GESIS Training Courses
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Scientific Coordination

Verena Kunz

Administrative Coordination

Janina Götsche

R 101

About
Location:
Online via Zoom
 
General Topics
Course Level
Format
Software used
Duration
Language
Fees
Students: 200 €
Academics: 300 €
Commercial: 600 €
 
Keywords
Additional links
Lecturer(s): Matthias Roth, Lukas Birkenmaier

About the lecturer - Matthias Roth

About the lecturer - Lukas Birkenmaier

Course description

R is a powerful, versatile, and open software environment for statistical computing. With R, it is possible to manage and transform data, perform a plethora of statistical analyses, and visualize scientific results. However, using R for the first time can be daunting. R is a programming language and thus works differently than many statistical software packages that primarily use graphical user interfaces (e.g., SPSS, Excel).
In this two-day crash course, we will introduce R to researchers with no or very little prior experience with R. The aim is to equip you with the skills to participate more readily in other introductory-level R courses (e.g., the GESIS Fall Seminar course Introduction to Computational Social Science in R).
 
We will start at the very basics by installing R and the popular development environment RStudio. Having R up and running, we will take a slow-paced approach to explaining the fundamentals of R usage: First, we will focus on how to use RStudio. Second, we will cover basic but important concepts of R programming.  Third, we will show how you can extend R's capabilities to perform analyses using R packages. Fourth, we introduce the popular R package “tidyverse”, which you can use to perform the most common data wrangling tasks: Reading in, sub-setting, and transforming data from various data sources. Finally, fifth, we will use the "tidyverse” for basic exploratory data analysis. 
 
The exercises you complete during the course provide reference material for common R programming tasks. Additionally, the focus is given to using online resources to find answers to programming problems.  
 
At the end of the course, you will know the fundamentals of R. You will be able to participate in other introductory-level R courses that often have a very steep learning curve for people with no prior exposure to programming (in R). Furthermore, you will be able to advance your R skills on your own.


Target group

Participants will find the course useful if:
  • They are new to the R language
  • They want to get a gentle practical introduction to R and RStudio before taking further introductory-level R courses, which often have a rather steep learning curve for programming beginners (e.g., the GESIS Fall Seminar course “Introduction to Computational Social Science in R”).
  • They would like to learn R in an open and cooperative environment, where questions are explicitly encouraged.


  • Learning objectives

    By the end of the course participants will:
  • Be familiar with setting up and using R and RStudio on their computers
  • Have a basic understanding of fundamental programming concepts and their application in R  
  • Be able to perform basic data wrangling tasks using the “tidyverse”
  • Be able to interpret error messages and find answers online  
  • Have the course material as a reference for basic R concepts
  •  
    Organizational Structure of the Course:
    The organizational structure of the course is specifically designed to provide participants with enough resources to get to know and explore the R environment. The interactive workshop comprises a mix of short lecture-style input sessions followed by corresponding practical lab sessions. Each topic will first shortly be introduced by the instructors. Afterward, participants will be provided with a set of exercises on each specific topic. We are going to discuss the solutions together in class before the start of the next input session.
    Throughout the seminar, both instructors will be available for consultations as well as practical advice.


    Prerequisites

    No prior knowledge is required.
     
    Software requirements:
    Participants will be requested to download R and RStudio in advance. However, detailed instructions will be sent beforehand via email, and there will be points of contact available for troubleshooting.