In this session, Facundo Muñoz (Cirad, France) describex tools and workflows to cumulatively improve the reproducibility of analyses performed in R. R is a mature, world-class, open-source statistical computing and data-analysis platform with a huge community of users from all areas of science and industry. Yet, most researchers rely only on its most basic scripting features, missing the opportunity to unleash its full potential, in particular concerning reproducible-research workflows. Specifically, we discuss encoding and platform-specific packages, the advantages of organising code into functions, using project-directories and relative paths, reproducible reports with RMarkdown, controlling package versions with Renv, organising code into a pipeline with targets, keeping track of changes from various collaborators with git, reproducibly publishing results with Continuous Integration in Git(Hu|La)b pages, reproducing the complete environment with docker, and controlling versions of the complete software stack with GNU Guix.