Introduction to R

Course code
Course fee
Course Level

This workshop will introduce participants to the R statistical programming language. R is a completely free and open-source programming language and environment for statistical analysis. In this workshop, participants will learn what R is and how it differs from other statistical software packages and programming languages. They will learn the basics of data I/O, manipulation, and visualization in R. Workshop participants will practice what they learn via practical exercises.

All content will be presented via live demonstrations of R programming and interactive R analyses. Workshop participants will practice on-the-fly by following along with the demonstration scripts and completing in-situ practical exercises. If the schedule permits, the participants are also welcome to ask the instructor for advice on how to incorporate R into their own data analyses.

Participants should install both R and Rstudio (the free desktop version) on their computers before the beginning of the course.

  • R can be downloaded here.
  • RStudio can be downloaded here.
  • No prior experience with R or programming are required.
Course director
Kyle Lang


Kyle Lang 

Target audience

Professionals seeking a master-level introduction to R programming.

For an overview of all our Winter school courses offered by the Department of Methodology and Statistics please click here.

Aim of the course

After completing this course, participants can:

  1. Describe what R is, how it differs from other statistical analysis software, and how it differs from other programming languages
  2. Describe common R data types and discuss their strengths and weaknesses
  3. Write simple R scripts to do the following tasks:
    - Read external data into R and write out data/results in various formats

    Calculate summary statistics
    - Programmatically manipulate data objects
    - Generate simple graphical visualizations of data/models.


Study load

Approximately eight hours of classroom time.

You will receive a certificate upon course completion. Please be aware that this course does not include graded activities, and therefore, we cannot provide a transcript of grades.


Course fee:
Fee covers
Course + course materials + lunch
Extra information about the fee

- Tuition fee for PhD students from the Faculty of Social and Behavioural Sciences from Utrecht University will be funded by the Graduate School of Social and Behavioural Sciences.

- The tuition fee for staff off the Faculty of Social and Behavioural Sciences from Utrecht University will be funded by FSBS.

Utrecht Summer School does not offer scholarships for this course.

Contact details

Irma Reyersen | E:

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Application deadline: 
Registration deadline
17 January 2024