Material overview

Author
Affiliation

Claudius Gräbner-Radkowitsch

EUF and JKU

📚 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

  • Békés, G., & Kézdi, G. (2021). Data Analysis for Business, Economics, and Policy. Cambridge University Press. https://gabors-data-analysis.com/

For more advanced details on the fundamentals of programming in R, I recommend the following:

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

The implementation lab takes place on Fridays, 11:15–12:45, in Room MAD 024 (Madrid Building) during the lecture period 18 September – 11 December 2026. The schedule below follows the official seminar timetable; topics and dates are matched to the main lecture. Please bring your laptop to every on-site lab session. The goals, rules and workload of the module are documented in the seminar description.

Note that the lab does not meet on site every week: the column Format below states whether a session is an on-site lab or a video lab that you work through on your own.

For information on how to use the exercise code, read this page.

There is no class in the week of 12–16 October 2026. The lab dates are unaffected by the rescheduling of the lecture that followed the cancelled session of 5 October (see the lecture overview). The three take-home examinations are published after lecture 04 (19 Oct, on logic and argumentation; due 4 Nov), lecture 08 (the video lecture released 2 Nov, on literature reviews; due 15 Nov) and lecture 09 (16 Nov, on statistics; due 22 Nov); you must pass two of them to be admitted to the final exam (see the seminar description).
Session Date Topic Format Exercise code
1 18 Sep 2026 General introduction & installation on-site
2 25 Sep 2026 Basics of R and R Studio video Basics, Functions
3 02 Oct 2026 Project management & recap on-site ProjectOrga
4 09 Oct 2026 Object types video ObjectTypes1, ObjectTypes2
5 23 Oct 2026 Quarto and R Markdown video Quarto
5a 23 Oct 2026 Recap session: paths & object types on-site
6 30 Oct 2026 Importing data video Wrangling1, Wrangling2
6a 30 Oct 2026 Recap session (optional) on-site
7 06 Nov 2026 Data preparation video Wrangling1, Wrangling2
8 13 Nov 2026 Data visualization video Visualization1
9 20 Nov 2026 Using AI for coding on-site
10 27 Nov 2026 Linear models in R on-site LinearRegression1, LinearRegression2
11 04 Dec 2026 Experimental data in R on-site
12 11 Dec 2026 Recap session & open Q&A (material TBA) on-site

There are also complementary recap exercises on data import, preparation and visualization in the recap session.