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https://github.com/qinwf/awesome-R

A curated list of awesome R packages, frameworks and software.
https://github.com/qinwf/awesome-R

List: awesome-R

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A curated list of awesome R packages, frameworks and software.

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# Awesome R



[![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)



A curated list of awesome R packages and tools. Inspired by [awesome-machine-learning](https://github.com/josephmisiti/awesome-machine-learning).



heart

for Top 50 CRAN downloaded packages or repos with 400+

star



- [Awesome R](#awesome-)

    - [2023](#2023)

    - [2020](#2020)

    - [2019](#2019)

    - [2018](#2018)

    - [Integrated Development Environments](#integrated-development-environments)

    - [Syntax](#syntax)

    - [Data Manipulation](#data-manipulation)

    - [Graphic Displays](#graphic-displays)

    - [Html Widgets](#html-widgets)

    - [Reproducible Research](#reproducible-research)

    - [Web Technologies and Services](#web-technologies-and-services)

    - [Parallel Computing](#parallel-computing)

    - [High Performance](#high-performance)

    - [Language API](#language-api)

    - [Database Management](#database-management)

    - [Machine Learning](#machine-learning)

    - [Natural Language Processing](#natural-language-processing)

    - [Bayesian](#bayesian)

    - [Optimization](#optimization)

    - [Finance](#finance)

    - [Bioinformatics and Biostatistics](#bioinformatics-and-biostatistics)

    - [Network Analysis](#network-analysis)

    - [Spatial](#spatial)

    - [R Development](#r-development)

    - [Logging](#logging)

    - [Data Packages](#data-packages)

    - [Other Tools](#other-tools)

    - [Other Interpreters](#other-interpreters)

    - [Learning R](#learning-r)

- [Resources](#resources)

    - [Websites](#websites)

    - [Books](#books)

    - [Podcasts](#podcasts)

    - [Reference Cards](#reference-cards)

    - [MOOCs](#moocs)

    - [Lists](#lists)

- [Other Awesome Lists](#other-awesome-lists)

- [Contributing](#contributing)



## 2023



* [Cookbook Polars for R](https://ddotta.github.io/cookbook-rpolars/)



## 2020



* [VSCode](https://code.visualstudio.com/) - [vscode-R](https://marketplace.visualstudio.com/items?itemName=Ikuyadeu.r) + [vscode-r-lsp](https://marketplace.visualstudio.com/items?itemName=REditorSupport.r-lsp) VSCode R Langauage Support

* [gt](https://github.com/rstudio/gt) - Easily generate information-rich, publication-quality tables from R

* [lightgbm heart](https://cran.r-project.org/web/packages/lightgbm/index.html) - Light Gradient Boosting Machine.

* [torch](https://cran.r-project.org/web/packages/torch/index.html) - Tensors and Neural Networks with 'GPU' Acceleration.



## 2019



* [ggforce](https://github.com/thomasp85/ggforce) - ggplot2 extension framework ![ggforce](https://cranlogs.r-pkg.org/badges/ggforce)

* [rayshader](https://github.com/tylermorganwall/rayshader) - 2D and 3D data visualizations via rgl ![rayshader](https://cranlogs.r-pkg.org/badges/rayshader)

* [vroom](https://github.com/r-lib/vroom) - Fast reading of delimited files  ![vroom](https://cranlogs.r-pkg.org/badges/vroom)



## Integrated Development Environments

*Integrated Development Environment*



* [VSCode heart](https://code.visualstudio.com/) - [vscode-R](https://marketplace.visualstudio.com/items?itemName=Ikuyadeu.r) + [vscode-r-lsp](https://marketplace.visualstudio.com/items?itemName=REditorSupport.r-lsp) VSCode R Langauage Support

* [RStudio heart](http://www.rstudio.org/) - A powerful and productive user interface for R. Works great on Windows, Mac, and Linux.

* [Emacs + ESS](http://ess.r-project.org/) - Emacs Speaks Statistics is an add-on package for emacs text editors.

* [Sublime Text + R-IDE](https://github.com/REditorSupport/sublime-ide-r) - Add-on package for Sublime Text 2/3.

* [TextMate + r.tmblundle](https://github.com/textmate/r.tmbundle) - Add-on package for TextMate 1/2.

* [StatET](http://www.walware.de/goto/statet) - An Eclipse based IDE for R.

* [R Commander](http://socserv.mcmaster.ca/jfox/Misc/Rcmdr/) - A package that provides a basic graphical user interface.

* [IRkernel heart](https://github.com/IRkernel/IRkernel) - R kernel for Jupyter.

* [Deducer](http://www.deducer.org/pmwiki/pmwiki.php?n=Main.DeducerManual?from=Main.HomePage) - A Menu driven data analysis GUI with a spreadsheet like data editor.

* [Radiant](https://radiant-rstats.github.io/docs) - A platform-independent browser-based interface for business analytics in R, based on the Shiny.

* [Nvim-R heart](https://github.com/jalvesaq/Nvim-R) - Neovim plugin for R.

* [Jamovi](https://www.jamovi.org/) and [JASP](https://jasp-stats.org/) - Desktop software for both Bayesian and Frequentist methods, using a UI familiar to SPSS users.

* [Bio7](http://www.bio7.org/) - An IDE contains tools for model creation, scientific image analysis and statistical analysis for ecological modelling.

* [RTVS](http://microsoft.github.io/RTVS-docs/) - R Tools for Visual Studio.

* [radian heart](https://github.com/randy3k/radian) (formerly rtichoke) - A modern R console with syntax highlighting.

* [RKWard](https://rkward.kde.org/) - An extensible IDE/GUI for R.



## Syntax

*Packages change the way you use R.*



* [magrittr heart](https://github.com/smbache/magrittr) - Let's pipe it.

* [pipeR](https://github.com/renkun-ken/pipeR) - Multi-paradigm Pipeline Implementation.

* [lambda.r](https://github.com/zatonovo/lambda.r) - Functional programming and simple pattern matching in R.

* [purrr](https://github.com/hadley/purrr) - A FP package for R in the spirit of underscore.js.



## Data Manipulation

*Packages for cooking data.*



* [dplyr heart](https://github.com/hadley/dplyr) - Fast data frames manipulation and database query.

* [data.table heart](https://github.com/Rdatatable/data.table) - Fast data manipulation in a short and flexible syntax.

* [reshape2  heart](https://github.com/hadley/reshape) - Flexible rearrange, reshape and aggregate data.

* [tidyr](https://github.com/hadley/tidyr) - Easily tidy data with spread and gather functions.

* [broom heart](https://github.com/dgrtwo/broom) - Convert statistical analysis objects into tidy data frames.

* [rlist](https://github.com/renkun-ken/rlist) - A toolbox for non-tabular data manipulation with lists.

* [ff](http://ff.r-forge.r-project.org/) - Data structures designed to store large datasets.

* [lubridate](https://github.com/tidyverse/lubridate) - A set of functions to work with dates and times.

* [stringi heart](https://github.com/gagolews/stringi) - ICU based string processing package.

* [stringr heart](https://github.com/hadley/stringr) - Consistent API for string processing, built on top of stringi.

* [bigmemory](https://github.com/kaneplusplus/bigmemory) - Shared memory and memory-mapped matrices. The big\* packages provide additional tools including linear models ([biglm](http://cran.r-project.org/web/packages/biglm/index.html)) and Random Forests ([bigrf](https://github.com/aloysius-lim/bigrf)).

* [fuzzyjoin](https://github.com/dgrtwo/fuzzyjoin) - Join tables together on inexact matching.

* [tidyverse](https://github.com/hadley/tidyverse) - Easily install and load packages from the tidyverse.

* [snakecase](https://github.com/Tazinho/snakecase) - Automatically parse and convert strings into cases like snake or camel among others.

* [DataExplorer](https://github.com/boxuancui/DataExplorer) - Fast exploratory data analysis with minimum code.



## Data Formats

*Packages for reading and writing data of different formats.*



* [arrow heart](https://arrow.apache.org/docs/r/) - An interface to the Arrow C++ library.

* [feather heart](https://github.com/wesm/feather) - Fast, interoperable binary data frame storage for Python, R, and more powered by Apache Arrow.

* [fst heart](www.fstpackage.org/fst/) - Lightning Fast Serialization of Data Frames for R.

* [haven](https://github.com/hadley/haven) - Improved methods to import SPSS, Stata and SAS files in R.

* [jsonlite](https://github.com/jeroenooms/jsonlite) - A robust and quick way to parse JSON files in R.

* [qs](https://github.com/traversc/qs) - Quick serialization of R objects.

* [readxl heart](https://readxl.tidyverse.org/) - Read excel files (.xls and .xlsx) into R.

* [readr heart](https://github.com/hadley/readr) - A fast and friendly way to read tabular data into R.

* [rio](https://github.com/leeper/rio) - A Swiss-Army Knife for Data I/O.

* [readODS](https://github.com/chainsawriot/readODS/) - Read OpenDocument Spreadsheets into R as data.frames.

* [RcppTOML](https://github.com/eddelbuettel/rcpptoml) - Rcpp Bindings to C++ parser for TOML files.

* [vroom](https://github.com/r-lib/vroom) - Fast reading of delimited files.

* [writexl](https://docs.ropensci.org/writexl/) - Portable, light-weight data frame to xlsx exporter for R.

* [yaml](https://github.com/viking/r-yaml) - R package for converting objects to and from YAML.



## Graphic Displays

*Packages for showing data.*



* [ggplot2 heart](https://github.com/hadley/ggplot2) - An implementation of the Grammar of Graphics.

* [ggfortify](https://github.com/sinhrks/ggfortify) - A unified interface to ggplot2 popular statistical packages using one line of code.

* [ggrepel](https://github.com/slowkow/ggrepel) - Repel overlapping text labels away from each other.

* [ggalt](https://github.com/hrbrmstr/ggalt) - Extra Coordinate Systems, Geoms and Statistical Transformations for ggplot2.

* [ggstatsplot](https://github.com/IndrajeetPatil/ggstatsplot) - ggplot2 Based Plots with Statistical Details

* [ggtree](https://github.com/GuangchuangYu/ggtree) - Visualization and annotation of phylogenetic tree.

* [ggtech](https://github.com/ricardo-bion/ggtech) - ggplot2 tech themes and scales

* [ggplot2 Extensions](https://ggplot2-exts.github.io/ggiraph.html) - Showcases of ggplot2 extensions.

* [lattice](https://github.com/deepayan/lattice) - A powerful and elegant high-level data visualization system.

* [corrplot](https://github.com/taiyun/corrplot) - A graphical display of a correlation matrix or general matrix. It also contains some algorithms to do matrix reordering.

* [rgl](http://cran.r-project.org/web/packages/rgl/index.html) - 3D visualization device system for R.

* [Cairo](http://cran.r-project.org/web/packages/Cairo/index.html) - R graphics device using cairo graphics library for creating high-quality display output.

* [extrafont](https://github.com/wch/extrafont) - Tools for using fonts in R graphics.

* [showtext](https://github.com/yixuan/showtext) - Enable R graphics device to show text using system fonts.

* [animation](https://github.com/yihui/animation) - A simple way to produce animated graphics in R, using [ImageMagick](http://imagemagick.org/).

* [gganimate](https://github.com/dgrtwo/gganimate) - Create easy animations with ggplot2.

* [misc3d](https://cran.r-project.org/web/packages/misc3d/index.html) - Powerful functions to deal with 3d plots, isosurfaces, etc.

* [xkcd](https://cran.r-project.org/web/packages/xkcd/index.html) - Use xkcd style in graphs.

* [imager](http://dahtah.github.io/imager/) - An image processing package based on CImg library to work with images and display them.

* [hrbrthemes](https://github.com/hrbrmstr/hrbrthemes) -  🔏 Opinionated, typographic-centric ggplot2 themes and theme components.

* [waffle](https://github.com/hrbrmstr/waffle) -  🍁 Make waffle (square pie) charts in R.

* [dendextend](https://github.com/talgalili/dendextend) -  visualizing, adjusting and comparing trees of hierarchical clustering.

* [idendro](https://github.com/tsieger/idendro) -  interactive exploration of dendrograms (trees of hierarchical clustering).

* [r2d3](https://rstudio.github.io/r2d3/) - R Interface to D3 Visualizations

* [Patchwork](https://github.com/thomasp85/patchwork) - Combine separate ggplots into the same graphic.

* [plot3D](http://www.rforscience.com/rpackages/visualisation/plot3d/) - Plotting Multi-Dimensional Data

* [plot3Drgl](https://cran.r-project.org/web/packages/plot3Drgl/index.html) - Plotting Multi-Dimensional Data - Using 'rgl'

* [httpgd](https://github.com/nx10/httpgd) - Asynchronous http server graphics device for R.



## HTML Widgets

*Packages for interactive visualizations.*



* [heatmaply](https://github.com/talgalili/heatmaply) - Interactive heatmaps with D3.

* [d3heatmap](https://github.com/rstudio/d3heatmap) - Interactive heatmaps with D3 (no longer maintained).

* [DataTables](http://rstudio.github.io/DT/) - Displays R matrices or data frames as interactive HTML tables.

* [DiagrammeR heart](https://github.com/rich-iannone/DiagrammeR) - Create JS graph diagrams and flowcharts in R.

* [dygraphs](https://github.com/rstudio/dygraphs) - Charting time-series data in R.

* [formattable heart](https://github.com/renkun-ken/formattable) - Formattable Data Structures.

* [ggvis heart](https://github.com/rstudio/ggvis) - Interactive grammar of graphics for R.

* [Leaflet](http://rstudio.github.io/leaflet/) - One of the most popular JavaScript libraries interactive maps.

* [MetricsGraphics](http://hrbrmstr.github.io/metricsgraphics/) - Enables easy creation of D3 scatterplots, line charts, and histograms.

* [networkD3](http://christophergandrud.github.io/networkD3/) - D3 JavaScript Network Graphs from R.

* [scatterD3](https://github.com/juba/scatterD3) - Interactive scatterplots with D3.

* [plotly heart](https://github.com/ropensci/plotly) - Interactive ggplot2 and Shiny plotting with [plot.ly](https://plot.ly).

* [rCharts heart](https://github.com/ramnathv/rCharts) - Interactive JS Charts from R.

* [rbokeh](http://hafen.github.io/rbokeh/) - R Interface to [Bokeh](http://bokeh.pydata.org/en/latest/).

* [threejs](https://github.com/bwlewis/rthreejs) - Interactive 3D scatter plots and globes.

* [timevis](https://github.com/daattali/timevis) - Create fully interactive timeline visualizations.

* [visNetwork](https://github.com/datastorm-open/visNetwork) - Using vis.js library for network visualization.

* [wordcloud2](https://github.com/Lchiffon/wordcloud2) - R interface to wordcloud2.js.

* [highcharter](https://github.com/jbkunst/highcharter) - R wrapper for highcharts based on htmlwidgets

* [echarts4r](https://github.com/JohnCoene/echarts4r) - R wrapper to Echarts version 4



## Reproducible Research

*Packages for literate programming and reproducible workflows.*



* [knitr heart](https://github.com/yihui/knitr) - Easy dynamic report generation in R.

* [redoc](https://github.com/noamross/redoc) - Reversible Reproducible Documents

* [tinytex](https://github.com/yihui/tinytex) - A lightweight and easy-to-maintain LaTeX distribution

* [xtable](http://cran.r-project.org/web/packages/xtable/index.html) - Export tables to LaTeX or HTML.

* [rapport](http://rapport-package.info/#intro) - An R templating system.

* [rmarkdown heart](http://rmarkdown.rstudio.com/) - Dynamic documents for R.

* [slidify heart](https://github.com/ramnathv/slidify) - Generate reproducible html5 slides from R markdown.

* [Sweave](https://www.statistik.lmu.de/~leisch/Sweave/) - A package designed to write LaTeX reports using R.

* [texreg](https://github.com/leifeld/texreg) - Formatting statistical models in LaTex and HTML.

* [checkpoint](https://github.com/RevolutionAnalytics/checkpoint) - Install packages from snapshots on the checkpoint server.

* [brew](https://cran.r-project.org/web/packages/brew/index.html) - Pre-compute data to enhance your report templates. Can be combined with knitr.

* [officer](https://davidgohel.github.io/officer/index.html) - An R package to generate Microsoft Word, Microsoft PowerPoint and HTML reports.

* [flextable](https://davidgohel.github.io/flextable/index.html) - An R package to embed complex tables (merged cells, multi-level headers and footers, conditional formatting) in Microsoft Word, Microsoft PowerPoint and HTML reports. It cooperates with the [officer] package and integrates with [rmarkdown] reports.

* [bookdown](https://bookdown.org/) - Authoring Books with R Markdown.

* [ezknitr](https://github.com/daattali/ezknitr) - Avoid the typical working directory pain when using 'knitr'

* [targets](https://docs.ropensci.org/targets/) - Make-like pipeline tool for organizing and running data science workflows, automatically skipping steps that have already been done. Supported by [rOpenSci](https://ropensci.org/).

* [R Suite](http://rsuite.io) - A package to design flexible and reproducible deployment workflows for R.

* [kable](https://cran.r-project.org/web/packages/kableExtra/vignettes/awesome_table_in_html.html) - Build fancy HTML or 'LaTeX' tables using 'kable()' from 'knitr'.



## Web Technologies and Services

*Packages to surf the web.*



* [Web Technologies List](https://github.com/ropensci/webservices) - Information about how to use R and the world wide web together.

* [shiny heart](https://github.com/rstudio/shiny) - Easy interactive web applications with R. See also [awesome-rshiny](https://github.com/grabear/awesome-rshiny)

* [shinyjs](https://github.com/daattali/shinyjs) -  Easily improve the user interaction and user experience in your Shiny apps in seconds.

* [RCurl](http://cran.r-project.org/web/packages/RCurl/index.html) - General network (HTTP/FTP/...) client interface for R.

* [curl](https://github.com/jeroen/curl) - A Modern and Flexible Web Client for R.

* [httr heart](https://github.com/hadley/httr) - User-friendly RCurl wrapper.

* [httpuv](https://github.com/rstudio/httpuv) - HTTP and WebSocket server library.

* [XML heart](http://cran.r-project.org/web/packages/XML/index.html) - Tools for parsing and generating XML within R.

* [xml2 heart](https://cran.r-project.org/web/packages/xml2/index.html) - Optimized tools for parsing and generating XML within R.

* [rvest heart](https://github.com/hadley/rvest) - Simple web scraping for R, using CSSSelect or XPath syntax.

* [OpenCPU heart](https://www.opencpu.org/) - HTTP API for R handling concurrent calls, based on the Apache2 web server, to expose R code as REST web services and create full-sized, multi-page web applications.

* [Rfacebook](https://github.com/pablobarbera/Rfacebook) - Access to Facebook API via R.

* [RSiteCatalyst](https://github.com/randyzwitch/RSiteCatalyst) - R client library for the Adobe Analytics.

* [plumber](https://github.com/trestletech/plumber) - A library to expose existing R code as web API.

* [golem](https://thinkr-open.github.io/golem/) - A framework for building production-grade Shiny apps.



## Parallel Computing

*Packages for parallel computing.*



* [parallel](http://cran.r-project.org/web/views/HighPerformanceComputing.html) - R started with release 2.14.0 which includes a new package parallel incorporating (slightly revised) copies of packages [multicore](http://cran.r-project.org/web/packages/multicore/index.html) and [snow](http://cran.r-project.org/web/packages/snow/index.html).

* [Rmpi](http://cran.r-project.org/web/packages/Rmpi/index.html) - Rmpi provides an interface (wrapper) to MPI APIs. It also provides interactive R slave environment.

* [foreach heart](http://cran.r-project.org/web/packages/foreach/index.html) - Executing the loop in parallel.

* [future heart](https://cran.r-project.org/package=future) - A minimal, efficient, cross-platform unified Future API for parallel and distributed processing in R; designed for beginners as well as advanced developers.

* [SparkR heart](https://github.com/amplab-extras/SparkR-pkg) - R frontend for Spark.

* [DistributedR](https://github.com/vertica/DistributedR) - A scalable high-performance platform from  HP Vertica Analytics Team.

* [ddR](https://github.com/vertica/ddR) - Provides distributed data structures and simplifies distributed computing in R.

* [sparklyr](http://spark.rstudio.com/) - R interface for Apache Spark from RStudio.

* [batchtools](https://cran.r-project.org/package=batchtools) - High performance computing with LSF, TORQUE, Slurm, OpenLava, SGE and Docker Swarm.



## High Performance

*Packages for making R faster.*



* [Rcpp heart](http://rcpp.org/) - Rcpp provides a powerful API on top of R, make function in R extremely faster.

* [Rcpp11](https://github.com/Rcpp11/Rcpp11) - Rcpp11 is a complete redesign of Rcpp, targetting C++11.

* [compiler](http://stat.ethz.ch/R-manual/R-devel/library/compiler/html/compile.html) - speeding up your R code using the JIT

* [cpp11](https://github.com/r-lib/cpp11) - cpp11 is a header-only R package that helps R package developers handle R objects with C++ code. It's similar to Rcpp but with different design trade-offs and features.



## Language API

*Packages for other languages.*



* [rJava](http://cran.r-project.org/web/packages/rJava/) - Low-level R to Java interface.

* [jvmr](https://github.com/cran/jvmr) - Integration of R, Java, and Scala.

* [reticulate heart](https://cran.r-project.org/web/packages/reticulate/index.html) - Interface to 'Python'.

* [rJython](http://cran.r-project.org/web/packages/rJython/index.html) - R interface to Python via Jython.

* [rPython](http://cran.r-project.org/web/packages/rPython/index.html) - Package allowing R to call Python.

* [runr](https://github.com/yihui/runr) - Run Julia and Bash from R.

* [RJulia](https://github.com/armgong/RJulia) - R package Call Julia.

* [JuliaCall](https://github.com/Non-Contradiction/JuliaCall) - Seamless Integration Between R and Julia.

* [RinRuby](https://sites.google.com/a/ddahl.org/rinruby-users/) - a Ruby library that integrates the R interpreter in Ruby.

* [R.matlab](http://cran.r-project.org/web/packages/R.matlab/index.html) - Read and write of MAT files together with R-to-MATLAB connectivity.

* [RcppOctave](https://github.com/renozao/RcppOctave) - Seamless Interface to Octave and Matlab.

* [RSPerl](http://www.omegahat.org/RSPerl/) - A bidirectional interface for calling R from Perl and Perl from R.

* [V8](https://github.com/jeroenooms/V8) - Embedded JavaScript Engine.

* [htmlwidgets](http://www.htmlwidgets.org/) - Bring the best of JavaScript data visualization to R.

* [rpy2](http://rpy.sourceforge.net/) - Python interface for R.



## Database Management

*Packages for managing data.*



* [RODBC](http://cran.r-project.org/web/packages/RODBC/) - ODBC database access for R.

* [DBI](https://github.com/rstats-db/DBI) - Defines a common interface between the R and database management systems.

* [elastic](https://github.com/ropensci/elastic) - Wrapper for the Elasticsearch HTTP API

* [mongolite](https://github.com/jeroenooms/mongolite) - Streaming Mongo Client for R

* [odbc](https://github.com/r-dbi/odbc) - Connect to ODBC databases (using the DBI interface)

* [RMariaDB](https://github.com/rstats-db/RMariaDB) - An R interface to MariaDB (a replacement for the old RMySQL package)

* [RMySQL](http://cran.r-project.org/web/packages/RMySQL/) - R interface to the MySQL database.

* [ROracle](http://cran.r-project.org/web/packages/ROracle/index.html) - OCI based Oracle database interface for R.

* [RPostgres](https://github.com/r-dbi/RPostgres) - an DBI-compliant interface to the postgres database.

* [RPostgreSQL](https://code.google.com/p/rpostgresql/) - R interface to the PostgreSQL database system.

* [RSQLite](http://cran.r-project.org/web/packages/RSQLite/) - SQLite interface for R

* [RJDBC](http://cran.r-project.org/web/packages/RJDBC/) - Provides access to databases through the JDBC interface.

* [rmongodb](https://github.com/mongosoup/rmongodb) - R driver for MongoDB.

* [redux](https://github.com/richfitz/redux) - Redis client for R.

* [RCassandra](http://cran.r-project.org/web/packages/RCassandra/index.html) - Direct interface (not Java) to the most basic functionality of Apache Cassandra.

* [RHive](https://github.com/nexr/RHive) - R extension facilitating distributed computing via Apache Hive.

* [RNeo4j](https://github.com/nicolewhite/Rneo4j) - Neo4j graph database driver.

* [rpostgis](https://github.com/mablab/rpostgis) - R interface to PostGIS database and get spatial objects in R.



## Machine Learning

*Packages for making R cleverer.*



* [anomalize](https://github.com/business-science/anomalize) - Tidy Anomaly Detection using Twitter's AnomalyDetection method.

* [AnomalyDetection heart](https://github.com/twitter/AnomalyDetection) - AnomalyDetection R package from Twitter.

* [ahaz](http://cran.r-project.org/web/packages/ahaz/index.html) - Regularization for semiparametric additive hazards regression.

* [arules](http://cran.r-project.org/web/packages/arules/index.html) - Mining Association Rules and Frequent Itemsets

* [bigrf](http://cran.r-project.org/web/packages/bigrf/index.html) - Big Random Forests: Classification and Regression Forests for

Large Data Sets

* [bigRR](http://cran.r-project.org/web/packages/bigRR/index.html) - Generalized Ridge Regression (with special advantage for p >> n

cases)

* [bmrm](http://cran.r-project.org/web/packages/bmrm/index.html) - Bundle Methods for Regularized Risk Minimization Package

* [Boruta](http://cran.r-project.org/web/packages/Boruta/index.html) - A wrapper algorithm for all-relevant feature selection

* [BreakoutDetection heart](https://github.com/twitter/BreakoutDetection) - Breakout Detection via Robust E-Statistics from Twitter.

* [bst](http://cran.r-project.org/web/packages/bst/index.html) - Gradient Boosting

* [CausalImpact heart](https://github.com/google/CausalImpact) - Causal inference using Bayesian structural time-series models.

* [C50](http://cran.r-project.org/web/packages/C50/index.html) - C5.0 Decision Trees and Rule-Based Models

* [caret heart](http://cran.r-project.org/web/packages/caret/index.html) - Classification and Regression Training

* [Clever Algorithms For Machine Learning](https://github.com/jbrownlee/CleverAlgorithmsMachineLearning)

* [CORElearn](http://cran.r-project.org/web/packages/CORElearn/index.html) - Classification, regression, feature evaluation and ordinal

evaluation

* [CoxBoost](http://cran.r-project.org/web/packages/CoxBoost/index.html) - Cox models by likelihood based boosting for a single survival

endpoint or competing risks

* [Cubist](http://cran.r-project.org/web/packages/Cubist/index.html) - Rule- and Instance-Based Regression Modeling

* [e1071](http://cran.r-project.org/web/packages/e1071/index.html) - Misc Functions of the Department of Statistics (e1071), TU Wien

* [earth](http://cran.r-project.org/web/packages/earth/index.html) - Multivariate Adaptive Regression Spline Models

* [elasticnet](http://cran.r-project.org/web/packages/elasticnet/index.html) - Elastic-Net for Sparse Estimation and Sparse PCA

* [ElemStatLearn](http://cran.r-project.org/web/packages/ElemStatLearn/index.html) - Data sets, functions and examples from the book: "The Elements

of Statistical Learning, Data Mining, Inference, and

Prediction" by Trevor Hastie, Robert Tibshirani and Jerome

Friedman

* [evtree](http://cran.r-project.org/web/packages/evtree/index.html) - Evolutionary Learning of Globally Optimal Trees

* [fable](https://github.com/tidyverts/fable/) - a collection of commonly used univariate and multivariate time series forecasting models

* [prophet heart](https://github.com/facebookincubator/prophet) - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

* [FSelector](https://cran.r-project.org/web/packages/FSelector/index.html) - A feature selection framework, based on subset-search or feature ranking approches.

* [frbs](http://cran.r-project.org/web/packages/frbs/index.html) - Fuzzy Rule-based Systems for Classification and Regression Tasks

* [GAMBoost](http://cran.r-project.org/web/packages/GAMBoost/index.html) - Generalized linear and additive models by likelihood based

boosting

* [gamboostLSS](http://cran.r-project.org/web/packages/gamboostLSS/index.html) - Boosting Methods for GAMLSS

* [gbm](http://cran.r-project.org/web/packages/gbm/index.html) - Generalized Boosted Regression Models

* [glmnet heart](http://cran.r-project.org/web/packages/glmnet/index.html) - Lasso and elastic-net regularized generalized linear models

* [glmpath](http://cran.r-project.org/web/packages/glmpath/index.html) - L1 Regularization Path for Generalized Linear Models and Cox

Proportional Hazards Model

* [GMMBoost](http://cran.r-project.org/web/packages/GMMBoost/index.html) - Likelihood-based Boosting for Generalized mixed models

* [grplasso](http://cran.r-project.org/web/packages/grplasso/index.html) - Fitting user specified models with Group Lasso penalty

* [grpreg](http://cran.r-project.org/web/packages/grpreg/index.html) - Regularization paths for regression models with grouped

covariates

* [h2o heart](http://cran.r-project.org/web/packages/h2o/index.html) - Deeplearning, Random forests, GBM, KMeans, PCA, GLM

* [hda](http://cran.r-project.org/web/packages/hda/index.html) - Heteroscedastic Discriminant Analysis

* [ipred](http://cran.r-project.org/web/packages/ipred/index.html) - Improved Predictors

* [kernlab](http://cran.r-project.org/web/packages/kernlab/index.html) - kernlab: Kernel-based Machine Learning Lab

* [klaR](http://cran.r-project.org/web/packages/klaR/index.html) - Classification and visualization

* [kohonen](http://cran.r-project.org/web/packages/kohonen/) - Supervised and Unsupervised Self-Organising Maps.

* [L0Learn](https://cran.r-project.org/web/packages/L0Learn/index.html) - Fast algorithms for best subset selection

* [lars](http://cran.r-project.org/web/packages/lars/index.html) - Least Angle Regression, Lasso and Forward Stagewise

* [lasso2](http://cran.r-project.org/web/packages/lasso2/index.html) - L1 constrained estimation aka ‘lasso’

* [LiblineaR](http://cran.r-project.org/web/packages/LiblineaR/index.html) - Linear Predictive Models Based On The Liblinear C/C++ Library

* [lightgbm heart](https://cran.r-project.org/web/packages/lightgbm/index.html) - Light Gradient Boosting Machine.

* [lme4 heart](https://github.com/lme4/lme4) - Mixed-effects models

* [nlme heart](https://cran.r-project.org/web/packages/nlme/index.html) - Mixed-effects models, handling user-specified matrix of residual covariance, relevant for the analysis of repeated observations in longitudinal trials

* [glmmTMB](https://cran.r-project.org/web/packages/glmmTMB/index.html) - Generalized mixed-effects models, handling user-specified matrix of residual covariance, relevant for the analysis of repeated observations in longitudinal trials

* [LogicReg](http://cran.r-project.org/web/packages/LogicReg/index.html) - Logic Regression

* [maptree](http://cran.r-project.org/web/packages/maptree/index.html) - Mapping, pruning, and graphing tree models

* [mboost](http://cran.r-project.org/web/packages/mboost/index.html) - Model-Based Boosting

* [Machine Learning For Hackers heart](https://github.com/johnmyleswhite/ML_for_Hackers)

* [mlr](https://github.com/mlr-org/mlr) - Extensible framework for classification, regression, survival analysis and clustering [DEPRECIATED]

* [mlr3 heart](https://github.com/mlr-org/mlr3) -  Next generation extensible framework for classification, regression, survival analysis and clustering

* [mvpart](http://cran.r-project.org/web/packages/mvpart/index.html) - Multivariate partitioning

* [MXNet heart](https://github.com/dmlc/mxnet/tree/master/R-package) - MXNet brings flexible and efficient GPU computing and state-of-art deep learning to R.

* [ncvreg](http://cran.r-project.org/web/packages/ncvreg/index.html) - Regularization paths for SCAD- and MCP-penalized regression

models

* [nnet](http://cran.r-project.org/web/packages/nnet/index.html) - eed-forward Neural Networks and Multinomial Log-Linear Models

* [oblique.tree](http://cran.r-project.org/web/packages/oblique.tree/index.html) - Oblique Trees for Classification Data

* [pamr](http://cran.r-project.org/web/packages/pamr/index.html) - Pam: prediction analysis for microarrays

* [party](http://cran.r-project.org/web/packages/party/index.html) - A Laboratory for Recursive Partytioning

* [partykit](http://cran.r-project.org/web/packages/partykit/index.html) - A Toolkit for Recursive Partytioning

* [penalized](http://cran.r-project.org/web/packages/penalized/index.html) - L1 (lasso and fused lasso) and L2 (ridge) penalized estimation

in GLMs and in the Cox model

* [penalizedLDA](http://cran.r-project.org/web/packages/penalizedLDA/index.html) - Penalized classification using Fisher's linear discriminant

* [penalizedSVM](http://cran.r-project.org/web/packages/penalizedSVM/index.html) - Feature Selection SVM using penalty functions

* [quantregForest](http://cran.r-project.org/web/packages/quantregForest/index.html) - quantregForest: Quantile Regression Forests

* [randomForest](http://cran.r-project.org/web/packages/randomForest/index.html) - randomForest: Breiman and Cutler's random forests for classification and regression.

* [randomForestSRC](http://cran.r-project.org/web/packages/randomForestSRC/index.html) - randomForestSRC: Random Forests for Survival, Regression and Classification (RF-SRC).

* [ranger](https://github.com/imbs-hl/ranger) - A Fast Implementation of Random Forests.

* [rattle](http://cran.r-project.org/web/packages/rattle/index.html) - Graphical user interface for data mining in R.

* [rda](http://cran.r-project.org/web/packages/rda/index.html) - Shrunken Centroids Regularized Discriminant Analysis

* [rdetools](http://cran.r-project.org/web/packages/rdetools/index.html) - Relevant Dimension Estimation (RDE) in Feature Spaces

* [REEMtree](http://cran.r-project.org/web/packages/REEMtree/index.html) - Regression Trees with Random Effects for Longitudinal (Panel)

Data

* [relaxo](http://cran.r-project.org/web/packages/relaxo/index.html) - Relaxed Lasso

* [rgenoud](http://cran.r-project.org/web/packages/rgenoud/index.html) - R version of GENetic Optimization Using Derivatives

* [rgp](http://cran.r-project.org/web/packages/rgp/index.html) - R genetic programming framework

* [Rmalschains](http://cran.r-project.org/web/packages/Rmalschains/index.html) - Continuous Optimization using Memetic Algorithms with Local

Search Chains (MA-LS-Chains) in R

* [rminer](http://cran.r-project.org/web/packages/rminer/index.html) - Simpler use of data mining methods (e.g. NN and SVM) in

classification and regression

* [ROCR](http://cran.r-project.org/web/packages/ROCR/index.html) - Visualizing the performance of scoring classifiers

* [RoughSets](http://cran.r-project.org/web/packages/RoughSets/index.html) - Data Analysis Using Rough Set and Fuzzy Rough Set Theories

* [rpart](http://cran.r-project.org/web/packages/rpart/index.html) - Recursive Partitioning and Regression Trees

* [RPMM](http://cran.r-project.org/web/packages/RPMM/index.html) - Recursively Partitioned Mixture Model

* [RSNNS](http://cran.r-project.org/web/packages/RSNNS/index.html) - Neural Networks in R using the Stuttgart Neural Network

Simulator (SNNS)

* [Rsomoclu](https://cran.r-project.org/web/packages/Rsomoclu/index.html) - Parallel implementation of self-organizing maps.

* [RWeka](http://cran.r-project.org/web/packages/RWeka/index.html) - R/Weka interface

* [RXshrink](http://cran.r-project.org/web/packages/RXshrink/index.html) - RXshrink: Maximum Likelihood Shrinkage via Generalized Ridge or Least

Angle Regression

* [sda](http://cran.r-project.org/web/packages/sda/index.html) - Shrinkage Discriminant Analysis and CAT Score Variable Selection

* [SDDA](http://cran.r-project.org/web/packages/SDDA/index.html) - Stepwise Diagonal Discriminant Analysis

* [SuperLearner](https://github.com/ecpolley/SuperLearner) and [subsemble](http://cran.r-project.org/web/packages/subsemble/index.html) - Multi-algorithm ensemble learning packages.

* [survminer](https://github.com/kassambara/survminer) - Survival Analysis & Visualization

* [survival](https://cran.r-project.org/web/packages/survival/index.html) - Survival Analysis

* [svmpath](http://cran.r-project.org/web/packages/svmpath/index.html) - svmpath: the SVM Path algorithm

* [tgp](http://cran.r-project.org/web/packages/tgp/index.html) - Bayesian treed Gaussian process models

* [tidymodels](https://cran.r-project.org/web/packages/tidymodels/index.html) - A collection of packages for modeling and statistical analysis that share the underlying design philosophy, grammar, and data structures of the tidyverse.

* [torch](https://cran.r-project.org/web/packages/torch/index.html) - Tensors and Neural Networks with 'GPU' Acceleration.

* [tree](http://cran.r-project.org/web/packages/tree/index.html) - Classification and regression trees

* [varSelRF](http://cran.r-project.org/web/packages/varSelRF/index.html) - Variable selection using random forests

* [xgboost heart](https://github.com/tqchen/xgboost/tree/master/R-package) - eXtreme Gradient Boosting Tree model, well known for its speed and performance.



## Natural Language Processing

*Packages for Natural Language Processing.*



* [text2vec](https://github.com/dselivanov/text2vec) - Fast Text Mining Framework for Vectorization and Word Embeddings.

* [tm](http://cran.r-project.org/web/packages/tm/index.html) - A comprehensive text mining framework for R.

* [openNLP](http://cran.r-project.org/web/packages/openNLP/index.html) - Apache OpenNLP Tools Interface.

* [koRpus](http://cran.r-project.org/web/packages/koRpus/index.html) - An R Package for Text Analysis.

* [zipfR](http://cran.r-project.org/web/packages/zipfR/index.html) - Statistical models for word frequency distributions.

* [NLP](http://cran.r-project.org/web/packages/NLP/index.html) - Basic functions for Natural Language Processing.

* [LDAvis](https://github.com/cpsievert/LDAvis) - Interactive visualization of topic models.

* [topicmodels](https://cran.r-project.org/web/packages/topicmodels/index.html) - Topic modeling interface to the C code developed by by David M. Blei for Topic Modeling (Latent Dirichlet Allocation (LDA), and Correlated Topics Models (CTM)).

* [syuzhet](https://cran.r-project.org/web/packages/syuzhet/index.html) - Extracts sentiment from text using three different sentiment dictionaries.

* [SnowballC](https://cran.rstudio.com/web/packages/SnowballC/index.html) - Snowball stemmers based on the C libstemmer UTF-8 library.

* [quanteda](https://github.com/kbenoit/quanteda) - R functions for Quantitative Analysis of Textual Data.

* [Topic Models Resources](https://github.com/trinker/topicmodels_learning) - Topic Models learning and R related resources.

* [NLP for :cn:](https://github.com/BZRLC/R-notes/blob/master/NLP/readme.md) - NLP related resources in R. @Chinese

* [MonkeyLearn](https://github.com/masalmon/monkeylearn) - 🐒 R package for text analysis with Monkeylearn 🐒.

* [tidytext](http://tidytextmining.com/index.html) - Implementing tidy principles of Hadley Wickham to text mining.

* [utf8](https://github.com/patperry/r-utf8) - Manipulating and printing UTF-8 text that fixes multiple bugs in R's UTF-8 handling.

* [corporaexplorer](https://kgjerde.github.io/corporaexplorer/) - Dynamic exploration of text collections



## Bayesian

*Packages for Bayesian Inference.*



* [brms](https://cran.r-project.org/web/packages/brms/index.html) - High-level interface for Bayesian regression models using Stan.

* [coda](http://cran.r-project.org/web/packages/coda/index.html) - Output analysis and diagnostics for MCMC.

* [mcmc](http://cran.r-project.org/web/packages/mcmc/index.html) - Markov Chain Monte Carlo.

* [MCMCpack](http://mcmcpack.berkeley.edu/) - Markov chain Monte Carlo (MCMC) Package.

* [R2WinBUGS](http://cran.r-project.org/web/packages/R2WinBUGS/index.html) - Running WinBUGS and OpenBUGS from R / S-PLUS.

* [BRugs](http://cran.r-project.org/web/packages/BRugs/index.html) - R interface to the OpenBUGS MCMC software.

* [rjags](http://cran.r-project.org/web/packages/rjags/index.html) - R interface to the JAGS MCMC library.

* [rstan heart](http://mc-stan.org/interfaces/rstan.html) - R interface to the Stan MCMC software.



## Optimization

*Packages for Optimization.*



* [lpSolve](https://cran.rstudio.com/web/packages/lpSolve/index.html) - Interface to `Lp_solve` to Solve Linear/Integer Programs.

* [minqa](https://cran.rstudio.com/web/packages/minqa/index.html) - Derivative-free optimization algorithms by quadratic approximation.

* [nloptr](https://cran.rstudio.com/web/packages/nloptr/index.html) - NLopt is a free/open-source library for nonlinear optimization.

* [ompr](https://cran.rstudio.com/web/packages/ompr/index.html) - Model mixed integer linear programs in an algebraic way directly in R.

* [Rglpk](https://cran.rstudio.com/web/packages/Rglpk/index.html) - R/GNU Linear Programming Kit Interface

* [ROI](https://cran.rstudio.com/web/packages/ROI/index.html) - The R Optimization Infrastructure ('ROI') is a sophisticated framework for handling optimization problems in R.



## Finance

*Packages for dealing with money.*



* [quantmod heart](http://www.quantmod.com/) - Quantitative Financial Modelling & Trading Framework for R.

* [pedquant](http://pedquant.com/) - Public Economic Data and Quantitative Analysis

* [TTR](http://cran.r-project.org/web/packages/TTR/index.html) - Functions and data to construct technical trading rules with R.

* [PerformanceAnalytics](http://cran.r-project.org/web/packages/PerformanceAnalytics/index.html) - Econometric tools for performance and risk analysis.

* [zoo heart](http://cran.r-project.org/web/packages/zoo/index.html) - S3 Infrastructure for Regular and Irregular Time Series.

* [xts](http://cran.r-project.org/web/packages/xts/index.html) - eXtensible Time Series.

* [tseries](http://cran.r-project.org/web/packages/tseries/index.html) - Time series analysis and computational finance.

* [fAssets](http://cran.r-project.org/web/packages/fAssets/index.html) - Analysing and Modelling Financial Assets.

* [scorecard](https://github.com/ShichenXie/scorecard) - Credit Risk Scorecard



## Bioinformatics and Biostatistics

*Packages for processing biological datasets.*



* [Bioconductor heart](http://www.bioconductor.org/) - Tools for the analysis and comprehension of high-throughput genomic data.

* [genetics](http://cran.r-project.org/web/packages/genetics/index.html) - Classes and methods for handling genetic data.

* [gap](http://cran.r-project.org/web/packages/gap/index.html) - An integrated package for genetic data analysis of both population and family data.

* [ape](http://cran.r-project.org/web/packages/ape/index.html) - Analyses of Phylogenetics and Evolution.

* [pheatmap](http://cran.r-project.org/web/packages/pheatmap/index.html) - Pretty heatmaps made easy.

* [lme4](https://github.com/lme4/lme4) - Generalized mixed-effects models.

* [nlme](https://cran.r-project.org/web/packages/nlme/index.html) - Mixed-effects models, handling user-specified matrix of residual covariance, relevant for the anaysis of repeated observations in longitudinal trials.

* [glmmTMB](https://cran.r-project.org/web/packages/glmmTMB/index.html) - Generalized mixed-effects models, handling user-specified matrix of residual covariance, relevant for the anaysis of repeated observations in longitudinal trials.



## Network Analysis

*Packages to construct, analyze and visualize network data.*



* [Network Analysis List](https://github.com/briatte/awesome-network-analysis) - Network Analysis related resources.

* [igraph heart](http://igraph.org/r/) - A collection of network analysis tools.

* [network](https://cran.r-project.org/web/packages/network/index.html) - Basic tools to manipulate relational data in R.

* [sna](https://cran.r-project.org/web/packages/sna/index.html) - Basic network measures and visualization tools.

* [netdiffuseR](https://github.com/USCCANA/netdiffuseR) - Tools for Analysis of Network Diffusion.

* [networkDynamic](https://cran.r-project.org/web/packages/networkDynamic/) - Support for dynamic, (inter)temporal networks.

* [ndtv](https://cran.r-project.org/web/packages/ndtv/) - Tools to construct animated visualizations of dynamic network data in various formats.

* [statnet](http://statnet.org/) - The project behind many R network analysis packages.

* [ergm](https://cran.r-project.org/web/packages/ergm/index.html) - Exponential random graph models in R.

* [latentnet](https://cran.r-project.org/web/packages/latentnet/index.html) - Latent position and cluster models for network objects.

* [tnet](https://cran.r-project.org/web/packages/tnet/index.html) - Network measures for weighted, two-mode and longitudinal networks.

* [rgexf](https://bitbucket.org/gvegayon/rgexf/wiki/Home) - Export network objects from R to [GEXF](http://gexf.net/format/), for manipulation with network software like [Gephi](https://gephi.org/) or [Sigma](http://sigmajs.org/).

* [visNetwork](https://github.com/datastorm-open/visNetwork) - Using vis.js library for network visualization.

* [tidygraph](https://github.com/thomasp85/tidygraph) - A tidy API for graph manipulation



## Spatial

*Packages to explore the earth.*



* [CRAN Task View: Analysis of Spatial Data](https://cran.r-project.org/web/views/Spatial.html)- Spatial Analysis related resources.

* [Leaflet](http://rstudio.github.io/leaflet/) - One of the most popular JavaScript libraries interactive maps.

* [ggmap](https://github.com/dkahle/ggmap) -  Plotting maps in R with ggplot2.

* [REmap](https://github.com/Lchiffon/REmap) - R interface to the JavaScript library ECharts for interactive map data visualization.

* [sf](https://cran.r-project.org/web/packages/sf/index.html) - Improved Classes and Methods for Spatial Data.

* [sp](https://edzer.github.io/sp/) - Classes and Methods for Spatial Data.

* [rgeos](https://cran.r-project.org/web/packages/rgeos/index.html) - Interface to Geometry Engine - Open Source

* [rgdal](https://cran.r-project.org/web/packages/rgdal/index.html) - Bindings for the Geospatial Data Abstraction Library

* [maptools](https://cran.r-project.org/web/packages/maptools/index.html) - Tools for Reading and Handling Spatial Objects

* [gstat](https://github.com/edzer/gstat) - Spatial and spatio-temporal geostatistical modelling, prediction and simulation.

* [spacetime](https://github.com/edzer/spacetime) - R classes and methods for spatio-temporal data.

* [RColorBrewer](https://cran.r-project.org/web/packages/RColorBrewer/index.html) - Provides color schemes for maps

* [spatstat](https://github.com/spatstat/spatstat) - Spatial Point Pattern Analysis, Model-Fitting, Simulation, Tests

* [spdep](https://cran.r-project.org/web/packages/spdep/index.html) - Spatial Dependence: Weighting Schemes, Statistics and Models

* [tigris](https://github.com/walkerke/tigris) - Download and use Census TIGER/Line shapefiles in R

* [GWmodel](https://cran.r-project.org/web/packages/GWmodel/) - Geographically-Weighted Models

* [tmap](https://github.com/mtennekes/tmap) - R package for thematic maps



## R Development

*Packages for packages.*



* [Package Development List](https://github.com/ropensci/PackageDevelopment) - R packages to improve package development.

* [promises](https://cran.r-project.org/web/packages/promises/index.html) - Abstractions for Promise-Based Asynchronous Programming

* [devtools heart](https://github.com/hadley/devtools) - Tools to make an R developer's life easier.

* [testthat heart](https://github.com/hadley/testthat) - An R package to make testing fun.

* [R6 heart](https://github.com/wch/R6) - simpler, faster, lighter-weight alternative to R's built-in classes.

* [pryr heart](https://github.com/hadley/pryr) - Make it easier to understand what's going on in R.

* [roxygen heart](https://github.com/klutometis/roxygen) - Describe your functions in comments next to their definitions.

* [lineprof](https://github.com/hadley/lineprof) - Visualise line profiling results in R.

* [renv heart](https://github.com/rstudio/renv) - Make your R projects more isolated, portable, and reproducible.

* [installr](https://github.com/talgalili/installr/) - Functions for installing softwares from within R (for Windows).

* [import](https://github.com/smbache/import/) - An import mechanism for R.

* [box heart](https://github.com/klmr/box) - A modern module system for R.

* [Rocker heart](https://github.com/rocker-org) - R configurations for [Docker](https://www.docker.com/).

* [RStudio Addins](https://github.com/daattali/rstudio-addins) - List of RStudio addins.

* [drat](https://github.com/eddelbuettel/drat) - Creation and use of R repositories on GitHub or other repos.

* [covr](https://github.com/jimhester/covr) - Test coverage for your R package and (optionally) upload the results to [coveralls](https://coveralls.io/) or [codecov](https://codecov.io/).

* [lintr](https://github.com/jimhester/lintr) - Static code analysis for R to enforce code style.

* [staticdocs](https://github.com/hadley/staticdocs) - Generate static html documentation for an R package.

* [sinew](https://github.com/metrumresearchgroup/sinew) - Generate roxygen2 skeletons populated with information scraped from the function script.



## Logging

*Packages for Logging*



* [futile.logger](https://github.com/zatonovo/futile.logger) - A logging package in R similar to log4j

* [log4r](https://github.com/johnmyleswhite/log4r) - A log4j derivative for R

* [logging](https://cran.r-project.org/web/packages/logging/index.html) - A logging package emulating the python logging package.



## Data Packages

*Handy Data Packages*



* [engsoccerdata](https://github.com/jalapic/engsoccerdata) - English and European soccer results 1871-2016.

* [gapminder](http://github.com/jennybc/gapminder) - Excerpt from the Gapminder dataset (data about countries through the past 50 years).

* [wbstats](https://cran.r-project.org/web/packages/wbstats/index.html) - Tools for searching and downloading data and statistics from the World Bank Data API and the World Bank Data Catalog API.

* [ICON](https://github.com/rrrlw/ICON) - complex systems & networks datasets from the Index of COmplex Networks (ICON) database [webpage](http://icon.colorado.edu).

* [RCOBOLDI](https://github.com/thospfuller/rcoboldi) - Import COBOL CopyBook data files directly into R as properly structured data frames. Package builds are available via [Drat](https://github.com/thospfuller/drat) and [DockerHub](https://hub.docker.com/r/thospfuller/rcoboldi-rocker-rstudio).



## Other Tools

*Handy Tools for R*



* [git2r](https://github.com/ropensci/git2r) - Gives you programmatic access to Git repositories from R.

* [Conda](https://anaconda.org/r/repo) - Most R packages are available through the Conda polyglot cross-platform dependency manager.



## Other Interpreters

*Alternative R engines.*



* [CXXR](https://www.cs.kent.ac.uk/projects/cxxr/) - Refactorising R into C++.

* [fastR](https://bitbucket.org/allr/fastr/wiki/Home) - FastR is an implementation of the R Language in Java atop Truffle and Graal.

* [pqR](http://www.pqr-project.org/) - a "pretty quick" implementation of R

* [renjin](http://www.renjin.org/) - a JVM-based interpreter for R.

* [rho](https://github.com/rho-devel/rho) - Refactor the interpreter of the R language into a fully-compatible, efficient, VM for R.

* [riposte](https://github.com/jtalbot/riposte) - a fast interpreter and JIT for R.

* [TERR](http://spotfire.tibco.com/discover-spotfire/what-does-spotfire-do/predictive-analytics/tibco-enterprise-runtime-for-r-terr) - TIBCO Enterprise Runtime for R.



## Learning R

*Packages for Learning R.*



* [swirl heart](http://swirlstats.com/) - An interactive R tutorial directly in your R console.

* [DataScienceR heart](https://github.com/ujjwalkarn/DataScienceR) - a list of R tutorials for Data Science, NLP and Machine Learning.



# Resources



Where to discover new R-esources.



## Websites



### Manuals



* [R-project](http://www.r-project.org/) - The R Project for Statistical Computing.

* [An Introduction to R](https://cran.r-project.org/doc/manuals/R-intro.pdf) - A very good introductory text on R, also covers some advanced topic. See also the `Manuals` section on [CRAN](https://cran.r-project.org/manuals.html)

* [CRAN Contributed Docs](https://cran.r-project.org/other-docs.html) - CRAN Contributed Documentation in many languages.

* [Quick-R](http://www.statmethods.net/) - An excellent quick reference

* [tryR](http://tryr.codeschool.com/) - A quick course for getting started with R.



### Tools and References



* [RDocumentation](https://www.rdocumentation.org/) - Search through all CRAN, Bioconductor, Github packages and their archives with RDocumentation.

* [rdrr.io](https://rdrr.io/) - Find R package documentation. Try R packages in your browser.

* [CRAN Task Views](http://cran.r-project.org/web/views/) - Task Views for CRAN packages.

* [rnotebook.io](https://rnotebook.io/) - Create online R Jupyter Notebooks for free.



### News and Info



* [R Weekly](https://rweekly.org) - Weekly updates about R and Data Science. R Weekly is openly developed on GitHub.

* [R Bloggers](http://www.r-bloggers.com/) - There are people scattered across the Web who blog about R. This is simply an aggregator of many of those feeds.

* [R-users](https://www.r-users.com/) - A job board for R users (and the people who are looking to hire them)



## Books



### Free and Online



* [_R for Data Science_ by Garrett Grolemund & Hadley Wickham](http://r4ds.had.co.nz/) - Free book from RStudio developers with emphasis on data science workflow.

* [_R Cookbook_ by Winston Chang](http://www.cookbook-r.com/) - A problem-oriented online book that supports his [R Graphics Cookbook, 2nd ed. (2018)](http://shop.oreilly.com/product/0636920063704.do).

* [_Advanced R_, 2nd ed. by Hadley Wickham (2019) heart](https://adv-r.hadley.nz/) - An online version of the Advanced R book.

* [_R Packages_, 2nd ed. by Hadley Wickham & Jennifer Bryan](https://r-pkgs.org/) - A book (in paper and website formats) on writing R packages.

* Books written as part of the Johns Hopkins Data Science Specialization:

  * [_Exploratory Data Analysis with R_ by Roger D. Peng (2016)](https://leanpub.com/exdata) - Basic analytical skills for all sorts of data in R.

  * [_R Programming for Data Science_ by Roger D. Peng (2019)](https://leanpub.com/rprogramming) - More advanced data analysis that relies on R programming.

  * [_Report Writing for Data Science in R_ by Roger D. Peng (2019)](https://leanpub.com/reportwriting) - R-based methods for reproducible research and report generation.

* [_R for SAS and SPSS users_ by Bob Muenchen (2012)](http://r4stats.com/books/free-version/) - An excellent resource for users already familiar with SAS or SPSS.

* [_Introduction to Statistical Learning with Application in R_ by Gareth James et al. (2017)](http://faculty.marshall.usc.edu/gareth-james/ISL/) - A simplified and "operational" version of *The Elements of Statistical Learning*. Free softcopy provided by its authors.

* [_The R Inferno_ by Patrick Burns (2011)](http://www.burns-stat.com/pages/Tutor/R_inferno.pdf) - Patrick Burns gives insight into R's ins and outs along with its quirks!

* [_Efficient R Programming_ by Colin Gillespie & Robin Lovelace (2017)](https://csgillespie.github.io/efficientR/) - An online version of the O’Reilly book: Efficient R Programming.

* [The R Programming Wikibook](https://en.wikibooks.org/wiki/R_Programming) - A collaborative handbook for R.



### Paid



* [The Art of R Programming](http://shop.oreilly.com/product/9781593273842.do) - It's a good resource for systematically learning fundamentals such as types of objects, control statements, variable scope, classes and debugging in R.

* [_R Cookbook_, 2nd ed. by JD Long & Paul Teetor (2019)](http://shop.oreilly.com/product/0636920174851.do) - A quick and simple introduction to conducting many common statistical tasks with R.

* [R in Action](http://www.manning.com/kabacoff2/) - This book aims at all levels of users, with sections for beginning, intermediate and advanced R ranging from "Exploring R data structures" to running regressions and conducting factor analyses.

* [_Use R!_ Series by Springer](http://www.springer.com/series/6991?detailsPage=titles) - This series of inexpensive and focused books from Springer publish shorter books aimed at practitioners. Books can discuss the use of R in a particular subject area, such as Bayesian networks, ggplot2 and Rcpp.

* [Learning R Programming](https://www.packtpub.com/big-data-and-business-intelligence/learning-r-programming) - Learning R as a programming language from basics to advanced topics.



### Book/monograph Lists and Reviews



* [R Books List](https://github.com/RomanTsegelskyi/rbooks) - List of R Books.

* [Readings in Applied Data Science](https://github.com/hadley/stats337) - These readings reflect Hadley's personal thoughts about applied data science.



## Podcasts



* [Not So Standard Deviations](https://soundcloud.com/nssd-podcast) - The Data Science Podcast.

  * [@Roger Peng](https://twitter.com/rdpeng) and [@Hilary Parker](https://twitter.com/hspter).

* [R World News](http://www.rworld.news/blog/) - R World News helps you keep up with happenings within the R community.

  * [@Bob Rudis](https://twitter.com/hrbrmstr) and [@Jay Jacobs](https://twitter.com/jayjacobs).

* [The R-Podcast](https://r-podcast.org/) - Giving practical advice on how to use R.

  * [@Eric Nantz](https://r-podcast.org/stories/contact.html).

* [R Talk](http://rtalk.org) - News and discussions of statistical software and language R.

  * [@Oliver Keyes](https://twitter.com/quominus), [@Jasmine Dumas](https://twitter.com/jasdumas), [@Ted Hart](https://twitter.com/emhrt_) and [@Mikhail Popov](https://twitter.com/bearloga).

* [R Weekly](https://rweekly.org) - Weekly news updates about the R community.



## Reference Cards



* [RStudio Cheat Sheets](https://www.rstudio.com/resources/cheatsheets/)

* [R Reference Card 2.0](http://cran.r-project.org/doc/contrib/Baggott-refcard-v2.pdf) - Material from R for Beginners by permission of Emmanuel Paradis (Version 2 by Matt Baggott).

* [Regression Analysis Refcard](http://cran.r-project.org/doc/contrib/Ricci-refcard-regression.pdf) - R Reference Card for Regression Analysis.

* [Reference Card for ESS](http://ess.r-project.org/refcard.pdf) - Reference Card for ESS.



## MOOCs

*Massive open online courses.*



* [Johns Hopkins University Data Science Specialization](https://www.coursera.org/specialization/jhudatascience/1) - 9 courses including: Introduction to R, literate analysis tools, Shiny and some more.

* [HarvardX Biomedical Data Science](http://simplystatistics.org/2014/11/25/harvardx-biomedical-data-science-open-online-training-curriculum-launches-on-january-19/) - Introduction to R for the Life Sciences.

* [Explore Statistics with R](https://www.edx.org/course/explore-statistics-r-kix-kiexplorx-0) - Covers introduction, data handling and statistical analysis in R.



## Lists

*Great resources for learning domain knowledge.*



* [Books](https://github.com/RomanTsegelskyi/rbooks) - List of R Books.

* [ggplot2 Extensions](https://ggplot2-exts.github.io/ggiraph.html) - Showcases of ggplot2 extensions.

* [Natural Language Processing :cn:](https://github.com/BZRLC/R-notes/blob/master/NLP/readme.md) - NLP related resources in R. @Chinese

* [Network Analysis](https://github.com/briatte/awesome-network-analysis) - Network Analysis related resources.

* [Open Data](https://github.com/ropensci/opendata) - Using R to obtain, parse, manipulate, create, and share open data.

* [Posts](https://github.com/qinwf/awesome-R/blob/master/misc/posts.md) - Great R blog posts or Rticles.

* [Package Development](https://github.com/ropensci/PackageDevelopment) - R packages to improve package development.

* [R Project Conferences](https://www.r-project.org/conferences.html) -  Information about useR! Conferences and DSC Conferences.

* [RStartHere](https://github.com/rstudio/RStartHere) - A guide to some of the most useful R packages, organized by workflow.

* [RStudio Addins](https://github.com/daattali/addinslist) - List of RStudio addins.

* [Topic Models](https://github.com/trinker/topicmodels_learning) - Topic Models learning and R related resources.

* [Web Technologies](https://github.com/ropensci/webservices) - Information about how to use R and the world wide web together.



## R Ecosystems



R communities and package collections (in alphabetical order):



 * [rOpenGov](http://ropengov.github.io/) Open government data, computational social science, digital humanities

 * [rOpenHealth](https://github.com/rOpenHealth) Public health data

 * [rOpenSci](https://ropensci.org) Open science



## 2018



* [fable](https://github.com/tidyverts/fable) - univariate and multivariate time series forecasting models ![fable](https://cranlogs.r-pkg.org/badges/fable)

* [r2d3](https://rstudio.github.io/r2d3/) - R Interface to D3 Visualizations ![r2d3](https://cranlogs.r-pkg.org/badges/r2d3)

* [rstats-ed](https://github.com/rstudio-education/rstats-ed) - List of courses teaching R

* [promises](https://cran.r-project.org/web/packages/promises/index.html) - Abstractions for Promise-Based Asynchronous Programming ![promises](https://cranlogs.r-pkg.org/badges/promises)

* [tinytex](https://yihui.name/tinytex/) - A lightweight and easy-to-maintain LaTeX distribution ![tinytex](https://cranlogs.r-pkg.org/badges/tinytex)

* [Readings in Applied Data Science](https://github.com/hadley/stats337) - These readings reflect Hadley's personal thoughts about applied data science.



## 2017



* [prophet](https://github.com/facebookincubator/prophet) - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

* [tidyverse](https://github.com/tidyverse/tidyverse) - Easily install and load packages from the tidyverse

* [purrr](https://github.com/tidyverse/purrr) - A functional programming toolkit for R

* [hrbrthemes](https://github.com/hrbrmstr/hrbrthemes) -  🔏 Opinionated, typographic-centric ggplot2 themes and theme components

* [xaringan](https://github.com/yihui/xaringan) - Create HTML5 slides with R Markdown and the JavaScript library

* [blogdown](https://github.com/rstudio/blogdown) - Create Blogs and Websites with R Markdown

* [glue](https://github.com/tidyverse/glue) -  Glue strings to data in R. Small, fast, dependency free interpreted string literals.

* [covr](https://github.com/jimhester/covr) - Test coverage reports for R

* [lintr](https://github.com/jimhester/lintr) - Static Code Analysis for R

* [reprex](https://github.com/jennybc/reprex) - Render bits of R code for sharing, e.g., on GitHub or StackOverflow.

* [reticulate](https://github.com/rstudio/reticulate) - R Interface to Python

* [tensorflow](https://github.com/rstudio/tensorflow) -  TensorFlow for R

* [utf8](https://github.com/patperry/r-utf8) - Manipulating and printing UTF-8 text that fixes multiple bugs in R's UTF-8 handling.

* [Patchwork](https://github.com/thomasp85/patchwork) - Combine separate ggplots into the same graphic.



# Other Awesome Lists



* [awesome-awesomeness](https://github.com/bayandin/awesome-awesomeness)

* [lists](https://github.com/jnv/lists)

* [awesome-rshiny](https://github.com/grabear/awesome-rshiny)



# Contributing

Your contributions are always welcome!



This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License - [CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/legalcode)