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Intro Reproducible Research in R

An introductory workshop on modern data analyses and workflows

A 3-day workshop for researchers introducing modern and reproducible data analysis tools and workflows with R.

Luke W. Johnston

Helene Baek Juel

Bettina Lengger

Daniel R. Witte

Hannah Chatwin

Malene Revsbech Christiansen

Anders Aasted Isaksen



License: CC BY 4.0 Zenodo DOI JOSE DOI

The course is designed as a series of participatory live-coding lessons, where the instructor and learner code together, along with reading activities and hands-on practical exercises interspersed throughout the course and a final group assignment to do a simple data analysis project. This website contains all of the material for the course, from reading material to exercises to code to images. It is structured as a book, with “chapters” as lessons, given in order of appearance. We make heavy use of the website throughout the course where code-along sessions almost identically follow the material on the website (with slight modifications for time or more detailed explanations).

The course material was created using Quarto to write the lessons and create the book format, GitHub to host the Git repository of the material, and GitHub Actions with Netlify to build and host the website. The original source material for this course is found on the r-cubed-intro Github repository.

Want to contribute to this course? Check out the README file as well as the CONTRIBUTING file on the GitHub repository for more details. The main way to contribute is by using GitHub and creating a new issue to make comments and give feedback on the material.

Target audiences

This website and its content are targeted to three groups:

  1. For the learners to use during the course, both to follow along in case they get lost and also to use as a reference after the course ends. The learner is someone who is currently or will soon actively be doing research (e.g. a PhD or postdoc), who is likely in biomedical research, and who has no or little knowledge on coding in R. A more detailed description of who the learner is can be found in Section 1.1.
  2. For the instructors to use as a guide for when they do the code-along sessions and lectures.
  3. For those who are interested in teaching, who may not have much experience or may not know where to start, to use this website as a guide to running and instructing their own workshops.

Re-use and licensing

Creative Commons License

The course material is licensed under the Creative Commons Attribution 4.0 International License, so the material can be used, re-used, and modified, as long as there is attribution to this source. Check out the CONTRIBUTING guidelines and the For Instructors section for more details and tips on using this material.


There were many sources of inspiration when creating this course, as well as subsequent modifications. Here is a list of some of these sources.

Content from multiple sources was used as inspiration for this course, including the R for Data Science book and the Fundamentals of Data Visualization.

The Danish Diabetes and Endocrinology Academy has hosted, organized, and sponsored running this course to researchers in Denmark versions multiple times. A huge thanks to them for their involvement, support, and sponsorship! Both Steno Diabetes Center Aarhus and Aarhus University are employers of Luke Johnston and Daniel Witte (the initial founders and creators).

Danish Diabetes and Endocrinology AcademySteno Diabetes Center AarhusAarhus University