R-Tutorial
Learning objectives
- get to know and be able to use the software R and the associated programming language
- dealing with data types and data structures
- combination of theoretical knowledge from Statistics I and Statistics II with practical implementations in R
Contents
- installation of R, explanation of the RStudio user interface
- data types and data structures in R
- tidyverse Approach (Wickham, H., Grolemund, G. (2017). R for Data Science, O’RILLEY, Sebastopol.):
- importing data using the packages readr, haven, readxl
- tidy data with tidyr
- data transformation with dplyr
- handling of strings, factors as well as dates and times with stringr, forcats, lubridate
- data visualization with ggplot2
- creation of reproducible research in the form of documents and presentations with knitr, rmarkdown
- programming (creating functions, parameters of a function, loops)
- introduction to the use of different statistical methods (such as analysis of variance, regression analysis etc.)
Prerequisites
There are no formal prerequisites for participating in the course. However, basic knowledge in the modules Statistics I and Statistics II is very helpful.
Course documents
Program scripts, screencasts for the respective content and exercises are available for download in Stud.IP in the respective semester.
Scope and evaluation
- 2 WCH
- certificate in case of regular and successful participation
- no evaluation
Dates
Dates are announced via Stud.IP.