An Introduction into R

01.02.2017 um 09:00 bis 02.02.2017 um 16:30

Prof. Dr. Alena Witzlack-Makarevich

Course description

R is a freely available software environment for statistical computing and graphics, which exceeds the capabilities of such environments as SPSS and SAS. Its usage increased substantially in recent years and nowadays R is very popular in the academia and is finding its way into commercial applications as well. R has advanced graphical capabilities thanks to for example packages like ggplot2. Due to its open-source nature, R has a large and supportive community. The latest techniques are developed and released very quickly.
R has a reputation for being hard to learn. On the one hand, R has a command line interface, which can be challenging to master at the beginning. On the other hand, instead of setting up a complete analysis at once, R users learn how to analyze data interactively. For most data analysts, this is a mind shift they first need to undergo.
The present introductory class (in English) is designed to get the students familiarized with the R environment and give them a head start in becoming proficient in R. The class is meant for the very beginners with no or minimal rudimentary familiarity with the R environment.
Topics to be covered

  • Creating and understanding a dataset in R
  • Data structures (vectors, matrices, lists, data frames, factors)
  • Entering data from the keyboard
  • Importing data from Excel, SPSS, etc.
  • Working with graphs
  • Basic graphs (bar plots, histograms, box plots, dot plots)
  • Graphical parameters
  • Combining graphs
  • Legend


  • Filtering data with logical expression
  •  Basic data management and dplyr
  • Frequency and contingency tables
  • Examples of tests (e.g. t-test)
  • Correlation and fitting regression models
  •  Additional packages
  • Dealing with missing values


In addition to short explanatory phases, the class is primarily composed of practical supervised units with one's own laptop. All participants will be provided with training datasets. Additionally, participants are encouraged to bring their own datasets to practice.
The following software needs to be installed on the laptops:

  • R
  • RStudio
  • A good text editor (e.g. Notepadd++ or Textmate)
  •  A spreadsheet application (e.g. Excel, Calc)


Datum Uhrzeit AE Themen Kursnummer Gebühren*
1.2.17 9.00 bis 16:30 Uhr 16

Wissenschaftliches Arbeiten

F171033 300 €
2.2.17 9.00 bis 16:30 Uhr        

* Kostenlos für Mitarbeiter*innen der CAU sowie Doktorand*innen, die im Graduiertenzentrum registriert sind.


Anmeldeschluss: 14 Tage vor Seminarbeginn


Diesen Termin meinem iCal-Kalender hinzufügen