ECPR Winter School
University of Bamberg, Bamberg
2 - 9 March 2018

WA106(A) - Introduction to R (entry level)

Instructor Details

Instructor Photo

Thorsten Schnapp

University of Bamberg

Instructor Bio

Thorsten is a research assistant at the University of Bamberg at the Chair of Statistics and Econometrics, where he teaches R courses both on bachelors’ and masters’ level. 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. His 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
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
Prerequisite Knowledge

Basic statistical knowledge on an undergrad level is desirable but not essential. Notice that this course will be taught for participants without experience in working with statistical (command line) software. This means also, depending on the necessary pace, it probably won’t cover all of the topics described. (Please make sure, that you are familiar with, generally, finding files in your operating systems’ folders and how to extract zip files.)

Short Outline

This course introduces students to many of the most commonly used features of R, a powerful and 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 work with some of the 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’re getting an overview of how R works and which tools are available to facilitate working with R (in particular we will use RStudio).  Afterwards, we will learn, reading in data from different data 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. In addition, we will learn about further objects (e.g., matrices, vectors) in R, which are necessary from time to time, when working with results but also inputs some commands provide or need.

In the second session we will 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. Additionally, we provide an overview of R’s basic graphical abilities.

In the final session we will talk about inference methods, i.e., how to implement regression analysis in R, how to access various elements of the outputs and, additionally, take a look at hypothesis tests.

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-to-Day Schedule

Friday AfternoonR 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 MorningData analysis, summary statistics, tables, regression analysis, data transformation, missing values

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

Saturday AfternoonRegression analysis and further inferential methods.

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

Day-to-Day Reading List


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

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 3.4.1 or higher from (License: GPL 2). In addition, we recommend downloading RStudio from (License: AGPL v3)

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


The information contained in this course description form may be subject to subsequent adaptations (e.g. taking into account new developments in the field, specific participant demands, group size etc.). Registered participants will be informed in due time in case of adaptations.

Note from the Academic Convenors

By registering to this course, you certify that you possess the prerequisite knowledge that is requested to be able to follow this course. The instructor will not teach these prerequisite items. If you are not sure if you possess this knowledge to a sufficient level, we suggest you contact the instructor before you proceed with your registration.

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