N.B. Since this is a seminar where we will be working on a research topic together, I will probably modify and adapt the readings and assignments as the semester proceeds. Readings are to be completed prior to class. Assignments are to be completed before the start of the next class.

Week 1 (Jan. 20): The problem stated

Read:

• Robert Baird, Religion in America; Or, An Account of the Origin, Relation to the State, and Present Condition of the Evangelical Churches in the United States. With Notices of the Unevangelical Denominations (New York: Harper & Brothers, 1856).
• Fletcher W. Hewes and Henry Garnett, Scribner’s Statistical Atlas of the United States Showing by Graphic Methods Their Present Condition and Their Political, Social and Industrial Development (New York: Charles Scribner’s Sons, 1883), plates 58 to 61.
• Herman Carl Weber, Presbyterian Statistics through One Hundred Years, 1826-1926 (Philadelphia: Presbyterian Church in the U.S.A., 1927), 438–539, 540–587.

Assignment:

• Find visualizations, data tables, data sets, or corpora about religion from the nineteenth century U.S. Post full citations and URLs in the Slack group, along with a sentence or two explaining what you’ve found. Examine the links that other people in the class post.

Week 2 (Jan. 27): Data visualization from historical actors

Read:

• Schulten, Mapping the Nation
• Shari Rabin, “’Let Us Endeavor to Count Them Up:’ Statistics and American Judaism in the Nineteenth Century” (presented at the Association for Jewish Studies, Boston, MA, 2013).
• Rodney Stark, “The Reliability of Historical United States Census Data on Religion,” Sociology of Religion 53, no. 1 (1992): 91–95, doi:10.2307/3711631.
• “Colleges, Universities and Churches” in Robert K. Nelson, Scott Nesbit, et al., Atlas of the Historical Geography of the United States (Digital Scholarship Lab).

Assignment:

• Find at least one instance of how historical U.S. religious data has been used by historians or sociologists. (Atlases are a sure bet, but be creative.) Post a scan or a photo of at least one visualization or data table and write a one paragraph critique.

Week 3 (Feb. 3): Data in social and digital history

Read:

• Graham, Milligan, Weingart, Macroscope, ch. 1–2.
• Roger Finke and Rodney Stark, The Churching of America, 1776-2005: Winners and Losers in Our Religious Economy (Rutgers University Press, 2005), at least chapters 1, 3, 5.
• Susan Hockey, “The History of Humanities Computing” in A Companion to Digital Humanities, ed. Susan Schreibman, Ray Siemens, John Unsworth (Oxford: Blackwell, 2004).
• William G. Thomas II, “Computing and the Historical Imagination,” in A Companion to Digital Humanities, ed. Susan Schreibman, Ray Siemens, John Unsworth (Oxford: Blackwell, 2004).
• Laurie F. Maffly-Kipp, “If It’s South Dakota You Must Be Episcopalian: Lies, Truth-Telling, and the Mapping of U.S. Religion.” Church History 71, no. 1 (2002): 132–42.
• Laurie F. Maffly-Kipp, “Putting Religion on the Map.” The Journal of American History 94, no. 2 (September 1, 2007): 522–29. doi:10.2307/25094964.
• Kieran Healy and James Moody, “Data Visualization in Sociology” Annual Review of Sociology, 40:105–128.
• Frederick W. Gibbs, “New Forms of History: Critiquing Data and Its Representations,” The American Historian, February 2016: http://tah.oah.org/february-2016/new-forms-of-history-critiquing-data-and-its-representations/.

Assignment:

• Complete the Try R exercises at Code School.
• Worksheet: Getting familiar with R

Week 4 (Feb. 10): Crash course in R

Read:

• Kaplan, Data Computing, chs. 1–4. If you want more of a challenge, try Arnold and Tilton, chs. 1–3 .
• Meirelles, Design for Information, ch. 1.

For reference:

• Hadley Wickham, Advanced R (Chapman & Hall, 2014).

Assignment:

• Worksheet: Data structures and Functions

Week 5 (Feb. 17): Visualization with the grammar of graphics

Read:

• Kaplan, Data Computing, chs. 5, 6, 8.
• Meirelles, Design for Information, ch. 3.
• Graham, Milligan, Weingart, Macroscope, ch 5.
• Johanna Drucker, “Humanities Approaches to Graphical Display” Digital Humanities Quarterly 5, no. 1 (2011).
• Fusion Charts, Principles of Data Visualization. But see the data vizualization papers below.

For reference:

• Slides on visualization with the Grammar of Graphics
• Joanna Zhao and Jennifer Bryan, R Graph Catalog
• ggplot2 documentation
• Winston Chang, companion website to R Graphics Cookbook
• Hadley Wickham, ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).

Data visualization research for reference:

• William S. Cleveland and Robert McGill. “Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods.” Journal of the American Statistical Association 79, no. 387 (September 1, 1984): 531–54. doi:10.1080/01621459.1984.10478080.
• C. G. Healey, K. S. Booth, and J. T. Enns, “High-Speed Visual Estimation Using Preattentive Processing,” ACM Transactions on Computer-Human Interaction 3, no. 2 (1996): 108–35.
• Ben Shneiderman, “The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations,” Proc. Visual Languages 96 (1996): 1–8.

Assignment:

• Worksheet: Basics of ggplot2

Week 6 (Feb. 24): Data manipulation

• Kaplan, Data Computing, chs. 1, 7, 9.
• Hadley Wickham, “Tidy Data” The Journal of Statistical Software 59, no. 10 (2014): 1–23.
• Hadley Wickham, “The Split-Apply-Combine Strategy for Data Analysis,” Journal of Statistical Software 40, no. 1 (2011): 1–29.
• David Mimno, “Data Carpentry,” August 19, 2014.

For reference:

• The Quartz guide to bad data

Assignment:

• Worksheet: Basic data manipulation with dplyr
• Begin transcribing historical data, chosen in consultation with me. This data should be the basis of at least one of your visualization essays in the class. The data that you transcribe will be used for the week on exploratory data analysis, and it must be amenable to manipulation with dplyr/tidyr and visualization with ggplot2. Transcription should be substantially begun by next meeting, and substantially completed by the end of spring break.

Week 7 (Mar. 2): Data manipulation continued; gathering historical data

Read:

• Kaplan, Data Computing 10–12.

Assignment:

• Worksheet: More data manipulation
• Complete transcription of historical data.

Spring break (Mar. 9)

Week 8 (Mar. 16): Exploratory data analysis and statistics

Read:

• Kaplan, Data Computing, ch. 14.
• Arnold and Tilton, Humanities Data in R, chs. 3–5.
• Peter Dalgaard, Introductory Statistics with R (Springer, 2008), chs. 4, 6, 3.

For reference:

• Roger D. Peng, Exploratory Data Analysis with R (Leanpub, 2015).
• Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani, An Introduction to Statistical Learning with Applications in R (Springer, 2013).

Assignment:

• Worksheet: Exploratory Data Analysis

Week 9 (Mar. 23): Mapping

Read:

• Meirelles, Design for Information, ch. 4.
• Arnold and Tilton, Humanities Data in R, ch. 7.
• Cameron Blevins, “Space, Nation, and the Triumph of Region: A View of the World from Houston,” Journal of American History 101, no. 1 (June 1, 2014): 122–47, doi:10.1093/jahist/jau184.
• Richard White “What is Spatial History?”

For reference:

• ggplot2 documentation
• Spatial Humanities Workshop
• leaflet documentation
• Roger S. Bivand, Edzer Pebesma, and Virgilio Gómez-Rubio, Applied Spatial Data Analysis with R (Springer, 2013).

Assignment:

• Worksheet: Basic Mapping

Week 10 (Mar. 30): Geocoding and georeferencing

Read:

• Meirelles, Design for Information, ch. 5.
• Todd Samuel Presner, David Shepard, and Yoh Kawano, HyperCities: Thick Mapping in the Digital Humanities, MetaLABprojects (Harvard University Press, 2014), 110–27.

Assignment:

• Worksheet

Week 11 (Apr. 6): Textual data

Read:

• Kaplan, Data Computing, ch. 16.
• Arnold and Tilton, Humanities Data in R, chs. 9 and 10.
• Meirelles, Design for Information, ch. 6.
• Graham, Milligan, Weingart, Macroscope, chs. 3–4.

For reference:

• Matthew Jockers, Text Analysis with R for Students of Literature (Springer, 2014). You may also wish to consult Matthew Jockers, Macroanalysis (University of Illinois Press, 2013).

Assignment:

• Worksheet

Week 12 (Apr. 13): Textual data continued

Read:

• Jean-Baptiste Michel et al., “Quantitative Analysis of Culture Using Millions of Digitized Books,” Science 331, no. 6014 (January 14, 2011): 176–82, doi:10.1126/science.1199644.
• Michael Whitmore, “Text: A Massively Addressable Object,” December 31, 2010.

Assignment:

• Worksheet

Week 13 (Apr. 20): Network data

Read:

• Meirelles, Design for Information, ch. 2.
• Arnold and Tilton, Humanities Data in R, ch. 6.
• Graham, Milligan, Weingart, Macroscope, chs. 6–7.

For reference:

• Eric D. Kolaczyk and Gábor Csárdi, Statistical Analysis of Network Data with R (Springer, 2014).

Assignment:

• Worksheet

Week 14 (Apr. 27): Final projects

TBD