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.

 

A basic understanding of quantitative social science and hypothesis testing, including introductory statistics (e.g., distributions, t-tests, cross tables, and linear regression).