GESIS Training Courses
user_jsdisabled
Search

Scientific Coordination

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

Administrative Coordination

Jacqueline Schüller
Tel: +49 0221 47694-160

Course 6: Introduction to R for Data Analysis

About
Location:
Online via Zoom
 
Course duration
Mo-Th: 09:30-15:30 CEST
Fr: 09:30-14:30 CEST
 
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

Please note: This Online-class is taught online only-live and in real time. Recordings will not be available.
 
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.
 
For additional details on the course and a day-to-day schedule, please download the full-length syllabus.


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.