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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JuliaConnectoR
A functionally oriented interface for calling Julia from R
This R-package provides a functionally oriented interface between R and Julia. The goal is to call functions from Julia packages directly as R functions. Julia functions imported via the JuliaConnectoR can accept and return R variables. It is also possible to pass R functions as arguments in place of Julia functions, which allows callbacks from Julia to R. From a technical perspective, R data structures are serialized with an optimized custom streaming format, sent to a (local) Julia TCP server, and translated to Julia data structures by Julia. The results of function calls are likewise translated back to R. Complex Julia structures can either be used by reference via proxy objects in R or fully translated to R data structures. -
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Awesome Network Analysis
A curated list of awesome network analysis resources
awesome-network-analysis is a curated list of resources focused on network and graph analysis, including libraries, frameworks, visualization tools, datasets, and academic papers. It covers multiple programming languages and domains like sociology, biology, and computer science. This repository serves as a central reference for researchers, analysts, and developers working with network data. -
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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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R Packages (r-pkgs)
Building R packages
rpkgs (in GitHub via hadley/r-pkgs) is the source (text + examples) for the book R Packages by Hadley Wickham and Jenny Bryan. The book teaches how to develop, document, test, and share R packages: the practices, tools, infrastructure, workflows, and best practices around package development in R. The repository contains the code, text, site content for building the book, examples, exercises, etc. It is not a software library to be loaded in R (except perhaps the examples), but a resource/guide/manual. The first edition is no longer available online. A second edition is under development and available. -
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blavaan
An R package for Bayesian structural equation modeling
blavaan is a free, open-source R package for Bayesian latent variable analysis. It relies on JAGS and Stan to estimate models via MCMC. The blavaan functions and syntax are similar to lavaan. The development version of blavaan (containing updates not yet on CRAN) can be installed via the command provided in the documentation. Compilation is required; this may be a problem for users who currently rely on a binary version of blavaan from CRAN. The blavaan package depends on the lavaan package for model specification and for some computations. This means that, if you already know lavaan, then you should already be able to do many things in blavaan. In particular, many blavaan commands add the letter “b” to the start of the lavaan command. It is also sometimes possible to use a lavaan command on a blavaan object, though the results may not always be what you expect. -
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esquisse
RStudio add-in to make plots interactively with ggplot2
The purpose of this add-in is to let you explore your data quickly to extract the information they hold. You can create visualization with {ggplot2}, filter data with {dplyr} and retrieve generated code. This addin allows you to interactively explore your data by visualizing it with the ggplot2 package. It allows you to draw bar plots, curves, scatter plots, histograms, boxplot and sf objects, then export the graph or retrieve the code to reproduce the graph. This addin allows you to interactively explore your data by visualizing it with the ggplot2 package. It allows you to draw bar plots, curves, scatter plots, histograms, boxplot and sf objects, then export the graph or retrieve the code to reproduce the graph. -
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gt R
Easily generate information-rich, publication-quality tables from R
With the gt package, anyone can make wonderful-looking tables using the R programming language. The gt philosophy: we can construct a wide variety of useful tables with a cohesive set of table parts. These include the table header, the stub, the column labels and spanner column labels, the table body, and the table footer. It all begins with table data (be it a tibble or a data frame). You then decide how to compose your gt table with the elements and formatting you need for the task at hand. Finally, the table is rendered by printing it at the console, including it in an R Markdown document, or exporting it to a file using gtsave(). Currently, gt supports the HTML, LaTeX, and RTF output formats. -
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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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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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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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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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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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Shiny
Build interactive web apps directly from R with Shiny framework
Shiny is an R package from RStudio that enables users to build interactive web applications using R without requiring knowledge of JavaScript, HTML, or CSS. It allows statisticians and data scientists to turn their analyses into fully functional web dashboards with reactive elements, data inputs, visualizations, and controls, making data communication more effective and dynamic. -
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caret
caret (Classification And Regression Training) R package
The caret (Classification And Regression Training) R package streamlines the process of building predictive machine learning models. It provides uniform interfaces for model training, tuning, evaluation, preprocessing, and variable importance. With support for over 200 models, caret is foundational for R workflows in modeling and machine learning. -
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ExData Plotting1
Plotting Assignment 1 for Exploratory Data Analysis
This repository explores household energy usage over time using the “Individual household electric power consumption” dataset from the UC Irvine Machine Learning Repository. The dataset covers nearly four years of minute-level measurements, including power consumption, voltage, current intensity, and detailed sub-metering values for different household areas. For analysis, focus is placed on a two-day period in February 2007, highlighting short-term consumption trends. The data requires careful handling due to its size of more than 2 million rows and coded missing values. By processing the date and time fields into proper formats, it becomes possible to generate clear time-series plots of energy usage. The repository demonstrates effective exploratory data analysis practices in R with a reproducible workflow for transforming raw data into visual insights. -
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IRkernel
R kernel for Jupyter
For detailed requirements and install instructions see irkernel.github.io. Per default IRkernel::installspec() will install a kernel with the name “ir” and a display name of “R”. Multiple calls will overwrite the kernel with a kernel spec pointing to the last R interpreter you called that commands from. You can install kernels for multiple versions of R by supplying a name and display name argument to the install spec() call (You still need to install these packages in all interpreters you want to run as a Jupyter kernel!): -
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ProgrammingAssignment2
Repository for Programming Assignment 2 for R Programming on Coursera
This repository contains the second programming assignment for an R course, focused on caching expensive computations by leveraging R’s scoping rules. The assignment walks you through creating a special matrix object that stores both a matrix and its cached inverse, avoiding repeated calls to costly operations. It builds on a worked example that caches the mean of a numeric vector, demonstrating how the operator preserves state across function calls. You then implement analogous logic for matrices via two functions, one to construct the cache-aware object and another to compute or retrieve the cached inverse. The instructions emphasize using solve for inversion and assuming that the supplied matrix is always invertible. The repository outlines the workflow for forking, editing the provided R stub, committing your solution, and submitting your repository URL as the final deliverable. -
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ShinyItemAnalysis
Test and Item Analysis via Shiny
ShinyItemAnalysis is an R package including functions and interactive shiny application for the psychometric analysis of educational tests, psychological assessments, health-related and other types of multi-item measurements, or ratings from multiple raters. Exploration of total and standard scores. Analysis of measurement error and reliability. Analysis of correlation structure and validity. Traditional item analysis. Item analysis with regression models. Item analysis with IRT models. Detection of differential item functioning. Number of toy datasets is available, the interactive application also allows the users to upload and analyze their own data and to automatically generate PDF or HTML reports. All methods include sample R code which is ready to copy and paste into R and run locally. Several toy data sets are ready to use. You can also upload and analyze your own data. ShinyItemAnalysis provides model equations, parameter estimates and their interpretation. -
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palmerpenguins
A great intro dataset for data exploration & visualization
palmerpenguins is an R package offering real-world ecological data from the Palmer Archipelago penguin species—Adélie, Chinstrap, and Gentoo. Designed as a more engaging alternative to the classical iris dataset, it provides size measurements, clutch information, and blood isotope data for teaching, visualization, and analytics practice. -
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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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sf (Simple Features)
Simple Features for R
sf is an R package that implements “simple features” (standardized vector spatial data) for R. It allows spatial vector data (points, lines, polygons etc.) to be represented as records in data frames (or tibbles) with geometry list columns, and performs spatial operations (geometry operations, coordinate reference system transformations, reading/writing spatial data, integration with spatial databases etc.). It interfaces to GDAL, GEOS, PROJ libraries for robust operations. Reading and writing spatial vector data via many file formats/drivers through GDAL, and spatial databases (PostGIS etc.) Supports all standard simple feature geometry types (points, linestrings, polygons, multi-geometries etc.) in various dimensions (XY, XYZ, XYM, XYZM). -
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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.
