Material overview
📚 General references
The following textbooks offer a good general reference to the course content, and I think its a good idea to read into these books in a general way. Moreover, I often point to chapters in the respective session pages.
Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G. (2023). R for data science: Import, tidy, transform, visualize, and model data (2nd edition). O’Reilly. https://r4ds.hadley.nz/
Ismay, C., & Kim, A. Y.-S. (2020). Statistical inference via data science: A ModernDive, into R and the tidyverse. CRC Press, Taylor and Francis Group. https://moderndive.com/index.html
For more advanced details on the fundamentals of programming in R, I recommend the following:
- Wickham, H. (2019). Advanced R (Second edition). CRC Press/Taylor & Francis Group. https://adv-r.hadley.nz/
For the model-related parts of the lecture I recommend the following book as a further reading reference:
- James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An introduction to statistical learning: With applications in R (Second edition). Springer. https://www.statlearning.com/
🔖 Session-specific material
For information on how to use the exercise code, read this tutorial.
| Session | Topic | Exercise code |
|---|---|---|
| 1 | General introduction | |
| 2 | Basics of R and R-Studio | Basics, Functions |
| 3 | Project management | ProjectOrga |
| 4 | Object types | ObjectTypes1, ObjectTypes2 |
| 5 | Importing data | Wrangling1, Wrangling2 |
| 6 | Preparing data | Wrangling1, Wrangling2 |
| 7 | Visualization | Visualization1 |
| 8 | Quarto | Quarto |