Open Source R Software - Page 5
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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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easystats
The R easystats-project
easystats is a meta‑package that installs and unifies a suite of R packages for post‑processing statistical models. It delivers a consistent API to assess model performance, effect sizes, parameters, and to generate reports and visualizations, all with minimal dependencies and maximum clarity. -
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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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future
R package: future: Unified Parallel and Distributed Processing in R
The future package in R provides a unified abstraction for asynchronous and/or parallel computation. It allows R expressions to be scheduled for future evaluation, with the result retrieved later, in a way decoupled from the specific backend used. This lets code be written in a way that works with sequential execution, multicore, multisession, cluster, or remote compute backends, without changing the high-level code. It handles automatic exporting of needed global variables/functions, managing of packages, RNG, etc. Automatic detection and export of global objects and functions needed by future expressions, so the user doesn’t need to manage that manually. Ability to control how futures are resolved. -
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generativeart
Create Generative Art with R
generativeart is an R package for creating algorithmic art by computing the positions of many thousands of points according to user-defined mathematical formulas with randomized parameters. Each render uses a seed to introduce controlled randomness, so every image is unique while remaining reproducible when the same seed and formula are reused. The package logs the seed, formula, and file name to a CSV, which makes it easy to catalog outputs, re-generate favorites, and track experiments. A small helper sets up a simple directory scaffold for “everything” versus “handpicked” images and a logfile folder, encouraging a tidy, iterative workflow. Rendering is performed with ggplot2, and users can select coordinate systems (Cartesian or polar), foreground/background colors, number of images to generate, and output format (PNG by default with other devices available). -
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geocompr
Geocomputation with R: an open source book
This repository hosts the source for Geocomputation with R, an open-source book covering spatial data analysis, visualization, and modeling using R. It teaches how to work with vector and raster data, coordinate systems, mapping, and geocomputation techniques using packages like sf, terra, tmap, and more. Actively maintained and updated for real-world geospatial workflows. -
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gganimate
A Grammar of Animated Graphics
gganimate extends the grammar of graphics as implemented by ggplot2 to include the description of animation. It does this by providing a range of new grammar classes that can be added to the plot object in order to customize how it should change with time. Here we take a simple boxplot of fuel consumption as a function of cylinders and let it transition between the number of gears available in the cars. As this is a discrete split (gear being best described as an ordered factor) we use transition_states and provide a relative length to use for transition and state view. As not all combinations of data are present there are states missing a box. We define that when a box appears it should fade into view, whereas it should shrink away when it disappears. Lastly, we decide to use a sinusoidal easing for all our aesthetics (here, only y is changing) gganimate is available on CRAN and can be installed with install.packages('gganimate'). -
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ggforce
Accelerating ggplot2
ggforce is an extension package for ggplot2 that introduces specialized statistical transforms, geoms, and layout utilities to enhance and complement the built-in ggplot2 offerings. It enables more advanced visualization techniques such as faceting enhancements, hulls, annotation marks, and novel layouts for network data and marked regions. -
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ggpubr
'ggplot2' Based Publication Ready Plots
ggpubr is an R package that provides easy-to-use wrapper functions around ggplot2 to create publication-ready visualizations with minimal code. It streamlines plot creation for researchers and analysts, allowing features such as statistical annotation, theme customization, and plot arrangement with fewer lines of code. -
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ggrepel
epel overlapping text labels away from each other in your ggplot2
ggrepel is an R package that provides “smart” repulsion for text and label geoms in ggplot2. When placing text labels on a plot (e.g. labeling points), the labels can often overlap; ggrepel ensures labels don’t overlap (or overlap less) by repelling labels / pushing them away, adding connecting lines or nudges, etc. It improves the readability of plots, especially when many labels are present. Support for point and segment geoms (so labels can be connected by lines when moved). Supports both plotting of labels inside or outside plot area, with trimming/clipping etc. -
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ggstatsplot
Enhancing {ggplot2} plots with statistical analysis
{ggstatsplot} is an extension of {ggplot2} package for creating graphics with details from statistical tests included in the information-rich plots themselves. In a typical exploratory data analysis workflow, data visualization and statistical modeling are two different phases: visualization informs modeling, and modeling in its turn can suggest a different visualization method, and so on and so forth. Bayesian hypothesis-testing. The central idea of {ggstatsplot} is simple: combine these two phases into one in the form of graphics with statistical details, which makes data exploration simpler and faster. Summary of statistical tests and effect sizes. -
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ggthemes
Additional themes, scales, and geoms for ggplot2
ggthemes is an R package that provides extra themes, scales, and geoms for ggplot2. It supplements the default ggplot2 offerings by allowing users to apply special themes (e.g., inspired by classic publications, external visualization styles), additional scale functions, and specialized geoms or color scales. It is often used to make ggplot2 plots adhere to aesthetic styles from famous news outlets, scientific journals, or presentation decks. Additional color scales and palettes for discrete and continuous data to match theme aesthetics. Extensive documentation and examples for each theme / scale so users can see how plots look and tweak them. -
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gm
R Package for Music Score and Audio Generation
Create music easily, and show musical scores and audio files in R Markdown documents, R Jupyter Notebooks and RStudio. -
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golem
A Framework for Building Robust Shiny Apps
golem is an opinionated framework for developing production-grade Shiny applications in R, treating the app like a full R package. It scaffolds project structure, testing, documentation, CI/CD, and supports containerization—streamlining the build-to-deploy pipeline while enforcing clean architecture and maintainability. -
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gptstudio
GPT RStudio addins that enable GPT assisted coding, writing & analysis
gptstudio is an R package and RStudio Addins interface that enables interactive use of large language models (OpenAI, HuggingFace, etc.) from within R. It includes a Chat add-in and source editing helpers to query models, generate code, comment or refactor code, and manage conversations—all integrated into RStudio using Shiny and bslib. -
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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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gtsummary
Presentation-Ready Data Summary and Analytic Result Tables
gtsummary is an R package for creating elegant, customizable, publication-ready summary tables of datasets and statistical models. It provides concise code to produce demographic tables (tbl_summary()), regression result tables, and more, with flexible styling options for reporting. -
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hrbrthemes
Opinionated, typographic-centric ggplot2 themes and theme components
hrbrthemes is a focused ggplot2 theme package with an emphasis on typography, layout precision, and visual polish. It includes themes like theme_ipsum and Font scales tailored for clean, high‑quality production graphics. -
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hui
hewies user interface - 3D scientific visualisation tool
Python project with goal to provide FOSS library to extract, analyse and visualise data in a 3D fashion. The instance will connect to a data source, ods sheet, csv, sql DB, pyodbc the instance will analyse and/or transform the data to be presented to the visualisation functionality the instance will visualise the data in a 3D fashion, likely using third party FOSS -
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janitor
Simple tools for data cleaning in R
janitor provides simple, convenient tools for data cleaning, formatting, and exploration in R. It is especially useful for cleaning messy data frames, removing duplicates, formatting column names, and producing frequency tables in a tidy workflow. -
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latex2exp
Use LaTeX in R graphics
latex2exp is an R package that lets you use LaTeX in plots. It parses and converts LaTeX to R’s custom plotmath expressions. You can read the full documentation on the package’s website. Expressions returned by latex2exp can be used to create formatted text and mathematical formulas and symbols to be rendered as axis labels, annotations, legends, titles, etc. throughout R’s plotting system. -
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lintr
Static Code Analysis for R
lintr is a static code analysis tool for R that identifies syntax errors, style inconsistencies, and other potential issues in R scripts and packages. It supports customizable lint rules and integrates with many editors to provide realtime feedback and enforce coding standards (e.g., tidyverse style). -
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magrittr
Improve the readability of R code with the pipe
magrittr introduces the pipe operator (%>%) and related functional utilities into R. It underlies the powerful piped syntax widely adopted in tidyverse workflows by enabling left-hand argument passing and providing helpers like compound assignment pipes and exposition pipes. -
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meditator
Miscellaneous tools for meditation
Miscellaneous tools for meditation and mental health. Optionally a very basic R package for meditation and mental health. Please close your eyes and take one deep breath. Feel how long the in-breath is. Feel how long the out-breath is. -
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miRPV
miRPV: An automated pipeline for miRNA Prediction and Validation in si
miRPV is an Automated tool that allows users to predict and validate microRNA from genome/gene sequence. System Requirement CPU: AMD64 (64bit) Memory: 2Gb RAM Storage: 5Gb Ubuntu 18.04 -
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mlr
Machine Learning in R
R does not define a standardized interface for its machine-learning algorithms. Therefore, for any non-trivial experiments, you need to write lengthy, tedious, and error-prone wrappers to call the different algorithms and unify their respective output. {mlr} provides this infrastructure so that you can focus on your experiments! The framework provides supervised methods like classification, regression, and survival analysis along with their corresponding evaluation and optimization methods, as well as unsupervised methods like clustering. It is written in a way that you can extend it yourself or deviate from the implemented convenience methods and construct your own complex experiments or algorithms.
