Advanced R helps you understand how R works at a fundamental level. It 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.

This book will teach you the foundations of R; three fundamental programming paradigms (functional, object-oriented, and metaprogramming); and powerful techniques for debugging and optimising
your code.

By reading this book, you will learn:

The difference between an object and its name, and why the distinction is important

The important vector data structures, how they fit together, and how you can pull them apart using subsetting

The fine details of functions and environments

The condition system, which powers messages, warnings, and errors

The powerful functional programming paradigm, which can replace many for loops

The three most important OO systems: S3, S4, and R6

The tidy eval toolkit for metaprogramming, which allows you to manipulate code and control evaluation

Effective debugging techniques that you can deploy, regardless of how your code is run

How to find and remove performance bottlenecks

The second edition is a comprehensive update:

New foundational chapters: "Names and values," "Control flow," and "Conditions"

comprehensive coverage of object oriented programming with chapters on S3, S4, R6, and how to choose between them

Much deeper coverage of metaprogramming, including the new tidy evaluation framework

use of new package like rlang [...] which provides a clean interface to low-level operations, and purr [...] for functional programming

Use of color in code chunks and figures

Hadley Wickham is Chief Scientist at RStudio, an Adjunct Professor at Stanford University and the University of Auckland, and a member of the R Foundation. He is the lead developer of the tidyverse, a collection of R packages, including ggplot2 and dplyr, designed to support data science. He is also the author of R for Data Science (with Garrett Grolemund), R Packages, and ggplot2: Elegant Graphics for Data Analysis.