Tutorials
Getting set up, and getting your bearings in R
These tutorials are not course material as such — they are the background you need before session 1, and the place to come back to when something does not work.
They live on Method and Inquiry, a collection of methods resources I maintain across all my courses. Keeping them in one place means they stay current, and you can use them again in later modules.
Before session 1
Work through the first two before the first session. If something goes wrong, do not spend hours on it — bring your laptop to session 1 and we will fix it together.
1. Installing R and RStudio
Everything you need to get both programs running, on Windows and macOS. Start here. You also need Quarto, which installs separately and takes about two minutes.
2. Installing R packages
What a package is, the difference between installing and loading, and what to do when an installation fails. For this course you need at least:
install.packages(c("tidyverse", "flextable", "here", "janitor", "knitr"))Further packages are announced in advance when we need them.
Your first steps in R
3. First steps in R
The basic grammar: objects, assignment, functions, vectors and data frames. This is the take-home task after session 1, and session 2 assumes it.
Statistik I and II taught you R in a different style. This tutorial will look partly familiar and partly new — the concepts are the same, some of the notation is not. Skim what you recognise, slow down where it looks unfamiliar.
Before session 4
4. Git and GitHub with RStudio
Git and GitHub with RStudio: installing and connecting (Gräbner-Radkowitsch 2026b)
Installing Git, and connecting RStudio to your GitHub account with a personal access token. Work through this before 19 October — session 4 starts from a working setup, and a room of thirty people installing software at once is not a good use of the time.
Allow about half an hour. You also need one more package:
install.packages("usethis")Most things on this page can be sorted out in the room. A Git installation is different — it is a download, an installer, and on some machines a restart. If it goes wrong, post in the Moodle forum before Monday 19 October.
If your statistics feels rusty
A recap of undergraduate statistics (Gräbner-Radkowitsch 2026e)
A full learning track covering conceptual foundations, data types and scales, descriptive statistics in R, probability, sampling and inference, and hypothesis testing. It is not required reading, but it is exactly the ground this course builds on.
There is no class on 5 and 12 October. Report 1 will not take both weeks. If anything in the first sessions felt shaky, that is the time to work through this track properly — before the modelling half of the course starts.