About the course | Intended audience | Prerequisites | Content details
About the course
A practical introduction to reproducible research workflows using R, R Markdown and GitHub.
Reproducibility is an essential component of modern computational research. This course introduces practical tools and workflows that help researchers organise analyses, document their work and collaborate effectively on shared projects.
Participants learn how to create dynamic notebooks and reports using R Markdown, combining code, results and written interpretation within a single document. The course also introduces version control using Git and GitHub, enabling participants to track changes, manage project histories and collaborate without creating conflicting file versions.
By the end of the course, participants should be able to:
- understand the importance of reproducible computational workflows
- create notebooks and reports using R Markdown
- combine code, figures and written interpretation within reproducible documents
- understand the principles of version control using Git
- create and manage repositories on GitHub
- collaborate on shared projects using version control workflows
Teaching is primarily hands-on, combining presentations, demonstrations and guided practical exercises.
Back to top Intended audience
This course is suitable for:
- researchers and students working with computational or data analysis workflows in R
- participants interested in improving the reproducibility and organisation of their research projects
- researchers who want to learn collaborative coding and version control practices
- participants looking to integrate analysis, figures and reports into reproducible documents
Prerequisites
Participants should have basic familiarity with R programming before attending this course.
Previous experience creating analyses or scripts in R is recommended, although prior knowledge of Git, GitHub or R Markdown is not required.
Content details
The course combines lectures, demonstrations and hands-on practical exercises focused on reproducible computational workflows.
Topics covered include:
- Introduction to reproducible research
Overview of reproducibility in computational research and the importance of organising analyses, documenting workflows and managing project histories effectively.
- Literate programming and notebooks
Introduction to literate programming concepts using R Markdown notebooks, combining narrative text, code and analysis outputs within a single reproducible document.
- Creating reports in R Markdown
Building dynamic reports that integrate figures, tables and statistical analyses directly from R code, enabling automated and reproducible reporting workflows.
- Introduction to version control with Git
Learning the core principles of version control, including tracking changes, managing file histories and understanding collaborative development workflows.
- Working with GitHub repositories
Creating and managing repositories on GitHub, sharing projects online and synchronising local and remote versions of files.
- Collaborative research workflows
Using Git and GitHub to work collaboratively on shared projects while avoiding conflicting file versions and maintaining reproducible research practices.
Topics include R programming, R Markdown, notebooks, GitHub, version control and reproducible research workflows.