Below is a list of free books, tutorials, packages, cheat sheets and other material to learn programming in R and improve your workflow. There are separate overviews for Python resources, SQL resources, and general Data Science, Machine Learning, & Statistics resources. If you have additions, please comment below or contact me!

Completely new to R? → Start here!

LAST UPDATED: 26-03-2018

R Basics

Cheat Sheets:

  • Base R cheat sheet by Mhairi McNeill***
  • Base R Functions cheat sheet by Tom Short
  • Basic R cheat sheet by Quandl.com
  • R Function Abbreviations Explained by Jeromy Anglim
  • RStudio cheat sheet by RStudio
  • RStudio Keyboard Shortcuts by RStudio***
  • Data Management in R cheat sheet
  • data.table wide cheat sheet by DataCamp.com
  • data.table long cheat sheet by DataCamp.com
  • Advanced R cheat sheet by Arianne Colton & Sean Chen
  • tidyverse cheat sheet by DataCamp.com
  • Data Import cheat sheet by RStudio with readr, tibble, and tidyr
  • Factor Manipulation with forcats cheat sheet by Lise Vaudor
  • Data Transformation cheat sheet by Rstudio with dplyr
  • Data Transformation cheat sheet 2 by Daniel Lüdecke with dplyr and sjmisc
  • Data Wrangling cheat sheet by RStudio with dplyr and tidyr
  • Character String Manipulation cheat sheet by RStudio with stringr
  • Dates and Times cheat sheet by RStudio with lubridate
  • Split-Apply-Combine cheat sheet by Ernest Adrogue Calvera
  • purr Functional Programming cheat sheet by RStudio
  • Tidy Evaluation by Edwin Thoen
  • R Syntax Comparison cheat sheet by Amelia McNamara
  • RStudio cheat sheet GitHub

Introductory Books:

  • Introduction to R (R Core Team, 1999)
  • R Language Definition (Manual) (R Core Team, 2000)
  • Data Import/Export (R Core Team, 2000)
  • SimpleR (Verzani, 2001-2)
  • R for Beginners (Paradis, 2002)
  • Introduction to R (Spector, 2004)
  • Ecological Models and Data in R (Bolker, 2007)
  • Software for Data Analysis: Programming with R (Chambers, 2008)
  • Econometrics in R (Farnsworth, 2008)
  • The Art of R Programming (Matloff, 2009)
  • R in a Nutshell (Adler, 2010)
  • R in Action: Data Analysis and Graphics with R (Kabacoff, 2011)
  • R for Psychology Experiments and Questionnaires (Baron, 2011)
  • The R Inferno (Burns, 2011)
  • Cookbook for R (Chang, ???)
  • The R Book (Crawley, 2013)
  • Introduction to Statistical Thought (Lavine, 2013)
  • A (very) short introduction to R (Torfs & Bauer, 2014)***
  • Advanced R (Wickham, 2014)
  • Introduction to R (Vaidyanathan, 2014)
  • Learning statistics with R: A tutorial for psychology students and other beginners (Navarro, 2014)
  • Introduction to R (Venables, Smith, & R Core Team, 2017)
  • The R Language Definition (R Core Team, 2017)
  • YaRrr! The Pirate’s Guide to R (Phillips, 2017)***

Online Courses: Introductory & Advanced:

  • swirl()***
  • Try R by Code School
  • Learn R by R-Exercises
  • R Tutorial by Cyclismo & DataCamp
  • 100 Tutorials for Learning R
  • Introduction to R by DataCamp
  • YaRrr! The Pirate’s Guide to R (Video)
  • R for Cats
  • A Psychologist’s Guide to R (pdf) by Sean Chris Murphy
  • Social Sciences: Critically Analyze Research and Results Using R by Coursera
  • A Hands-on Introduction to Statistics with R by DataCamp.com
  • R Course in Statistics by PagePiccini.com
  • Mini courses in R by UCLA
  • Data Science: R Basics @edX
  • Introduction to R for Data Science @edX
  • Data Analysis and Visualization Using R @VarianceExplained
  • R Programming @Coursera*** by Roger Peng, Jeff Leek, & Brian Caffo
  • DIY: 37 Youtube R Tutorials Overview by Flavio Azevedo***

Style Guides:

  • Google’s R Style Guide
  • Advanced R Style Guide by Hadley Wickham
  • A guide for switching from base R to the tidyverse.

Data Visualization in R

  • R Graph Gallery & Code Examples***
  • Fundamentals of Data Visualization (Wilke, 2018)
  • R Base Plots Wiki Reference Guide
  • CRAN Task View – Graphics & Visualization
  • R Graphical Parameters Cheatsheet by Flowingdata.com

Colors

  • R Color Guide***
  • colourpicker – widget that allows users to choose colours
  • ggplot2 colour guide***
  • multicolored annotated text in ggplot2 by Andrew Whitby & Visuelle Data

Interactive / HTML widgets:

  • R HTML Widgets Gallery***
  • plotly – interactive plots
  • d3heatmap – interactive d3 heatmaps
  • DT – interactive tables
  • DiagrammeR – interactive diagrams
  • dygraphs – interactive time series plots
  • formattable – formattable data structures
  • ggvis – interactive ggplot2
  • Highcharter – interactive Highcharts plots
  • leaflet – interactive maps
  • metricsgraphics – interactive JavaScript bare-bones line, scatterplot and bar charts
  • networkD3 – interative d3 network graphs
  • scatterD3 – interactive scatterplots with d3
  • rbokeh – interactive Bokeh plots
  • rCharts – interactive Javascript charts
  • rcdimple – interactive JavaScript bar charts and others
  • rglwidget – interactive 3d plots
  • threejs – interactive 3d plots and globes
  • visNetwork – interactive network graphs
  • wordcloud2 – interface to wordcloud2.js.
  • timevis – interactive timelines

ggplot2

  • Code examples of top-50 ggplot2 visualizations***
  • ggplot2 Cheatsheet by RStudio
  • ggplot2 Quick Reference Guide 
  • ggplot2 Code Snippets
  • ggplot2 Code Snippets 2
  • A practical introduction with R and ggplot2 (Healy, 2017)
  • Data Vizualization: A practical introduction (Healy, 2018)
  • Hitchhiker’s Guide to ggplot2 in R (Burchell & Vargas, 2016)
  • Setting custom ggplot themes with ggthemr
  • Creating custom, reproducible color palettes by Simon Jackson
  • Rearranging values within ggplot2 facets
  • Combine plots using patchwork or cowplot
  • ggplot2 extensions overview***
    • ggthemes – plot style themes
    • hrbrthemes – opinionated, typographic-centric themes
    • ggmap – maps with Google Maps, Open Street Maps, etc.
    • ggiraph – interactive ggplots
    • ggstance – horizontal versions of common plots
    • GGally – scatterplot matrices
    • ggalt – additional coordinate systems, geoms, etc.
    • ggforce – additional geoms, etc.
    • ggrepel – prevent plot labels from overlapping
    • ggraph – graphs, networks, trees and more
    • ggpmisc – photo-biology related extensions
    • geomnet – network visualization
    • ggExtra – marginal histograms for a plot
    • gganimate – animations
    • ggspectra – tools for plotting light spectra
    • ggnetwork – geoms to plot networks
    • ggradar – radar charts
    • ggsurvplot (survminer) – survival curves
    • ggseas – seasonal adjustment tools
    • ggtech – style themes for plots
    • ggtern – ternary diagrams
    • ggTimeSeries – time series visualizations
    • ggtree – tree visualizations
    • treemapify – wilcox’s treemaps

Miscellaneous

  • coefplot – visualizes model statistics
  • circlize – circular visualizations for categorical data
  • quantmod – candlestick financial charts
  • colorspace – HSL based color palettes
  • viridis – Matplotlib viridis color pallete for R
  • munsell – Munsell color palettes for R.
  • Cairo – high-quality display output
  • igraph – Network Analysis and Visualization
  • lattice – Trellis graphics
  • tmap – thematic maps
  • trelliscopejs – interactive alternative for ggplot2::facet_wrap
  • rgl – interactive 3D plots
  • corrplot – graphical display of a correlation matrix
  • googleVis – Google Charts API
  • plotROC – interactive ROC plots
  • extrafont – fonts in R graphics
  • showtext – text using system fonts
  • animation – animated graphics using ImageMagick.
  • misc3d – 3d plots, isosurfaces, etc.
  • xkcd – xkcd style graphics
  • imager – CImg library to work with images

Dashboards & Shiny

  • Shiny Cheat Sheet by RStudio
  • More Shiny Resources by Rob Gilmore
  • More Shiny Resources for Statistics by Yingjie Hu
  • Shiny Tutorial
  • Building Shiny apps – an interactive tutorial by Dean Attali
  • flexdashboard – dashboard creation simplified
  • colourpicker – widget that allows users to choose colours.

Regular Expressions in R

    • R Regular Expression Cheatsheet by Lise Vaudor
    • R Regular Expression Cheatsheet
    • R Regular Expression Cheatsheet (page 2) by RStudio
    • Regular Expressions in R – Part 1: Introduction and base R functions
    • R Regular Expressions by Jon M. Calder in swirl()
    • R Regular Expression Video Tutorial by Roger Peng
    • General Regular Expression Cheatsheet
    • General Regular Expression Video Tutorial by Roger Peng
    • General Regular Expression Cheatsheet by OverAPI.com

R Markdown & Documentation

  • R Markdown Cheatsheet by RStudio
  • R Markdown Reference Guide by RStudio
  • R Markdown Basics
  • Markdown Tutorial by RStudio
  • Markdown Gallery by RStudio
  • The knitr book (Xie, 2015)
  • Creating slides with R Markdown (Video) by Brian Caffo
  • General Markdown Cheatsheet
  • blogdown: Creating Websites with R Markdown (Xie, Thomas, & Hill, 2018)
  • blogdown tutorials
  • Packages:
    • tidystats – automating updating of model statistics
    • papaja – preparing APA journal articles
    • blogdown – build websites with Markdown & Hugo
    • huxtable – create Excel, html, & LaTeX tables

R Database Linking

  • Tutorial: Database Queries with R
  • Introduction to sparklyr by DataCamp
  • dbplot – leverages dplyr to process calculations of plot inside database

Functional Programming

  • Functional Programming lecture by Duke University
  • Functional Programming: purrr tutorial by Jenny Bryan
  • purr Functional Programming cheat sheet by RStudio
  • Writing Function in R by DataCamp.com

Statistical Learning, Machine Learning, & Data Science

  • Machine Learning with R: An Irresponsibly Fast Tutorial by Will Stanton***
  • CRAN Task View – Machine Learning & Statistical Learning
  • R Packages for Machine Learning by Joseph Misiti
  • Introduction to Data Science with R (Video)
  • Course: Introduction to R for Data Science @Microsoft
  • Course: Introduction to R for Data Science @FutureLearn by Hadley Wickham
  • Introduction to Statistical Learning (James, Witten, Hastie, & Tibshirani, 2013)
  • Course: Introduction to Statistical Learning (Hastie & Tibshirani, 2014)***
  • Data Science Live Book (Casas, 2017)
  • 100 Tutorials for Learning R
  • R Programming for Data Science (Peng, 2016)
  • R for Data Science (Grolemund & Wickham, 2017)***
  • Elements of Statistical Learning (Hastie, Tibshirani, & Friedman, 2001)
  • Statistical Foundations of Machine Learning (Bontempi & Taieb, 2017)
  • Machine Learning with R (Lantz, 2013)
  • R Functions for Regression Analysis Cheatsheet by Vito Ricci
  • Regression Models for Data Science in R (Caffo, 2015)
  • Applied Biostatistical Analyses using R (Cox, 2017)
  • Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery (Williams, 2011)
  • Machine Learning Algorithms R Implementation by Ajitesh Kumar
  • R Data Mining Reference Card
  • R Data Mining: Examples & Case Studies (Zhao, 2015)
  • Statistical modelling in R (Zhao, 2015) @RDataMining
  • Predictive modelling in R with caret
  • Machine Learning with caret cheat sheet by Max Kuhn
  • R sparklyr cheat sheet by RStudio
  • Machine Learning modelling cheat sheet by Arnaud Amsellem
  • Machine Learning with mlr cheat sheet by Aaron Coley
  • Deep Learning with keras cheat sheet by RStudio
  • R Interface to Keras

Natural Language Processing & Text Mining

  • Text Mining Tutorial with tm
  • Tidy Text Mining (Silges & Robinson, 2017) with tidytext
  • Text Analysis with R for Students of Literature (Jockers, 2014)
  • 100 Tutorials for Learning R
  • 21 Recipes for Mining Twitter Data (Rudis, 2017) with rtweet
  • Course: Introduction to Text Analytics with R @DataScienceDojo
  • Course: Twitter Text Mining and Social Network Analysis (Zhoa, 2016) @RDataMining with twitteR
  • List of resources for NLP & Text Mining by Stephen Thomas
  • Packages — for an overview: CRAN Task View – Natural Language Processing:
    • tm – text mining.
    • tidytext – text mining using tidyverse principles
    • quanteda –  framework for quantitative text analysis
    • gutenbergr – public domain works (free books to practice on)
    • corpora – statistics and data sets for corpus frequency data.
    • tau – Text Analysis Utilities
    • Sentiment140 – headache-free sentiment analysis
    • sentimentr – sentiment analysis using text polarity
    • openNLP – sentence detector, tokenizer, pos-tagger, shallow and full syntactic parser, named-entity detector, and maximum entropy models with OpenNLP.
    • cleanNLP – natural language processing via tidy data models
    • RSentiment – English lexicon-based sentiment analysis with negation and sarcasm detection functionalities.
    • RWeka – data mining tasks with Weka
    • wordnet – a large lexical database of English with WordNet .
    • stringi – language processing wrappers
    • textcat – provides support for n-gram based text categorization.
    • text2vec – text vectorization, topic modeling (LDA, LSA), word embeddings (GloVe), and similarities.
    • lsa – Latent Semantic Analysis
    • topicmodels -Latent Dirichlet Allocation (LDA) and Correlated Topics Models (CTM)
    • lda -Latent Dirichlet Allocation and related models

Time series

  • CRAN Task View – TimeSeries
  • R xts Cheatsheet
  • Forecasting: Principles and Practice (Hyndman & Athanasopoulos, 2017)
  • A little book of R for time series (tutorial)
  • Tutorials: Part 1, Part 2, Part 3, & Part 4 of tidy time series @Business-Science.io with tidyquant

Survival analysis

  • CRAN Task View – Survival
  • R Survival Analysis Cheatsheet by Przemysław Biecek
  • survival – functionality for survival and hazard models
  • ggsurvplot (survminer) – survival curves

Geographical / Spatial mapping

  • Making Maps with R (tutorial) with ggmaps, maps, and mapdata
  • Importing OpenStreetMap data (tutorial) with osmar 

Other applications

  • Image featurization
  • Introduction to Empirical Bayes (Robinson, ???)
  • CRAN Task View – Bayesian Inference

R Development, Optimization & Other

  • R Package Development Cheatsheet with devtools
  • Mastering Software Development in R (Peng, Kross, & Anderson, 2017)
  • Efficient R Programming (Gillespie & Lovelace, 2017)
  • Happy Git and GitHub for the useR (Jenny Bryan, 2017)
  • RStudio Addins by Dean Attali

R Help, Connect, & Inspiration

  • R Help Mailing List
  • R Seek – search engine for R-related websites
  • R site search – search engine for help files, manuals, and mailing lists
  • Nabble – mailing list archive and forum
  • R User Groups & Conferences
  • Stack Overflow – a FAQ for all your R struggles (programming)
  • Cross Validated – a FAQ for all your R struggles (statistics)
  • CRAN Task Views – discover new packages per topic
  • The R Journal – open access, refereed journal of R
  • Twitter: #rstats, RStudio, Hadley Wickham, Yihui Xie, Mara Averick, Julia Silge, Jenny Bryan, David Smith, Hilary Parker, R-bloggers
  • Facebook: R Users Psychology
  • Youtube: Ben Lambert, Roger Peng
  • Reddit: rstats, rstudio, statistics, machinelearning, dataisbeautiful

R Blogs

  • http://adamleerich.com
  • http://njtierney.github.io/
  • https://trinkerrstuff.wordpress.com
  • https://rollingyours.wordpress.com
  • https://www.r-statistics.com
  • https://beckmw.wordpress.com
  • http://rgraphgallery.blogspot.com
  • http://onertipaday.blogspot.com
  • https://learnr.wordpress.com
  • http://padamson.github.io
  • http://www.r-datacollection.com/blog/
  • http://www.thertrader.com
  • https://fronkonstin.com
  • https://nicercode.github.io
  • http://www.rblog.uni-freiburg.de
  • https://advanceddataanalytics.net
  • http://r4stats.com/blog/
  • http://blog.revolutionanalytics.com/
  • http://www.r-bloggers.com/
  • http://kbroman.org/blog/
  • https://juliasilge.com/blog/
  • http://andrewgelman.com/
  • http://www.statsblogs.com/author/eric-cai-the-chemical-statistician/
  • https://www.statmethods.net/
  • http://www.stats-et-al.com/search/label/R

 

R Jobs

  • R Jobs on StackOverflow.com
  • R Jobs on R-users.com
  • Jobs on Kaggle