The workshop focuses on reproducible research in the quantitative social and behavioral sciences. In the context of this workshop, reproducibility means that other researchers can fully understand and rerun your data preparation and statistical analyses. However, the workflows and tools covered in this workshop will also help in facilitating your own work as they allow you to automate and track analysis and reporting tasks. In addition to a conceptual introduction to the methods and key terms around reproducible research, this workshop focuses on procedures for maximizing the reproducibility of data analyses using R. After discussing essential definitions and dimensions of reproducibility, we will cover some computer literacy and project organization basics that are helpful for conducting reproducible research (e.g., folder structures, naming schemes, or command-line interfaces). After that, we will focus on version control, dependency management, and computational reproducibility. The tools we will use for that include Git and GitHub, R packages for dependency management as well as Binder, a tool to package and share reproducible and interactive analysis environments.

The workshop is targeted at participants who have (at least some) experience with R and want to learn (more) about workflows and tools for making the results of their research reproducible.

Participants should have some basic knowledge of R and RStudio (e.g., installing and loading packages, importing different data types, basic data wrangling, and analyses). Other than that, the only other prerequisite is that you should not be afraid to use your keyboard more often than your mouse. To facilitate applying the methods covered in the workshop to their own work, we recommend that participants install all necessary software on their computers. Information on how to do this will be shared with all registered participants prior to the workshop.