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Introduction to R (entry level)

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

Thorsten Schnapp

University of Bamberg

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

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

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

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 very powerful and versatile computing environment, and is 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. Users can write their own R code, or adjust existing code according to their needs, and share it with others.

But this 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 course. However, by the end of the course you shoud have sufficient knowledge to teach yourself techniques and / or follow other courses taught in R.

First part (scheduled for Friday)

I will give an overview of how R works, and which tools facilitate working with R (in particular RStudio). You 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. You will learn about further objects (e.g. matrices, vectors) in R.

Second part (scheduled for Saturday morning)

We start analysing 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. The descriptive part ends with an overview of R’s basic graphical abilities.

Third part (scheduled for Saturday afternoon)

In the last session we will discuss inference methods, i.e. how to facilitate hypothesis testing and conduct regression analysis in R.

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.

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

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

Saturday Morning Data analysis, summary statistics, graphics.

One and multidimensional descriptions, descriptive relationships, summary commands and several graphics.

Saturday Afternoon Regression analysis and further inferential methods.

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

Note: All sessions are intertwined with hands-on exercises for students.

Day Readings

You will be given a pdf script to accompany every major part of the course. Additional literature (below) is helpful but not mandatory.

Software Requirements

If you want to use your own computer or we’re not in a computer lab, download R Version 3.5.1 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.