RSS001.O1 ​​Crash course in R – a gentle introduction​

​​The guiding logic of the course is to give practical knowledge of the whole data analysis workflow:

  • ​Monday – Getting to know R |Importing data
  • Tuesday – Data wrangling/cleaning
  • Wednesday – Exploratory analysis
  • Thursday – Visualization
  • Friday – Analysis | Reporting the results

​Reflecting on the realities of typical research projects, the beginning of the course focuses on data cleaning and getting data into a shape which allows us to analyze and visualize it properly. The exploratory analysis and data visualization parts are heavily intertwined. 

​We will also review how to get various different types of datafiles into R (from Stata, SPSS, Excel).

​We will learn how to make descriptive statistics, how to group data, and how to explore a given dataset. The course puts strong emphasis on visualization components and we will learn to use the ggplot2 package to produce wonderful looking graphs.

​While this is a general R intro, we will look into how to carry out some of the most common analysis in R (hypothesis testing, linear regressions) and how to get that output into a nicely formatted academic paper. RMarkdown provides an intuitive workflow that allows us to export the final results to a Word file, a pdf, or html.​


12 June 2023 - 16 June 2023
Course Fee

Regular: €560
Students & PhD's: €395

Early Bird Regular: €504 (application deadline* April 1st) 
Early Bird Students & PhD's: €355,50 (application deadline* April 1st)

Scholarships and discounts

Please note that Erasmus+ scholarships are not possible for this course

Find more information here

Application deadline

May 1st

*Your application is only completed when the course fee has been paid

Course leader Akos Mate
Level of participant
  • Master
  • PhD
  • PostDoc
  • Professional
Admission requirements ​There are no requirements for this course, other than to have a laptop capable of running R (basically anything made after 1998).​
Admission documents
  • ​To get the student/PhD discount you need to upload a copy of your Student card or other proof of registration
  • If you are not a student/PhD, you can upload an empty document under 'Student Card'.
Mode of Study Online
ECTS Find more information here
Location Online