Sociol 232: Visualizing Social Data
Instructor
- Kieran Healy
- 268 Reuben-Cooke
- kieran.healy@duke.edu
- kjhealy
Dates and Location
- January 12th–April 24th, 2024
- Wed/Fri
- 10:05am-11:20am
- Perkins LINK 088 (Classroom 4)
About this course
This course will teach you how to use modern, widely-used tools to create insightful, beautiful, reproducible visualizations of social science data. You will also learn about the theory and practice of efforts to visualize social-scientific data, and society more generally. We will think about different ways of looking at data, about where social science data comes from in the first place, and about the implications of choosing to represent it in different ways.
By the end of the course you will
- Understand the basic principles behind effective data visualization.
- Know how to create a wide range of plots in R using ggplot2.
- Know a fair amount about how to use R for things other than data visualization.
- Have a good understanding of issues surrounding the collection and representation of data in the social sciences and beyond.
Core texts
I recommend (but do not require you buy) three books. Draft versions of all of them are available for free online.
Kieran Healy, Data Visualization: A Practical Introduction (Princeton: Princeton University Press, 2019), http://socviz.co/. The print version can be purchased at Amazon and other bookshops.
Hadley Wickham, Garrett Grolemund, and Mine Çetinkaya-Rundel, R for Data Science: Import, Tidy, Transform, Visualize, and Model Data, Second. (Sebastopol, CA: O’Reilly Media, 2023), https://r4ds.hadley.nz. The print version can be purchased at Amazon and other bookshops.
Claus E. Wilke, Fundamentals of Data Visualization (Sebastopol, California: O’Reilly Media, 2019), https://serialmentor.com/dataviz/. The print version can be purchased at Amazon and other bookshops.
Software
We will do all of our visualization work in this class using R and use RStudio to manage our code and projects. R is a freely-available programming language that is designed for statistical computing and widely used across the natural and social sciences, as well as in the rapidly-growing world of “data science” generally. RStudio is an integrated development environment, or IDE, for R, a kind of control center from which you can manage the engine-room of R itself. It is also freely available. If you haven’t used these tools before, don’t worry. The course does not presuppose any familiarity with them. We will get up and running with them during the first week.
Schedule
The weekly schedule can be viewed on its own page, which has more details on readings, examples, and problem sets.
| Week | Date | Topic |
|---|---|---|
| Week 1 | - / Jan 12 | Orientation |
| Week 2 | Jan 17 / Jan 19 | Make Some Graphs in R |
| Week 3 | Jan 24 / Jan 26 | Ways of Seeing |
| Week 4 | Jan 31 / Feb 2 | How ggplot Thinks |
| Week 5 | Feb 7 / Feb 9 | Show the Right Numbers |
| Week 6 | Feb 14 / Feb 16 | Expanding your Vocabulary |
| Midterm Assignment | - / - | Midterm Assignment |
| Week 7 | Feb 21 / Feb 23 | Counting People |
| Week 8 | Feb 28 / Mar 2 | Trends and Time Series |
| Week 9 | Mar 6 / Mar 8 | Maps and Spatial Data |
| Week 10 | Mar 13 / Mar 15 | Spring Break |
| Week 11 | Mar 20 / Mar 22 | Iteration and Missing Data |
| Week 12 | Mar 27 / Mar 29 | Text as Data |
| Week 13 | Apr 3 / Apr 5 | Social Networks |
| Week 14 | Apr 10 / Apr 12 | Project prep |
| Week 15 | Apr 17 / Apr 19 | Catch-up |
| Final Project | - / - | Final Project |
Course policies
- Attendance is required, and important. I am a reasonable person; if you need to be absent please let me know in advance insofar as that is possible.
- Do the assigned readings in advance of class.
- Submit problem sets, or other assignments, on time.
Required work and grading
Three kinds of work are required: problem sets and class participation, a midterm project, and a final project.
- Weekly Class Participation and Problem Sets will let you reflect on the reading and practice your coding and visualization skills. Problem sets are due by end of day the Monday after they are assigned.
- A Midterm Project.
- A Final Project. There is no final exam.
Grade components: Problem Sets and Class Participation: 50% / Midterm Project 20% / Final Project 30%.
How you should approach this course
The material covered in the course has a lot of continuity and it is cumulative. You will be learning a set of practical skills. This means that techniques we learn early on will be necessary for understanding things that come later. It also means that regular practice will help you a lot. So, this is not a “Topic of the week” course where you can tune out for a few weeks while expecting to be able to easily drop back in later. The material we cover each week will not be overwhelming. If you participate during class and keep up with the weekly assignments you’ll be in a very strong position to do well in the class. If you don’t, it’ll be harder than you expected.
Duke community standard
Like all classes at the university, this course is conducted under the Duke Community Standard. Duke University is a community dedicated to scholarship, leadership, and service and to the principles of honesty, fairness, respect, and accountability. Citizens of this community commit to reflect upon and uphold these principles in all academic and nonacademic endeavors, and to protect and promote a culture of integrity. To uphold the Duke Community Standard you will not lie, cheat, or steal in academic endeavors; you will conduct yourself honorably in all your endeavors; and you will act if the Standard is compromised.