Reproducible Research in R: An intermediate workshop on modern approaches and workflows to processing data
Reproducibility and open scientific practices are increasingly being requested or required of scientists and researchers, but training on these practices has not kept pace. This course intends to help bridge that gap and covers the fundamentals and workflow of data analysis in R.
This repository contains the lesson, lecture, and assignment material for the course, including the website source files and other associated course administration files.
For more detail on the course, check out the syllabus.
The lectures and lessons in this course are designed to be presented primarily with a participatory live-coding approach. This involves an instructor typing and running code in RStudio in front of the class, while the class follows along using their own computers. Challenges are interspersed in the lesson material, allowing participants to collaboratively work on smaller coding problems for a few minutes. All lesson materials are provided ahead of time on the course website for participants to refer to during lectures. Throughout the course, participants work in small groups to complete the exercises together.
The teaching material is found mainly in the project folders:
preamble/: Contains the syllabus and the schedule files.sessions/: Contains the code-along teaching material, as well as associated links to the lecture slides.slides/: Contains the slides, created as Revealjs HTML slides by using Quarto.
The website is generated from Quarto, so follows the file and folder structure conventions from that package.
Packages used and depended on for this course are included in the
DESCRIPTION file. To install the packages, run this function in the
root directory (where the r-cubed-intermediate.Rproj file is located:
# install.packages("pak")
pak::pak()If you are interested in contributing to the course material, please refer to the contributing guidelines. Please note that the project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
Much of the lesson material was taken and modified from multiple sources (most of which I've created, been involved in, or contributed to), including:
- UofTCoders Reproducible Quantitative Methods for EEB (which I helped develop and is the inspiration and basis for this course)
- Software and Data Carpentry workshop material
- UofTCoders material and AU CRU material, from the peer led and contributed lessons
- Some material from my "Reproducible Quantitative Methods: Data analysis workflow using R"