Assignments
Overview
Weekly coding assignments reinforce what you learn in each session. They’re designed to take 1-2 hours outside of class, and you’ll have time during class to get started and ask questions.
Grading: 8 assignments total. Worth 35% of your final grade.
Assignment Schedule
| Assignment | Topic | Assigned | Due |
|---|---|---|---|
| 1: Getting Started | RStudio, ggplot2 basics | Wed Apr 1 | Sun Apr 5 |
| 2: Data Transformation | dplyr verbs, pipe | Wed Apr 8 | Sun Apr 12 |
| 3: Tidying, Import & Quarto | pivot, read_csv, Quarto | Mon Apr 20 | Sun Apr 26 |
| 4: Visualization Deep Dive | geoms, scales, design | Mon Apr 27 | Sun May 3 |
| 5: Exploratory Data Analysis | distributions, relationships | Mon May 4 | Sun May 10 |
| 6: Data Types & Wrangling | logicals, strings, factors | Mon May 11 | Sun May 17 |
| 7: Joins & Missing Data | joins, NA handling | Wed May 13 | Sun May 24 |
| 8: Reproducible Report | Quarto document | Wed May 27 | Sun May 31 |
Submission Guidelines
- Submit all assignments through Canvas by 11:59 PM on the due date
- Submit your
.Rscript or.qmdfile (depending on the assignment) - Include your name and the assignment number at the top of your file
- Make sure your code runs without errors from a fresh R session
AI Tools Policy
AI tools (ChatGPT, Claude, GitHub Copilot, etc.) are not permitted on any assignments in this course. The goal is to build your own foundational skills — AI is most useful once you already understand what you’re doing. See the syllabus for the full policy and rationale.
Getting Help
- Start assignments during class work time — I’m there to help!
- Attend office hours
- Post questions on the Canvas discussion board
- Work with classmates (but submit your own code)