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: 2021-09-24

---

Table of Contents (clickable)

• Beginner
• Advanced
• Cheat sheets
• Data manipulation
• Data visualization
• Dashboards & Shiny
• Markdown
• Database connections
• Machine learning
• Text mining
• Geospatial analysis
• Bioinformatics
• R IDEs
• Software & language connections
• Help
• Blogs
• Conferences, Events, & Groups
• Jobs
• Other tips & tricks

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

• swirl()***
• Join #TidyTuesday***
• 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
• Chromebook Data Science (CBDS) – Introduction to R
• Learning R by Doing – A Learning Experiment in RStudio and GitHub
• R Bootcamp by Jared Knowles
• Hands-on Introduction to Statistics with R by DataCamp.com
• R Course in Statistics by PagePiccini.com
• Data Analysis in R by DataQuest.io
• Data Science: R Basics @edX
• Introduction to R for Data Science @edX
• Introduction to R workshop by Chris Bilder
• Data Analysis and Visualization Using R @VarianceExplained
• Programming @Coursera*** by Roger Peng, Jeff Leek, & Brian Caffo
• Intro to R by Bradley Boehmke
• Intermediate R by Bradley Boehmke
• Advanced R by Bradley Boehmke
• Wrangling data in the Tidyverse – useR! 2018 tutorial by Simon Jackson
• Winter R Bootcamp 2015 by Sean Cross
• RStudio’s Data Science Course in a Box
• Data Carpentry Social Science Workshop in R

• Youtube R classes by Chris Bilder
• 37 Youtube R Tutorials by Flavio Azevedo***
• Essential R tutorials by Gilad Feldman
• Data Carpentry Social Science in R
• Statistics and R, by Rafael Irizarry and Michael Love
• Learn R via R-coder.com

• A Psychologist’s Guide to R (pdf) by Sean Chris Murphy
• Learn R for Psychology research: A crash course
• Social Sciences: Critically Analyze Research and Results Using R by Coursera
• LearnR – R Programming for Behavioral Scientists
• Data Science for Social Scientists

• Boston University workshop – Tricks for cleaning your data
• University of Oklahoma – Econometrics lab sessions by Tyler Ransom
• University of British Colombia – STAT 545A and 547M – Data wrangling, exploration, and analysis with R
• University of California – Business Analytics R Programming Guide
• University of Oregon – Summer School 2018 R Bootcamp by Jessica Kosie
• University of Oregon – Data science for economists
• Oregon Health & Science University – CS631 Principles & Practice of Data Visualization
• Brooklyn College of CUNY – Psych7709 Using R for Reproducible Research
• University of Illinois – A language, not a letter: Learning Statistics in R
• University College London – Statistical Computing with R Programming Language: A Gentle Introduction
• University of Glasgow – psyTeachR Teaching Reproducible Research

• GitHub repository rstats-ed, including many additional courses and learning materials
• Gilad Feldman’s How To R Guides

Style Guides

• Google’s R style guide
• Tidyverse style guide by Hadley Wickham
• Advanced R style guide by Hadley Wickham
• R style guide for stat405 by Hadley Wickham
• R style guide by Collin Gillespie
• Best practices for R Coding by Arnaud Amsellem / The R Trader
• The State of Naming Conventions in R (Bååth, 2012)
• A guide for switching from base R to the tidyverse

BACK TO TABLE OF CONTENTS

---

Advanced R

• Advanced R – 1st ed. (Wickham, 2014)
• Advanced R – 2nd ed. (Wickham, 2018)***
• Efficient R Programming (Gillespie & Lovelace, 2017)
• Writing R extensions
• Happy Git and GitHub for the useR (Jenny Bryan, 2017)
• RStudio addins by Dean Attali

Package Development

• Mastering Software Development in R (Peng, Kross, & Anderson, 2017)
• R Packages (Wickham & Bryan, ???)
• rOpenSci Packages: Development, Maintenance, and Peer Review
• How to develop good R packages (for open science) by Maëlle Salmon
• Tutorial on creating R packages by Friedrich Leisch
• Developing R Packages by Jeff Leek
• Writing an R package from scratch by Hilary Parker
• Write your own R package by STAT545
• Making an R Package, by R.M. Ripley
• Prepare your package for CRAN
• Introduction to roxygen2 by Hadley Wickham
• How to build package vignettes with knitr by Yihui Xie
• knitr in a nutshell: a minimal tutorial by Karl Broman
• Rtools: Building R for Windows by Brian Ripley, Duncan Murdoch, and Jeroen Ooms
• devtools – tools to make an R developer’s life easier
• roxygen2 – tools for describing functions in comments next to their definitions
• Rd2roxygen – tools for converting Rd to roxygen documentation
• testthat – tools that simplify the testing of R packages

Non-standard Evaluation

• Tidy evaluation explained in 5 minutes via YouTube 
• Tidy evaluation (Henry & Wickham, 2018)
• Tidy evaluation webinar by RStudio
• IV metaprogramming chapters of Advanced R (Wickham, 2014)
• tidyeval tutorial by Ian Lyttle

Functional Programming

• Writing Functions in R by Hadley Wickham via DataCamp.com
• R for Data Science chapters on Functions and Iteration 
  (Grolemund & Wickham, 2018)***
• Advanced R chapter on Functions (Wickham, 2014)
• Lesson on writing, testing, and documenting custom functions by Software-Carpentry.org
• User-defined R fuctions tutorial by Carlo Fanara via DataCamp.com
• Functional programming lecture by Duke University
• purrr tutorial by Jenny Bryan***
• Intro to purrr tutorial by Emorie Beck
• Learn purrr tutorial by Dan Ovando
• purrr cheat sheet by RStudio

BACK TO TABLE OF CONTENTS

Cheat Sheets

• Getting started in R, by Saghir Bashir***
• 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 cheat sheet by Jeromy Anglim
• RStudio cheat sheet by RStudio
• RStudio keyboard shortcuts by RStudio***
• Data management in R cheat sheet
• data.table cheat sheet by Erik Petrovski
• data.table wide cheat sheet by DataCamp
• data.table long cheat sheet by DataCamp
• Advanced R cheat sheet by Arianne Colton & Sean Chen
• tidyverse cheat sheet by DataCamp
• 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 visualization cheat sheet by RStudio with ggplot2
• Data wrangling cheat sheet by RStudio with dplyr and tidyr
• Automate random assignment and sampling cheat sheet with randomizr by Alex Coppock.
• Cheat sheet for the mosaic package teaching math, stats, computation, and modelling, by Michael Laviolette
• 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
• purrr functional programming cheat sheet by RStudio
• Tidy evaluation cheet sheet by Edwin Thoen
• cartography cheat sheet by Timothee Giraud
• bayesplot cheat sheet by Edward Roualdes
• R package development cheat sheet with devtools
• R syntax comparison cheat sheet by Amelia McNamara
• xts cheat sheet for time series by DataCamp
• RStudio cheat sheet GitHub
• reticulate cheat sheet by RStudio

Many of the above cheat sheets are hosted in the official RStudio cheat sheet overview.

---

Data Manipulation

• Introduction to data.table
• Comparison between data.table & dplyr code
• data.table cheat sheet by Erik Petrovski
• data.table wide cheat sheet by DataCamp
• data.table long cheat sheet by DataCamp
• dplyr cheat sheet by RStudio
• Pipes in R Tutorial For Beginners

---

Data Visualization

• R graph gallery & code examples***
• R charts: A collection of charts and graphs made with R code
• Fundamentals of Data Visualization (Wilke, 2018)
• Exploratory Data Analysis and Visualization (Bogart & Robbins, 2018)
• R base plots wiki reference guide
• Guide to (base) graphics and visualization in R, by StatisticsGlobe
• CRAN Task View – Graphics & Visualization
• R graphical parameters cheat sheet by Flowingdata.com
• MPA 635: Data Visualization course by Andrew Heiss

Colors

• R Color Guide***
• colourpicker – widget that allows users to choose colours
• paletteer –  comprehensive collection of color palettes in R***
• ggplot2 colour guide***
• Canva’s 100 color palette included in ggthemes::scale_color_canva
• Wes Anderson color palettes
• Multicolored annotated text in ggplot2 by Andrew Whitby & Visuelle Data
• Picular.co – Google, but for colors

Interactive / HTML / JavaScript widgets

• R HTML Widgets Gallery***
• plotly – interactive plots
• billboarder – easy interface to billboard.js, a JavaScript chart library based on D3
• d3heatmap – interactive D3 heatmaps
• altair – Vega-Lite visualizations via Python
• DT – interactive tables
• DiagrammeR – interactive diagrams (DiagrammeR cheat sheet)
• 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
• Hitchhiker’s Guide to ggplot2 in R (Burchell & Vargas, 2016)
• A practical introduction with R and ggplot2 (Healy, 2017)
• Data Vizualization: A practical introduction (Healy, 2018)
• Complete ggplot2 Tutorial
• Principles & Practice of Data Visualization CS631 at Oregon Health & Science University
• Data visualization cheat sheet by RStudio with ggplot2
• 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
• equisse – RStudio addin to interactively explore data with ggplot2 without coding

ggplot2 extensions

• ggplot2 extensions overview***
• ggthemes – plot style themes
• hrbrthemes – opinionated, typographic-centric themes
• ggmap – maps with Google Maps, Open Street Maps, etc.
• ggiraph – interactive ggplots
• gghighight – highlight lines or values, see vignette
• ggstance – horizontal versions of common plots
• GGally – scatterplot matrices
• ggalt – additional coordinate systems, geoms, etc.
• ggbeeswarm – column scatter plots or voilin scatter plots
• ggforce – additional geoms, see visual guide
• 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, see also the gganimate wiki page
• ggpage – pagestyled visualizations of text based data
• ggpmisc – useful additional geom_* and stat_* functions
• ggstatsplot – include details from statistical tests in plots
• ggspectra – tools for plotting light spectra
• ggnetwork – geoms to plot networks
• ggpoindensity – cross between a scatter plot and a 2D density plot
• ggradar – radar charts
• ggsurvplot (survminer) – survival curves
• ggseas – seasonal adjustment tools
• ggthreed – (evil) 3D geoms
• ggtech – style themes for plots
• ggtern – ternary diagrams
• ggTimeSeries – time series visualizations
• ggtree – tree visualizations
• treemapify – wilcox’s treemaps
• seewave – spectograms

Miscellaneous

• coefplot – visualizes model statistics
• circlize – circular visualizations for categorical data
• clustree – visualize clustering analysis
• quantmod – candlestick financial charts
• dabestr– Data Analysis using Bootstrap-Coupled ESTimation
• devoutsvg – an SVG graphics device (with pattern fills)
• devoutpdf – an PDF graphics device
• cartography – create and integrate maps in your R workflow
• 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
• graphlayouts – new layout algorithms for network visualization
• lattice – Trellis graphics
• tmap – thematic maps
• trelliscopejs – interactive alternative for 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
• rvg – produces Vector Graphics that allow further editing in PowerPoint or Excel
• 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
• ungeviz – tools for visualize uncertainty
• waffle – square pie charts a.k.a. waffle charts
• Creating spectograms in R with hht, warbleR, soundgen, signal, seewave, or phonTools

BACK TO TABLE OF CONTENTS

---

Shiny, Dashboards, & Apps

• Shiny Cheat Sheet by RStudio
• Shiny Tutorial
• A collection of links to Shiny applications that have been shared on Twitter.
• Enterprise-ready dashboards with Shiny and databases
• Several packages to upgrade your Shiny dashboards
• More Shiny Resources by Rob Gilmore
• More Shiny Resources for Statistics by Yingjie Hu
• Building Shiny apps – an interactive tutorial by Dean Attali
• Advanced Shiny tips & tricks by Dean Attali (version 2)
• flexdashboard – dashboard creation simplified
• colourpicker – widget that allows users to choose colours
• brighter – toolbox with helpful functions for shiny development
• DesktopDeployR – self-contained R-based desktop applications

---

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 Yan
• Pandoc syntax highlighting examples by Garrick Aden-Buie
• Creating slides with R Markdown (Video) by Brian Caffo
• Introduction to xaringan by Yihui Xie
• A quick demonstration of xarigan
• General Markdown cheat sheet
• blogdown websites with R Markdown (Xie, Thomas, & Hill, 2018)
• blogdown tutorials
• How to build a website with blogdown in R, by Storybench
• radix – online publication format designed for scientific and technical communication
• A template RStudio project with data analysis and manuscript writing by Thomas Julou
• Multiple reports from a single Markdown file (example 1) (example2)

• tidystats – automating updating of model statistics
• papaja – preparing APA journal articles
• blogdown – build websites with Markdown & Hugo
• huxtable – create Excel, html, & LaTeX tables
• xaringan – make slideshows via remark.js and markdown
• summarytools – produces neat, quick data summary tables
• citr – RStudio Addin to Insert Markdown Citations

---

Cloud, Server, & Database

• Access and manage Google spreadsheets from R with googlesheets
• Tutorial: Database Queries with R
• Introduction to sparklyr by DataCamp
• Running R on AWS
• AWS EC2 Tutorial For Beginners
• Using RStudio on Amazon EC2 under the Free Usage Tier
• Getting started with databases using R, by RStudio
  • RMySQL – connects to MySQL and MariaDB
  • RPostgreSQL – connects to Postgres and Redshift.
  • RSQLite – embeds a SQLite database.
  • odbc – connects to many commercial databases via the open database connectivity protocol.
  • bigrquery – connects to Google’s BigQuery.
  • DBI – separates the connectivity to the DBMS into a “front-end” and a “back-end”.
  • dbplot – leverages dplyr to process calculations of plot inside database
  • dplyr – also works with remote on-disk data stored in databases
  • tidypredict – run predictions inside the database

BACK TO TABLE OF CONTENTS

---

Statistical Modeling & Machine Learning

• 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)
• 100 Tutorials for Learning R
• Machine Learning Algorithms R Implementation by Ajitesh Kumar
• R Data Mining: Examples & Case Studies (Zhao, 2015)
• Statistical modelling in R (Zhao, 2015) @RDataMining
• Predictive modelling in R with caret
• R interface to Keras
• Tensorflow for R gallery
• Image featurization
• R for Data Science Online Learning Community
• R statistical programming resources by Michael Clark

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

• Introduction to Statistical Learning*** at Stanford University by Trevor Hastie and Rob Tibshirani
• Introduction to R for Data Science @Microsoft
• Introduction to R for Data Science @FutureLearn by Hadley Wickham
• PSY2002: Advanced Statistics at University of Toronto by Elizabeth Page-Gould
• STAT 450/870: Regression Analysis at University of Nebraska-Lincoln by Chris Bilder
• STAT 850: Computing Tools for Statisticians at University of Nebraska-Lincoln by Chris Bilder
• STAT 873: Applied Multivariate Statistical Analysis at University of Nebraska-Lincoln by Chris Bilder
• STAT 875: Categorical Data Analysis at University of Nebraska-Lincoln by Chris Bilder
• STAT 950: Computational Statistics at University of Nebraska-Lincoln by Chris Bilder
• Joint Statistical Meetings: Analysis of Categorical Data by Chris Bilder

Cheat sheets

• R functions for regression analysis cheat sheet by Vito Ricci
• Machine Learning modeling cheat sheet by Arnaud Amsellem
• Machine Learning with mlr cheat sheet by Aaron Coley
• Cheat sheet for h20’s algorithms for big data and parallel computing in R by Juan Telleria
• Deep Learning with keras cheat sheet by RStudio
• Machine Learning with caret cheat sheet by Max Kuhn
• Nonlinear cointegrating autoregressive distributed lag models with nardl cheat sheet by Taha Zaghdoudi
• R survival analysis with survminer cheat sheet by Przemysław Biecek
• R Data Mining reference card
• R sparklyr cheat sheet by RStudio

Time series

• CRAN Task View – TimeSeries
• R xts cheat sheet
• Forecasting: Principles and Practice (Hyndman & Athanasopoulos, 2017)
• A little book of R for time series (tutorial)
• ARIMA forecasting in R (6-part Youtube series)
• Introduction to the tsfeatures package
• Tutorials: Part 1, Part 2, Part 3, & Part 4 of tidy time series @Business-Science.io with tidyquant
• Packages:
  • xts – extensible time series
  • tsfeatures – methods for extracting various features from time series data
  • tidyquant – tidyverse-style financial analysis

Survival analysis

• CRAN Task View – Survival
• R survival analysis cheat sheet by Przemysław Biecek
• Packages:
  • survival – functionality for survival and hazard models
  • ggsurvplot (survminer) – survival curves

Bayesian

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

Miscellaneous

• corrr – easier correlation matrix management and exploration

BACK TO TABLE OF CONTENTS

---

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 @DataScienceDojo
• Course: 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 Benoit
• 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

Regular Expressions

• R Regular Expression cheat sheet by Lise Vaudor
• R Regular Expression cheat sheet
• R Regular Expression cheat sheet (page 2) by RStudio
• regexplain – interactive RStudio addin for regular expressions
• 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 cheat sheet
• General Regular Expression Video Tutorial by Roger Peng
• General Regular Expression cheat sheet by OverAPI.com

BACK TO TABLE OF CONTENTS

---

Geographic & Spatial mapping

• Making Maps with R (tutorial) with ggmaps, maps, and mapdata
• Importing OpenStreetMap data (tutorial) with osmar 
• Geocomputation with R (Lovelace, Nowosad, & Muenchow, 2018)
• Spatial manipulation with Simple Features (sf) cheat sheet by Ryan Garnett

---

Bioinformatics & Computational Biology

• Applied statistics for Bioinformatics using R (Krijnen, 2009)
• A little book of R for Bioinformatics (Coghlan, ???)
• Bioinformatics and Functional Genomics (Pevsner, 2015)
• Applied Biostatistical Analyses using R (Cox, 2017)
• Molecular data analysis using R (Ortutay & Ortutay, 2017)
• Modern statistics for Biology (Holmes & Huber, 2019)

BACK TO TABLE OF CONTENTS

---

Integrated Development Environments (IDEs) &
Graphical User Inferfaces (GUIs)

Descriptions mostly taken from their own websites:

• RStudio*** – Open source and enterprise ready professional software
• Jupyter 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 IDE
• R Plugins for Vim, Emax, and Atom editors
• Rattle*** – GUI for data mining
• equisse – RStudio add-in to interactively explore and visualize data
• R Analytic Flow – data flow diagram-based IDE
• RKWard – easy to use and easily extensible IDE and GUI
• Eclipse StatET – Eclipse-based IDE
• OpenAnalytics 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 GUI
• JGR – Java-based GUI for R
• jamovi & jmv – free and open statistical software to bridge the gap between researcher and statistician
• Exploratory.io – cloud-based data science focused GUI
• Stagraph – GUI for ggplot2 that allows you to visualize and connect to databases and/or basic file types
• ggraptr – 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

• BERT – Basic Excel R Toolkit
• A Comprehensive Guide to Transitioning from Excel to R by Alyssa Columbus
• readxl – package to load in Excel data
• xlsx – package to read and write Excel data
• rvg – produces Vector Graphics which can be modified in Excel
• devoutpdf – an PDF graphics device
• tidyxl – imports non-tabular (e.g., format) data from Excel files into R
• unpivotr – unpivot complex and irregular data layouts in R
• unheadr – handle data with embedded subheaders

R & Python

• Python for R users
• reticulate cheat sheet by RStudio
• reticulate – tools for interoperability between Python and R

R & SQL

• sqldf – running SQL statements on R data frames

BACK TO TABLE OF CONTENTS

---

---

R Help, Connect, & Inspiration

• RStudio Community
• 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
• 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 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://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
• http://www.brodrigues.co/
• https://datasharkie.com/
• https://www.programmingwithr.com/

---

R Conferences, Events, & Meetups

• Overview of R conferences by JumpingRivers
• Overview of R virtual events by JumpingRivers
• Overview of R user groups by JumpingRivers
• Overview of R-Ladies groups by JumpingRivers

R Jobs

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

BACK TO TABLE OF CONTENTS