Open Source R Software
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R Software
Browse free open source R Software and projects below. Use the toggles on the left to filter open source R Software by OS, license, language, programming language, and project status.
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ggplot2
An implementation of the Grammar of Graphics in R
ggplot2 is a system written in R for declaratively creating graphics. It is based on The Grammar of Graphics, which focuses on following a layered approach to describe and construct visualizations or graphics in a structured manner. With ggplot2 you simply provide the data, tell ggplot2 how to map variables to aesthetics, what graphical primitives to use, and it will take care of the rest. ggplot2 is over 10 years old and is used by hundreds of thousands of people all over the world for plotting. In most cases using ggplot2 starts with supplying a dataset and aesthetic mapping (with aes()); adding on layers (like geom_point() or geom_histogram()), scales (like scale_colour_brewer()), and faceting specifications (like facet_wrap()); and finally, coordinating systems. ggplot2 has a rich ecosystem of community-maintained extensions for those looking for more innovation. ggplot2 is a part of the tidyverse, an ecosystem of R packages designed for data science. -
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dplyr
dplyr: A grammar of data manipulation
dplyr is an R package that provides a consistent and intuitive grammar for data manipulation, enabling users to filter, arrange, summarize, and transform data efficiently. Part of the tidyverse ecosystem, dplyr simplifies complex data operations through a clear and readable syntax, whether working with data frames, tibbles, or databases. It is widely used in data science and statistical analysis workflows. -
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rmarkdown
Dynamic Documents for R
R Markdown is an R package for creating dynamic, reproducible documents that combine code (R, Python, SQL, etc.), results (figures, tables), and narrative text. Built on Knitr and Pandoc, it supports generating HTML, PDF, Word, slideshows, dashboards, and more. It’s widely used in data science and reproducible reporting workflows. -
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Seurat
R toolkit for single cell genomics
Seurat is a comprehensive R toolkit for single-cell genomics analysis, introduced by the Satija Lab at NYGC. It supports quality control, normalization, clustering, integration of multimodal data (e.g., scRNA‑seq, spatial, CITE‑seq), and visualization. Seurat v5 introduces scalable workflows and spatial transcriptomics support, commonly used in academic and industry research for single-cell studies. -
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LabPlot
Data Visualization and Analysis
LabPlot is a FREE, open source and cross-platform Data Visualization and Analysis software accessible to everyone. -
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TinyTeX
Cross-platform, portable, and easy-to-maintain LaTeX distribution
A lightweight, cross-platform, portable, and easy-to-maintain LaTeX distribution based on TeX Live. TinyTeX, is a custom LaTeX distribution based on TeX Live that is small in size but still functions well in most cases. Even if you run into the problem of missing LaTeX packages, it should be super clear to you what you need to do. In fact, if you are an R Markdown user, there is nothing you need to do, because missing packages will just be installed automatically. You may not even know the existence of LaTeX at all since it should rarely bother you. Currently, TinyTeX works best for R users. Other users can use it, too—it is just that missing LaTeX packages won’t be automatically installed, and you need to install them manually. Or you can go to the extreme to install all packages, but remember there are thousands of them. -
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forecast
Forecasting Functions for Time Series and Linear Models
The forecast package is a comprehensive R package for time series analysis and forecasting. It provides functions for building, assessing, and using univariate forecasting models (e.g. ARIMA, exponential smoothing, etc.), tools for automatic model selection, diagnostics, plotting, forecasting future values, etc. It's widely used in statistics, economics, business forecasting, environmental science, etc. Exponential smoothing state space models (ETS) including seasonal components. Residual checks, model accuracy, plots, forecast error measures etc. -
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sparklyr
R interface for Apache Spark
sparklyr is an R package that provides seamless interfacing with Apache Spark clusters—either local or remote—while letting users write code in familiar R paradigms. It supplies a dplyr-compatible backend, Spark machine learning pipelines, SQL integration, and I/O utilities to manipulate and analyze large datasets distributed across cluster environments. -
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Data Science Specialization
Course materials for the Data Science Specialization on Coursera
The Data Science Specialization Courses repository is a collection of materials that support the Johns Hopkins University Data Science Specialization on Coursera. It contains the source code and resources used throughout the specialization’s courses, covering a broad range of data science concepts and techniques. The repository is designed as a shared space for code examples, datasets, and instructional materials, helping learners follow along with lectures and assignments. It spans essential topics such as R programming, data cleaning, exploratory data analysis, statistical inference, regression models, machine learning, and practical data science projects. By providing centralized resources, the repo makes it easier for students to practice concepts and replicate examples from the curriculum. It also offers a structured view of how multiple disciplines—programming, statistics, and applied data analysis—come together in a professional workflow. -
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Introduction to Zig
An open, technical and introductory book for the Zig programming lang
This is the official repository for the book "Introduction to Zig: a project-based Book", written by Pedro Duarte Faria. To know more about the book, check out the About this book section below. You can read the current version of the book in your web browser. The book is built using the publishing system Quarto in conjunction with a little bit of R code (zig_engine.R), which is responsible for calling the Zig compiler to compile and run the Zig code examples. -
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R Source
Read-only mirror of R source code
The wch/r-source repository is a read-only mirror of the official R language source code, maintained to reflect the upstream Subversion (SVN) R core development tree. This mirror provides public visibility into R’s internals—everything from the interpreter, base and recommended packages, documentation, and C/Fortran code under the hood. It is updated hourly to stay in sync with the upstream SVN. Although it mirrors the R source for browsing and reference, it is not the “canonical development repo* (i.e. you can’t submit pull requests via that mirror). The repository includes build instructions, the full directory structure (src, src/library, doc, etc.), licensing information (GPL-2.0), and documentation. Developers, package authors, and curious users often browse this mirror to inspect implementation details, debug issues, or see how base functions are implemented in C or Fortran. -
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devtools
Tools to make an R developer's life easier
devtools is an R package designed to simplify R package development by providing functions for creating, building, testing, and installing packages from various sources (e.g., CRAN, GitHub). It integrates with usethis, roxygen2, testthat, and simplifies workflows for developers and contributors to the R ecosystem. -
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knitr
A general-purpose tool for dynamic report generation in R
knitr is an R package that acts as a literate programming engine, combining code execution and document generation. It executes code embedded in Markdown, LaTeX, or other formats and produces output with results interleaved into final documents. It powers R Markdown and supports caching, chunk options, graphics, and extensibility for reproducible analysis. -
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mathpix
Query the mathpix API to convert math images to LaTeX
Query the mathpix API to convert math images to LaTeX. -
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plumber
Turn your R code into a web API
plumber is an R package that enables rapid creation of HTTP APIs by decorating existing R functions with special roxygen-style comments. It transforms R scripts into RESTful web services with minimal setup and integrates easily with RStudio or CI environments. -
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tidyverse
Easily install and load packages from the tidyverse
tidyverse is a meta‑package that installs and loads a cohesive suite of R packages designed for data science, sharing underlying design principles, grammar, and data structures. Core components include ggplot2, dplyr, tidyr, readr, purrr, tibble, stringr, forcats, and more. It promotes tidy data workflows and consistency across tasks. -
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Mastering Shiny
Mastering Shiny: a book
Mastering Shiny is a book (and its accompanying source repository) by Hadley Wickham that teaches people how to build interactive web applications using Shiny in R. It starts from basics (your first app, UI components, reactivity) and progresses to more advanced topics (dynamic UIs, modules, testing, security, performance). It is intended to help data scientists, analysts, or R users who may not have deep experience in web technologies become expert Shiny developers. The source code is open, and the book is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. -
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RStudio Cheatsheets
Curated collection of official cheat sheets for data science tools
The cheatsheets repository from RStudio is a curated collection of official cheat sheets for R, RStudio, the tidyverse, Shiny, and related data science tools. Each cheat sheet is a single (or double) page PDF that condenses important syntax, functions, workflows, and best practices into a visually organized format ideal for quick reference. The repository contains source files (R Markdown or LaTeX) that generate the cheat sheets, version history, and metadata (title, author, description) for each. It covers topics such as data wrangling, data import, modeling, visualization, RStudio IDE shortcuts, Shiny development, and the tidyverse suite (dplyr, ggplot2, tidyr, purrr). These cheat sheets are widely used by R learners, educators, and practitioners as quick reference tools, and they often ship with RStudio by default or are linked from RStudio’s help/documentation pages. Users can also contribute new cheat sheet proposals, corrections, or translations via pull requests. -
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purrr
A functional programming toolkit for R
purrr enhances R’s functional programming capabilities by providing a consistent set of tools for working with lists and vectors, enabling safer and more expressive iteration compared to base R’s loop functions. -
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DiagrammeR
Graph and network visualization using tabular data in R
DiagrammeR is an R package to create, manipulate, and visualize network graphs, flowcharts, diagrams, and more using Graphviz and Mermaid syntax. Integrates with RMarkdown and Shiny apps, supports node/edge traversal, and graph analysis algorithms, making it ideal for documenting processes, causal relationships, or data pipelines. -
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NYC Taxi Data
Import public NYC taxi and for-hire vehicle (Uber, Lyft)
The nyc-taxi-data repository is a rich dataset and exploratory project around New York City taxi trip records. It collects and preprocesses large-scale trip datasets (fares, pickup/dropoff, timestamps, locations, passenger counts) to enable data analysis, modeling, and visualization efforts. The project includes scripts and notebooks for cleaning and filtering the raw data, memory-efficient processing for large CSV/Parquet files, and aggregation workflows (e.g. trips per hour, heatmaps of pickups/dropoffs). It also contains example analyses—spatial and temporal visualizations like maps, time-series plots, and hotspot detection—highlighting insights such as patterns of demand, peak times, and geospatial distributions. The repository is often used as a benchmark dataset and example for teaching, benchmarking, and demonstration purposes in the data science and urban analytics communities. -
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Paper2GUI
Convert AI papers to GUI
Convert AI papers to GUI，Make it easy and convenient for everyone to use artificial intelligence technology。让每个人都简单方便的使用前沿人工智能技术 Paper2GUI: An AI desktop APP toolbox for ordinary people. It can be used immediately without installation. It already supports 40+ AI models, covering AI painting, speech synthesis, video frame complementing, video super-resolution, object detection, and image stylization. , OCR recognition and other fields. Support Windows, Mac, Linux systems. Paper2GUI: 一款面向普通人的 AI 桌面 APP 工具箱，免安装即开即用，已支持 40+AI 模型，内容涵盖 AI 绘画、语音合成、视频补帧、视频超分、目标检测、图片风格化、OCR 识别等领域。支持 Windows、Mac、Linux 系统。 -
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R4DS (R for Data Science)
R for data science: a book
“R for Data Science” (r4ds) is the source material (book + examples) by Hadley Wickham et al., intended to teach data science using R and the tidyverse. It covers the workflow from importing data, tidying, transforming, visualizing, modelling, communicating results, and programming in R. The repository contains the source files (Quarto / RMarkdown), example datasets, visualizations, exercises, and all content needed to build the book. Includes many example datasets, diagrams, code samples, and “hands-on” exercises. Comprehensive coverage of data-science workflow: data import, cleaning, transformation, exploration, modelling etc. Includes topics beyond basics: relational data (joins), date/time, strings, working with missing values, visualizing data, etc. -
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RStan
RStan, the R interface to Stan
RStan is the R interface to Stan, a C++ library for statistical modeling and high-performance statistical computation. It lets users specify models in the Stan modeling language (for Bayesian inference), compile them, and perform inference from R. Key inference approaches include full Bayesian inference via Hamiltonian Monte Carlo (specifically the No-U-Turn Sampler, NUTS), approximate Bayesian inference via variational methods, and optimization (penalized likelihood). RStan integrates with Stan’s automatic differentiation library, provides diagnostics, model comparison, posterior predictive checks, etc. It is used in research, applied statistics, and modelling workflows where flexibility and rigor in Bayesian methods are required. -
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httr
httr: a friendly http package for R
httr is superseded: only changes necessary to keep it on CRAN will be made. We recommend using httr2 instead. The aim of httr is to provide a wrapper for the curl package, customized to the demands of modern web APIs. Functions for the most important http verbs: GET(), HEAD(), PATCH(), PUT(), DELETE() and POST(). Automatic connection sharing across requests to the same website (by default, curl handles are managed automatically), cookies are maintained across requests, and an up-to-date root-level SSL certificate store is used. Requests return a standard reponse object that captures the http status line, headers and body, along with other useful information. Support for OAuth 1.0 and 2.0 with oauth1.0_token() and oauth2.0_token(). The demo directory has eight OAuth demos: four for 1.0 (twitter, vimeo, withings and yahoo) and four for 2.0 (facebook, github, google, linkedin). OAuth credentials are automatically cached within a project.
