Introduction to R

Course code
Course fee
Course Level
This course is closed and you can't apply anymore. Please check our other courses.

This course has already taken place in 2022. It will be available again in 2023.

This workshop will introduce students to the R statistical programming language. R is a completely free and open-source programming language and environment for statistical analysis. In this course, students 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 visualisation in R. We will also cover basic statistical analyses such as t-tests, correlation, ANOVA, and linear regression. Students will practice what they learn via practical exercises.

In the morning/early afternoon, new content will be presented via interactive lectures. In the afternoon, the students will practice what they learned via practical exercises. If the schedule permits, the students are also welcome to ask the instructor for advice on how to incorporate R into their own data analyses.

  • Participants should bring their own laptop computer with both R and RStudio installed.
  • No prior programming experience is required.

Please note that there are no graded activities included in this course. Therefore, we are not able to provide participants with a transcript of grades. You will obtain a certificate upon completion of this course.

Day-to-day programme (PDF)
Course director
Kyle Lang


Kyle Lang 

Target audience

Professionals seeking a master-level introduction to R programming

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

Aim of the course

After completing this course, students 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 visualisations of data/models
  • Conduct simple statistical analyses (e.g., t-test, correlation, ANOVA, regression)

Study load

Approximately 8 hours of classroom time.


Course fee:
Fee covers
Course + course materials
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.

Utrecht Summer School does not offer scholarships for this course.

More information

Irma Reyersen | E:


Application deadline: 
Registration deadline
17 January 2022