R is a software environment for statistical computing that is both powerful and versatile, as well as open source. It allows users to manage and manipulate data, conduct a wide range of statistical analyses, and present scientific results in various forms. However, for individuals who are new to R, the experience can be challenging as it is a programming language that operates differently from commercial statistical software packages like SPSS or Excel that primarily use graphical user interfaces.

This three-day workshop addresses researchers who have little to no prior experience with R. During this time, we will start by introducing R and the popular development environment RStudio. We will move at a slow pace, explaining the fundamental concepts of R usage, including basic programming concepts and how to use RStudio. Additionally, we will show participants how to extend R's capabilities to perform analyses using R packages. We will also cover the popular R package "tidyverse", which is useful for performing common data wrangling tasks, such as reading-in, sub-setting, and transforming data from various sources. Finally, we will use the "tidyverse" package to conduct basic exploratory data analysis and visualizations.

Throughout the workshop, participants will complete exercises that will provide them with reference material for common R programming tasks. 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 R, including how to work with tabular data, such as reading in, transforming, and analyzing data. Our goal is to equip participants with all the tools and resources they need to continue advancing their R skills on their own.