Data analysis with R

Methodology courses and philosophy of science

Course information

ECTS: 2.5
Number of sessions: 4
Hours per sessions: 3
Course fee:

  • free for PhD candidates of the Graduate School
  • € 525,- for non-members
  • consult our enrolment policy for more information


Telephone: +31 (0)10 4082607

Session 1
March 2 (Monday) 2020
Mandeville building (directions), room T19-01

Session 2
March 6 (Friday) 2020
Mandeville building (directions), room T19-01

Session 3
March 9 (Monday) 2020
Mandeville building (directions), room T19-01

Session 4
March 13 (Friday) 2020
Mandeville building (directions), room T19-01


The open-source software environment R is a powerful platform for data analysis and statistical graphics that has become the global standard in statistical computing. It combines a powerful programming language with flexible graphical capabilities.

Aims and working method

The instructor will illustrate the application of these techniques with practical examples of R. Participants will gain practical experience with R by conducting analyses on provided datasets or data from the participants’ PhD project.

Learning objectives

After completion of this course, you will be able to:

  • Understand basic R functionality for reading and manipulating data sets;
  • Explore data with descriptive statistics and graphics, and
  • to use R for more advanced analyses (such as, linear regression modelling and mediation and moderation).

How to prepare

  • Bring your laptop to all sessions
  • Download and install RStudio

Installation instruction:

  • Log in to your remote desktop and open the application catalog
  • Search for 'RStudio'
  • Download and install R 3.4.1 / RStudio 1.0.143 
  • Search for 'application catalog' and/or 'remote desktop' in for more information.
  • Please do this well in advance and notify the course instructor if there are any problems

Session description

Session 1-2
Understanding R

Session 3-4
Using R for data analysis

About the instructor

Pieter Schoonees is an assistant professor in the Department of Marketing Management at RSM, Erasmus University. His expertise lie in the fields of computational statistics, machine learning and psychometrics. Pieter's research focuses on developing statistical and machine learning algorithms and applying these to secondary data. A special interest is the use of such techniques for the analysis of data gathered from neuroscienfic studies.