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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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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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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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. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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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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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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mlr3
mlr3: Machine Learning in R - next generation
mlr3 is a modern, object-oriented R framework for machine learning. It provides core abstractions (tasks, learners, resamplings, measures, pipelines) implemented using R6 classes, enabling extensible, composable machine learning workflows. It focuses on clean design, scalability (large datasets), and integration into the wider R ecosystem via extension packages. Users can do classification, regression, survival analysis, clustering, hyperparameter tuning, benchmarking etc., often via companion packages. -
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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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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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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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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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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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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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pointblank
Data quality assessment and metadata reporting for data frames
With the pointblank package it’s really easy to methodically validate your data whether in the form of data frames or as database tables. On top of the validation toolset, the package gives you the means to provide and keep up-to-date with the information that defines your tables. For table validation, the agent object works with a large collection of simple (yet powerful!) validation functions. We can enable much more sophisticated validation checks by using custom expressions, segmenting the data, and by selective mutations of the target table. The suite of validation functions ensures that everything just works no matter whether your table is a data frame or a database table. Sometimes, we want to maintain table information and update it when the table goes through changes. For that, we can use an informant object plus associated functions to help define the metadata entries and present it as a data dictionary. -
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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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Open Intro Statistics
An open-source textbook written at the college level
OpenIntro Statistics is a dynamic take on the traditional curriculum, being successfully used at Community Colleges to the Ivy League. Each chapter's content is in one of the eight chapter folders that start with "ch_". Within each folder, there is a "figures" folder and a "TeX" folder. The TeX folder contains the text files that are used to typeset the chapters in the textbook. In many cases, R code is supplied with figures to regenerate the figure. It will often be necessary to install the "openintro" R package that is available from GitHub (https://github.com/OpenIntroOrg) if you would like to regenerate a figure. Other packages may also occasionally be required. -
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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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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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nichenetr
NicheNet: predict active ligand-target links between interacting cells
nichenetr: the R implementation of the NicheNet method. The goal of NicheNet is to study intercellular communication from a computational perspective. NicheNet uses human or mouse gene expression data of interacting cells as input and combines this with a prior model that integrates existing knowledge on ligand-to-target signaling paths. This allows to predict ligand-receptor interactions that might drive gene expression changes in cells of interest. This model of prior information on potential ligand-target links can then be used to infer active ligand-target links between interacting cells. NicheNet prioritizes ligands according to their activity (i.e., how well they predict observed changes in gene expression in the receiver cell) and looks for affected targets with high potential to be regulated by these prioritized ligands. -
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pagedown
Paginate the HTML Output of R Markdown with CSS for Print
Paginate the HTML Output of R Markdown with CSS for Print. You only need a modern web browser (e.g., Google Chrome or Microsoft Edge) to generate PDF. No need to install LaTeX to get beautiful PDFs. This R package stands on the shoulders of two giants to support typesetting with CSS for R Markdown documents: Paged.js and ReLaXed (we only borrowed some CSS from the ReLaXed repo and didn't really use the Node package). -
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performance
Models' quality and performance metrics (R2, ICC, LOO, AIC, BF, ...)
performance is part of the easystats ecosystem and offers model quality assessment tools for R. It computes metrics like R², RMSE, ICC, and conducts diagnostics such as overdispersion, zero‑inflation, convergence, and singularity checks, complementing model workflows with comprehensive evaluation. -
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swirl
Learn R, in R
swirl is an R package that allows interactive, in-R learning of statistics, data science, R programming etc. The idea is that you load swirl in R, and it presents you with lessons (within R’s console or RStudio) that ask you to type commands, check results, and progress through tutorial material—without leaving the R environment. It is used for teaching R, especially for beginners, as well as for self-paced learning of packages, data manipulation, visualization, etc. Lessons and content are stored locally or can be downloaded and used without a continuous internet connection. Content includes quizzes, multiple-choice questions, coding exercises etc. to reinforce learning. -
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OmicSelector
Feature selection and deep learning modeling for omic biomarker study
OmicSelector is an environment, Docker-based web application, and R package for biomarker signature selection (feature selection) from high-throughput experiments and others. It was initially developed for miRNA-seq (small RNA, smRNA-seq; hence the name was miRNAselector), RNA-seq and qPCR, but can be applied for every problem where numeric features should be selected to counteract overfitting of the models. Using our tool, you can choose features, like miRNAs, with the most significant diagnostic potential (based on the results of miRNA-seq, for validation in qPCR experiments).
