This workshop introduces 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. We will also cover basic statistical analyses such as t-tests, correlation, ANOVA, and linear regression. 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
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.
Lecturers
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, participants can:
- Describe what R is, how it differs from other statistical analysis software, and how it differs from other programming languages
- Describe common R data types and discuss their strengths and weaknesses
- 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
- Conduct simple statistical analyses (e.g., t-test, correlation, ANOVA, regression)
Study load
Approximately eight hours of classroom time.
Costs
- 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: ms.summerschool@uu.nl