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README.md
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<div align="center">
# awesome-reproducible-R [![Awesome][awesome-badge]](https://github.com/sindresorhus/awesome)
An awesome list of resources for reproducible research in R, which includes a comparison table of different tools.
</div>
## Contents
- [Comprehensive Tools](#comprehensive-tools)
- [Environment & Package Management](#environment--package-management)
- [Project Management](#project-management)
- [Data Management](#data-management)
- [Documentation & Reporting](#documentation--reporting)
- [Code Style](#code-style)
### Comprehensive Tools
These packages or frameworks incorporate package, project, and data management into one tool/
- [rrtools](https://github.com/benmarwick/rrtools) - The goal of rrtools is to provide instructions, templates, and functions for making a basic compendium suitable for writing reproducible research with R
### Environment & Package Management
These tools are designed to manage packages or package repositories.
- [reproducible](https://github.com/PredictiveEcology/reproducible) – A set of tools for R that enhance reproducibility beyond package management.
- [packrat](https://github.com/rstudio/packrat) - A dependency management system for creating isolated and reproducible R environments.
- [renv](https://rstudio.github.io/renv/) - A dependency management toolkit for self-contained, portable, and shareable R projects.
- [beRi](https://github.com/datasnakes/beRi) - beRi "beri environments for R installations" is an R environment, R installation, and R package management system for R.
### Project Management
These tools are designed to manage projects.
- [template](https://github.com/Pakillo/template) – A template for research projects structured as R packages.
- [workflowr](https://github.com/jdblischak/workflowr) - Organize your project into a research website.
- [ProjectTemplate](https://github.com/KentonWhite/ProjectTemplate) - Automatically build a directory for a new R project with a standardized subdirectory structure.
- [rworkflows](https://github.com/r-lib/pkgdown) - A suite of tools to make it easy for R developers to implement reproducible best practices on GitHub.
### Data Management
These tools are designed to manage data.
- [DataPackageR](https://github.com/ropensci/DataPackageR) – An R package to enable reproducible data processing, packaging and sharing.
- [archivist](https://github.com/pbiecek/archivist) - An R package that stores copies of all objects along with their metadata.
- [charlatan](https://github.com/ropensci/charlatan/) - An R package to create fake data.
### Documentation & Reporting
These tools focus on creating reproducible and shareable documentation or reports.
- [knitr](https://yihui.org/knitr/) - A general-purpose tool for dynamic report generation in R.
- [rmarkdown](https://rmarkdown.rstudio.com/) - An ecosystem for creating reproducible documents, presentations, and dashboards.
- [pkgdown](https://pkgdown.r-lib.org/) - Generate static HTML documentation for an R package.
- [rticles](https://pkgs.rstudio.com/rticles/index.html) - A suite of R Markdown templates for various types of articles.
- [grateful](https://github.com/Pakillo/grateful) - Facilitate citation of R packages.
- [report](https://easystats.github.io/report/) - Automatically produces reports of models and data frames according to best practices guidelines (e.g., APA’s style), ensuring standardization and quality in results reporting.
### Code Style
- [styler](https://styler.r-lib.org/) - A package that formats R code according to a consistent style.
- [lintr](https://lintr.r-lib.org/) - Static code analysis for R to enforce a consistent style.
## Contribute
Contributions are welcome! Please read the [contribution guidelines](CONTRIBUTING.md) first.
## License
[](https://creativecommons.org/publicdomain/zero/1.0/)
To the extent possible under law, the Datasnakes have waived all copyright
and related or neighboring rights to this work. See [LICENSE](LICENSE).
[awesome-badge]: https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg