A curated list of awesome R frameworks, packages and software. Inspired by awesome-machine-learning.
Integrated Development Environment
- RStudio - A powerful and productive user interface for R. Works great on Windows, Mac, and Linux.
- JGR - JGR (speak 'Jaguar') is a Java Gui for R.
- Emacs + ESS - Emacs Speaks Statistics is an add-on package for emacs text editors.
- StatET - An Eclipse based IDE (integrated development environment) 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.
Packages for cooking data.
- dplyr - Blazing 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.
- tidyr - Easily tidy data with spread and gather functions.
Packages for showing data.
- ggplot2 - An implementation of the Grammar of Graphics.
- ggvis - Interactive grammar of graphics for R.
- rCharts - Interactive JS Charts from R.
- 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.
Packages to surf the web.
- shiny - Easy interactive web applications with R.
- RCurl - General network (HTTP/FTP/...) client interface for R.
- httpuv - HTTP and WebSocket server library.
- XML - Tools for parsing and generating XML within R.
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.
- compliler - speeding up your R code using the JIT
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
Packages for managing data.
- RODBC - ODBC database access for R.
- DBI - Defines a common interface between the R and database management systems.
- 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.
Packages for making R cleverer.
- h2o - Deeplearning, Random forests, GBM, KMeans, PCA, GLM
- Clever Algorithms For Machine Learning
- Machine Learning For Hackers
- nnet - nnet: Feed-forward Neural Networks and Multinomial Log-Linear Models
- rpart - rpart: Recursive Partitioning and Regression Trees
- randomForest - randomForest: Breiman and Cutler's random forests for classification and regression
- lasso2 - lasso2: L1 constrained estimation aka ‘lasso’
- gbm - gbm: Generalized Boosted Regression Models
- e1071 - e1071: Misc Functions of the Department of Statistics (e1071), TU Wien
- tgp - tgp: Bayesian treed Gaussian process models
- rgp - rgp: R genetic programming framework
- arules - arules: Mining Association Rules and Frequent Itemsets
- frbs - frbs: Fuzzy Rule-based Systems for Classification and Regression Tasks
- e1071 - e1071: Misc Functions of the Department of Statistics (e1071), TU Wien
- rattle - rattle: Graphical user interface for data mining in R
- ahaz - ahaz: Regularization for semiparametric additive hazards regression
- arules - arules: Mining Association Rules and Frequent Itemsets
- bigrf - bigrf: Big Random Forests: Classification and Regression Forests for Large Data Sets
- bigRR - bigRR: Generalized Ridge Regression (with special advantage for p >> n cases)
- bmrm - bmrm: Bundle Methods for Regularized Risk Minimization Package
- Boruta - Boruta: A wrapper algorithm for all-relevant feature selection
- bst - bst: Gradient Boosting
- C50 - C50: C5.0 Decision Trees and Rule-Based Models
- caret - caret: Classification and Regression Training
- CORElearn - CORElearn: Classification, regression, feature evaluation and ordinal evaluation
- CoxBoost - CoxBoost: Cox models by likelihood based boosting for a single survival endpoint or competing risks
- Cubist - Cubist: Rule- and Instance-Based Regression Modeling
- e1071 - e1071: Misc Functions of the Department of Statistics (e1071), TU Wien
- earth - earth: Multivariate Adaptive Regression Spline Models
- elasticnet - elasticnet: Elastic-Net for Sparse Estimation and Sparse PCA
- ElemStatLearn - 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 - evtree: Evolutionary Learning of Globally Optimal Trees
- frbs - frbs: Fuzzy Rule-based Systems for Classification and Regression Tasks
- GAMBoost - GAMBoost: Generalized linear and additive models by likelihood based boosting
- gamboostLSS - gamboostLSS: Boosting Methods for GAMLSS
- gbm - gbm: Generalized Boosted Regression Models
- glmnet - glmnet: Lasso and elastic-net regularized generalized linear models
- glmpath - glmpath: L1 Regularization Path for Generalized Linear Models and Cox Proportional Hazards Model
- GMMBoost - GMMBoost: Likelihood-based Boosting for Generalized mixed models
- grplasso - grplasso: Fitting user specified models with Group Lasso penalty
- grpreg - grpreg: Regularization paths for regression models with grouped covariates
- hda - hda: Heteroscedastic Discriminant Analysis
- ipred - ipred: Improved Predictors
- kernlab - kernlab: Kernel-based Machine Learning Lab
- klaR - klaR: Classification and visualization
- lars - lars: Least Angle Regression, Lasso and Forward Stagewise
- lasso2 - lasso2: L1 constrained estimation aka ‘lasso’
- LiblineaR - LiblineaR: Linear Predictive Models Based On The Liblinear C/C++ Library
- LogicReg - LogicReg: Logic Regression
- maptree - maptree: Mapping, pruning, and graphing tree models
- mboost - mboost: Model-Based Boosting
- mvpart - mvpart: Multivariate partitioning
- ncvreg - ncvreg: Regularization paths for SCAD- and MCP-penalized regression models
- nnet - nnet: Feed-forward Neural Networks and Multinomial Log-Linear Models
- oblique.tree - oblique.tree: Oblique Trees for Classification Data
- pamr - pamr: Pam: prediction analysis for microarrays
- party - party: A Laboratory for Recursive Partytioning
- partykit - partykit: A Toolkit for Recursive Partytioning
- penalized - penalized: L1 (lasso and fused lasso) and L2 (ridge) penalized estimation in GLMs and in the Cox model
- penalizedLDA - penalizedLDA: Penalized classification using Fisher's linear discriminant
- penalizedSVM - 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 - rattle: Graphical user interface for data mining in R
- rda - rda: Shrunken Centroids Regularized Discriminant Analysis
- rdetools - rdetools: Relevant Dimension Estimation (RDE) in Feature Spaces
- REEMtree - REEMtree: Regression Trees with Random Effects for Longitudinal (Panel) Data
- relaxo - relaxo: Relaxed Lasso
- rgenoud - rgenoud: R version of GENetic Optimization Using Derivatives
- rgp - rgp: R genetic programming framework
- Rmalschains - Rmalschains: Continuous Optimization using Memetic Algorithms with Local Search Chains (MA-LS-Chains) in R
- rminer - rminer: Simpler use of data mining methods (e.g. NN and SVM) in classification and regression
- ROCR - ROCR: Visualizing the performance of scoring classifiers
- RoughSets - RoughSets: Data Analysis Using Rough Set and Fuzzy Rough Set Theories
- rpart - rpart: Recursive Partitioning and Regression Trees
- RPMM - RPMM: Recursively Partitioned Mixture Model
- RSNNS - RSNNS: Neural Networks in R using the Stuttgart Neural Network Simulator (SNNS)
- RWeka - RWeka: R/Weka interface
- RXshrink - RXshrink: Maximum Likelihood Shrinkage via Generalized Ridge or Least Angle Regression
- sda - sda: Shrinkage Discriminant Analysis and CAT Score Variable Selection
- SDDA - SDDA: Stepwise Diagonal Discriminant Analysis
- svmpath - svmpath: svmpath: the SVM Path algorithm
- tgp - tgp: Bayesian treed Gaussian process models
- tree - tree: Classification and regression trees
- varSelRF - varSelRF: Variable selection using random forests
- caret - Unified interface to ~150 ML algorithms in R.
- SuperLearner and subsemble - Multi-algorithm ensemble learning packages.
- Introduction to Statistical Learning
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.
Where to discover new R-esources.
- 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.
- CRAN Task Views - Task Views for CRAN packages.
- 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.
- 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.
Your contributions are always welcome!