Help yourself to these free books, tutorials, packages, cheat sheets, and many more materials for R programming. There’s a separate overview for handy R programming tricks . If you have additions, please comment below or contact me !
LAST UPDATED: 2020-08-24
Table of Contents (clickable)
Completely new to R? → Start learning here!
Introductory R
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 Data Technologies (Murrel, 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 (Navarro, 2014) Programming for Psychologists (Crump, 2014) IPSUR: Introduction to Probability and Statistics Using R (Kerns, 2014) Hands-On Programming with R (Grolemund, 2014)
Getting used to R, RStudio, and R Markdown (2016) Introduction to R (Venables, Smith, & R Core Team, 2017) The R Language Definition (R Core Team, 2017) Functional Programming and Unit Testing for Data Munging with R (Rodrigues, 2017) YaRrr! The Pirate’s Guide to R (Phillips, 2017) ***R for Data Science (Grolemund & Wickham, 2017) ***An Introduction to Statistical and Data Sciences via R (Ismay & Kim, 2018) by ModernDive Answering questions with data (Crump, 2018) Statistical Thinking for the 21st Century (Poldrack, 2018) R Notes for Professionals book (Goalkicker, 2018) Learning Statistics with R (Navarro, 2019) R Graphics Cookbook – 2nd edition (Chang, 2019) Introduction to Open Data Science (The Ocean Health Index Team, 2019) Data Science with R: A Resource Compendium (Monkman, 2019) R in Action: Third Edition (Kabacoff, 2019) A Practical Extension of Introductory Statistics in Psychology using R (Pongpipat, Miranda, & Kmiecik, 2019) R for Marketing Students (Samuel Franssens, ????)
Online Courses
Style Guides
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Advanced R
Package Development
Non-standard Evaluation
Functional Programming
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Cheat Sheets
Many of the above cheat sheets are hosted in the official RStudio cheat sheet overview .
Data Manipulation
Data Visualization
Colors
Interactive / HTML / JavaScript widgets
ggplot2
ggplot2 extensions
ggplot2 extensions overview *** ggthemes
– plot style themeshrbrthemes
– opinionated, typographic-centric themesggmap
– maps with Google Maps, Open Street Maps, etc.ggiraph
– interactive ggplotsgghighight
– highlight lines or values, see vignette ggstance
– horizontal versions of common plotsGGally
– scatterplot matricesggalt
– additional coordinate systems, geoms, etc.ggbeeswarm
– column scatter plots or voilin scatter plotsggforce
– additional geoms, see visual guide ggrepel
– prevent plot labels from overlappingggraph
– graphs, networks, trees and moreggpmisc
– photo-biology related extensionsgeomnet
– network visualizationggExtra
– marginal histograms for a plotgganimate
– animations, see also the gganimate wiki page ggpage
– pagestyled visualizations of text based dataggpmisc
– useful additional geom_*
and stat_*
functionsggstatsplot
– include details from statistical tests in plotsggspectra
– tools for plotting light spectraggnetwork
– geoms to plot networksggpoindensity
– cross between a scatter plot and a 2D density plotggradar
– radar chartsggsurvplot (survminer)
– survival curvesggseas
– seasonal adjustment toolsggthreed
– (evil) 3D geomsggtech
– style themes for plotsggtern
– ternary diagramsggTimeSeries
– time series visualizationsggtree
– tree visualizationstreemapify
– wilcox’s treemapsseewave
– spectograms
Miscellaneous
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Shiny, Dashboards, & Apps
Markdown & Other Output Formats
R Markdown cheat sheet by RStudio R Markdown reference guide by RStudio R Markdown Basics R Markdown tutorial by RStudio R Markdown gallery by RStudio The knitr
book (Xie, 2015) Getting used to R, RStudio, and R Markdown (2016) R Markdown: The Definitive Guide (Xie, Allaire, & Grolemund, 2018) Introduction to R Markdown (Clark, 2018) R Markdown for Scientists (Tierney, 2019) R Markdown Tips and Tricks Pimp my RMD by Holtz YanPandoc syntax highlighting examples by Garrick Aden-BuieCreating slides with R Markdown (Video) by Brian CaffoIntroduction to xaringan
by Yihui Xie A quick demonstration of xarigan
General Markdown cheat sheet blogdown
websites with R Markdown (Xie, Thomas, & Hill, 2018)blogdown
tutorialsHow to build a website with blogdown
in R, by Storybench radix – online publication format designed for scientific and technical communicationA template RStudio project with data analysis and manuscript writing by Thomas JulouMultiple reports from a single Markdown file (example 1 ) (example2 )
tidystats
– automating updating of model statisticspapaja
– preparing APA journal articlesblogdown
– build websites with Markdown & Hugohuxtable
– create Excel, html, & LaTeX tablesxaringan
– make slideshows via remark.js and markdown summarytools
– produces neat, quick data summary tables citr
– RStudio Addin to Insert Markdown Citations
Cloud, Server, & Database
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Statistical Modeling & Machine Learning
Books
Elements of Statistical Learning (Hastie, Tibshirani, & Friedman, 2001) Introduction to Statistical Learning (James, Witten, Hastie, & Tibshirani, 2013) Machine Learning with R (Lantz, 2013) Regression Models for Data Science in R (Caffo, 2015) R Programming for Data Science (Peng, 2016) Data Science Live Book (Casas, 2017) Statistical Foundations of Machine Learning (Bontempi & Taieb, 2017) R for Data Science (Grolemund & Wickham, 2017) Introduction to Data Science (Irizarry, 2018)
Courses
Cheat sheets
Time series
Survival analysis
Bayesian
Miscellaneous
corrr
– easier correlation matrix management and exploration
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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) Tidytext tutorials by computational journalism 21 Recipes for Mining Twitter Data (Rudis, 2017) with rtweet
Emil Hvitfeldt’s R-text-data GitHub repository Course: Introduction to Text Analytics with R @DataScienceDojoCourse: Twitter Text Mining and Social Network Analysis (Zhoa, 2016) @RDataMining with twitteR
Quantitative Analysis of Textual Data with quanteda
cheat sheet by Stefan Müller and Kenneth BenoitList 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
principlesquanteda
– framework for quantitative text analysisgutenbergr
– public domain works (free books to practice on)corpora
– statistics and data sets for corpus frequency data.tau
– Text Analysis UtilitiesSentiment140
– headache-free sentiment analysissentimentr
– sentiment analysis using text polarityopenNLP
– 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 modelsRSentiment
– 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 wrapperstextcat
– provides support for n-gram based text categorization.text2vec
– text vectorization, topic modeling (LDA, LSA), word embeddings (GloVe), and similarities.lsa
– Latent Semantic Analysistopicmodels
-Latent Dirichlet Allocation (LDA) and Correlated Topics Models (CTM)lda
-Latent Dirichlet Allocation and related models
Regular Expressions
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Geographic & Spatial mapping
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Integrated Development Environments (IDEs) & Graphical User Inferfaces (GUIs)
Descriptions mostly taken from their own websites:
RStudio *** – Open source and enterprise ready professional softwareJupyter Notebook *** – open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text across dozens of programming languages.Microsoft R tools for Visual Studio – turn Visual Studio into a powerful R IDER Plugins for Vim , Emax , and Atom editors Rattle *** – GUI for data mining equisse – RStudio add-in to interactively explore and visualize dataR Analytic Flow – data flow diagram-based IDERKWard – easy to use and easily extensible IDE and GUI Eclipse StatET – Eclipse-based IDEOpenAnalytics Architect – Eclipse-based IDE TinnR – open source GUI and IDE DisplayR – cloud-based GUI BlueSkyStatistics – GUI designed to look like SPSS and SAS ducer – GUI for everyone R commander (Rcmdr) – easy and intuitive GUIJGR – Java-based GUI for Rjamovi & jmv
– free and open statistical software to bridge the gap between researcher and statisticianExploratory.io – cloud-based data science focused GUIStagraph – GUI for ggplot2 that allows you to visualize and connect to databases and/or basic file typesggraptr – GUI for visualization (Rapid And Pretty Things in R)ML Studio – interactive Shiny platform for data visualization, statistical modeling and machine learning
R & other software and languages
R & Excel
R & Python
R & SQL
sqldf
– running SQL statements on R data frames
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R Help, Connect, & Inspiration
RStudio Community R help mailing list R seek – search engine for R-related websitesR site search – search engine for help files, manuals, and mailing listsNabble – mailing list archive and forumR User Groups & Conferences R for Data Science Online Learning Community 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 topicThe R Journal – open access, refereed journal of RTwitter: #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
R Conferences, Events, & Meetups
R Jobs
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