GESIS Training Courses
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Wiss. Koordination

Verena Kunz

Administrative Koordination

Janina Götsche

Introduction to R

About
Veranstaltungsort:
Online via Zoom
 
General Topics:
Course Level:
Format:
Software used:
Duration:
Language:
Fees:
Students: 330 €
Academics: 495 €
Commercial: 990 €
 
Keywords
Additional links
Dozent(en): Natalia Umansky, Christian Pipal

Referenteninformationen - Natalia Umansky

Referenteninformationen - Christian Pipal

Seminarinhalt

R is a powerful, versatile, and open-source software environment tailored for statistical computing. It enables users to efficiently manage, manipulate, and analyze data, offering diverse options for presenting scientific results. Despite its capabilities, beginners may find R challenging due to its programming language structure, which differs significantly from commercially available statistical software like SPSS or Excel and their graphical user interfaces.
This three-day workshop addresses researchers who have little to no prior experience with R. We will start by introducing R and the popular development environment RStudio. We will move at a slow pace, using real-world data analysis examples. We will walk through the typical steps of analyzing data, while learning the fundamental concepts of R and the popular R package “tidyverse”.  A significant focus during this workshop will be on the “tidyverse”, now a standard toolkit for data wrangling tasks in R, including data importing, sub-setting, and transformation from various sources. After introducing each new set of tools, we will revisit underlying R programming concepts, such as data types, functions, and control structures. We will also explore how to enhance R's capabilities using additional R packages. Finally, we will introduce participants to basic data modelling, and use the "tidyverse" package to conduct basic exploratory data analysis and visualizations.
Throughout the workshop, participants will complete exercises that provide them with reference material for common R programming tasks. Throughout these exercises we will have a dedicated space for troubleshooting, answering in-depth questions, and more advanced coding challenges. We will also emphasize the use of online resources to help participants find answers to programming problems. By the end of the course, participants will have a solid understanding of the fundamentals of data analysis with R (reading in and saving data, transforming data, and analyzing data). Overall, this course equips participants with all the tools and resources necessary to continue advancing their R skills on their own.
 
Organizational structure of the course
The course's structure aims to equip participants with ample resources to acquaint themselves with the R environment. The workshop is interactive and incorporates brief lectures on various topics, followed by corresponding practical lab sessions. Instructors will introduce each topic briefly and then assign a set of exercises to the participants. These solutions will be discussed in class before the start of the next input session. During the seminar, both instructors will be on hand to provide guidance and practical advice to the participants.


Zielgruppe

Participants will find the course useful if:
  • they are new to the R language.
  • they want to get a practical introduction to R and RStudio.
  • they want to use R to import, manage, transform, explore, analyze, and visualize data.
  • they would like to learn R in an open and cooperative environment, where questions are explicitly encouraged.


  • Lernziel

    By the end of the course participants will:
  • be familiar with setting up and using R and RStudio on their computer.
  • 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 visualize data using “ggplot2”.
  • be able to interpret error messages and find answers online.


  • Voraussetzungen

    A basic understanding of quantitative social science and hypothesis testing, including introductory statistics (e.g., distributions, t-tests, cross tables, and linear regression).
     
    Software requirements
    Participants will be requested to download R and RStudio in advance. Participants will receive a detailed instruction beforehand via e-mail, and there will be points of contact available for troubleshooting. There will also be a dedicated space for installation troubleshooting during the first workshop day.


    Zeitplan

    Literaturempfehlungen