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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).
for CRAN downloaded packages or repos with 400+
- 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  (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  (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  (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  (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  (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  (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/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  (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/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  (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 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  (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 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  (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  (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  (https://github.com/renkun-ken/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/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  (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  (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.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  (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  (https://github.com/hadley/httr) - User-friendly RCurl wrapper.
⟡ httpuv (https://github.com/rstudio/httpuv) - HTTP and WebSocket server library.
⟡ XML  (http://cran.r-project.org/web/packages/XML/index.html) - Tools for parsing and generating XML within R.
⟡ xml2  (https://cran.r-project.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 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  (http://cran.r-project.org/web/packages/foreach/index.html) - Executing the loop in parallel.
⟡ future  (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  (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  (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  (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  (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  (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  (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  (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  (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  (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  (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  (https://cran.r-project.org/web/packages/lightgbm/index.html) - Light Gradient Boosting Machine.
⟡ lme4  (https://github.com/lme4/lme4) - 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 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  (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  (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  (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  (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  (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  (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  (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  (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  (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  (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  (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, lighter-weight alternative to R's built-in 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/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  (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 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)  (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  (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)
R Github: https://github.com/qinwf/awesome-R