Beginner’s guide to R: Introduction – by Sharon Machlis – “Our aim here isn’t R mastery, but giving you a path to start using R for basic data work: Extracting key statistics out of a data set, exploring a data set with basic graphics and reshaping data to make it easier to analyze.”
Beautiful Data chapter: “Bay area blues: the effect of the housing crisis” – by Hadley Wickham, Deborah F. Swayne and David Poole – A chapter from the book, Beautiful Data: The Stories Behind Elegant Data Solutions, which serves as an excellent narrative on how to work with data in the real-world. The actual R code is at https://github.com/hadley/sfhousing [buy it]
Beautiful plotting in R: A ggplot2 cheatsheet – by ZevRoss – One of the most thorough and helpful cheatsheets I’ve ever seen; this one is focused on making visualizations with ggplot2.
Sharon Machlis’s ggplot2 cheat sheet with interactive search-by-task – by Sharon Machlis – “Here’s your easy-to-use guide to dozens of useful ggplot2 R data visualization commands in a handy, searchable table. Plus, download code snippets to save yourself a boatload of typing.”
An Introduction to R – by The R-Core group – “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. We have made a number of small changes to reflect differences between the R and S programs, and expanded some of the material.”
An Introduction on How to Make Beautiful Charts With R and ggplot2 – by Max Woolf – Max Woolf’s short and readable intro to working with ggplot2.
Impatient R – by Burns Statistics – This is a tutorial (previously known as “Some hints for the R beginner”) for beginning to learn the R programming language. It is a tree of pages — move through the pages in whatever way best suits your style of learning.
A Layered Grammar of Graphics – by Hadley Wickham – R has been one of my least favorite languages to learn but being exposed to Wilkinson’s Grammar of Graphics via Hadley Wickham’s sublime implementation in ggplot2 has been worth the price of admission. This paper acts as an abbreviated form of Wickham’s 2009 manual on ggplot2. [buy it]
Tidy Data – by Hadley Wickham – Less about R and more about Hadley Wickham’s assertions about the ideal shape of data and how that informs the many R-lang libraries he’s created. A great read for both data novices and experts, as data cleaning/munging ends up being one of the hardest data science problems.
Advanced R – by Hadley Wickham – “The book is designed primarily for R users who want to improve their programming skills and understanding of the language. It should also be useful for programmers coming to R from other languages, as it explains some of R’s quirks and shows how some parts that seem horrible do have a positive side.” [buy it]
R Packages – by Hadley Wickham – “Packages are the fundamental units of reproducible R code. They include reusable R functions, the documentation that describes how to use them, and sample data. In this section you’ll learn how to turn your code into packages that others can easily download and use. Writing a package can seem overwhelming at first. So start with the basics and improve it over time. It doesn’t matter if your first version isn’t perfect as long as the next version is better.” [buy it]
“These notes are designed to allow individuals who have a basic grounding in statistical methodology to work through examples that demonstrate the use of R for a range of types of data manipulation, graphical presentation and statistical analysis. “ [buy it]
An Introduction to Statistical Learning with Applications in R – by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani – “This book provides an introduction to statistical learning methods. It is aimed for upper level undergraduate students, masters students and Ph.D. students in the non-mathematical sciences. The book also contains a number of R labs with detailed explanations on how to implement the various methods in real life settings, and should be a valuable resource for a practicing data scientist.” [buy it]
The Elements of Statistical Learning: Data Mining, Inference, and Prediction. – by Trevor Hastie, Robert Tibshirani, and Jerome Friedman – A more advanced treatment of the topics covered in “An Introduction to Statistical Learning with Applications in R” [buy it]
The R Inferno – by Burns Statistics – If you are using R and you think you’re in hell, this is a map for you. A book about trouble spots, oddities, traps, glitches in R.
Jenny Bryan’s Teaching materials for the R package ggplot2 – by Jenny Bryan – Slides, documents, and code used in UBC stats professor Jenny Bryan’s R-data-workshops.