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Introduction to R (entry level) - FULLY BOOKED - contact to be added to a waiting list

Thorsten Schnapp

University of Bamberg

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.

Course Dates and Times

Course Dates and Times

Friday 3 March: 13:00-15:00 and 15:30-17:00
Saturday 4 March: 09:30-12:00 and 13:00-14:30
7.5 hours over two days

Prerequisite Knowledge

Basic statistical knowledge on an undergrad level is desirable but not essential, because depending on the participants needs this course will be taught on a slower pace for participants without experience in statistical (command line) software. This means also it probably won’t cover all of the topics described. If, of course, everyone is experienced in using statistical software to some extent, this course will be taught in a normal pace like Florian Weiler’s course.

Short Outline

This course introduces students to many of the most commonly used features of R, a powerful and very versatile statistical computing environment. At the beginning of the course, 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 summarize and tabulate the data, and how to use them for statistical inference. We will also learn how to use some of the very powerful plotting tools of R. At the end of the course, students should be able to use R with some confidence. But be aware that “Introduction to R” is not designed to specifically prepare participants for their main courses, but only a general introduction into the R programming language.

Long Course Outline

R is a very powerful and versatile computing environment, and is widely used by statisticians, economists, political scientists, etc. 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. What this means it that user can write their own R code, or adjust existing code according to their needs, and share this code with others. But this also means that there are a vast number of statistical tools and methods implemented in R, and we will only be able to scratch the surface of R’s vast potential in this short introductory course. However, the goal is to provide students with enough knowledge of how R works so they can learn new techniques themselves and/or follow other courses taught in R.

In the first session, we will cover how R works and which tools are available to facilitate working with R (in particular we will use 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. In addition, reading in data from other data formats (such as SPSS and STATA) will be covered, as well as how to save data.

In the second session we will start analysing the data, first using simple summary commands and descriptive statistics, but also through the use of various tabulating tools available in R and from additional R packages. Then we will cover how to implement regression analysis in R, and how to access various elements of our statistical models for further analysis.

In the final session we will talk about merging and reshaping data of different sources. Additionally, we provide an overview of R’s basic graphical abilities, and if there is time left, we will also talk about other more powerful graphics packages such as lattice and ggplot2.

At the end of the course students should be confident users of the basic functions of R. They should also know how to get help, and thus how to learn techniques not covered in the course without further guidance. At the least students should be able to participate in and follow other courses requiring a basic knowledge of R taught during the Winter School. But be aware that “Introduction to R” is not a course 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, we want to introduce participants to the general logic of the programming language R, and give them a basic understanding of how the program works. Since R is a very comprehensive program, additional time after completing the course is necessary in order to obtain a good working knowledge of R.


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

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

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

Day Readings
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.

Appropriate PDFs will be provided for each session.

Software Requirements

If students want to use their own computer or we’re not able to be in a computer lab, you should download R Version 3.3.0 or higher from .In addition, we recommend downloading RStudio from
If we need additional R packages, further information will follow regarding these free-of-charge packages, when the online course area will be available.


Hardware Requirements

Any fairly modern computer able to run R should be good enough for this course. Students will need an internet connection if they use their own computer, as we will need to download R packages during the course.
If you are unfamiliar with the German power supply: we have 230 volts and 50 hertz, and the 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 .Greenwich, CT: Manning Publications.


Additional Information


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 in due time.

Note from the Academic Conveners

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, contact the instructor before registering.