Open and Reproducible Science in R

The Open and Reproducible Science· in R module of the MSc in International Health and Tropical Medicine is designed to equip students with a foundational understanding and appreciation of the pillars of Open Science more broadly and within that the concepts, methods and tools for Reproducible Research more specifically. To further the students’ learning, practical examples and exercises are discussed and walked through using the R language for statistical computing as a way to practically demonstrate these concepts.

Module organisation

The module is organised in two parts. The first part focuses on teaching the basics of using R and RStudio. These topics are covered in the first 5 sessions of the module during Michealmas term. The second part, discussed in the last 5 sesions of the module during Hilary Term, focuses on giving on overview of open and reproducible science.

Part 1: All about R

Session 1: Getting the right tools for the job

This session describes the set of tools useful for open and reproducible science.

Session 2: Learning the basics of R - Part 1

This session focuses on the basics of using R through RStudio for working with data.

Session 3: Learning the basics of R - Part 2

This session introduces the use of R packages to extend functionalities for data handling and analysis.

Session 4: Learning the basics of R - Part 3

This session teaches how to write functions and provides a challenge to write a function given a specific task.

Session 5: Creating basic R workflows and literate programming

This session demonstrates the basics of project-oriented workflows and introduces literate programming in R.

Part 2: Open and Reproducible Science

Session 6: Open Science and Reproducible Research in R

This session describes the what, why, and how of Open Science and Reproducible Research in R.

Session 7: Git and GitHub for use with R

This session goes deeper into the use of git and GitHub for code versioning and code sharing.

Session 8: Reproducible scientific workflows in R - Part 1

This session introduces best practices in reproducible scientific workflows and their implementation using the {targets} package.

Session 9: Reproducible scientific workflows in R - Part 2

This session will be the second day of Hackathon 2025.

Session 10: Making your R-based research project portable

This session introduces R code portability using the {renv} package.