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Introduction to R (for participants with some prior knowledge in command-line programming)

Course Dates and Times

Friday 22 February 13:00–15:00 and 15:30–18:00

Saturday 23 February 09:00–12:30 and 14:00–17:30

Florian Weiler

Rijksuniversiteit Groningen

R is a powerful and versatile statistical computing environment. This course will introduce you to many of its most commonly used features. 

First, we will learn what R is and how it works, how data can be read into R, and how these data can be accessed and manipulated.

Next, we will cover various ways to summarise and tabulate the data, and how to use them for statistical inference. We will also learn how to work with some of R's plotting tools.

By the end of the course, you should be able to use R with some confidence.

Tasks for ECTS Credits

1 credit (pass/fail grade). Attend at least 90% of course hours, participate fully in in-class activities, and carry out the necessary reading and/or other work prior to, and after, class.

Instructor Bio

Florian Weiler is a senior researcher at the University of Basel, where he teaches statistics and content courses. He earned his doctoral degree at ETH Zurich.

Before joining the University of Basel, he worked as a lecturer in Quantitative Politics at the University of Kent, and as a postdoctoral researcher at the University of Bamberg. 

Florian's main research interests are in the fields of environmental politics and interest group research.

R is a powerful and versatile computing environment, widely used by statisticians, economists, and political scientists.

R is developed by its users, and researchers from many different fields have contributed to making R into the powerful statistical program it is today. This means that users can write their own R code, or adjust existing code according to their needs, and share this code with others. But it also means that there are a vast number of statistical tools and methods implemented in R, and we will only scratch the surface of R’s vast potential in this short introductory course. However, I aim to provide you with enough knowledge of how R works to learn new techniques yourself, and/or follow other courses in R.

First session

I'll give an overview of how R works and which tools are available to facilitate working with R (in particular RStudio).

Then we will learn about objects in R, how they (or a subset of their elements) can be accessed and manipulated, and how they can be transformed into datasets for further analysis. I will cover reading in data from other data formats such as SPSS and STATA, as well as how to save data.

Once we are familiar with data generation and data access, we will learn how to merge different datasets, and bring them into the desired data format.

Second session

We will start analysing the data, first using simple summary commands and descriptive statistics, but also using various tabulating tools available in R.

Then we will implement regression analysis in R, and access various elements of our statistical models for further analysis.

We will also cover data transformation and how to deal with special values in R.

Final session

I will talk about the powerful graphic tools provided by R.

We will use the basic plot function that comes with the standard R distribution, but I will also talk about other graphics packages such as lattice and ggplot2.

Finally, we will briefly talk about writing our own simple functions in R.

By the end of the course, you should have knowledge of the basic functions of R, sufficient to follow other Winter School courses which require this basic knowledge.

You should also know how to get help, and thus how to learn for yourself techniques not covered during the course.


Some basic knowledge of R, or any other command-line programming language such as Stata or SPSS.

If you are an absolute beginner, take course WA105(A), taught by Thorsten Schnapp.

The two courses are essentially the same: both start with the absolute basics, but in this course we will progress faster and cover more ground.

Each course includes pre-course assignments, including readings and pre-recorded videos, as well as daily live lectures totalling at least three hours. The instructor will conduct live Q&A sessions and offer designated office hours for one-to-one consultations.

Please check your course format before registering.

Online courses

Live classes will be held daily for three hours on a video meeting platform, allowing you to interact with both the instructor and other participants in real-time. To avoid online fatigue, the course employs a pedagogy that includes small-group work, short and focused tasks, as well as troubleshooting exercises that utilise a variety of online applications to facilitate collaboration and engagement with the course content.

In-person courses

In-person courses will consist of daily three-hour classroom sessions, featuring a range of interactive in-class activities including short lectures, peer feedback, group exercises, and presentations.


This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc.). Registered participants will be informed at the time of change.

By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, please contact us before registering.

Day Topic Details
Friday Afternoon R and RStudio, accessing and manipulating data, merging and reshaping data

The lecture will be intertwined with hands-on exercises for students

Saturday Morning Data analysis, summary statistics, tables, regression analysis, data transformation, missing values

The lecture will be intertwined with hands-on exercises for students

Satruday Afternoon Graphics in R (in particular plot and lattice), simple functions

The lecture will be intertwined with hands-on exercises for students

Day Readings

Appropriate PDFs will be provided for each session.

Friday afternoon

Required: Chapters 1 and 2

Saturday morning

Required: Chapters 3 and 4

Saturday afternoon

Required: Chapters 6 and 7

All the required readings are from Fox, John, & Sanford Weisberg (2011): An R Companion to Applied Regression 2nd edition, Thousand Oaks: Sage Publications, Inc.

Software Requirements

If you want to use your own computer or we’re not in a computer lab, download R Version 3.4.1 or higher (License: GPL 2).

I also recommend downloading RStudio (License: AGPL v3).

I will send you more information on free-of-charge R packages if necessary, when the online course area becomes available.

Hardware Requirements

Any fairly modern computer should able to run R well enough for this course.

If you're using your own laptop, you will need an internet connection because we'll be downloading R packages during the course.

German power supply: 230 volts / 50 hertz. Sockets typically accept Schuko (CEE 7/4) or Europlug (CEE 7/16).


Fox, John, & Sanford Weisberg (2011): An R Companion to Applied Regression 2nd edition, Thousand Oaks: Sage Publications, Inc.

Kleiber C, Zeileis A (2008): Applied econometrics with R New York: Springer

Zuur, Alain, Elena Ieno, & Erik Meesters (2009): A Beginner’s Guide to R Dordrecht, Heidelberg, London, New York: Springer.

Kabacoff, Robert (2011): R in Action: Data Analysis and Graphics with R Greenwich, CT: Manning Publications.