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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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Short Course A: Introduction to R for Data Analysis

About
Location:
Cologne / Unter Sachsenhausen 6-8
 
General Topics:
Data analysis, Research Data Management
Course Level:
Beginner
Format:
Summer School
Software used:
R
Duration:
2 days
Language:
English
Fees:
Students: 220 €
Academics: 330 €
Commercial: 660 €
 
Keywords
Additional links
Lecturer(s): Dennis Abel, Franziska Quoß

About the lecturer - Dennis Abel

About the lecturer - Franziska Quoß

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. In this course, we will first introduce the basic concepts and functionalities of R. Following this, we will go through a prototypical data analysis workflow: import, wrangling, exploration, (basic) analysis, and reporting.
 
For additional details on the course and a day-to-day schedule, please download the full-length syllabus.
 
Organizational structure of the course
This is a two-day course with a total amount of 12 hours of class time. 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. You will then receive a set of exercises on each topic that you work on alone. The solution of the exercises will be discussed before the start of the next lecture part.


Target group

You will find the course useful if:
  • you want to use R to wrangle, explore, visualize, and analyze your quantitative data.


    • Learning objectives

    By the end of the course, you will:
  • be comfortable with using R and RStudio,
  • be able to import, wrangle, and explore your data with R,
  • be able to conduct basic visualizations and analyses of your data with R.


    • 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 required.
    •  
    Software and hardware requirements
    You will need a computer or laptop with R (https://cran.r-project.org/) and RStudio installed (https://www.rstudio.com/) prior to the course. Both programs are free and open source.


    Schedule

    Recommended readings