Data Analytics in R

This course is full. A similar course will be offered as part of VU Amsterdam Summer School. You can check the updated website of the summer school from 1 December onwards for more information.

With the increasing use of alternative software packages like R in data analysis, now is the time to learn their ins and outs. The large number of active programmers creating R packages makes this an up-to-date programme providing a huge range of statistical analyses useful for professionals and students in conducting advanced research analysis. Researchers also use R to write functions for analysing data, or to create professional plots.

Course level
Master, PhD candidates and professionals from all disciplines
Session 2
11 January to 18 January 2020  (the course is full)
Coordinating lecturer                                      Dr. Mariken A.C.G. van der Velden
Other lecturers
Dr. Wouter van Atteveldt and Dr. Kasper Welbers
Form(s) of instructionLectures and practical programming sessions
Form(s) of assessmentMorning seminar, afternoon tutorials
ECTS3 credits
Contact hours30 hours (5 days, 3 hours in the morning, 3 hours in the afternoon)
Tuition fee

€800 - non-VU students and staff

€500 - VU students and staff

Students with an interest in quantitative methods. Students will learn to work with R, and have the opportunity to select into either visualizations of their data, the basics of quantitative text analysis, or work with advanced statistics in R. If you have doubts about your eligibility for the course, please contact us: [email protected]
In possession of a laptop

The explosion of digital communication and increasing efforts to digitize existing material has produced a deluge of material such as digitized historical news archives, policy and legal documents, political debates and millions of social media messages by politicians, journalists, and citizens. This has the potential of putting theoretical predictions about the societal roles played by information, and the development and effects of communication to rigorous quantitative tests that were impossible before. Besides providing an opportunity, the analysis of such “big data” sources also poses methodological challenges. Traditional manual content analysis does not scale to very large data sets due to high cost and complexity. For this reason, many researchers turn to automatic text analysis using techniques such as dictionary analysis, automatic clustering and scaling of latent traits, and machine learning. To properly use such techniques, however, requires a very specific skill set.

This course aims to give students an introduction to data analytics. Students are able to choose between visualization of data (option A), text analysis (option B) and advanced statistics (option C). R will be used as platform and language of instruction, but the basic principles and methods are easily generalizable to other languages and tools such as python. Participants will be given handouts with examples based on pre-existing data to follow along, but are encouraged to work on their own data and problems using the techniques offered. 

Possibility to have a social activity with the instructors on Wednesday.

Upon successful completion of the course, students will be able to:

  • Understand the R programming language and software environment;
  • Organize, transform and merge data with R;
  • Conduct simple analyses with R (i.e., descriptive statistics, correlations, chisquare, (in)dependent t-test, one-way ANOVA, linear regression and more complex analyses relevant to one’s projects);
  • Create (animated) visuals with for various types of data using R; [OPTION A]
  • Perform web scraping (e.g., news articles, social media responses), topic modeling and machine learning with R; [OPTION B]
  • Use R packages to conduct more complex analyses that are relevant to their own project (e.g., factor analysis, multilevel analysis, time series analysis). [OPTION C]

Healy,   K.   (2018).   Data   visualization:   a   practical   introduction.

Princeton University Press. Also available online.

Wickham,  H.,  &  Grolemund,  G.  (2016).  R  for data science: Import, tidy, transform, visualize, and model data. O'Reilly Media, Inc. Free online.


Before the course starts, download R and R Studio. Moreover,  please complete this online Datacamp Introduction to R course.This online course covers basic knowledge of R and its programming language that you will need to successfully complete this Data Analysis in R course. 

Dr. Mariken A.C.G. van der Velden

"Communicating data by means of visuals is an essential skill for researchers and professionals. " 

Dr. Wouter van Atteveldt

"R is to social science what the microscope was to biology. And it’s free too!”


D. Kasper Welbers

"SPSS is dead!"

It is not possible to apply anymore as the course has reached its maximum capacity.