due before class: Worksheet 2 (Data Structures), by 4pm to Canvas.
Wed · Feb 11 — Tech Table
Due: GitHub questions to Slack by 10am · reasonable start on Worksheet 3.
W07Feb 16–18Information as data; tidy dataTidy data principles & relational structure; Wed Tech Table.Worksheet 3›
Mon · Feb 16 — Information as data, tidy data principles
- Broman & Woo, “Data Organization in Spreadsheets,” American Statistician 72, no. 1 (2018).
- Wickham, “Tidy Data,” Journal of Statistical Software 50, no. 10 (2014).
- Watch Wickham, “Tidy Data and Tidy Tools,” NYC Open Statistical Computing Meetup, Dec. 2011.
- Airtable, “What is a relational database?”
Due before class: Worksheet 3 (Loops, Conditionals & Functions), by 4pm to Canvas.
Wed · Feb 18 — Tech Table
Due: GitHub questions to Slack by 10am.
W08Feb 23–25Data manipulation with dplyrThe tidyverse approach to wrangling; Wed Tech Table.—›
Mon · Feb 23 — Basic data manipulation with dplyr
- Wickham & Grolemund, R for Data Science: chs. 5, 10, 16, 17.
- The Tidy Tools Manifesto.
- Welcome to the Tidyverse.
- Introduction to dplyr.
Wed · Feb 25 — Tech Table
Due: GitHub questions to Slack by 10am · reasonable start on Worksheet 4.
W09Mar 2–4Advanced data manipulationJoins, reshaping, and pipelines; Wed Tech Table.Worksheet 4›
Mon · Mar 2 — Advanced data manipulation
Due before class: Worksheet 4 (Basic Data Manipulation), by 4pm to Canvas.
Wed · Mar 4 — Tech Table
Due: GitHub questions to Slack by 10am · reasonable start on Worksheet 5.
W10Mar 9–11AI coding tools & intro to HugoAI guardrails introduced; static site generators.Worksheet 5›
Mon · Mar 9 — AI coding tools
Due before class: Worksheet 5 (Advanced Data Manipulation), by 4pm to Canvas. Guardrails for AI coding tools begin this week — see the AI use policy below.
Wed · Mar 11 — Hugo: static site generators, setup, structure, theming
- Hugo docs: Introduction.
- Hugo docs: Getting Started.
W11Mar 16–18Spring Break — no class—›
W12Mar 23–25Building your Hugo siteTemplating, content, deployment, polish; Wed Tech Table.—›
Mon · Mar 23 — Hugo: templating, content, deployment, polish
Wed · Mar 25 — Tech Table: Hugo
Due: GitHub questions to Slack by 10am.
W13Mar 30–Apr 1Data visualization with ggplotThe grammar of graphics; Wed Tech Table.—›
Mon · Mar 30 — Data visualization with ggplot & the grammar of graphics
- Wickham & Grolemund, R for Data Science: chs. 1 & 3.
- Healy, Data Visualization: chs. 1, 3, 4.
- Look at the R Graph Gallery and Top 50 ggplot2 Visualizations.
Wed · Apr 1 — Tech Table
Due: GitHub questions to Slack by 10am · reasonable start on Worksheet 6.
W14Apr 6–8Text analysis ITokenization, frequency & TF-IDF; Wed Tech Table.Worksheet 6›
W15Apr 13–15Text analysis II — topic modelingUnsupervised models & interpretation; Wed Tech Table.Worksheet 7›
Mon · Apr 13 — Topic modeling
- Explore Quiet Transformations and the related project Signs@40: Feminist Scholarship through Four Decades.
- Antoniak, “Topic Modeling for the People.”
- Review the documentation for
topicmodelsby Grün & Hornik.
Due before class: Worksheet 7 (Text Analysis), by 4pm to Canvas.
Wed · Apr 15 — Tech Table
Due: GitHub questions to Slack by 10am · reasonable start on Worksheet 8 (Topic Modeling).
W16Apr 20–22Maps & geographic data in RSpatial data, projections, leaflet; Wed Tech Table.Worksheet 8›
Mon · Apr 20 — Maps & geographic data in R
- Review docs for leaflet (and the leaflet website).
- Review docs for sf.
- Review docs for tigris.
Due before class: Worksheet 8 (Topic Modeling), by 4pm to Canvas.
Wed · Apr 22 — Tech Table
Due: GitHub questions to Slack by 10am · reasonable start on Worksheet 9 (Mapping).
FINFinalsFinal project presentationsPortfolio, proposal & mapping due.3 deliverables›
- Present your dataset proposal and portfolio to the seminar.
Artificial intelligence (AI) use ›
All students in the Digital History Ph.D. program are bound by the program’s AI Use Policy (handbook, p. 10); this course aligns with it. In Weeks 1–9, AI tools are strictly prohibited — you must develop foundational computational-thinking skills without AI assistance. Any use before the instructor grants permission results in automatic failure of the assignment and is reported as an academic-integrity violation.
Beginning Week 10, the instructor introduces guardrails. You may then use AI tools to understand code structure, debug, and improve technical skills — but you must understand every line you submit, verify accuracy, and document your AI use in your weekly coding log. AI use is forbidden for historical analysis, interpretation, or writing. Always be the historian in the loop.
Late work ›
Academic integrity ›
Student accessibility services ›
Conduct, diversity & emergency preparedness ›
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