Road2R

A curated list of amazingly awesome R libraries, resources and package collection.

Contributing Information

Please see author's details.
Collaboration and pull requests are always welcomed!

Workflow for Data Science

Workflow of Text Mining

Workflow of R Package Development

Workflow of Typical Modeling Process

Table of Contents

• Road2R
  • Contributing Information
  • Workflow for Data Science
  • Workflow of Text Mining
  • Workflow of R Package Development
  • Workflow of Typical Modeling Process
  • Table of Contents
    • Data manipulation
    • Visualization
    • Import Data
    • Network graph
    • Interactive Graphics
    • Word cloud
    • Text mining
    • Machine learning
    • Time series
    • Markdown
    • Development in R
    • Online blogs
    • Learning materials
    • Cheatsheet
    • Shiny
    • Bioinformatics
    • Single-cell

Data manipulation

Packages that enable you to manipulate data

• dplyr - a grammar of data manipulation.
• tibble - efficient display structure for tabular data.
• dbplyr - A 'dplyr' Back End for Databases.
• plyr - the split-apply-combine paradigm for R.
• tidyr - Easily Tidy Data with 'spread()' and 'gather()' Functions.
• jsonlite - A Robust, High Performance JSON Parser and Generator for R.
• stringr - Simple, Consistent Wrappers for Common String Operations.
• stringi - Character String Processing Facilities.
• data.table - Extension of "data.frame".
• magrittr - A Forward-Pipe Operator for R.
• reshape2 - Flexibly Reshape Data: A Reboot of the Reshape Package.
• forcats - Tools for Working with Categorical Variables (Factors).
• broom - Convert Statistical Analysis Objects into Tidy Data Frames.
• Matrix - Sparse and Dense Matrix Classes and Methods.
• lubridate - Make Dealing with Dates a Little Easier.
• xts - tools for time series based data.
• hashmap - The Faster Hash Map.
• rlist - A Toolbox for Non-Tabular Data Manipulation.
• yaml - Methods to Convert R Data to YAML and Back.
• xml2 - Parse XML.
• lobstr - lobstr provides tools in the same vein as str(), which allow you to dig into the detail of an object.
• reticulate - Interface to 'Python'.
• moderndive - An R package of datasets and wrapper functions for tidyverse-friendly introductory linear regression.

Visualization

Packages that enable you to plot

• ggplot2 - Create Elegant Data Visualisations Using the Grammar of Graphics.
• gganinate - A Grammar of Animated Graphics.
• ggedit - Interactive 'ggplot2' Layer and Theme Aesthetic Editor.
• ggfittext - Fit Text Inside a Box in 'ggplot2'.
• ggforce - Accelerating 'ggplot2'.
• gggenes - Draw Arrow Maps in 'ggplot2'.
• ggnetwork - Geometrics to Plot Networks with ''ggplot2''.
• ggmap - Spatial Visualization with ''ggplot2''.
• ggpubr - ''ggplot2'' Based Publication Ready Plots.
• ggrepel - Automatically Position Non-Overlapping Text Labels with 'ggplot2'.
• ggridge - Ridgeline Plots in 'ggplot2'.
• ggsci - Scientific Journal and Sci-Fi Themed Color palettes for 'ggplot2'.
• ggsignif - Significance Brackets for 'ggplot2'.
• ggthemes - Extra Themes, Scales and Geoms for 'ggplot2'.
• ggvis - Interactive Grammar of Graphics.
• cowplot - Streamlined Plot Theme and Plot Annotations for 'ggplot2'.
• treemapify - Draw Treemaps in 'ggplot2'.
• ggalluvial - Alluvial Diagrams in 'ggplot2'.
• GGally - Extension to 'ggplot2'.
• alluvial - Alluvial Diagrams.
• corrplot - Visualization of a Correlation Matrix.
• arulesViz - Visualizing Association Rules and Frequent Itemsets.
• gplots - Various R Programming Tools for Plotting Data.
• rpart.plot - Plot 'rpart' Models: An Enhanced Version of 'plot.rpart'.
• survival - Survival Analysis.
• pheatmap - Pretty Heatmap.
• ROCR - Visualization the Performance of Scoring Classifiers.
• pROC - Display and Analyze ROC Curves.
• ggstance - Horizontal versions of common plots.
• ggExtra - Marginal histograms for a plot.
• patchwork - Simple to combine separate ggplots into the same graphic.

Import Data

Packages that enable you to import, load and save data

• readr - Read Rectangular Text Data.
• openxlsx - Read, Write and Edit XLSX Files
• readxl - Read Excel Files.
• clipr - Read and Write from the System Clipboard.
• data.table - Extension of "data.frame".
• feather - R Bindings to the Feather 'API'.
• haven - Import and Export 'SPSS', 'Stata' and 'SAS' Files.
• httr - Tools for Working with URLs and HTTP.
• rvest - Easily Harvest (Scrape) Web Pages.
• DBI - R Database Interface
• RMySQL - MySQL driver for DBI
• RPostgres - Postgres driver for DBI
• RSQLite - SQlite driver for DBI
• git2r - Provides Access to Git Repositories.
• RCurl - General Network (HTTP/FTP/...) Client Interface for R.

Network graph

packages that enable you to visualize network

• igraph - Network Analysis and Visualization.
• ggnetwork - Geometrics to Plot Networks with ''ggplot2''.
• network3D - D3 JavaScript Network Graphs from R.
• network - Classes for Relational Data.
• sna - Tools for Social Network Analysis.
• snow - Simple Network of Workstations.
• visNetwork - Network Visualization using 'vis.js' Library.

Interactive Graphics

packages that enable you to make interactive graphics

• htmlwidgets - framework for creating JavaScript widgets with R.
• networkD3 - Interative d3 network graphs.
• d3heatmap - Interactive d3 heatmaps.
• DT - Interactive tables.
• REmap - Create html maps by Echarts.
• echarts4r - EChart htmlwidget.
• leaflet - Interactive maps.
• plotly - Interactive plots.
• Highcharter - Interactive Highcharts plots.
• visNetwork - Interactive network graphs.

Word cloud

packages that enable you to draw word cloud

• wordcloud - Word Clouds.
• wordcloud2 - Create Word Cloud by 'htmlwidget'.

Text mining

packages that enable you to do text mining

• tidytext - Text Mining using 'dplyr', 'ggplot2', and Other Tidy Tools.
• widyr - Widen, Process, then Re-Tidy Data.
• tm - Text Mining Package.
• NLP - Natural Language Processing Infrastructure.
• janeaustenr - Jane Austen's Complete Novels.

Machine learning

packages that enable you to deploy machine learning algorithms

• caret - Classification and Regression Training.
• xgboost - Extreme Gradient Boosting.
• glmnet - Lasso and Elastic-Net Regularized Generalized Linear Models.
• e1071 - Misc Functions of the Department of Statistics, ProbabilityTheory Group (Formerly: E1071), TU Wien.
• arules - Mining Association Rules and Frequent Itemsets.
• car - Companion to Applied Regression.
• igraph - PageRank algotirhm.
• kernlab - Kernel-Based Machine Learning Lab.
• modelr - Modelling Functions that Work with the Pipe.
• gbm - gradient boosted regression models.
• nlme - Linear and Nonlinear Mixed Effects Models.
• randomForest - Breiman and Cutler's Random Forests for Classification and Regression.
• rpart - Recursive Partitioning and Regression Trees.
• boot - Bootstrap Functions (Originally by Angelo Canty for S).
• gbm - gradient boosted regression models.
• nnet - Feed-Forward Neural Networks and Multinomial Log-Linear Models.
• neuralnet - Training of Neural Networks.
• h2o - parallel distributed machine learning algorithms.
• mlr - Machine Learning in R.
• ggpomological - Pomological Colors.
• visdat - Preliminary Exploratory Visualisation of Data.
• lares - R Package for Analytics and Machine Learning.
• mlr3 - A clean, object-oriented rewrite of mlr.
• mlrCPO - Composable Preprocessing Operators for MLR.

Time series

packages that enable you to play with time

• xts - eXtensible Time Series.
• forecast - Forecasting Functions for Time Series and Linear Models.
• zoo - S3 Infrastructure for Regular and Irregular Time Series (Z's Ordered Observations).
• tsfeatures - The R package tsfeatures provides methods for extracting various features from time series data.

Markdown

packages that enable you to write markdwon in R

• rmarkdown - Dynamic Documents for R.
• rsconnect - Deployment Interface for R Markdown Documents and Shiny Applications.
• knit - A General-Purpose Package for Dynamic Report Generation in R.
• tinytex - tinytex: Helper Functions to Install and Maintain TeX Live, and Compile LaTeX Documents.

Development in R

packages that enable you to develop your own project

• devtools - Tools to Make Developing R Packages Easier.
• Rcpp - Seamless R and C++ Integration.
• testthat - Unit Testing for R.
• roxygen2 - In-Line Documentation for R.
• bindrcpp - An 'Rcpp' Interface to Active Bindings.
• RStudio Desktop IDE - IDE.
• RStudio Server Open Source - server IDE.
• Anaconda - The World's Most Popular Python/R Data Science Platform.
• Visual Studio Code - Open source code editor.
• goodpractice - Advice on R Package Building.
• usethis - Automate Package and Project Setup.

Online blogs

Online blogs.

• RPubs - Easy web publishing from R.
• Shinyapps - Share your Shiny Applications Online.
• R-bloggers - R news and tutorials contributed by (750) R bloggers.
• RWeekly - Weekly Updates from the Entire R Community.
• RDocumentation - Search all 16,944 CRAN, Bioconductor and GitHub packages.
• Rstudio Community - The Rstudio Community.
• 生信爱好者周刊 - 生信爱好者周刊，Weekly of bioinformatics lovers.

Learning materials

Online learning materials

• Writing R extension - The online pdf of telling you how to write your own packages.
• R packages - This is the book site for “R packages”. It was published with O’Reilly in April 2015.
• R for Data Science - This is the website for “R for Data Science”.
• Text Mining with R - A Tidy Approach - This is the website for Text Mining with R.
• R Start Here - A guide to some of the most useful R Packages that we know about.
• ggplot2 Doc - Reference of ggplot2.
• Plotly for R - plotly for R.
• Efficient R programming - Efficient R programming by Colin Gillespie and Robin Lovelace.
• Advanced R - Advanced R.
• Advanced R Solutions - Advanced R Solutions.
• bookdown: Authoring Books and Technical - Documents with R Markdown.
• Geocomputation with R - Geocomputation with R.
• knitr - knitr: elegant, flexible, and fast dynamic report generation with R.
• Machine Learning with TensorFlow(R version) - This repository is for practicing R tensorflow modeling exercises.
• R的极客理想-高级开发篇 - R的极客理想-高级开发篇.
• swirl - swirl is a platform for learning (and teaching) statistics and R simultaneously and interactively.
• stats337 - Stats 337: Readings in Applied Data Science.
• Data Science in Education Book - Book on data science (or data analysis) in education using R.
• datascience-box - Data Science Course in a Box.
• R語言翻轉教室 - 全中文R語言互動式教材.
• Statistical Rethinking with brms ggplot2 and the tidyverse - Statistical Rethinking with brms, ggplot2, and the tidyverse.
• DataScienceR - a curated list of R tutorials for Data Science, NLP and Machine Learning.
• MLwR - Machine Learning with R.
• ml-course-r - R code for the assignments of Ng's Coursera machine learning course.
• Book on data science (or data analysis) in education using R - Book on data science (or data analysis) in education using R.
• R Workshops for Beginners - No programming experience required!
• Shiny Tutorial in Chinese - Shiny中文教程.
• R语言教程 - R Tutorial From Peking University (Mandarin Chinese).
• Tidy evaluation - This guide is now superseded by more recent efforts at documenting tidy evaluation in a user-friendly way.
• DIYtranscriptomics - A full course on the analysis of RNA-seq data using R programming environment and the Bioconductor suite of software packages – a versatile and robust collection of tools for bioinformatics, statistics, and plotting.

Cheatsheet

Cheatsheets of Rstudio packages

• RStudio IDE Cheat Sheet - RStudio IDE Cheat Sheet.
• R Markdown Cheat Sheet - R Markdown Cheat Sheet.
• Shiny - Shiny Cheat Sheet.
• Package Development Cheat Sheet - Package Development Cheat Sheet.
• Data Import Cheat Sheet - Data Import Cheat Sheet.
• Data Transformation Cheat Sheet - Data Transformation Cheat Sheet(dplyr).
• Data Visualization Cheat Sheet - Data Visualization Cheat Sheet with ggplot2.
• Apply Functions Cheat Sheet with purrr - Apply Functions Cheat Sheet.
• Deep Learning with Keras Cheat Sheet - Deep Learning with Keras Cheat Sheet.
• Sparklyr Cheat Sheet - Sparklyr Cheat Sheet.
• Work with Strings - Work with Strings Cheat Sheet.
• Dates and times with lubridate - Dates and Times Cheat Sheet.
• Advanced R - Environments, data Structures, Functions, Subsetting and more by Arianne Colton and Sean Chen.
• Caret - Modeling and Machine Learning in R with the caret package by Max Kuhn.
• data.table - Data manipulation with data.table, cheatsheet by Erik Petrovski.
• Base R - Base R.
• cartography - Thematic maps with spatial objects by Timothée Giraud.
• estimatr - Fast, robust estimators for common models.
• eurostat - R tools to access the eurostat database, by rOpenGov.
• h2o - The R interface to h20’s algorithms for big data and parallel computing.
• How big is your graph? - Graph sizing with base R.
• LaTeX - A reference to the LaTeX typesetting language, useful in combination with knitr and R Markdown.
• leaflet - Interactive maps in R with leaflet, by Kejia Shi.
• Machine Learning Modelling - A tabular guide to machine learning algorithms in R, by Arnaud Amsellem.
• mlr - The mlr package offers a unified interface to R’s machine learning capabilities.
• Mosaic - The mosaic package is for teaching mathematics, statistics, computation and modeling.
• nardl - The nardl package estimates the nonlinear cointegrating autoregressive distributed lag model.
• xplain - Explain statistical functions with XML files and xplain.
• Parallel Computation - Parallel computing in R with the parallel, foreach, and future packages. By Ardalan Mirshani.
• quanteda - Quantitative Analysis of Textual Data in R with the quanteda package by Stefan Müller and Kenneth Benoit.
• randomizr - Automate random assignment and sampling with randomizr. By Alex Coppock.
• Regular Expressions - Basics of regular expressions and pattern matching in R by Ian Kopacka.
• Simple Features (sf) - Tools for working with spatial vector data: points, lines, polygons, etc. Cheatsheet by Ryan Garnett.
• sjmisc - dplyr friendly Data and Variable Transformation, by Daniel Lüdecke.
• survminer - Elegant survival plots, by Przemyslaw Biecek.
• Syntax Comparison - Three code styles compared: $, formula, and tidyverse. By Amelia McNamara.
• Teach R - Concise advice on how to teach R or anything else. By Adi Sarid.

Shiny

Shiny Applications Development Materials

• Shiny - Web Application Framework for R.
• Shiny Tutorials - Shiny Tutorials.
• shinyAce - Ace editor bindings to enable a rich text editing environment within Shiny.
• shinyBS - Twitter Bootstrap Components for Shiny.
• shinydashboard - Create Dashboards with 'Shiny'.
• shinyjs - Easily Improve the User Experience of Your Shiny Apps in Seconds.
• Shiny Developer Conference - Shiny Developer Conference.
• ShinyTrials - Shiny applications with shiny & shinydashboard.
• shiny.io - Share your Shiny Applications Online.
• Shiny Applications Examples - Examples of interactive web apps using shiny.
• shiny server - Host Shiny applications over the web.
• Mastering Shiny - Mastering Shiny: a book.
• Awesome Shiny Extensions - Awesome Shiny Extensions.

Bioinformatics

Bioinformatics Tools Implemented in R

• CMSclassifier - An R package and an example data set are provided to run the CMSclassifier.
• CMScaller - an R package for consensus molecular subtyping of colorectal cancer pre-clinical models.
• genefu - R package providing various functions relevant for gene expression analysis with emphasis on breast and ovarian cancers.
• CancerSubtypes - Cancer subtypes identification, validation and visualization based on multiple genomic data sets.
• consensusOV - Gene expression-based subtype classification for high-grade serous ovarian cancer.
• ConsensusClusterPlus - ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking.
• WGCNA - WGCNA: an R package for weighted correlation network analysis.
• ImmuLncRNA - The R package 'ImmulancRNA' contains 3 method which can caculate the tumor purity, partial correlation coefficient and identify the lncRNA-pathway pairs.
• randomForestSRC - R-package for random forest analysis of regression, classification, survival and more.
• plsRcox - Partial Least Squares Regression for Cox Models and Related Techniques.
• superpc - Supervised Principal Components.
• CoxBoost - Cox models by likelihood based boosting for a single survival endpoint or competing risks.
• survivalsvm - Survival Support Vector Analysis.
• GSVA - Gene Set Variation Analysis for microarray and RNA-seq data.
• isomiRseeker - Detect miRNA isoforms from small RNA-seq data.
• TCGAbiolinks - TCGAbiolinks - An R/Bioconductor package for integrative analysis with TCGA data.
• clusterProfiler - clusterProfiler: an R package for comparing biological themes among gene clusters.
• glmnet - glmnet: Lasso and Elastic-Net Regularized Generalized Linear Models.
• OmicCircos - an R application and package for generating high-quality circular plots for omics data.
• maftools - maftools - An R package to summarize, analyze and visualize MAF files.
• RTN - RTN: Reconstruction of Transcriptional regulatory Networks and analysis of regulons.
• RedeR - Interactive visualization and manipulation of nested networks.
• GCclassifier - GCclassifier: an R package for the prediction of molecular subtypes of gastric cancer.
• immunedeconv - an R package for unified access to computational methods for estimating immune cell fractions from bulk RNA sequencing data.
• NbClust - NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set.
• BLCAsubtyping - Transcriptomic tools to classify bladder tumours according to six published molecular classifications.
• consensusMIBC - This package implements a nearest-centroid transcriptomic classifier, that assigns class labels according to the consensus molecular classification of Muscle-Invasive Bladder Cancer (Manuscript submitted). The consensus classification identifies 6 molecular classes : Luminal Papillary (LumP), Luminal Non Specified (LumNS), Luminal Unstable (LumU), Stroma-rich, Basal/Squamous (Ba/Sq), Neuroendocrine-like (NE-like).
• classifyNMIBC - This package implements a Pearson nearest-centroid classifier that assigns class labels to single samples according to the four transcriptomic UROMOL2021 classes of non-muscle-invasive bladder cancer (NMIBC): class 1, class 2a, class 2b and class 3.
• oncoPredict - Predict Response from Expression Data and Identify Cell line/Clinical Targets and Trends.
• Mime - Mime: A flexible machine-learning framework to construct and visualize models for clinical characteristics prediction and feature selection.

Single-cell

Singl-cell RNA-seq & ATAC in R

• Signac - Signac is a comprehensive R package for the analysis of single-cell chromatin data. Signac includes functions for quality control, normalization, dimension reduction, clustering, differential activity, and more.
• Seurat - Seurat is an R toolkit for single cell genomics, developed and maintained by the Satija Lab at NYGC.
• ccRemover - Removes the Cell-Cycle Effect from Single-Cell RNA-Sequencing Data.
• cellTree - Cell population analysis and visualization from single cell RNA-seq data using a Latent Dirichlet Allocation model.
• SingleR - Reference-Based Single-Cell RNA-Seq Annotation.
• CellChat - CellChat is an R package designed for inference, analysis, and visualization of cell-cell communication from single-cell data. CellChat aims to enable users to identify and interpret cell-cell communication within an easily interpretable framework, with the emphasis of clear, attractive, and interpretable visualizations.
• Harmony - Fast, sensitive and accurate integration of single-cell data with Harmony.