Course: Kick start R (4th edition)


19 May 2017


VU campus, Initium (building nr 1077), Room 3B45
De Boelelaan 1077, 1081 HV Amsterdam
Map of VU campus


The goal of this kick-start course is to:

  1. Introduce the structure and philosophy behind the R-language.
  2. Introduce a number of tools to work with R.
  3. Give hands-on experience in solving data problems from the lab

This is an introductory course to speed up the study of R.


Dr. Douwe Molenaar (VU)

Target audience

Primarily targeting researchers from academia, but participants from the private sector are also welcome.


This is a one-day introduction to the possibilities of the statistical calculation environment R for those who would like to use it, or have already started using R for statistical data analysis but would like to obtain a bit more background. We will start with a lecture in which the philosophy behind the R-language will be explained. An understanding of the data structures in R allows you to work more efficiently with R. We will show some of the advanced possibilities of R as well as where to find tutorials if you want to know more about those techniques. Most of the day will be devoted to a hands-on tutorial. The tutorial contains material from an introductory master course that is taught at the VU University. The manual contains material and references to material for self-study.

Topics that we will discuss: R language basics, installing R and R-packages, finding help, using R-studio, making dynamic reports, model fitting, reading/writing data from/to files, making publication-ready graphs, books and tutorials on the use of R.

Participants will get a certificate after successfully completing this course.

Course programme

9:45  Coffee/tea
10:00 Welcome
10:10 Introductory lecture: philosophy of R, understanding data structures and methods
11:15 Tools for using R (RStudio, Version control, Making dynamic documents)
12:00 Afternoon: Solving some every-day problems from the lab in R, using basic statistical techniques. In these demos we use skeleton scripts and available data sets. However, we encourage you to bring and use your own data.

    • Linear modeling and ANOVA
    • Nonlinear fitting
    • Unsupervised learning
    • Supervised learning
12:30 Lunch
13:30 Continuation of the program
15:00 Coffee/tea
15:30 Continuation of the program
17:00 End

Course material will be provided for and will be online available.

More information

For more information about the course programme you can contact Douwe Molenaar


Maximum number of participants is 35, so register soon to be sure of a course seat! Coffee, tea and soft drinks and lunch will be provided.

Registration is closed.

Previous editions