Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/qinwf/awesomeR
A curated list of awesome R packages, frameworks and software.
https://github.com/qinwf/awesomeR
List: awesomeR
awesome awesomelist dataanalysis datascience list r rstats
Last synced: about 2 months ago
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A curated list of awesome R packages, frameworks and software.
 Host: GitHub
 URL: https://github.com/qinwf/awesomeR
 Owner: qinwf
 Created: 20140719T06:02:47.000Z (about 10 years ago)
 Default Branch: master
 Last Pushed: 20240229T09:37:56.000Z (8 months ago)
 Last Synced: 20240520T04:02:41.366Z (5 months ago)
 Topics: awesome, awesomelist, dataanalysis, datascience, list, r, rstats
 Language: R
 Homepage:
 Size: 1.1 MB
 Stars: 5,811
 Watchers: 419
 Forks: 1,501
 Open Issues: 12

Metadata Files:
 Readme: README.md
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README
# 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 [awesomemachinelearning](https://github.com/josephmisiti/awesomemachinelearning).
for Top 50 CRAN downloaded packages or repos with 400+ [Awesome R](#awesome)
 [2023](#2023)
 [2020](#2020)
 [2019](#2019)
 [2018](#2018)
 [Integrated Development Environments](#integrateddevelopmentenvironments)
 [Syntax](#syntax)
 [Data Manipulation](#datamanipulation)
 [Graphic Displays](#graphicdisplays)
 [Html Widgets](#htmlwidgets)
 [Reproducible Research](#reproducibleresearch)
 [Web Technologies and Services](#webtechnologiesandservices)
 [Parallel Computing](#parallelcomputing)
 [High Performance](#highperformance)
 [Language API](#languageapi)
 [Database Management](#databasemanagement)
 [Machine Learning](#machinelearning)
 [Natural Language Processing](#naturallanguageprocessing)
 [Bayesian](#bayesian)
 [Optimization](#optimization)
 [Finance](#finance)
 [Bioinformatics and Biostatistics](#bioinformaticsandbiostatistics)
 [Network Analysis](#networkanalysis)
 [Spatial](#spatial)
 [R Development](#rdevelopment)
 [Logging](#logging)
 [Data Packages](#datapackages)
 [Other Tools](#othertools)
 [Other Interpreters](#otherinterpreters)
 [Learning R](#learningr)
 [Resources](#resources)
 [Websites](#websites)
 [Books](#books)
 [Podcasts](#podcasts)
 [Reference Cards](#referencecards)
 [MOOCs](#moocs)
 [Lists](#lists)
 [Other Awesome Lists](#otherawesomelists)
 [Contributing](#contributing)## 2023
* [Cookbook Polars for R](https://ddotta.github.io/cookbookrpolars/)
## 2020
* [VSCode](https://code.visualstudio.com/)  [vscodeR](https://marketplace.visualstudio.com/items?itemName=Ikuyadeu.r) + [vscoderlsp](https://marketplace.visualstudio.com/items?itemName=REditorSupport.rlsp) VSCode R Langauage Support
* [gt](https://github.com/rstudio/gt)  Easily generate informationrich, publicationquality tables from R
* [lightgbm ](https://cran.rproject.org/web/packages/lightgbm/index.html)  Light Gradient Boosting Machine.
* [torch](https://cran.rproject.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.rpkg.org/badges/ggforce)
* [rayshader](https://github.com/tylermorganwall/rayshader)  2D and 3D data visualizations via rgl ![rayshader](https://cranlogs.rpkg.org/badges/rayshader)
* [vroom](https://github.com/rlib/vroom)  Fast reading of delimited files ![vroom](https://cranlogs.rpkg.org/badges/vroom)## Integrated Development Environments
*Integrated Development Environment** [VSCode ](https://code.visualstudio.com/)  [vscodeR](https://marketplace.visualstudio.com/items?itemName=Ikuyadeu.r) + [vscoderlsp](https://marketplace.visualstudio.com/items?itemName=REditorSupport.rlsp) VSCode R Langauage Support
* [RStudio ](http://www.rstudio.org/)  A powerful and productive user interface for R. Works great on Windows, Mac, and Linux.
* [Emacs + ESS](http://ess.rproject.org/)  Emacs Speaks Statistics is an addon package for emacs text editors.
* [Sublime Text + RIDE](https://github.com/REditorSupport/sublimeider)  Addon package for Sublime Text 2/3.
* [TextMate + r.tmblundle](https://github.com/textmate/r.tmbundle)  Addon 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 ](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://radiantrstats.github.io/docs)  A platformindependent browserbased interface for business analytics in R, based on the Shiny.
* [NvimR ](https://github.com/jalvesaq/NvimR)  Neovim plugin for R.
* [Jamovi](https://www.jamovi.org/) and [JASP](https://jaspstats.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/RTVSdocs/)  R Tools for Visual Studio.
* [radian ](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 ](https://github.com/smbache/magrittr)  Let's pipe it.
* [pipeR](https://github.com/renkunken/pipeR)  Multiparadigm 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 ](https://github.com/hadley/dplyr)  Fast data frames manipulation and database query.
* [data.table ](https://github.com/Rdatatable/data.table)  Fast data manipulation in a short and flexible syntax.
* [reshape2 ](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 ](https://github.com/dgrtwo/broom)  Convert statistical analysis objects into tidy data frames.
* [rlist](https://github.com/renkunken/rlist)  A toolbox for nontabular data manipulation with lists.
* [ff](http://ff.rforge.rproject.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 ](https://github.com/gagolews/stringi)  ICU based string processing package.
* [stringr ](https://github.com/hadley/stringr)  Consistent API for string processing, built on top of stringi.
* [bigmemory](https://github.com/kaneplusplus/bigmemory)  Shared memory and memorymapped matrices. The big\* packages provide additional tools including linear models ([biglm](http://cran.rproject.org/web/packages/biglm/index.html)) and Random Forests ([bigrf](https://github.com/aloysiuslim/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 ](https://arrow.apache.org/docs/r/)  An interface to the Arrow C++ library.
* [feather ](https://github.com/wesm/feather)  Fast, interoperable binary data frame storage for Python, R, and more powered by Apache Arrow.
* [fst ](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 ](https://readxl.tidyverse.org/)  Read excel files (.xls and .xlsx) into R.
* [readr ](https://github.com/hadley/readr)  A fast and friendly way to read tabular data into R.
* [rio](https://github.com/leeper/rio)  A SwissArmy 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/rlib/vroom)  Fast reading of delimited files.
* [writexl](https://docs.ropensci.org/writexl/)  Portable, lightweight data frame to xlsx exporter for R.
* [yaml](https://github.com/viking/ryaml)  R package for converting objects to and from YAML.## Graphic Displays
*Packages for showing data.** [ggplot2 ](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/ricardobion/ggtech)  ggplot2 tech themes and scales
* [ggplot2 Extensions](https://ggplot2exts.github.io/ggiraph.html)  Showcases of ggplot2 extensions.
* [lattice](https://github.com/deepayan/lattice)  A powerful and elegant highlevel 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.rproject.org/web/packages/rgl/index.html)  3D visualization device system for R.
* [Cairo](http://cran.rproject.org/web/packages/Cairo/index.html)  R graphics device using cairo graphics library for creating highquality 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.rproject.org/web/packages/misc3d/index.html)  Powerful functions to deal with 3d plots, isosurfaces, etc.
* [xkcd](https://cran.rproject.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, typographiccentric 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 MultiDimensional Data
* [plot3Drgl](https://cran.rproject.org/web/packages/plot3Drgl/index.html)  Plotting MultiDimensional 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 ](https://github.com/richiannone/DiagrammeR)  Create JS graph diagrams and flowcharts in R.
* [dygraphs](https://github.com/rstudio/dygraphs)  Charting timeseries data in R.
* [formattable ](https://github.com/renkunken/formattable)  Formattable Data Structures.
* [ggvis ](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 ](https://github.com/ropensci/plotly)  Interactive ggplot2 and Shiny plotting with [plot.ly](https://plot.ly).
* [rCharts ](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/datastormopen/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 ](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 easytomaintain LaTeX distribution
* [xtable](http://cran.rproject.org/web/packages/xtable/index.html)  Export tables to LaTeX or HTML.
* [rapport](http://rapportpackage.info/#intro)  An R templating system.
* [rmarkdown ](http://rmarkdown.rstudio.com/)  Dynamic documents for R.
* [slidify ](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.rproject.org/web/packages/brew/index.html)  Precompute 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, multilevel 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/)  Makelike 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.rproject.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 ](https://github.com/rstudio/shiny)  Easy interactive web applications with R. See also [awesomershiny](https://github.com/grabear/awesomershiny)
* [shinyjs](https://github.com/daattali/shinyjs)  Easily improve the user interaction and user experience in your Shiny apps in seconds.
* [RCurl](http://cran.rproject.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 ](https://github.com/hadley/httr)  Userfriendly RCurl wrapper.
* [httpuv](https://github.com/rstudio/httpuv)  HTTP and WebSocket server library.
* [XML ](http://cran.rproject.org/web/packages/XML/index.html)  Tools for parsing and generating XML within R.
* [xml2 ](https://cran.rproject.org/web/packages/xml2/index.html)  Optimized tools for parsing and generating XML within R.
* [rvest ](https://github.com/hadley/rvest)  Simple web scraping for R, using CSSSelect or XPath syntax.
* [OpenCPU ](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 fullsized, multipage 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://thinkropen.github.io/golem/)  A framework for building productiongrade Shiny apps.## Parallel Computing
*Packages for parallel computing.** [parallel](http://cran.rproject.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.rproject.org/web/packages/multicore/index.html) and [snow](http://cran.rproject.org/web/packages/snow/index.html).
* [Rmpi](http://cran.rproject.org/web/packages/Rmpi/index.html)  Rmpi provides an interface (wrapper) to MPI APIs. It also provides interactive R slave environment.
* [foreach ](http://cran.rproject.org/web/packages/foreach/index.html)  Executing the loop in parallel.
* [future ](https://cran.rproject.org/package=future)  A minimal, efficient, crossplatform unified Future API for parallel and distributed processing in R; designed for beginners as well as advanced developers.
* [SparkR ](https://github.com/amplabextras/SparkRpkg)  R frontend for Spark.
* [DistributedR](https://github.com/vertica/DistributedR)  A scalable highperformance 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.rproject.org/package=batchtools)  High performance computing with LSF, TORQUE, Slurm, OpenLava, SGE and Docker Swarm.## High Performance
*Packages for making R faster.** [Rcpp ](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/Rmanual/Rdevel/library/compiler/html/compile.html)  speeding up your R code using the JIT
* [cpp11](https://github.com/rlib/cpp11)  cpp11 is a headeronly R package that helps R package developers handle R objects with C++ code. It's similar to Rcpp but with different design tradeoffs and features.## Language API
*Packages for other languages.** [rJava](http://cran.rproject.org/web/packages/rJava/)  Lowlevel R to Java interface.
* [jvmr](https://github.com/cran/jvmr)  Integration of R, Java, and Scala.
* [reticulate ](https://cran.rproject.org/web/packages/reticulate/index.html)  Interface to 'Python'.
* [rJython](http://cran.rproject.org/web/packages/rJython/index.html)  R interface to Python via Jython.
* [rPython](http://cran.rproject.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/NonContradiction/JuliaCall)  Seamless Integration Between R and Julia.
* [RinRuby](https://sites.google.com/a/ddahl.org/rinrubyusers/)  a Ruby library that integrates the R interpreter in Ruby.
* [R.matlab](http://cran.rproject.org/web/packages/R.matlab/index.html)  Read and write of MAT files together with RtoMATLAB 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.rproject.org/web/packages/RODBC/)  ODBC database access for R.
* [DBI](https://github.com/rstatsdb/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/rdbi/odbc)  Connect to ODBC databases (using the DBI interface)
* [RMariaDB](https://github.com/rstatsdb/RMariaDB)  An R interface to MariaDB (a replacement for the old RMySQL package)
* [RMySQL](http://cran.rproject.org/web/packages/RMySQL/)  R interface to the MySQL database.
* [ROracle](http://cran.rproject.org/web/packages/ROracle/index.html)  OCI based Oracle database interface for R.
* [RPostgres](https://github.com/rdbi/RPostgres)  an DBIcompliant interface to the postgres database.
* [RPostgreSQL](https://code.google.com/p/rpostgresql/)  R interface to the PostgreSQL database system.
* [RSQLite](http://cran.rproject.org/web/packages/RSQLite/)  SQLite interface for R
* [RJDBC](http://cran.rproject.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.rproject.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/businessscience/anomalize)  Tidy Anomaly Detection using Twitter's AnomalyDetection method.
* [AnomalyDetection ](https://github.com/twitter/AnomalyDetection)  AnomalyDetection R package from Twitter.
* [ahaz](http://cran.rproject.org/web/packages/ahaz/index.html)  Regularization for semiparametric additive hazards regression.
* [arules](http://cran.rproject.org/web/packages/arules/index.html)  Mining Association Rules and Frequent Itemsets
* [bigrf](http://cran.rproject.org/web/packages/bigrf/index.html)  Big Random Forests: Classification and Regression Forests for
Large Data Sets
* [bigRR](http://cran.rproject.org/web/packages/bigRR/index.html)  Generalized Ridge Regression (with special advantage for p >> n
cases)
* [bmrm](http://cran.rproject.org/web/packages/bmrm/index.html)  Bundle Methods for Regularized Risk Minimization Package
* [Boruta](http://cran.rproject.org/web/packages/Boruta/index.html)  A wrapper algorithm for allrelevant feature selection
* [BreakoutDetection ](https://github.com/twitter/BreakoutDetection)  Breakout Detection via Robust EStatistics from Twitter.
* [bst](http://cran.rproject.org/web/packages/bst/index.html)  Gradient Boosting
* [CausalImpact ](https://github.com/google/CausalImpact)  Causal inference using Bayesian structural timeseries models.
* [C50](http://cran.rproject.org/web/packages/C50/index.html)  C5.0 Decision Trees and RuleBased Models
* [caret ](http://cran.rproject.org/web/packages/caret/index.html)  Classification and Regression Training
* [Clever Algorithms For Machine Learning](https://github.com/jbrownlee/CleverAlgorithmsMachineLearning)
* [CORElearn](http://cran.rproject.org/web/packages/CORElearn/index.html)  Classification, regression, feature evaluation and ordinal
evaluation
* [CoxBoost](http://cran.rproject.org/web/packages/CoxBoost/index.html)  Cox models by likelihood based boosting for a single survival
endpoint or competing risks
* [Cubist](http://cran.rproject.org/web/packages/Cubist/index.html)  Rule and InstanceBased Regression Modeling
* [e1071](http://cran.rproject.org/web/packages/e1071/index.html)  Misc Functions of the Department of Statistics (e1071), TU Wien
* [earth](http://cran.rproject.org/web/packages/earth/index.html)  Multivariate Adaptive Regression Spline Models
* [elasticnet](http://cran.rproject.org/web/packages/elasticnet/index.html)  ElasticNet for Sparse Estimation and Sparse PCA
* [ElemStatLearn](http://cran.rproject.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.rproject.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 ](https://github.com/facebookincubator/prophet)  Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or nonlinear growth.
* [FSelector](https://cran.rproject.org/web/packages/FSelector/index.html)  A feature selection framework, based on subsetsearch or feature ranking approches.
* [frbs](http://cran.rproject.org/web/packages/frbs/index.html)  Fuzzy Rulebased Systems for Classification and Regression Tasks
* [GAMBoost](http://cran.rproject.org/web/packages/GAMBoost/index.html)  Generalized linear and additive models by likelihood based
boosting
* [gamboostLSS](http://cran.rproject.org/web/packages/gamboostLSS/index.html)  Boosting Methods for GAMLSS
* [gbm](http://cran.rproject.org/web/packages/gbm/index.html)  Generalized Boosted Regression Models
* [glmnet ](http://cran.rproject.org/web/packages/glmnet/index.html)  Lasso and elasticnet regularized generalized linear models
* [glmpath](http://cran.rproject.org/web/packages/glmpath/index.html)  L1 Regularization Path for Generalized Linear Models and Cox
Proportional Hazards Model
* [GMMBoost](http://cran.rproject.org/web/packages/GMMBoost/index.html)  Likelihoodbased Boosting for Generalized mixed models
* [grplasso](http://cran.rproject.org/web/packages/grplasso/index.html)  Fitting user specified models with Group Lasso penalty
* [grpreg](http://cran.rproject.org/web/packages/grpreg/index.html)  Regularization paths for regression models with grouped
covariates
* [h2o ](http://cran.rproject.org/web/packages/h2o/index.html)  Deeplearning, Random forests, GBM, KMeans, PCA, GLM
* [hda](http://cran.rproject.org/web/packages/hda/index.html)  Heteroscedastic Discriminant Analysis
* [ipred](http://cran.rproject.org/web/packages/ipred/index.html)  Improved Predictors
* [kernlab](http://cran.rproject.org/web/packages/kernlab/index.html)  kernlab: Kernelbased Machine Learning Lab
* [klaR](http://cran.rproject.org/web/packages/klaR/index.html)  Classification and visualization
* [kohonen](http://cran.rproject.org/web/packages/kohonen/)  Supervised and Unsupervised SelfOrganising Maps.
* [L0Learn](https://cran.rproject.org/web/packages/L0Learn/index.html)  Fast algorithms for best subset selection
* [lars](http://cran.rproject.org/web/packages/lars/index.html)  Least Angle Regression, Lasso and Forward Stagewise
* [lasso2](http://cran.rproject.org/web/packages/lasso2/index.html)  L1 constrained estimation aka ‘lasso’
* [LiblineaR](http://cran.rproject.org/web/packages/LiblineaR/index.html)  Linear Predictive Models Based On The Liblinear C/C++ Library
* [lightgbm ](https://cran.rproject.org/web/packages/lightgbm/index.html)  Light Gradient Boosting Machine.
* [lme4 ](https://github.com/lme4/lme4)  Mixedeffects models
* [nlme ](https://cran.rproject.org/web/packages/nlme/index.html)  Mixedeffects models, handling userspecified matrix of residual covariance, relevant for the analysis of repeated observations in longitudinal trials
* [glmmTMB](https://cran.rproject.org/web/packages/glmmTMB/index.html)  Generalized mixedeffects models, handling userspecified matrix of residual covariance, relevant for the analysis of repeated observations in longitudinal trials
* [LogicReg](http://cran.rproject.org/web/packages/LogicReg/index.html)  Logic Regression
* [maptree](http://cran.rproject.org/web/packages/maptree/index.html)  Mapping, pruning, and graphing tree models
* [mboost](http://cran.rproject.org/web/packages/mboost/index.html)  ModelBased Boosting
* [Machine Learning For Hackers ](https://github.com/johnmyleswhite/ML_for_Hackers)
* [mlr](https://github.com/mlrorg/mlr)  Extensible framework for classification, regression, survival analysis and clustering [DEPRECIATED]
* [mlr3 ](https://github.com/mlrorg/mlr3)  Next generation extensible framework for classification, regression, survival analysis and clustering
* [mvpart](http://cran.rproject.org/web/packages/mvpart/index.html)  Multivariate partitioning
* [MXNet ](https://github.com/dmlc/mxnet/tree/master/Rpackage)  MXNet brings flexible and efficient GPU computing and stateofart deep learning to R.
* [ncvreg](http://cran.rproject.org/web/packages/ncvreg/index.html)  Regularization paths for SCAD and MCPpenalized regression
models
* [nnet](http://cran.rproject.org/web/packages/nnet/index.html)  eedforward Neural Networks and Multinomial LogLinear Models
* [oblique.tree](http://cran.rproject.org/web/packages/oblique.tree/index.html)  Oblique Trees for Classification Data
* [pamr](http://cran.rproject.org/web/packages/pamr/index.html)  Pam: prediction analysis for microarrays
* [party](http://cran.rproject.org/web/packages/party/index.html)  A Laboratory for Recursive Partytioning
* [partykit](http://cran.rproject.org/web/packages/partykit/index.html)  A Toolkit for Recursive Partytioning
* [penalized](http://cran.rproject.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.rproject.org/web/packages/penalizedLDA/index.html)  Penalized classification using Fisher's linear discriminant
* [penalizedSVM](http://cran.rproject.org/web/packages/penalizedSVM/index.html)  Feature Selection SVM using penalty functions
* [quantregForest](http://cran.rproject.org/web/packages/quantregForest/index.html)  quantregForest: Quantile Regression Forests
* [randomForest](http://cran.rproject.org/web/packages/randomForest/index.html)  randomForest: Breiman and Cutler's random forests for classification and regression.
* [randomForestSRC](http://cran.rproject.org/web/packages/randomForestSRC/index.html)  randomForestSRC: Random Forests for Survival, Regression and Classification (RFSRC).
* [ranger](https://github.com/imbshl/ranger)  A Fast Implementation of Random Forests.
* [rattle](http://cran.rproject.org/web/packages/rattle/index.html)  Graphical user interface for data mining in R.
* [rda](http://cran.rproject.org/web/packages/rda/index.html)  Shrunken Centroids Regularized Discriminant Analysis
* [rdetools](http://cran.rproject.org/web/packages/rdetools/index.html)  Relevant Dimension Estimation (RDE) in Feature Spaces
* [REEMtree](http://cran.rproject.org/web/packages/REEMtree/index.html)  Regression Trees with Random Effects for Longitudinal (Panel)
Data
* [relaxo](http://cran.rproject.org/web/packages/relaxo/index.html)  Relaxed Lasso
* [rgenoud](http://cran.rproject.org/web/packages/rgenoud/index.html)  R version of GENetic Optimization Using Derivatives
* [rgp](http://cran.rproject.org/web/packages/rgp/index.html)  R genetic programming framework
* [Rmalschains](http://cran.rproject.org/web/packages/Rmalschains/index.html)  Continuous Optimization using Memetic Algorithms with Local
Search Chains (MALSChains) in R
* [rminer](http://cran.rproject.org/web/packages/rminer/index.html)  Simpler use of data mining methods (e.g. NN and SVM) in
classification and regression
* [ROCR](http://cran.rproject.org/web/packages/ROCR/index.html)  Visualizing the performance of scoring classifiers
* [RoughSets](http://cran.rproject.org/web/packages/RoughSets/index.html)  Data Analysis Using Rough Set and Fuzzy Rough Set Theories
* [rpart](http://cran.rproject.org/web/packages/rpart/index.html)  Recursive Partitioning and Regression Trees
* [RPMM](http://cran.rproject.org/web/packages/RPMM/index.html)  Recursively Partitioned Mixture Model
* [RSNNS](http://cran.rproject.org/web/packages/RSNNS/index.html)  Neural Networks in R using the Stuttgart Neural Network
Simulator (SNNS)
* [Rsomoclu](https://cran.rproject.org/web/packages/Rsomoclu/index.html)  Parallel implementation of selforganizing maps.
* [RWeka](http://cran.rproject.org/web/packages/RWeka/index.html)  R/Weka interface
* [RXshrink](http://cran.rproject.org/web/packages/RXshrink/index.html)  RXshrink: Maximum Likelihood Shrinkage via Generalized Ridge or Least
Angle Regression
* [sda](http://cran.rproject.org/web/packages/sda/index.html)  Shrinkage Discriminant Analysis and CAT Score Variable Selection
* [SDDA](http://cran.rproject.org/web/packages/SDDA/index.html)  Stepwise Diagonal Discriminant Analysis
* [SuperLearner](https://github.com/ecpolley/SuperLearner) and [subsemble](http://cran.rproject.org/web/packages/subsemble/index.html)  Multialgorithm ensemble learning packages.
* [survminer](https://github.com/kassambara/survminer)  Survival Analysis & Visualization
* [survival](https://cran.rproject.org/web/packages/survival/index.html)  Survival Analysis
* [svmpath](http://cran.rproject.org/web/packages/svmpath/index.html)  svmpath: the SVM Path algorithm
* [tgp](http://cran.rproject.org/web/packages/tgp/index.html)  Bayesian treed Gaussian process models
* [tidymodels](https://cran.rproject.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.rproject.org/web/packages/torch/index.html)  Tensors and Neural Networks with 'GPU' Acceleration.
* [tree](http://cran.rproject.org/web/packages/tree/index.html)  Classification and regression trees
* [varSelRF](http://cran.rproject.org/web/packages/varSelRF/index.html)  Variable selection using random forests
* [xgboost ](https://github.com/tqchen/xgboost/tree/master/Rpackage)  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.rproject.org/web/packages/tm/index.html)  A comprehensive text mining framework for R.
* [openNLP](http://cran.rproject.org/web/packages/openNLP/index.html)  Apache OpenNLP Tools Interface.
* [koRpus](http://cran.rproject.org/web/packages/koRpus/index.html)  An R Package for Text Analysis.
* [zipfR](http://cran.rproject.org/web/packages/zipfR/index.html)  Statistical models for word frequency distributions.
* [NLP](http://cran.rproject.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.rproject.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.rproject.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 UTF8 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 ](https://github.com/BZRLC/Rnotes/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/rutf8)  Manipulating and printing UTF8 text that fixes multiple bugs in R's UTF8 handling.
* [corporaexplorer](https://kgjerde.github.io/corporaexplorer/)  Dynamic exploration of text collections## Bayesian
*Packages for Bayesian Inference.** [coda](http://cran.rproject.org/web/packages/coda/index.html)  Output analysis and diagnostics for MCMC.
* [mcmc](http://cran.rproject.org/web/packages/mcmc/index.html)  Markov Chain Monte Carlo.
* [MCMCpack](http://mcmcpack.berkeley.edu/)  Markov chain Monte Carlo (MCMC) Package.
* [R2WinBUGS](http://cran.rproject.org/web/packages/R2WinBUGS/index.html)  Running WinBUGS and OpenBUGS from R / SPLUS.
* [BRugs](http://cran.rproject.org/web/packages/BRugs/index.html)  R interface to the OpenBUGS MCMC software.
* [rjags](http://cran.rproject.org/web/packages/rjags/index.html)  R interface to the JAGS MCMC library.
* [rstan ](http://mcstan.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)  Derivativefree optimization algorithms by quadratic approximation.
* [nloptr](https://cran.rstudio.com/web/packages/nloptr/index.html)  NLopt is a free/opensource 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 ](http://www.quantmod.com/)  Quantitative Financial Modelling & Trading Framework for R.
* [pedquant](http://pedquant.com/)  Public Economic Data and Quantitative Analysis
* [TTR](http://cran.rproject.org/web/packages/TTR/index.html)  Functions and data to construct technical trading rules with R.
* [PerformanceAnalytics](http://cran.rproject.org/web/packages/PerformanceAnalytics/index.html)  Econometric tools for performance and risk analysis.
* [zoo ](http://cran.rproject.org/web/packages/zoo/index.html)  S3 Infrastructure for Regular and Irregular Time Series.
* [xts](http://cran.rproject.org/web/packages/xts/index.html)  eXtensible Time Series.
* [tseries](http://cran.rproject.org/web/packages/tseries/index.html)  Time series analysis and computational finance.
* [fAssets](http://cran.rproject.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 ](http://www.bioconductor.org/)  Tools for the analysis and comprehension of highthroughput genomic data.
* [genetics](http://cran.rproject.org/web/packages/genetics/index.html)  Classes and methods for handling genetic data.
* [gap](http://cran.rproject.org/web/packages/gap/index.html)  An integrated package for genetic data analysis of both population and family data.
* [ape](http://cran.rproject.org/web/packages/ape/index.html)  Analyses of Phylogenetics and Evolution.
* [pheatmap](http://cran.rproject.org/web/packages/pheatmap/index.html)  Pretty heatmaps made easy.
* [lme4](https://github.com/lme4/lme4)  Generalized mixedeffects models.
* [nlme](https://cran.rproject.org/web/packages/nlme/index.html)  Mixedeffects models, handling userspecified matrix of residual covariance, relevant for the anaysis of repeated observations in longitudinal trials.
* [glmmTMB](https://cran.rproject.org/web/packages/glmmTMB/index.html)  Generalized mixedeffects models, handling userspecified 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/awesomenetworkanalysis)  Network Analysis related resources.
* [igraph ](http://igraph.org/r/)  A collection of network analysis tools.
* [network](https://cran.rproject.org/web/packages/network/index.html)  Basic tools to manipulate relational data in R.
* [sna](https://cran.rproject.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.rproject.org/web/packages/networkDynamic/)  Support for dynamic, (inter)temporal networks.
* [ndtv](https://cran.rproject.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.rproject.org/web/packages/ergm/index.html)  Exponential random graph models in R.
* [latentnet](https://cran.rproject.org/web/packages/latentnet/index.html)  Latent position and cluster models for network objects.
* [tnet](https://cran.rproject.org/web/packages/tnet/index.html)  Network measures for weighted, twomode 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/datastormopen/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.rproject.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.rproject.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.rproject.org/web/packages/rgeos/index.html)  Interface to Geometry Engine  Open Source
* [rgdal](https://cran.rproject.org/web/packages/rgdal/index.html)  Bindings for the Geospatial Data Abstraction Library
* [maptools](https://cran.rproject.org/web/packages/maptools/index.html)  Tools for Reading and Handling Spatial Objects
* [gstat](https://github.com/edzer/gstat)  Spatial and spatiotemporal geostatistical modelling, prediction and simulation.
* [spacetime](https://github.com/edzer/spacetime)  R classes and methods for spatiotemporal data.
* [RColorBrewer](https://cran.rproject.org/web/packages/RColorBrewer/index.html)  Provides color schemes for maps
* [spatstat](https://github.com/spatstat/spatstat)  Spatial Point Pattern Analysis, ModelFitting, Simulation, Tests
* [spdep](https://cran.rproject.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.rproject.org/web/packages/GWmodel/)  GeographicallyWeighted 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.rproject.org/web/packages/promises/index.html)  Abstractions for PromiseBased Asynchronous Programming
* [devtools ](https://github.com/hadley/devtools)  Tools to make an R developer's life easier.
* [testthat ](https://github.com/hadley/testthat)  An R package to make testing fun.
* [R6 ](https://github.com/wch/R6)  simpler, faster, lighterweight alternative to R's builtin classes.
* [pryr ](https://github.com/hadley/pryr)  Make it easier to understand what's going on in R.
* [roxygen ](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 ](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 ](https://github.com/klmr/box)  A modern module system for R.
* [Rocker ](https://github.com/rockerorg)  R configurations for [Docker](https://www.docker.com/).
* [RStudio Addins](https://github.com/daattali/rstudioaddins)  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.rproject.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 18712016.
* [gapminder](http://github.com/jennybc/gapminder)  Excerpt from the Gapminder dataset (data about countries through the past 50 years).
* [wbstats](https://cran.rproject.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/rcoboldirockerrstudio).## 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 crossplatform 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.pqrproject.org/)  a "pretty quick" implementation of R
* [renjin](http://www.renjin.org/)  a JVMbased interpreter for R.
* [rho](https://github.com/rhodevel/rho)  Refactor the interpreter of the R language into a fullycompatible, efficient, VM for R.
* [riposte](https://github.com/jtalbot/riposte)  a fast interpreter and JIT for R.
* [TERR](http://spotfire.tibco.com/discoverspotfire/whatdoesspotfiredo/predictiveanalytics/tibcoenterpriseruntimeforrterr)  TIBCO Enterprise Runtime for R.## Learning R
*Packages for Learning R.** [swirl ](http://swirlstats.com/)  An interactive R tutorial directly in your R console.
* [DataScienceR ](https://github.com/ujjwalkarn/DataScienceR)  a list of R tutorials for Data Science, NLP and Machine Learning.# Resources
Where to discover new Resources.
## Websites
### Manuals
* [Rproject](http://www.rproject.org/)  The R Project for Statistical Computing.
* [An Introduction to R](https://cran.rproject.org/doc/manuals/Rintro.pdf)  A very good introductory text on R, also covers some advanced topic. See also the `Manuals` section on [CRAN](https://cran.rproject.org/manuals.html)
* [CRAN Contributed Docs](https://cran.rproject.org/otherdocs.html)  CRAN Contributed Documentation in many languages.
* [QuickR](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.rproject.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.rbloggers.com/)  There are people scattered across the Web who blog about R. This is simply an aggregator of many of those feeds.
* [Rusers](https://www.rusers.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.cookbookr.com/)  A problemoriented 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) ](https://advr.hadley.nz/)  An online version of the Advanced R book.
* [_R Packages_, 2nd ed. by Hadley Wickham & Jennifer Bryan](https://rpkgs.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)  Rbased methods for reproducible research and report generation.
* [_R for SAS and SPSS users_ by Bob Muenchen (2012)](http://r4stats.com/books/freeversion/)  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/garethjames/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.burnsstat.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/bigdataandbusinessintelligence/learningrprogramming)  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/nssdpodcast)  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 RPodcast](https://rpodcast.org/)  Giving practical advice on how to use R.
* [@Eric Nantz](https://rpodcast.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.rproject.org/doc/contrib/Baggottrefcardv2.pdf)  Material from R for Beginners by permission of Emmanuel Paradis (Version 2 by Matt Baggott).
* [Regression Analysis Refcard](http://cran.rproject.org/doc/contrib/Riccirefcardregression.pdf)  R Reference Card for Regression Analysis.
* [Reference Card for ESS](http://ess.rproject.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/harvardxbiomedicaldatascienceopenonlinetrainingcurriculumlaunchesonjanuary19/)  Introduction to R for the Life Sciences.
* [Explore Statistics with R](https://www.edx.org/course/explorestatisticsrkixkiexplorx0)  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://ggplot2exts.github.io/ggiraph.html)  Showcases of ggplot2 extensions.
* [Natural Language Processing ](https://github.com/BZRLC/Rnotes/blob/master/NLP/readme.md)  NLP related resources in R. @Chinese
* [Network Analysis](https://github.com/briatte/awesomenetworkanalysis)  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/awesomeR/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.rproject.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.rpkg.org/badges/fable)
* [r2d3](https://rstudio.github.io/r2d3/)  R Interface to D3 Visualizations ![r2d3](https://cranlogs.rpkg.org/badges/r2d3)
* [rstatsed](https://github.com/rstudioeducation/rstatsed)  List of courses teaching R
* [promises](https://cran.rproject.org/web/packages/promises/index.html)  Abstractions for PromiseBased Asynchronous Programming ![promises](https://cranlogs.rpkg.org/badges/promises)
* [tinytex](https://yihui.name/tinytex/)  A lightweight and easytomaintain LaTeX distribution ![tinytex](https://cranlogs.rpkg.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 nonlinear 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, typographiccentric 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/rutf8)  Manipulating and printing UTF8 text that fixes multiple bugs in R's UTF8 handling.
* [Patchwork](https://github.com/thomasp85/patchwork)  Combine separate ggplots into the same graphic.# Other Awesome Lists
* [awesomeawesomeness](https://github.com/bayandin/awesomeawesomeness)
* [lists](https://github.com/jnv/lists)
* [awesomershiny](https://github.com/grabear/awesomershiny)# Contributing
Your contributions are always welcome!This work is licensed under the Creative Commons AttributionNonCommercialShareAlike 4.0 International License  [CC BYNCSA 4.0](http://creativecommons.org/licenses/byncsa/4.0/legalcode)