Blogs

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• Alex Cookson - Alex Cookson loves making beautiful visualizations and easy-to-read walkthroughs of R concepts. He's particularly interested in data about media, like books, movies, and musicals.
• Tony ElHabr - Tony ElHabr is passionate mostly about energy markets and sports analytics. His blog provides detailed tutorials, project explanations, and presentations.
• Cédric Scherer - Cédric Scherer is a graduated computational ecologist and freelance data visualization expert who has created visualizations across all disciplines, purposes, and styles and regularly teaches data visualization principles, R, and ggplot2.
• Data Imaginist - Thomas Lin Pedersen is a data scientist turned software engineer who focuses on improving researchers’ interactions with the data they produce.
• Julia Silge - Julia Silge is a data scientist and software engineer at RStudio where she work on open source modeling tools. She is passionate about making beautiful charts, the statistical programming language R, Jane Austen, black coffee, and red wine.
• rweekly - Weekly Updates from the Entire R Community by Bruce Zhao, Colin Fay, Eric Nantz, Hao Zhu, Jon Calder, Jonathan Carroll, Maëlle Salmon, Ryo Nakagawara, and Wolfram Qin.
• r-bloggers - R-Bloggers.com was created by Tal Galili and is a blog aggregator of content contributed by bloggers who write about R (in English). The site helps R bloggers and users to connect and follow the R blogosphere.
• Ryo Nakagawara - Ryo Nakagawara is a Data Scientist and has been doing work as both a reporting analyst and a software developer in R and SQL to improve ACDI and VOCA data pipelines, create R packages, reproducible reports, dashboards, and Shiny apps to communicate how his projects worldwide are progressing.
• Statistics Globe - Joachim Schork started this platform to share his statistical know-how and to improve his own statistical skills by discussing with other statisticians and programmers.
• Musings on R - A blog on all things R and Data Science by Martin Chan. Topics covered include comparing dplyr and data.table, Shiny apps, ggplot, data cleaning, using RStudio, interviews with other R users/data scientists, and web scraping.
• Avery Robbins - Avery Robbins loves to learn and to share useful or awesome things that have benefited him personally. This website is a tool for him to actively do just that: share knowledge, ideas, and tips that are helpful.

Books

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• Advanced R - This book is designed for R programmers who want to deepen their understanding of the language, and programmers experienced in other languages who want to understand what makes R different and special. Exercise Solutions Author: Hadley Wickham.
• An Introduction to R - This introduction to R is derived from an original set of notes describing the S and S-Plus environments written in 1990–2 by Bill Venables and David M. Smith when at the University of Adelaide.
• An Introduction to R - The aim of this book is to introduce you to using R, a powerful and flexible interactive environment for statistical computing and research. Authors: Alex Douglas, Deon Roos, Francesca Mancini, Ana Couto & David Lusseau
• Data Science in a Box - The core content of the course focuses on data acquisition and wrangling, exploratory data analysis, data visualization, inference, modelling, and effective communication of results.
• Data Science in Education Using R - This book is primarily about learning to use R as a tool for data science in education. Authors: Ryan A. Estrellado, Emily A. Bovee, Jesse Mostipak, Joshua M. Rosenberg, and Isabella C. Velásquez.
• Engineering Production-Grade Shiny Apps - This book covers the process of building a Shiny application that will later be sent to production. Authors: Colin Fay, Sébastien Rochette, Vincent Guyader, Cervan Girard.
• Exploratory Data Analysis with R - This book covers the essential exploratory techniques for summarizing data with R. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Author: Roger D. Peng.
• Forecasting: Principles and Practice - This textbook is intended to provide a comprehensive introduction to forecasting methods and to present enough information about each method for readers to be able to use them sensibly. Authors: Rob J Hyndman and George Athanasopoulos.
• ggplot2: Elegant Graphics for Data Analysis - This book provides a hands-on introduction to ggplot2 with lots of example code and graphics. It also explains the grammar on which ggplot2 is based. Author: Hadley Wickham.
• Happy Git and GitHub for the useR - Happy Git provides opinionated instructions on how to install Git and get it working smoothly with GitHub, in the shell and in the RStudio IDE, develop a few key workflows that cover your most common tasks, and integrate Git and GitHub into your daily work with R and R Markdown. Authors: Jenny Bryan, the STAT 545 TAs, Jim Hester.
• Introduction to R & Spatial Data with Raster and Terra - This document provides a concise introduction to R. It emphasizes what you need to know to be able to use the language in any context. Author: Professor Robert Hijmans.
• JavaScript for R - The ultimate aim of this work is to demonstrate to the reader the many great benefits one can reap by inviting JavaScript into their data science workflow. Author: John Coene.
• Learning Statistics with R - Learning Statistics with R covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software. Author: Danielle Navarro.
• Mastering Shiny - This is the online version of Mastering Shiny, a book currently under early development and intended for a late 2020 release. This book complements the Shiny online documentation and is intended to help app authors develop a deeper understanding of Shiny. Author: Hadley Wickham. Mastering Shiny Exercise solutions
• Practical Data Science with R - The intent of this book is to present data science from a pragmatic, practice-oriented viewpoint. The book concentrates on the process of data science, from the planning stages of a project, through the data collection and exploration, to the modeling, and finally to deployment and the sharing of results. Authors: Nina Zumel and John Mount.
• Practical Regression and Anova using R - The emphasis of this text is on the practice of regression and analysis of variance. The objective is to learn what methods are available and more importantly, when they should be applied. Author: Julian Faraway.
• R Cookbook, 2nd Edition - This book is full of how-to recipes, each of which solves a specific problem. The recipe includes a quick introduction to the solution followed by a discussion that aims to unpack the solution and give you some insight into how it works. Authors: James (JD) Long and Paul Teetor.
• R for Data Science - This book will teach you how to do data science with R. You will learn how to get your data into R, get it into the most useful structure, transform it, visualize it and model it. Exercise Solutions Authors: Garrett Grolemund and Hadley Wickham.
• R Programming for Data Science - This book brings the fundamentals of R programming to you, using the same material developed as part of the industry-leading Johns Hopkins Data Science Specialization. Author: Roger Peng.
• R Tutorial – Be a Data Science rock star with R - A tour of the R programming language that explores its different and essential concepts. This R DataFlair Tutorial Series is designed to help beginners to get started with R and experienced to brush up their R programming skills and gain perfection in the language.
• Statistical Inference via Data Science - This is intended to be a gentle introduction to the practice of analyzing data and answering questions using data the way data scientists, statisticians, data journalists, and other researchers would. Authors: Chester Ismay and Albert Y. Kim.
• Supervised Machine Learning for Text Analysis in R - This book focuses on supervised or predictive modeling for text, using text data to make predictions about the world around us. Authors: Emil Hvitfeldt and Julia Silge.
• Text Mining with R - This book serves as an introduction of text mining using the tidytext package and other tidy tools in R. Authors: Julia Silge and David Robinson.
• The Book of R - The aim of The Book of R: A First Course in Programming and Statistics is to provide a relatively gentle yet informative exposure to the statistical software environment R, alongside some common statistical analyses, so that readers may have a solid foundation from which to eventually become experts in their own right. Exercise solutions Author: Tilman M. Davies.
• The R Language - An introduction to R written by the authors of the R language.
• Tidy Modeling with R - This book is a guide to using a new collection of software in the R programming language for model building.
• A Sufficient Introduction to R - This book is intended to guide people that are completely new to programming along a path towards a useful skill level using R. Author: Derek L. Sonderegger.
• Efficient R programming - Efficient R Programming is about increasing the amount of work you can do with R in a given amount of time. It’s about both computational and programmer efficiency. Authors: Colin Gillespie, Robin Lovelace.
• Practicals and Exercises - This series of exercises reviews some of the content discussed during the author's lectures, and introduces some other basic concepts about working with data in R. Author: Charles DiMaggio, PhD.
• Quantitative Politics with R - The aim of this book is to provide an easily accessible introduction to R for the
• Introduction to Data Science - Data Analysis and Prediction Algorithms with R - This book started out as the class notes used in the HarvardX Data Science Series. It introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling with dplyr, data visualization with ggplot2, algorithm building with caret, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation with knitr and R markdown. Author: Professor Rafael A. Irizarry.
• Introduction to Probability and Statistics Using R - The book can be subdivided into three basic parts. The first part includes the introductions and elementary descriptive statistics; I want the students to be knee-deep in data right out of the gate. The second part is the study of probability, which begins at the basics of sets and the equally likely model, journeys past discrete/continuous random variables, and continues through to multivariate distributions. The chapter on sampling distributions paves the way to the third part, which isinferential statistics. This last part includes point and interval estimation, hypothesis testing, and finishes with introductions to selected topics in applied statistics. Author: G. Jay Kerns.

Communities of Practice

Uncategorized

• TidyTuesday - TidyTuesday is a weekly data project aimed at the R ecosystem with an emphasis placed on understanding how to summarize and arrange data to make meaningful charts.
• R for Data Science (R4DS) Online Learning Community - Founded by Jessie Mostipak (@kierisi) to create a supportive and responsive online space for learners and mentors to gather and work through the R for Data Science book by Garrett Grolemund and Hadley Wickham. Grown into a community of R learners at all skill levels working together to improve their skills.

Contributing

Uncategorized

• contributing.md

Podcasts

Uncategorized

• The R-Podcast - Practical advice on how to take advantage of R to accomplish innovative and robust data analyses. Hosted by Eric Nantz.
• Not so Standard Deviations - A data science podcast where Roger Peng and Hilary Parker talk about the latest in data science and data analysis in academia and industry.

Topic Areas

Comprehensive R Tutorials

• Data Flair - The tutorials are grouped by skill level (beginner, intermediate, expert).
• Intro to R course by Fabio Votta - part 1 - A fun introduction to R programming grouped into categories (operators, objects, functions, exercises, and data frames).
• Intro to R course by Fabio Votta - part 2 - A fun introduction to R programming grouped into categories (data manipulation and cleaning featuring the janitor, tidyr, and dplyr packages).
• Introduction to Data Analysis with R - This is a lecture series with videos, scripts and exercises introducing R and the tidyverse as well as statistical concepts.
• Tutorials Point - The tutorials are grouped into categories (R tutorial, R Data Interfaces, R Charts & Graphs, R Statistics Examples, R Useful Resources) that cover in-depth all the basic needs for someone starting learning the R programming language.
• R CODER - The tutorials are grouped into categories (introduction, data structures, data wrangling, programming, import & export, graphics) that cover in-depth all the basic needs for someone starting learning the R programming language.

Functions

• stat.berkeley - Introduction to Functions - An introduction to functions in the R language by the organizers of Integrating Computing into the Statistics Curricula (U.C. Berkeley).

Joining Data

• Joining Data in R with dplyr - Course notes from the Joining Data in R with dplyr course on DataCamp. Topics include mutating joins, filtering joins and set operations, assembling data, advanced joining. Author: William Surles.

Math

• Descriptive Statistics - A tutorial of descriptive statistics which are used to summarize data in a way that provides insight into the information contained in the data. Author: Salvatore S. Mangiafico.
• Descriptive statistics in R - This article explains how to compute the main descriptive statistics in R and how to present them graphically. Author - Antoine Soetewey.
• Gallery of Statistical Distributions - Author: NIST/SEMATECH.
• Essential Math for Data Science - An article discussing the key mathematical topics to master to become a better data scientist. Author: Tirthajyoti Sarkar.

Shiny

• Building Shiny apps - an interactive tutorial - This tutorial is a hands-on activity complement to a set of presentation slides for learning how to build Shiny apps. Author: Dean Attali.
• How to Start with Shiny - Detailed introductory video tutorial. Author: Garrett Grolemund.
• Awesome R Shiny - A curated list of resources for R Shiny. Author: Rob Gilmore.

Spatial

• An Introduction to Choropleth maps in R - Author: Henry Cann.
• Getting latitude & longitude for any address - Author: Brecht Vermeire.
• Map Plots Created With R And Ggmap - Author: Laura Ellis.
• Plot Spatial Data / Shapefiles in R - From the "math et al" YouTube channel.

Uncategorized

• Data.Table and Dplyr Tour - A detailed comparison of R packages data.table and dplyr. Author: Atrebas.
• data.table: A gentle introduction - A quick introduction to data.table. The main objective is to present the data.table syntax, showing how to perform basic, but essential, data wrangling tasks. Author: Atrebas.
• From base R to stringr - This vignette compares stringr functions to their base R equivalents to help users transitioning from using base R to stringr. Author: Sara Stoudt.
• Help me help you: creating reproducible examples - Making a great reprex is both an art and a science and this webinar will cover both aspects. A reprex makes a conversation about code more efficient and pleasant for all. This comes up whenever you ask someone for help, report a bug in software, or propose a new feature. The reprex package (https://reprex.Tidyverse.org) makes it especially easy to prepare R code as a reprex, in order to share on sites such as https://community.rstudio.com, https://github.com, or https://stackoverflow.com. Author: Jenny Bryan.
• Subreddit - r/Rlanguage - R Programming Language - A Reddit subreddit focused on implementing the R programming language for statistics and data science.
• Subreddit - r/programming - The R Project for Statistical Computing - A Reddit subreddit focused on using R for statistical computing.
• Syntax equivalents: base R vs Tidyverse - A detailed comparison of base R and tidyverse. Author: Hugo Tavares.
• Fakir - Create Fake Data in R for Tutorials - Author: Colin Fay.

Viz

• A ggplot2 Tutorial for Beautiful Plotting in R - A comprehensive and easy to follow tutorial that covers working with axes, titles, legends, backgrounds, grid lines, margins, multi-panel plots, colors, themes, lines, text, coordinates, chart types, ribbons, smoothings, and interactive plots. Author: Cédric Scherer.
• Chart Suggestions — A thought-starter on choosing the way to show your data - Author: Andrew Abela, Ph.D.
• Color Hex Color Codes - Author: Color-Hex.
• Combine Multiple GGPlots into a Figure - Author: Alboukadel Kassambara.
• Coolors - The super fast color schemes generator! Create the perfect palette or get inspired by thousands of beautiful color schemes. Features include color picker, pick palette from photo, create a collage, make your own gradient palette, create a gradient, contrast checker, etc.
• From Data to Viz - From Data to Viz leads you to the most appropriate graph for your data. Author: Yan Holtz.
• ggplot2 extensions - gallery - Maintained by Daniel Emaasit.
• ggplot2 - Modify components of a theme - How to modify components of a theme in ggplot2. Author: the developers of Tidyverse.
• htmlwidgets for R - Showcase and gallery of the various interactive web visualizations you can build using R.
• The Data Visualization Catalogue - The Data Visualization Catalogue is a project developed by Severino Ribecca to create a library of different information visualization types.
• The Graphic Continuum - The Graphic Continuum shows the many different types of visualizations available to us when we encode and present data. Authors: Jonathan Schwabish, and Severino Ribecca.
• Time Based Heatmaps in R - Author: Laura Ellis.
• Awesome ggplot2 - A curated list of awesome ggplot2 tutorials, packages etc. Author: Erik Gahner Larsen.
• r-color-palettes - Comprehensive list of color palettes available in r. Author: Emil Hvitfeldt.
• AISEKA - Discover the best Color Palette & Color Tools. Author: meetqy.
• Top 50 ggplot2 Visualizations - The Master List (With Full R Code) - This tutorial helps you choose the right type of chart for your specific objectives and how to implement it in R using ggplot2. Author: Selva Prabhakaran.
• The R Graph Gallery - A collection of charts made with the R programming language. Author: Yan Holtz.

Web Scraping

• Web Scraping Reference: Cheat Sheet for Web Scraping using R - Guide, reference and cheatsheet on web scraping using rvest, httr and Rselenium. Author: yifyan et al.

Wrangling

• Data Wrangling Part 1: Basic to Advanced Ways to Select Columns - Author: Suzan Baert.
• Data Wrangling Part 2: Transforming your columns into the right shape - Author: Suzan Baert.
• Data Wrangling Part 3: Basic and more advanced ways to filter rows - Author: Suzan Baert.
• Data Wrangling Part 4: Summarizing and slicing your data - Author: Suzan Baert.