Informations about the modules


Module (6 Credits)

Advanced R for Econometricians

Name in diploma supplement
Advanced R for Econometricians
Admission criteria
See exam regulations.
180 hours of student workload, in detail:
  • Attendance: 60 hours
  • Preparation, follow up: 60 hours
  • Exam preparation: 60 hours
The module takes 1 semester(s).
Qualification Targets


  • know the strengths and limitations of the high-level statistical programming language R
  • thoroughly understand the R ecosystem and have a profound understanding in selected fields of advanced R programming
  • can apply their skills in advanced statistical and econometric applications
  • are able to document and communicate scientific results in a reproducible manner
  • are prepared for implementing big data applications using R
Module Exam

Weighted average of a (group) R-project (70%) and a presentation (30%, usually about 20 minutes).

Usage in different degree programs
  • BWL EaFWahlpflichtbereich1st-3rd Sem, Elective
  • ECMXWahlpflichtbereichME6 Applied Econometrics1st-3rd Sem, Elective
  • VWLWahlpflichtbereich I1st-3rd Sem, Elective
  • WiInfWahlpflichtbereichWahlpflichtbereich II: Informatik, BWL, VWLWahlpflichtmodule der Volkswirtschaftslehre1st-3rd Sem, Elective
Name in diploma supplement
Advanced R for Econometricians
Organisational Unit
Participants at most
Preliminary knowledge

A solid understanding of basic R programming as, for example, taught in our Master-level econometrics courses is required.


This course teaches advanced topics in R programming that become increasingly relevant for everyday applications in both applied and theoretical econometrics and empirical economics.

The first part of the course covers intermediate concepts in functional and object orientated programming, error handling, profiling and benchmarking as well as a treatment of selected R packages tailored for big data applications. Students are also introduced to reporting with dynamic documents. Part II deals with the tidyverse, a collection of packages designed for modern applications in data science. The third part introduces topics such as multi-core computing, C++ integration and other cutting-edge R extensions.

Students are prepared for applications in future studies and are able to efficiently tackle research-related programming tasks.


Part I

  • R at its Heart: Functional Programming
  • Getting it right: debugging, profiling and testing
  • Reporting: Reproducible Research with R Markdown

Part II

  • A Grammar of graphics: ggplot2
  • Keep it clean: selected tidyverse packages
  • Getting data: webscraping and text mining

Part III

  • Version control: git and github
  • Need for speed: Rcpp and RcppArmadillo
  • Harnessing power: parallelization
  • Show it to others: Shiny, R Packages
  • Eddelbuettel, D. (2013). Seamless R and C++ Integration with Rcpp. Springer
  • Grolemund, G.; Wickham, H. (2017); R for Data Science. O’Reilly
  • Matloff, N. (2011). The Art of R Programming. No Starch Press
  • Wickham, H. (2019). Advanced R. CRC Press
  • Wickham, H. (2009). ggplot2 - Elegant Graphics for Data Analysis. Springer
  • Xie, Y. (2018); R Markdown: The Definitive Guide. CRC Press
Teaching concept

Presentation, discussion and joint solving of programming exercises.

Die Veranstaltung entspricht einem Vorlesungsanteil von 2 SWS und einem Übungsanteil von 2 SWS.

Lecture with integrated exercise: Advanced R for Econometricians (WIWI‑C1138)
Module: Advanced R for Econometricians (WIWI‑M0887)