Sommersemester 26

Lecture with integrated exercise

Advanced R for Econometricians (ARE)

Lecturer:
Dr. Martin Christopher Arnold
M.Sc. Martin Schmelzer
Contact:
Prof. Dr. Christoph Hanck
Dr. Martin Christopher Arnold
M.Sc. Martin Schmelzer
Term:
Summer Semester 2026
Cycle:
Block course
Time:
See description
Room:
See description
Language:
English
Moodle:
Veranstaltung in Moodle
LSF:
Veranstaltung im LSF
Participants
Module Advanced R for Econometricians in the degree programs

Important Notes:

Admission: 

The number of participants is limited. Please apply in advance by emailing a one-page letter of motivation to Martin Schmelzer by April 1. Please state your study program! 

Additionally, the completion of a self-assessment is mandatory (outcomes not relevant for the admission decision).

Times/Dates: 9:30 – 17:00 on

11.04.26 – R12 R06 A48
18.04.26 – R12 R06 A48
25.04.26 – R12 R06 A48

09.05.26 – S06 S00 B08
23.05.26 – S06 S00 B08
29.05.26 – S06 S00 B08
30.05.26 – S06 S00 B08

27.06.26 – S06 S00 B08

Please see the location info for PC-Hall A-003, directions from Berliner Platz and directions to building S06 from U11 stop Universität Essen.

Description:

The course teaches advanced topics in R programming that become increasingly relevant for everyday applications in both applied and theoretical econometrics and empirical economics. It covers, amongst other topics, advanced concepts in programming (object orientation, profiling, debugging), packages for modern applications in data science and cutting-edge R extensions, e.g., for parallel computing and C++ integration.

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

Learning Targets:

Students

  • 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  modern applications using R

Outline:

  • Advanced R concepts
  • Functional Programming
  • Profiling and Debugging
  • Reproducible Research with Rmarkdown
  • ggplot2, dplyr and other popular tidyverse packages
  • Webscraping
  • Working with databases
  • Talking to LLMs via code
  • Developing Shiny applications

Literature:

  • Eddelbuettel, D. (2013). Seamless R and C++ Integration with Rcpp. Springer
  • Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G. (2023). R for data science : import, tidy, transform, visualize, and model data (2nd edition.). O’Reilly
  • Matloff, N. (2011). The Art of R Programming. No Starch Press
  • Wickham, H. (2019). Advanced R. CRC Press
  • Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis (2nd ed. 2016). Springer Nature
  • Xie, Y., Allaire, J. J., & Grolemund, G. (2019). R Markdown : the definitive guide. CRC

Methods of Assessment:

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

Formalities:

Solid working knowledge of basic R programming is required.

See the module manual for further info.