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Warning: The course content is currently being revised and updated, but will be ready shortly before the course.
Reproducible Research in R
An intermediate workshop on modern approaches and workflows to processing data
Updated: May 07 2021
Reproducibility and open scientific practices are increasingly demanded of, and needed by, scientists and researchers in our modern research environments. As we our tools for generating data become more sophisticated and powerful, we also need to start using more sophisticated and powerful tools for processing it. Training on how to use these tools and build modern data analysis skills is lacking for researchers, even though this work is highly time-consuming and technical. As a consequence of this unawareness of the need for these skills, how exactly data is processed is poorly, if at all, described in scientific studies. This hidden aspect of research could have major impacts on the reproducibility of studies. This course was created specifically to start addressing these types of problems.
The course is designed as a series of participatory live-coding lessons, where the instructor and learner code together, and is interspersed with hands-on exercises using some real-world datasets. This website contains all of the material for the course, from reading material to exercises, 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 follow the material on the website nearly exactly (with slight modifications for time or more detailed explanations).
The course material was created using rmarkdown to write the lessons,
bookdown to create the book format, GitLab to host the Git
repository of the material, and GitLab CI with Netlify to create the website.
The original source material for this course is found on the
r-cubed-intermediate GitLab repository.
Want to contribute to this course? Check out the README file as well as the CONTRIBUTING file on the GitLab repository for more details. The main way to contribute is by using GitLab and creating a new Issue to make comments and give feedback for the material.
1.1 Re-use and licensing
The course material is licensed under the Creative Commons Attribution 4.0 International License and the course code is licensed under a MIT License, so the material can be used, re-used, and modified, as long as there is attribution to this source.
- Luke Johnston: Brainstormed most of the course material and structure; set up the website; organized and coordinated the course; wrote and developed the code-along sessions; and, taught the final data analysis code-along session as well as the lectures.
- Signe Freja Storgaard: Taught and edited the data import code-along session material.
- Andreas Halgreen Eiset: Taught and edited the “Don’t Repeat Yourself” code-along session material.
- Omar Silverman: Taught and edited the processing and cleaning code-along session material.
Some of the material was taken and modified from multiple sources, including:
- UofTCoders Reproducible Quantitative Methods for EEB
- Software and Data Carpentry workshop material
- UofTCoders material and AU CRU material
- From version 1 of the “Reproducible Quantitative Methods: Data analysis workflow using R” course
The course material also draws inspiration from these excellent resources:
The Danish Diabetes Academy hosted, organized, and sponsored this course. A huge thanks to them for their involvement, support, and sponsorship! Both Steno Diabetes Center Aarhus and Aarhus University employ the instructors.