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Programming with R for Reproducible Research
Last Updated: 2026-02-05 16:37:17
Abstract
Deeper understanding of R: Function calls, rather than "commands".Reproducible research and data analysis via Sweave and Rmarkdown.Limits of floating point arithmetic.Understanding how functions work. Environments, packages, namespaces.Closures, i.e., Functions returning functions.Lists and [mc]lapply() for easy parallelization.Performance measurement and improvements.
Objective
Learn to understand R as a (very versatile and flexible) programming language and learn about some of its lower level functionalities which are needed to understand *why* R works the way it does.
Content
See "Skript": https://github.com/mmaechler/ProgRRR/tree/master/ETH
Resources
Lecture Notes
Material available from Githubhttps://github.com/mmaechler/ProgRRR/tree/master/ETH(typically will be updated during course)
Literature
Norman Matloff (2011) The Art of R Programming - A tour of statistical software design. no starch press, San Francisco. on stock at Polybuchhandlung (CHF 42.-). More material, notably H.Wickam's "Advanced R" : see my ProgRRR github page.
Learning Materials (Links)
- Main link
- Lecture webpage with all materials
General Information
- Language
- English
- Levels
- MSC
- Frequency
- Yearly recurring
Examination
- Type
- graded semester performance
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise | Programming with R for Reproducible Research |
|
14 h semesterly |
Offered In
-
Statistics Master (The following courses belong to the curriculum of the Master's Programme in Statistics. The corresponding credits do not count as external credits even for course units where an enrolment at ETH Zurich is not possible.)