Last updated: 2019-04-09

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Knit directory: rrresearch/

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Rmd 2462ad6 Anna Krystalli 2019-02-16 Start workflowr project.

Welcome to the R for Reproducible Research Resource site.


In order to ensure robustness of outputs and maximise the benefits of ACCE research to future researchers and society more generally, it is important to share the underlying code and data. But for sharing to have any impact, such materials need to be created FAIR (findable, accessible, interoperable, reusable), i.e. they must be adequately described, archived, and made discoverable to an appropriate standard.

Additionally, if analyses are to be deemed robust, they must be at the very least reproducible, but ideally well documented and reviewable.

R and Rstudio tools and conventions offer a powerful framework for making modern, open, reproducible and collaborative computational workflows more accessible to researchers.

This course focuses on data and project management through R and Rstudio, will introduce students to best practice and equip them with modern tools and techniques for managing data and computational workflows to their full potential. The course is designed to be relevant to students with a wide range of backgrounds, working with anything from relatively small sets of data collected from field or experimental observations, to those taking a more computational approach and bigger datasets.

By the end of the workshop, participants will be able to:

  • Understand the basics of good research data management and be able to produce clean datasets with appropriate metadata.

  • Manage computational projects for reproducibility, reuse and collaboration.

  • Use version control to track the evolution of research projects.

  • Use R tools and conventions to document code and analyses and produce reproducible reports.

  • Be able to publish, share materials and collaborate through the web.

  • Understand why this all matters!


Day 1


Research Data Management

  • Data Hygiene
  • Metadata

Research Code Management

Literate Programming

  • Intoduction to long form documentation in Rmarkdown

Managing projects

  • Version control
  • Sharing code
  • Collaborating on code

Day 2

Packaging code

  • Writing & documenting functions
  • Capturing metadata incl. dependencies
  • Checking & Testing functions

Putting it all together: a Research Compendium

  • Creating a research compendium