Workplan and coaching

You will build this TikTok-data project step by step during the course. This workplan describes what needs to be done each week. Also check out the grading criteria for further guidance.

Note

Deliverables for Weeks 2-4 are due on the day before the next coaching session.

  • Week 2 deliverable -> due the day before Coaching #2
  • Week 3 deliverable -> due the day before Coaching #3
  • Week 4 deliverable -> due the day before Coaching #4

For Week 5, there is no additional deliverable due before Coaching #5. Use Coaching #5 for feedback before final submission.

Check Canvas for exact date/time cutoffs and final submission deadlines.

Week 2 (Coaching #1) - Team work

  • Instructor will have provided a public template repository with starter code, assignment instructions, and initial GitHub issues.
  • Fork the template repository and add all team members as collaborators.
  • Update README.md with installation and run instructions.
  • Complete Week 2 issues, write short completion summaries, and close finished issues.
  • Download starter datasets and produce a clean Quarto summary for one selected CSV file.
  • Use Git frequently (more than 5 meaningful commits) with correct use of git status, git add, git commit, git push, and git pull.
  • Ensure scripts follow setup-input-transformation-output principles.
  • Deliverable due: day before Coaching #2.

Week 3 (Coaching #2) - Individual work

  • Instructor will have created and assigned new individual issues focused on data visualization.
  • Work only on your assigned issue(s) and keep task scope clearly separated.
  • Create visualization outputs with ggplot2 and export .png files.
  • Use dir.create, ggsave, and a mix of chart types (e.g., histogram, bar chart, time series).
  • Work in a feature branch, push your branch, and open a pull request to main.
  • Do not merge unfinished individual work directly into main.
  • Add a Makefile for your visualization workflow.
  • Deliverable due: day before Coaching #3.

Week 4 (Coaching #3) - Individual work and team work

Individual work

  • Instructor will have assigned reviewers for each issue/pull request.
  • Review peers’ pull requests and provide actionable feedback.
  • Update README.md based on what was learned during review.
  • Improve your code using recent tutorial techniques (e.g., loops, regular expressions, interpolation).
  • Check out another team member’s branch, make changes, push updates, and update the linked issue/pull request.
  • Confirm that the Makefile runs after your revisions.

Team work (collaboration and integration)

  • Hold an integration meet-up.
  • Merge all relevant pull requests.
  • Resolve merge conflicts jointly.
  • Merge one stable final integrated version to main.
  • Close all open issues related to this stage.
  • Deliverable due: day before Coaching #4.

Week 5 (Coaching #4) - Team work

  • Instructor will have created new issues on regression analysis, modular project structure, output/reporting, database input, and reproducible package versions.
  • Self-assign issues and review each other’s work.
  • Finalize README.md.
  • Add regression analysis and implement modular folders: data_prep, analysis, paper.
  • Generate analysis output as PDF (or advanced option: interactive dashboard).
  • Replace CSV input with the provided database input.
  • Use feature branches per task with clean commits and merges.
  • Deliver a full end-to-end Makefile, include make clean, and ensure reproducible R package versioning.

Week 6 (Coaching #5, optional) - Team work

  • Bring your near-final project state for targeted feedback.
  • Verify end-to-end reproducibility on a clean machine/environment.
  • Resolve remaining integration or automation issues.
  • Prepare final submission according to Canvas instructions.