RSS02.D2 Introduction to R - Confirmed


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

Monday – Importing data 
Tuesday – Data wrangling/cleaning 
Wednesday – Visualisation | Exploratory analysis 
Thursday – Analysis | Writing our own functions 
Friday – Reporting the results

Reflecting on the realities of typical research projects, 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 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 (as an example, most of the Financial Times' charts are made with R in ggplot2).

As part of learning a programming language, it is inevitable that we must learn how to write our own functions. It is not the most intuitive part, and I will focus on making it as accessible as possible without relying on too much computer science/programming jargon. Alongside this, we’ll look at a few statistical applications in R (t-test and OLS regression).

At the end of the course, we will export our results from R or even write an academic paper or report using RMarkdown.


26 June 2023 - 30 June 2023
Course Fee

Regular: €995
Students & PhD's: €645

Early Bird Regular: €895 (application deadline* April 1st) 
Early Bird Students & PhD's: €580,50 (application deadline* May 15th)

Scholarships and discounts Find more information here
Application deadline

May 15th

*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 On Campus
ECTS 2 or 4 Find more information here
Location Radboud University