Resources
Guides
Step-by-step instructions for common tasks in this course.
| Guide | When to use it |
|---|---|
| Setting Up an R Project | Every assignment — start here |
| Using R Scripts for Assignments | Assignments 1–2 |
| Using Quarto Documents for Assignments | Assignments 3–8 and Final Project |
| Common R Errors (and What They Mean) | When you get a red error message and don’t know what it means |
Session Handouts
Printable reference sheets for each class session — key functions, syntax, and tips on one page.
| Session | Handout |
|---|---|
| 2: Your First Visualization | |
| 3: Data Transformation I | |
| 4: Data Transformation II | |
| 5: Data Tidying | |
| 6: Data Import |
| Session | Handout |
|---|---|
| 7: Quarto & Reproducibility | |
| 8: Layers & Aesthetics | |
| 9: Perception & Design | |
| 10: EDA — Variation | |
| 11: EDA — Covariation | |
| 12: Data Types |
| Session | Handout |
|---|---|
| 13: Strings, Factors & Text | |
| 14: Joins | |
| 15: Missing Data | |
| 16: Storytelling with Data | |
| 17: Correlation & Regression | |
| 18: Putting It All Together |
| Handout | Description |
|---|---|
| APA Figure Guidelines | Formatting figures for journal submissions |
| Data Visualization Principles | Key design principles at a glance |
Course Textbook
- R for Data Science (2nd edition) — free online. Weekly readings are listed on the schedule.
Cheatsheets
One-page visual references for the packages we use most. Print these out or keep them open while you work.
- ggplot2 Cheatsheet — geoms, aesthetics, scales, themes
- dplyr Cheatsheet — filter, select, mutate, summarize, group_by
- tidyr Cheatsheet — pivot_longer, pivot_wider, separate, unite
- RStudio IDE Cheatsheet — keyboard shortcuts, panes, project setup
- Quarto Cheatsheet — code chunks, Markdown formatting, YAML options
- Browse all cheatsheets
Extra Practice
If you want more practice beyond the assignments, these are all free.
- Posit Primers — interactive R tutorials in your browser. Great for reinforcing the basics.
- Cédric Scherer’s ggplot2 Tutorial — a beautifully illustrated deep dive into ggplot2. Useful when you’re working on the visualization assignments.
- Quarto Tutorial — Quarto’s own getting-started guide, if you want more detail than our in-class introduction.
Finding Datasets
For your final project, you’ll need a psychology or social science dataset. Here are good places to look:
- Open Science Framework (OSF) — published research data, often with codebooks
- ICPSR — large archive of social and behavioral science data
- General Social Survey (GSS) — decades of survey data on American attitudes and behaviors
- World Values Survey — cross-cultural survey data
- Kaggle Datasets — wide variety; filter for social science topics
- Your own research lab (with advisor permission)
See the Final Project page for dataset requirements.
Software
Getting Help
- In class: Start assignments during work time — the instructor and TA are there to help
- Office hours: See the syllabus for times and location
- Canvas discussion board: Post questions anytime — classmates and the teaching team can respond
- Classmates: Work together, but write your own code