https://github.com/fhdsl/tools_for_reproducible_workflows_in_r
https://github.com/fhdsl/tools_for_reproducible_workflows_in_r
Last synced: about 2 months ago
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- Host: GitHub
- URL: https://github.com/fhdsl/tools_for_reproducible_workflows_in_r
- Owner: fhdsl
- License: cc-by-4.0
- Created: 2022-11-21T17:48:10.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2025-08-01T00:42:50.000Z (11 months ago)
- Last Synced: 2025-08-01T02:37:44.143Z (11 months ago)
- Language: CSS
- Homepage: https://hutchdatascience.org/Tools_for_Reproducible_Workflows_in_R/
- Size: 104 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 15
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Metadata Files:
- Readme: README.md
- License: LICENSE.md
- Code of conduct: code_of_conduct.md
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README
## Tools for Reproducible Workflows
This is the GitHub repository for the Tools for Reproducible Workflows course. This course explores a variety of tools that can assist with reproducible data analysis from a broad range of fields. This course was funded as part of a series of courses in the [Training Module for Reproducible Data Science Research project](https://reporter.nih.gov/search/k_pXzn8wfUeEvaWpnzIToA/project-details/10663171).
Except where otherwise indicated, the contents of this course are available for use under the Creative Commons Attribution 4.0 license. You are free to adapt and share the work, but you must give appropriate credit, provide a link to the license, and indicate if changes were made. Sample attribution: Tools for Reproducible Workflows in R by [Fred Hutchinson Data Science Lab](https://hutchdatascience.org/) and [University of Texas, Austin](https://stat.utexas.edu/) (CC-BY 4.0). You can download the illustrations by clicking [here](https://docs.google.com/presentation/d/1MNHf8JpolaEP_vQ_kB-1xRBF9wo3haCArRu117hBoHA/edit?usp=sharing).
## Learning Objectives
This course will demonstrate how to:
1. Explain best practices for making analyses more reproducible and transparent
2. Use special features in RStudio for efficiency and reproducibility
3. Configure and organize projects for data analysis using the here package and the ProjectTemplate package
4. Create reproducible reports using RMarkdown and Quarto
5. Write custom functions for reuse of code
6. Test functions with the testthat package
7. Setup and use Git and GitHub to track changes over time 8. Share data and code publicly
This course was built with [OTTR](https://www.ottrproject.org/)
All materials in this course are licensed under a Creative Commons Attribution 4.0 International License unless noted otherwise.