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Introduction to R (entry level)

Course Dates and Times

Friday 2 March
13:00–15:00 and 15:30–17:00

Saturday 3 March
09:00–10:30 / 11:00–12:00 and 13:00–14:30

Thorsten Schnapp

University of Bamberg

R is a powerful and versatile statistical computing environment. This course introduces students 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.

NB – This course is not designed to prepare you for a main course. It is only a general introduction into the R programming language.

Instructor Bio

Thorsten Schnapp is a research assistant at the University of Bamberg at the Chair of Statistics and Econometrics, where he teaches undergraduate and Master's-level courses on R.

He holds a Diploma in Sociology from the University of Munich and a Master of Science degree in Survey Statistics from the University of Bamberg.

Thorsten's main research interests are in the fields of scientific computing, statistical algorithms and dynamical systems, like Markov Chain Monte Carlo methods and their application to Multiple Imputation and Bayesian estimation procedures.

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 how to read in data from different formats (e.g., R’s data sets, SPSS and STATA files), how columns / variables get accessed and transformed, as well as how to save data.

We will also learn about objects such as matrices and vectors in R, which are necessary from time to time.

Second session

We start analysing the data from a statistical point of view.

First, we use simple summary commands and descriptive statistics standard to R, but also from additional R packages.

I will also provide an overview of R’s basic graphical abilities.

Final session

We'll discuss inference methods, i.e., how to implement regression analysis in R, and how to access various elements of the outputs.

We will also take a look at hypothesis tests.

By the end of the course, you should be a confident user of the basic functions of R.

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

This course should enable you to follow other Winter School courses requiring only a basic knowledge of R. Please note, however, that it is not a specific introduction to follow-up courses that use R. Since many other instructors in the Winter School use R, we are unable to prepare all participants individually for their main courses. Instead, this course aims to introduce you to the general logic of the programming language, and give you a basic understanding of how R works.

R is a very comprehensive program, so you will need to put in additional time after the course to obtain a good working knowledge.


Basic statistical knowledge at undergrad level is desirable but not essential.

This course is for those with no experience in statistical (command line) software, which means that we probably won’t have time to cover absolutely all of the topics described below.

You should be familiar with, generally, finding files in your operating systems’ folders and extracting zip files.

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.

Overview about are, load RData files, dta, sav (and probably Excel files), accessing elements and changing the content.

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

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

Saturday Afternoon Regression analysis and further inferential methods.

lm and summary.lm, output interpretation, t-Tests, chi-squared Test.

Day Readings

We will provide an own script for every session, accompanied by an appropriate Pdf.

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.