02. Data Viz

Examples, good and bad. Theory underlying what makes a viz good and bad. Tools to implement viz tasks.

Artwork by @allison_horst.

Agenda

August 27, 2025

1. Grammar of graphics
2. ggplot

Readings

• Class notes: Visualization

• Wickham (2017) Data Visualization in R for Data Science.

Reflection questions

• When creating a graph, try to sketch / image the graph before you code it. What do you want R to do (what is the goal)? In order to do that, what does R need to know?

• What should the goal of a plot be? What should the goal of your plot be?

• What different aspects deconstruct a plot?

Ethics considerations

• Does your plot make the comparison of interest: easily? and accurately?

• Did you add alt text to your images (see Writing Alt Text for Data Visualization)?

• Is your plot accessible to those who are color blind or looking at the image in black and white?

Slides

• In class slides for 8/27/25.

Additional Resources

• Tufte (1997) Visual and Statistical Thinking: Displays of Evidence for Making Decisions.
• DuBois’s images online and in W.E.B. Du Bois’s Data Portraits: Visualizing Black America, a compilation edited by Whitney Battle-Baptiste.
• Flowcharts for choosing appropriate plots, brief tutorials of viz types, and source code in R and Python: from Data to Viz
• See something or say something
• Census trends visualized
• Visualization Internship (Fall 2021) at 538
• visualizing data: Digest of the best visualizations from the past month
• A regular NYT column on visualizations: What’s Going on in this Graph?
• Studies about visualizations and perception
• Fundamentals of Data Visualization
• Allison Horst’s amazing illustrations
• Makeover Mondays, a weekly data project for making more effective visualizations.