Introduction

Roles & organisation

Project based learning is about learning by doing, but also about peer instruction. This means that you will be both a learner and a teacher. There will be differences in levels among participants, but because of that, some will learn efficiently from people that have just learned, and others will teach and increase their understanding.

Each project has hints and guiding questions. However, if interesting questions pop up during the project, you are encouraged to work on those. Also, you don’t have to perform all the tasks and answer all the questions.

You will get to know each other, define how you like to collaborate and start on the project. On day 2 and 3 you can work on the project, and apply the skills and knowlege you have learned during these days. We will conclude the projects on day 3 with a 10-minute presentation of each group.

Working directories

Each group has a shared directory on the course server, /group_work/groupX (replace X with your group number). Your home directory has a shortcut to it called group_work, so you can open it from the Files pane (bottom right).

  • Download the project data once per group, into your group directory. In the Console, go there first, e.g. setwd("~/group_work/group1"), and then run the download code on your project’s page.
  • Each of you then creates your own RStudio project for your own scripts (File → New Project… → New Directory), and reads the data from the group directory. Use the group directory to share scripts and results (e.g. figures, or objects saved with saveRDS()) with the rest of your group.
  • Everyone on the course can open all group directories, so stick to your own, and don’t delete other people’s files.

Your home directory and the group directories are removed after the course. Download anything you want to keep (in the Files pane: select files, More → Export…).

Download the project data into a directory of your choice, with the download code on your project’s page, and create an RStudio project in the projectX directory that appears (File → New Project… → Existing Directory). To work together, share scripts and results the way your group prefers, for example through a shared drive or a Git repository.

Using an AI assistant

During the project work, you are welcome to use an AI coding assistant, together with the LLM packs of this course. Using AI assistants with LLM packs explains how to set this up, on the course server or on your own computer, and what to check in what it produces.

An assistant can write code quickly, but deciding whether that code does the right thing for your data is still up to you and your group. For your presentation:

  • Be ready to explain every step of your analysis, and why it is right for your dataset — whether you or the assistant wrote the code.
  • Say where you used an assistant, and what you checked.