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Scientific Coordination

Sebastian E. Wenz
Tel: +49 221 47694-159

Administrative Coordination

Jacqueline Schüller
Tel: +49 0221 47694-160

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Course 7: Introduction to R for Data Analysis

About
Location:
Cologne / Unter Sachsenhausen 6-8
 
Course duration:
10:00-17:00 CEST
General Topics:
Computational Social Science, Data Analysis, Research Data Management
Course Level:
Beginner
Format:
Summer School
Software used:
R
Duration:
5 days
Language:
English
Fees:
Students: 500 €
Academics: 750 €
Commercial: 1500 €
 
Keywords
Additional links
Lecturer(s): Jan Schwalbach, Dennis Abel

About the lecturer - Jan Schwalbach

About the lecturer - Dennis Abel

Course description

The open-source software package R is free of charge and offers a variety of standard data analysis procedures as well as a comprehensive repertoire of highly specialized processes and procedures, even for complex applications. After introducing the basic concepts and functionalities of R, we will go through a prototypical data analysis workflow in the course: import, wrangling, exploration, (basic) analysis, reporting.
 
A detailed syllabus with course times and literature will soon be available for download here.


Target group

Participants will find the course useful if they want to use R to wrangle, explore, visualize, and analyse their data.


Learning objectives

By the end of the course participants should be:
  • comfortable with using R and RStudio.
  • able to import, wrangle, and explore their data with R.
  • able to conduct basic visualizations and analyses of their data with R.
  • able to generate reproducible research reports using R Markdown.
    •  
    Organizational structure of the course
    The best way to learn R is to try things out and apply the presented concepts. Therefore, we will have a mixture of lectures and hands-on exercises. More specifically, each topic will be introduced in a lecture by the instructors. Participants will then receive a set of exercises on each topic that they work on alone. The solution of the exercises will be discussed before the start of the next lecture part.


    Prerequisites

  • prior experience with quantitative data analysis, basic statistics, and regression
  • experience with using other statistical packages (e.g., SPSS or Stata) is helpful, but not a requirement
    •  
    Software and hardware requirements
    Course participants will need to bring a laptop with R (https://cran.r-project.org/) and RStudio installed (https://www.rstudio.com/). Both programs are free and open source. Participants will need to be able to download files from the internet (free Wifi is provided by GESIS) and have the rights to install packages on their laptops during the course.