Module: Advanced R for Econometricians (6 Credits)

Name in diploma supplement

Advanced R for Econometricians

Responsible

 Prof. Dr. Christoph Hanck

Admission criteria

See exam regulations.

Workload

 180 hours of student workload, in detail:
  • Attendance: 60 hours
  • Preparation, follow up: 60 hours
  • Exam preparation: 60 hours

Duration

The module takes 1 semester(s).

Qualification 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 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 EaF Master > Wahlpflichtbereich > 1.-3. Sem, Elective
  • ECMX Master > Wahlpflichtbereich > ME6 Applied Econometrics > 1.-3. Sem, Elective
  • VWL Master > Wahlpflichtbereich I > 1.-3. Sem, Elective
  • WiInf Master > Wahlpflichtbereich > Wahlpflichtbereich II: Informatik, BWL, VWL > Wahlpflichtmodule der Volkswirtschaftslehre > 1.-3. Sem, Elective

Elements

  • Lecture with integrated exercise Advanced R for Econometricians (6 Credits)

Module: Advanced R for Econometricians (WIWI‑M0887)

Lecture with integrated exercise: Advanced R for Econometricians (6 Credits)

Name in diploma supplement

Advanced R for Econometricians

Organisational Unit

Lehrstuhl für Ökonometrie

Lecturers

Prof. Dr. Christoph Hanck,

M.Sc. Martin Christopher Arnold

Hours per week

4

Language

English

Cycle

irregular

Participants at most

30

Preliminary knowledge

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

Abstract

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.

Contents

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

Literature

  • 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)