A curated guide to essential R packages organized by their role in the data science workflow.
RStartHere is a curated guide to essential R packages organized by the data science workflow. It helps data scientists and R users discover the best tools for tasks like data import, visualization, modeling, and communication. The project addresses the challenge of navigating R's vast ecosystem by providing a structured, opinionated list of high-quality packages.
Data scientists, statisticians, and analysts who use R for data analysis and need guidance on selecting effective packages. It's especially helpful for intermediate R users looking to expand their toolkit and follow best practices.
Unlike generic package lists, RStartHere organizes packages by practical workflow stages and applies clear quality criteria. It saves time by filtering the R ecosystem to proven, interoperable tools recommended by the RStudio team and community.
A guide to some of the most useful R Packages that we know about
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Packages are categorized by data science steps like Import, Tidy, and Visualize, making it easy to find tools for specific tasks based on the README's structured sections.
Each listed package meets standards for performance, intuitive syntax, and compatibility, as outlined in the Criteria section emphasizing speed, stability, and interoperability.
Includes hundreds of packages from visualization (ggplot2 extensions) to modeling (caret, xgboost) and communication (rmarkdown, shiny), providing a comprehensive toolkit for data science.
Accepts suggestions via GitHub contributions, allowing users to propose new packages, which fosters ongoing relevance and crowd-sourced improvements.
As a manually curated GitHub README, updates depend on maintainer effort, so recommendations may not reflect the latest package versions or emerging alternatives.
Provides only brief package lists with links, missing tutorials, performance benchmarks, or detailed comparisons to help users choose between similar tools.
Heavily features packages from the tidyverse ecosystem (e.g., dplyr, ggplot2), potentially overlooking other valid approaches or niche packages outside this scope.