CRAN is a network of ftp and web servers around the world that store identical, up-to-date, versions of R and its documentation.
This official and up-to-date tutorial, gives an introduction to the language and how to use R for doing statistical analysis and graphics.
RStudio is an integrated development environment (IDE) for R. It includes a console, syntax-highlighting editor that supports direct code execution, as well as tools for plotting, history, debugging and workspace management.
A trove of cheat sheets below to make it easy to learn about and use some of R’s most useful packages.
The tidyverse is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures.
Our mission is to create a fun and supportive environment where we develop programming and statistics skills together, using R.
We want to replace statistics anxiety and code fear with inspiration and motivation to learn, and here we will share our experience.
Both sites provide interactive lessons that will get you writing real code in minutes. They are a great place to make mistakes and test out new skills. You are told immediately when you go wrong and given a chance to fix your code.
This course will teach you the fundamentals of writing functions in R so that, among other things, you can make your code more readable, avoid coding errors, and automate repetitive tasks.
This is an introduction to the dplyr and ggplot2 packages through exploration and visualization of country data over time. This is a suitable course for people who have no or limited experience in R and are interested in learning to perform data analysis.
Covers the basics of ggplot2. Followed by part 2 which covers more advanced topics.
This course brings ggplot2 and dplyr into action in an in-depth analysis of United Nations voting data. The course also introduces broom for tidying model output and the tidyr package for wrangling data into an explorable shape.
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. Hadley Wickham
This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science. Garrett Grolemund and Hadley Wickham
The Rcpp package has become the most widely used language extension for R, the powerful environment and language for computing with data. As of May 2017, 1026 packages on CRAN and a further 91 on BioConductor deploy Rcpp to extend R, to accelerate computations and to connect to other C++ projects.