GESIS Training Courses
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Scientific Coordination

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

Administrative Coordination

Angelika Ruf
Tel: +49 221 47694-162

Course 6: Introduction to R for Data Analysis

About
Location:
Online via Zoom
 
General Topics:
Course Level:
Format:
Software used:
R
Duration:
5 days
Language:
Fees:
Students: 400 €
Academics: 600 €
Commercial: 1200 €
Keywords:
Additional links
Lecturer(s): Dr. Johannes Breuer, Dr. Stefan Jünger, Veronika Batzdorfer

About the lecturer - Dr. Johannes Breuer

About the lecturer - Dr. Stefan Jünger

About the lecturer - Veronika Batzdorfer

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 providing an introduction to 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.
 
This course has 24 hours (instead of the usual 30) of total class time.
 
The detailed syllabus with course times and literature will be available soon.


Target group

Participants will find the course useful if they want to use R to wrangle, explore, visualize, and analyze 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 need access to a computer with R (https://cran.r-project.org/) and RStudio installed (https://www.rstudio.com/). Both programs are free and open source.