Practical #13

Advanced Statistical Programming using R — Final Submission & Reflection Work Session

Author

Leonhard Kestel, Lisa Bondo Andersen, Cynthia Huang

Published

July 17, 2026

Overview

This session is dedicated work time — no new content is introduced. Use it to finish your final submission and reflection documents before the deadlines this week.

The session is structured into four parts:

  1. Housekeeping — rAI platform usage survey, final submission checklist, and deadline reminders
  2. Work time — final report polish, group-reflection.qmd, individual contribution statements, and your individual reflection log
  3. In-class discussion — talk through your project experience with other groups
  4. Open floor — questions on submission logistics, the oral exam format, or anything left over from yesterday’s lecture
ImportantDeadlines this week
  • Final submission (group repo) due 22 Jul
  • Individual contribution statement due 23 Jul via Moodle
  • Oral exam on 29 Jul

Part 1: Housekeeping (15 min)

Before you start working, fill out the rAI platform usage survey and make sure you know what’s due and where it goes.

rAI platform usage survey (10 min)

  • Sign in to the rAI learning space
  • A survey about your usage of the rAI platform will be prompted on sign-in — please complete it before moving on to the rest of the session

Final submission checklist

  • Your group repo is rendered as a website and published to GitHub Pages
  • Open your live GitHub Pages link in a private/incognito window and check it actually loads — pages, figures, and tables included
  • README.md, CONTRIBUTING/, and .gitignore are in place and up to date
  • group-reflection.qmd is added to the repo and answers the prompts below
  • CONTRIBUTING/ includes each member’s contribution statement and AI disclosure
  • Submit the link to your live rendered website on Moodle by 22 Jul

Individual contribution statement

  • Due 23 Jul via Moodle, separately from the group submission
  • Should be consistent with what’s documented in CONTRIBUTING/
  • Submit a rendered PDF of your individual contribution statement

Part 2: Work time (50 min)

Work in your project group. At least one person should have the group repo open in RStudio. Prioritise in this order:

  • final submission website and/or report
  • group-reflection.qmd
  • contribution statements
  • individual reflection log.

Exercise 2.1: Polish your final submission

Render your project and read it as a stranger would. Fix anything that would confuse a reader who hasn’t seen your dataset before:

  • Do your research questions, methods, and conclusions still line up?
  • Are figures and tables captioned, labelled, and referenced from the text?
  • Does the site render cleanly end to end — no broken links, missing images, or leftover TODOs?

Exercise 2.2: Write your group reflection

Add group-reflection.qmd to your repository (see the group project reflection prompts for the full skills-inventory template). It does not need to be polished — specific answers are more useful than complete ones.

Exercise 2.3: Update your contribution statement

In CONTRIBUTING/, make sure each member has one file. List the files/sections they owned, 1–3 key tasks, and any collaborative tasks they participated in, alongside the AI disclosure table (Tool | Purpose | Scope).

Exercise 2.4: Catch up your reflection log

Check your individual reflection log is up to date, including the Week 13 prompts.

Tip

If you’re stuck on what to write, look back at your own git log and commit messages — they’re usually the fastest way to remember what you actually did and decided.

Part 3: In-class discussion (20 min)

In this final session, you will discuss with other groups your project experiences in class. This is not a formal presentation of results — it is a structured conversation about what you learned, what surprised you, and what you would do differently.

Pair up with someone from a different group and take turns picking one question to discuss from each of the 3 topics below.

About your data and analysis

  • What did you learn about your dataset that you did not expect when you chose it?
  • Which analytical decision was hardest to make, and how did you reason through it?
  • What is one thing your analysis cannot answer, and why?

About your workflow and collaboration

  • What worked well in how your group coordinated? What was harder than expected?
  • How did you use LLM assistance during the project — what was useful, what was not, and when did you have to override or correct it?

About your own skill development

  • What is one concept or tool from the course that made more sense once you applied it to your own data?
  • What would you want to learn next, given what this project revealed?
  • One error or mistake in LLM output that you caught

The reflection is also good preparation for the oral exam: the questions above are representative of the kinds of reasoning you will be asked to demonstrate.

Part 4: Open floor (5 min)

Bring any remaining questions on submission logistics, the oral exam format, or material from yesterday’s lecture. If you have nothing outstanding, keep using the time to work.

Students without a group project

If you did not do the group project:

  • Use this time to review weekly practicals and prepare for the oral exam individually.
  • Revisit a practical’s dataset (or pick a new one) and practise reasoning from scratch: what questions could you ask of it, what methods would apply, what would you report?
  • The instructor is available for one-on-one questions during work time