GESIS Training Courses

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André Ernst

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Claudia O'Donovan-Bellante
Tel: +49 621 1246-221

Introduction to R (Online-Workshop!)

Online via Zoom
General Topics:
Course Level:
Software used:
Students: 160 €
Academics: 240 €
Commercial: 480 €
Additional links
Lecturer(s): Prof. Dr. Merlin Schaeffer, Friedolin Merhout

About the lecturer - Prof. Dr. Merlin Schaeffer

About the lecturer - Friedolin Merhout

Course description

This course introduces participants to the statistical programming language R and its use in the integrated development environment RStudio. R is a versatile language offering both the full range of established statistical procedures and cutting-edge techniques for data collection, processing, and analysis. RStudio wraps the language in a well-structured and easier to use environment. R's breadth of applications relies on a large catalogue of (user contributed-)packages, with notable recent contributions going towards a more unified framework for these packages.
The course aims to familiarize participants with the broad potential of these tools in general and for their personal needs and applications. After completing the course, participants will understand how to implement all steps of data analysis in RStudio. This includes learning R techniques for:
  • Data import from multiple sources
  • Data manipulation and visualization
  • Basic statistical analyses and linear regression
  • Automatic and interactive report generation
  • In addition to the technical programming skills, participants will also learn to apply a conceptual framework to data analysis, where all the steps of a data analysis are automated via a programmatic pipeline.
    Core elements participants will gain familiarity with are:
    RStudio: An integrated development environment for R supporting interactive data analysis, building of data analysis pipelines, and R software development
  • Tidyverse: A framework and collection of R packages centered on the concept of tidy data
  • Visualization: Generating and tailoring high-quality figures using the R package ggplot2
  • Reproducible analysis: Writing and generating automatic and reproducible reports using R Markdown

  • Learning objectives

    After completing the course, participants will understand how to implement all steps of their data analysis projects in R using RStudio. This includes loading data into RStudio, processing and visualizing data, and conducting basic statistical analyses. Participants will be able to wrap all of this into R Markdown scripts that automate the analysis and allow it to be shared and reproduced.


    Participants should be familiar with basic approaches in quantitative empirical social. Ideally, participants have some experience with using scripting for data analysis, e.g. from using Stata or SAS. Fluency in English, for instructional purposes, is required.


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