ujjwalkarn/awesome-R

Awesome R

A curated list of awesome R frameworks, packages and software. Inspired by awesome-machine-learning.

• Awesome R
  • Integrated Development Environments
  • Syntax
  • Data Manipulation
  • Graphic Displays
  • Html Widgets
  • Reproducible Research
  • Web Technologies and Services
  • Parallel Computing
  • High Performance
  • Language API
  • Database Management
  • Machine Learning
  • Natural Language Processing
  • Bayesian
  • Finance
  • Bioinformatics
  • Network Analysis
  • R Development
  • Other Interpreters
  • Learning R
• Resources
  • Websites
  • Books
  • Reference Cards
  • MOOCs
• Other Awesome Lists
• Contributing

Integrated Development Environments

Integrated Development Environment

• RStudio - A powerful and productive user interface for R. Works great on Windows, Mac, and Linux.
• Emacs + ESS - Emacs Speaks Statistics is an add-on package for emacs text editors.
• Sublime Text + R-Box - Add-on package for Sublime Text 2/3.
• TextMate + r.tmblundle - Add-on package for TextMate 1/2.
• StatET - An Eclipse based IDE for R.
• Revolution R Enterprise - Revolution R would be offered free to academic users and commercial software would focus on big data, large scale multiprocessor functionality.
• R Commander - A package that provides a basic graphical user interface.
• IPython - An interactive Python interpreter, and it supports execution of R code while capturing both output and figures.
• Deducer - A Menu driven data analysis GUI with a spreadsheet like data editor.
• Radiant - A platform-independent browser-based interface for business analytics in R, based on the Shiny.
• Vim-R - Vim plugin for R.
• JASP - A complete package for both Bayesian and Frequentist methods, that is familiar to users of SPSS.

Syntax

Packages change the way you use R.

• magrittr - Let's pipe it.
• pipeR - Multi-paradigm Pipeline Implementation.
• lambda.r - Functional programming and simple pattern matching in R.
• purrr - A FP package for R in the spirit of underscore.js.

Data Manipulation

Packages for cooking data.

• dplyr - Fast data frames manipulation and database query.
• data.table - Fast data manipulation in a short and flexible syntax.
• reshape2 - Flexible rearrange, reshape and aggregate data.
• readr - A fast and friendly way to read tabular data into R.
• haven - Improved methods to import SPSS, Stata and SAS files in R.
• tidyr - Easily tidy data with spread and gather functions.
• broom - Convert statistical analysis objects into tidy data frames.
• rlist - A toolbox for non-tabular data manipulation with lists.
• jsonlite - A robust and quick way to parse JSON files in R.
• ff - Data structures designed to store large datasets.
• lubridate - A set of functions to work with dates and times.
• stringi - ICU based string processing package.
• stringr - Consistent API for string processing, built on top of stringi.

Graphic Displays

Packages for showing data.

• ggplot2 - An implementation of the Grammar of Graphics.
• lattice - A powerful and elegant high-level data visualization system.
• rgl - 3D visualization device system for R.
• Cairo - R graphics device using cairo graphics library for creating high-quality display output.
• extrafont - Tools for using fonts in R graphics.
• showtext - Enable R graphics device to show text using system fonts.
• animation - A simple way to produce animated graphics in R, using ImageMagick.
• misc3d - Powerful functions to deal with 3d plots, isosurfaces, etc.
• xkcd - Use xkcd style in graphs.
• imager - An image processing package based on CImg library to work with images and display them.

HTML Widgets

Packages for interactive visualizations.

• d3heatmap - Interactive heatmaps with D3.
• DataTables - Displays R matrices or data frames as interactive HTML tables.
• DiagrammeR - Create JS graph diagrams and flowcharts in R.
• dygraphs - Charting time-series data in R.
• formattable - Formattable Data Structures.
• ggvis - Interactive grammar of graphics for R.
• Leaflet - One of the most popular JavaScript libraries interactive maps.
• MetricsGraphics - Enables easy creation of D3 scatterplots, line charts, and histograms.
• networkD3 - D3 JavaScript Network Graphs from R.
• plotly - Interactive ggplot2 and Shiny plotting with plot.ly.
• rCharts - Interactive JS Charts from R.
• rbokeh - R Interface to Bokeh.
• threejs - Interactive 3D scatter plots and globes.

Reproducible Research

Packages for literate programming.

• knitr - Easy dynamic report generation in R.
• xtable - Export tables to LaTeX or HTML.
• rapport - An R templating system.
• rmarkdown - Dynamic documents for R.
• slidify - Generate reproducible html5 slides from R markdown.
• Sweave - A package designed to write LaTeX reports using R.
• texreg - Formatting statistical models in LaTex and HTML.
• checkpoint - Install packages from snapshots on the checkpoint server.
• brew - Pre-compute data to enhance your report templates. Can be combined with knitr.
• ReporteRs - An R package to generate Microsoft Word, Microsoft PowerPoint and HTML reports.

Web Technologies and Services

Packages to surf the web.

• shiny - Easy interactive web applications with R.
• RCurl - General network (HTTP/FTP/...) client interface for R.
• httr - User-friendly RCurl wrapper.
• httpuv - HTTP and WebSocket server library.
• XML - Tools for parsing and generating XML within R.
• rvest - Simple web scraping for R, using CSSSelect or XPath syntax.
• OpenCPU - HTTP API for R.
• Rfacebook - Access to Facebook API via R.

Parallel Computing

Packages for parallel computing.

• parallel - R started with release 2.14.0 which includes a new package parallel incorporating (slightly revised) copies of packages multicore and snow.
• Rmpi - Rmpi provides an interface (wrapper) to MPI APIs. It also provides interactive R slave environment.
• foreach - Executing the loop in parallel.
• SparkR - R frontend for Spark.

High Performance

Packages for making R faster.

• Rcpp - Rcpp provides a powerful API on top of R, make function in R extremely faster.
• Rcpp11 - Rcpp11 is a complete redesign of Rcpp, targetting C++11.
• compiler - speeding up your R code using the JIT

Language API

Packages for other languages.

• rJava - Low-level R to Java interface.
• jvmr - Integration of R, Java, and Scala.
• rJython - R interface to Python via Jython.
• rPython - Package allowing R to call Python.
• runr - Run Julia and Bash from R.
• RJulia - R package Call Julia.
• RinRuby - a Ruby library that integrates the R interpreter in Ruby.
• R.matlab - Read and write of MAT files together with R-to-MATLAB connectivity.
• RcppOctave - Seamless Interface to Octave and Matlab.
• RSPerl - A bidirectional interface for calling R from Perl and Perl from R.
• V8 - Embedded JavaScript Engine.
• htmlwidgets - Bring the best of JavaScript data visualization to R.
• rpy2 - Python interface for R.

Database Management

Packages for managing data.

• RODBC - ODBC database access for R.
• DBI - Defines a common interface between the R and database management systems.
• elastic - Wrapper for the Elasticsearch HTTP API
• mongolite - Streaming Mongo Client for R
• RMySQL - R interface to the MySQL database.
• ROracle - OCI based Oracle database interface for R.
• RPostgreSQL - R interface to the PostgreSQL database system.
• RSQLite - SQLite interface for R
• RJDBC - Provides access to databases through the JDBC interface.
• rmongodb - R driver for MongoDB.
• rredis - Redis client for R.
• RCassandra - Direct interface (not Java) to the most basic functionality of Apache Cassanda.
• RHive - R extension facilitating distributed computing via Apache Hive.
• RNeo4j - Neo4j graph database driver.

Machine Learning

Packages for making R cleverer.

• AnomalyDetection - AnomalyDetection R package from Twitter.
• ahaz - Regularization for semiparametric additive hazards regression.
• arules - Mining Association Rules and Frequent Itemsets
• bigrf - Big Random Forests: Classification and Regression Forests for Large Data Sets
• bigRR - Generalized Ridge Regression (with special advantage for p >> n cases)
• bmrm - Bundle Methods for Regularized Risk Minimization Package
• Boruta - A wrapper algorithm for all-relevant feature selection
• BreakoutDetection - Breakout Detection via Robust E-Statistics from Twitter.
• bst - Gradient Boosting
• CausalImpact - Causal inference using Bayesian structural time-series models.
• C50 - C5.0 Decision Trees and Rule-Based Models
• caret - Classification and Regression Training
• Clever Algorithms For Machine Learning
• CORElearn - Classification, regression, feature evaluation and ordinal evaluation
• CoxBoost - Cox models by likelihood based boosting for a single survival endpoint or competing risks
• Cubist - Rule- and Instance-Based Regression Modeling
• e1071 - Misc Functions of the Department of Statistics (e1071), TU Wien
• earth - Multivariate Adaptive Regression Spline Models
• elasticnet - Elastic-Net for Sparse Estimation and Sparse PCA
• ElemStatLearn - 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 - Evolutionary Learning of Globally Optimal Trees
• FSelector - A feature selection framework, based on subset-search or feature ranking approches.
• frbs - Fuzzy Rule-based Systems for Classification and Regression Tasks
• GAMBoost - Generalized linear and additive models by likelihood based boosting
• gamboostLSS - Boosting Methods for GAMLSS
• gbm - Generalized Boosted Regression Models
• glmnet - Lasso and elastic-net regularized generalized linear models
• glmpath - L1 Regularization Path for Generalized Linear Models and Cox Proportional Hazards Model
• GMMBoost - Likelihood-based Boosting for Generalized mixed models
• grplasso - Fitting user specified models with Group Lasso penalty
• grpreg - Regularization paths for regression models with grouped covariates
• h2o - Deeplearning, Random forests, GBM, KMeans, PCA, GLM
• hda - Heteroscedastic Discriminant Analysis
• Introduction to Statistical Learning
• ipred - Improved Predictors
• kernlab - kernlab: Kernel-based Machine Learning Lab
• klaR - Classification and visualization
• kohonen - Supervised and Unsupervised Self-Organising Maps.
• lars - Least Angle Regression, Lasso and Forward Stagewise
• lasso2 - L1 constrained estimation aka ‘lasso’
• LiblineaR - Linear Predictive Models Based On The Liblinear C/C++ Library
• LogicReg - Logic Regression
• maptree - Mapping, pruning, and graphing tree models
• mboost - Model-Based Boosting
• Machine Learning For Hackers
• mvpart - Multivariate partitioning
• ncvreg - Regularization paths for SCAD- and MCP-penalized regression models
• nnet - eed-forward Neural Networks and Multinomial Log-Linear Models
• oblique.tree - Oblique Trees for Classification Data
• pamr - Pam: prediction analysis for microarrays
• party - A Laboratory for Recursive Partytioning
• partykit - A Toolkit for Recursive Partytioning
• penalized - L1 (lasso and fused lasso) and L2 (ridge) penalized estimation in GLMs and in the Cox model
• penalizedLDA - Penalized classification using Fisher's linear discriminant
• penalizedSVM - Feature Selection SVM using penalty functions
• quantregForest - quantregForest: Quantile Regression Forests
• randomForest - randomForest: Breiman and Cutler's random forests for classification and regression.
• randomForestSRC - randomForestSRC: Random Forests for Survival, Regression and Classification (RF-SRC).
• rattle - Graphical user interface for data mining in R.
• rda - Shrunken Centroids Regularized Discriminant Analysis
• rdetools - Relevant Dimension Estimation (RDE) in Feature Spaces
• REEMtree - Regression Trees with Random Effects for Longitudinal (Panel) Data
• relaxo - Relaxed Lasso
• rgenoud - R version of GENetic Optimization Using Derivatives
• rgp - R genetic programming framework
• Rmalschains - Continuous Optimization using Memetic Algorithms with Local Search Chains (MA-LS-Chains) in R
• rminer - Simpler use of data mining methods (e.g. NN and SVM) in classification and regression
• ROCR - Visualizing the performance of scoring classifiers
• RoughSets - Data Analysis Using Rough Set and Fuzzy Rough Set Theories
• rpart - Recursive Partitioning and Regression Trees
• RPMM - Recursively Partitioned Mixture Model
• RSNNS - Neural Networks in R using the Stuttgart Neural Network Simulator (SNNS)
• RWeka - R/Weka interface
• RXshrink - RXshrink: Maximum Likelihood Shrinkage via Generalized Ridge or Least Angle Regression
• sda - Shrinkage Discriminant Analysis and CAT Score Variable Selection
• SDDA - Stepwise Diagonal Discriminant Analysis
• SuperLearner and subsemble - Multi-algorithm ensemble learning packages.
• svmpath - svmpath: the SVM Path algorithm
• tgp - Bayesian treed Gaussian process models
• tree - Classification and regression trees
• varSelRF - Variable selection using random forests
• xgboost - eXtreme Gradient Boosting Tree model, well known for its speed and performance.

Natural Language Processing

Packages for Natural Language Processing.

• tm - A comprehensive text mining framework for R.
• openNLP - Apache OpenNLP Tools Interface.
• koRpus - An R Package for Text Analysis.
• zipfR - Statistical models for word frequency distributions.
• tmcn - A Text mining toolkit for international characters especially for Chinese.
• Rwordseg - Chinese word segmentation.
• NLP - Basic functions for Natural Language Processing.
• LDAvis - Interactive visualization of topic models.
• topicmodels - 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 - Extracts sentiment from text using three different sentiment dictionaries.

Bayesian

Packages for Bayesian Inference.

• coda - Output analysis and diagnostics for MCMC.
• mcmc - Markov Chain Monte Carlo.
• MCMCpack - Markov chain Monte Carlo (MCMC) Package.
• R2WinBUGS - Running WinBUGS and OpenBUGS from R / S-PLUS.
• BRugs - R interface to the OpenBUGS MCMC software.
• rjags - R interface to the JAGS MCMC library.
• rstan - R interface to the Stan MCMC software.

Finance

Packages for dealing with money.

• quantmod - Quantitative Financial Modelling & Trading Framework for R.
• TTR - Functions and data to construct technical trading rules with R.
• PerformanceAnalytics - Econometric tools for performance and risk analysis.
• zoo - S3 Infrastructure for Regular and Irregular Time Series.
• xts - eXtensible Time Series.
• tseries - Time series analysis and computational finance.
• fAssets - Analysing and Modelling Financial Assets.

Bioinformatics

Packages for processing biological datasets.

• Bioconductor - Tools for the analysis and comprehension of high-throughput genomic data.
• genetics - Classes and methods for handling genetic data.
• gap - An integrated package for genetic data analysis of both population and family data.
• ape - Analyses of Phylogenetics and Evolution.
• pheatmap - Pretty heatmaps made easy.

Network Analysis

Packages to construct, analyze and visualize network data.

• igraph - A collection of network analysis tools.
• network - Basic tools to manipulate relational data in R.
• sna - Basic network measures and visualization tools.
• networkDynamic - Support for dynamic, (inter)temporal networks.
• ndtv - Tools to construct animated visualizations of dynamic network data in various formats.
• statnet - The project behind many R network analysis packages.
• ergm - Exponential random graph models in R.
• latentnet - Latent position and cluster models for network objects.
• tnet - Network measures for weighted, two-mode and longitudinal networks.
• rgexf - Export network objects from R to GEXF, for manipulation with network software like Gephi or Sigma.

R Development

Packages for packages.

• devtools - Tools to make an R developer's life easier.
• testthat - An R package to make testing fun.
• R6 - simpler, faster, lighter-weight alternative to R's built-in classes.
• pryr - Make it easier to understand what's going on in R.
• roxygen - Describe your functions in comments next to their definitions.
• lineprof - Visualise line profiling results in R.
• packrat - Make your R projects more isolated, portable, and reproducible.
• installr - Functions for installing softwares from within R (for Windows).
• import - An import mechanism for R.
• Rocker - R configurations for Docker.
• drat - Creation and use of R repositories on GitHub or other repos.
• covr - Test coverage for your R package and (optionally) upload the results to coveralls or codecov.
• lintr - Static code analysis for R to enforce code style.

Other Interpreters

Alternative R engines.

• renjin - a JVM-based interpreter for R.
• pqR - a "pretty quick" implementation of R
• fastR - FastR is an implementation of the R Language in Java atop Truffle and Graal.
• riposte - a fast interpreter and JIT for R.
• TERR - TIBCO Enterprise Runtime for R.
• RRE - Revolution R Enterprise.
• CXXR - Refactorising R into C++.

Learning R

Packages for Learning R.

• swirl - An interactive R tutorial directly in your R console.

Resources

Where to discover new R-esources.

Websites

• R-project - The R Project for Statistical Computing.
• R Bloggers - There are people scattered across the Web who blog about R. This is simply an aggregator of many of those feeds.
• DataCamp - Learn R data analytics online.
• Quick-R - An excellent quick reference.
• Advanced R - An in-progress book site for Advanced R.
• CRAN Task Views - Task Views for CRAN packages.
• The R Programming Wikibook - A collaborative handbook for R.
• R-users - A job board for R users (and the people who are looking to hire them)
• R Cookbook - A problem-oriented website that supports the R Graphics Cookbook.
• tryR - A quick course for getting started with R.

Books

• The Art of R Programming - It's a good resource for systematically learning fundamentals such as types of objects, control statements, variable scope, classes and debugging in R.
• Free Books - CRAN Contributed Documentation in many languages.
• R Cookbook - A quick and simple introduction to conducting many common statistical tasks with R.
• Books written as part of the Johns Hopkins Data Science Specialization:
  • Exploratory Data Analysis with R - Basic analytical skills for all sorts of data in R.
  • R Programming for Data Science - More advanced data analysis that relies on R programming.
  • Report Writing for Data Science in R - R-based methods for reproducible research and report generation.
• R Packages - A book (in paper and website formats) on writing R packages.
• R in Action - 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! - 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.
• R for SAS and SPSS users - An excelllent resource for users already familiar with SAS or SPSS.
• An Introduction to R - A very good introductory text on R, also covers some advanced topics.

Reference Cards

• R Reference Card 2.0 - Material from R for Beginners by permission of Emmanuel Paradis (Version 2 by Matt Baggott).
• Data Mining Refcard - R Reference Card for Data Mining.
• Regression Analysis Refcard - R Reference Card for Regression Analysis.
• Reference Card for ESS - Reference Card for ESS.
• R Markdown Cheat sheet - Quick reference guide for writing reports with R Markdown.
• Shiny Cheat sheet - Quick reference guide for building Shiny apps.
• ggplot2 Cheat sheet - Quick reference guide for data visualisation with ggplot2.
• devtools Cheat sheet - Quick reference guide to package development in R.

MOOCs

Massive open online courses.

• The Analytics Edge - Hands-on introduction to data analysis with R from MITx.
• Johns Hopkins University Data Science Specialization - 9 courses including: Introduction to R, literate analysis tools, Shiny and some more.
• HarvardX Biomedical Data Science - Introduction to R for the Life Sciences.
• Explore Statistics with R - Covers introduction, data handling and statistical analysis in R.

Other Awesome Lists

• awesome-awesomeness
• lists

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