A curated list of awesome R packages, frameworks, and software for data science and statistical computing.
Awesome R is a curated directory of high-quality R packages, frameworks, and software tools for data analysis, visualization, and statistical computing. It helps R users quickly discover essential resources across domains like machine learning, bioinformatics, web development, and reproducible research by aggregating community-vetted tools with usage metrics.
R programmers, data scientists, statisticians, and researchers looking to explore or expand their toolkit with well-maintained R packages and learning resources.
It saves time by providing a centralized, quality-filtered list of R tools—avoiding the need to search through scattered sources—and includes popularity indicators to help users identify widely-adopted packages.
A curated list of awesome R packages, frameworks and software.
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
Aggregates top R packages with indicators like GitHub stars and CRAN downloads, ensuring only well-maintained, widely-used resources are listed, as seen in the heart and star icons for popular packages.
Organized into intuitive sections such as Data Manipulation, Machine Learning, and Web Technologies, making it easy to browse tools by domain without searching scattered sources.
Includes dedicated sections for recent years (e.g., 2023, 2020) with new additions like gt and torch, helping users stay current with evolving tools.
Provides links to books, MOOCs, podcasts, and reference cards under 'Learning R', offering a holistic approach to mastering R beyond package discovery.
Presented as a static markdown file without interactive filtering or search functionality, requiring manual navigation through long lists, which can be inefficient for large-scale exploration.
Updates depend on community contributions and are not real-time; some sections, like the 2023 list with only one entry, may lag behind the latest package releases or trends.
While it lists many packages, it doesn't provide usage examples, benchmarks, or recommendations on when to choose one over another, leaving users to seek additional sources for decision-making.