This repository contains Jupyter notebooks for a very short introduction to R and statistics (in German).
The course is designed to get students of philosophy and other social sciences, who have no previous experience in programming and statistics, up and running in R. It covers core concepts of R, visualizations, and statistics to analyze simple estimation, comparison and relational claims.
The course was written by Paul Hasselkuß, Isabelle Keßels, and Daian Vlad Bica. It was supported by Heinrich Heine University's E-Learning Support Fund (ELFF, 2023-I).
The course consists of five notebooks:
- The first notebook explains how to use the notebooks and introduces R.
- The second notebook introduces variables and functions.
- The third notebook explains how data can be loaded into R and how R can be used to create different plots.
- The fourth notebook introduces summary statistics and confidence intervals
- The fifth and final notebook explains p-values and (simple) linear regression.
- Use
tidyverserather than R's core functions (especially for plotting) - Expand the modeling part to cover additional types of models/variables
- Properly discuss differences between normal and non-normal distributions and how to check these
- Explain how data can be cleaned-up
- Translate to English
Except for some images (see images/README.md), the course is available under Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0).