rstats-ed
A curated repository of university courses, workshops, and online materials for learning and teaching R programming and data science.
What is rstats-ed?
rstats-ed is a curated collection of educational resources for learning and teaching the R programming language. It compiles university courses, workshops, MOOCs, and tutorials from various disciplines to help educators and students find quality materials. The project addresses the challenge of discovering structured R courses across different institutions and formats.
Educators designing R-based curricula, students seeking courses, and self-learners looking for structured learning paths in R and data science.
It provides a centralized, community-maintained directory of R courses, saving time for those searching for educational materials. Unlike generic learning platforms, it focuses specifically on R and includes academic courses with syllabi and resources.
Overview
List of courses teaching R
Use Cases
Best For
- University instructors looking for R course syllabi to adapt
- Students searching for semester-long R courses in specific fields like ecology or social sciences
- Self-learners seeking structured MOOCs or tutorials on R and tidyverse
- Workshop organizers needing examples of short-format R training materials
- Researchers wanting to teach reproducible data analysis with R Markdown and Git
- Academic departments designing data science curricula with R
Not Ideal For
- Developers seeking hands-on, interactive coding platforms with immediate feedback and exercises
- Teams needing corporate training materials with certifications or professional development credits
- Individuals looking for up-to-the-minute tutorials on the latest R packages or niche advanced topics
- Projects requiring a fully integrated learning environment with built-in code execution and grading
Pros & Cons
Pros
Aggregates courses from various disciplines like psychology, ecology, and social sciences, and includes global offerings in multiple languages, as seen in listings from Brazil to the UK.
Features semester-long university courses with detailed syllabi and resources, such as those from Duke University and Stanford, providing solid foundational learning paths.
Many courses integrate Git, R Markdown, and project management, promoting best practices in reproducible research, highlighted in courses like ESPM 288 at UC Berkeley.
Focuses on modern R data science workflows using the tidyverse, with examples from University of Edinburgh and other institutions teaching data wrangling and visualization.
Cons
Relies on community pull requests, so listings can become stale; many courses are from 2017-2020 and may not reflect current R versions or practices.
As a user-submitted directory, there's no vetting process for course quality or accuracy, risking inclusion of outdated or poorly maintained resources.
The directory only lists external resources without built-in tutorials or exercises, so users must navigate to other platforms for hands-on practice.
Frequently Asked Questions
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