The 15 Best R Programming Books Based on Real User Reviews

Our editors have compiled this directory of the best R programming books based on Amazon user reviews, rating, and ability to add business value.

There are loads of free resources available online (such as Solutions Review’s Data Analytics and Business Intelligence Software Buyer’s Guide, Visual Comparison Matrix, and best practices section) and those are great, but sometimes it’s best to do things the old fashioned way. There are few resources that can match the in-depth, comprehensive detail of one of the best R programming books.

The editors at Solutions Review have done much of the work for you, curating this comprehensive directory of the best R programming books on Amazon. Titles have been selected based on the total number and quality of reader user reviews and ability to add business value. Each of the books listed in this compilation have met a minimum criteria of 100 reviews and a 4-star-or-better ranking.

Below you will find a library of titles from recognized industry analysts, experienced practitioners, and subject matter experts spanning the depths of R for data science all the way to R for machine learning and other use cases. This directory includes publications for practitioners of all skill levels.

The Best R Programming Books

An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

OUR TAKE: The most popular R-focused book on Amazon touts a nearly perfect rating of 4.8 stars, this title is targeted at statisticians and non-statisticians alike and was written by the authors of The Elements of Statistical Learning.

“This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented.”

R for Data Science: Import, Tidy, Transform, Visualize, and Model Data

OUR TAKE: This title is the number-one seller in Amazon’s Mathematical & Statistical Software category. Author Hadley Wickham is an Assistant Professor and the Dobelman FamilyJunior Chair in Statistics at Rice University.

“Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details.”

Discovering Statistics Using R

OUR TAKE: This book received rave reviews from some of the foremost experts in the field. Author Andy Field is Professor of Quantitative Methods at the University of Sussex and has published more than 150 research papers, book chapters and full-length titles.

“The R version of Andy Field′s hugely popular Discovering Statistics Using SPSS takes students on a journey of statistical discovery using the freeware R. Like its sister textbook, Discovering Statistics Using R is written in an irreverent style and follows the same ground breaking structure and pedagogical approach. The core material is enhanced by a cast of characters to help the reader on their way, hundreds of examples, self assessment tests to consolidate knowledge, and additional website material for those wanting to learn more.”

R For Dummies

OUR TAKE: The second edition of this popular text will teach you how to reshape and manipulate data, merge data sets, split and combine data, and perform calculations on vectors and arrays.

“R is an open source statistical environment and programming language that has become very popular in varied fields for the management and analysis of data. RFor Dummies provides a quick and painless way to master all the R you’ll ever need. Requiring no prior programming experience and packed with tons of practical examples, step-by-step exercises, and sample code, this friendly and accessible guide shows you how to know your way around lists.”

The Art of R Programming: A Tour of Statistical Software Design

OUR TAKE: Author Norman Matloff is a Professor of Computer Science at the University of California, Davis and the creator of several popular software packages and web tutorials on computer graphics.

“The Art of R Programming takes you on a guided tour of software development with R, from basic types and data structures to advanced topics like closures, recursion, and anonymous functions. No statistical knowledge is required, and your programming skills can range from hobbyist to pro. Along the way, you’ll learn about functional and object-oriented programming, running mathematical simulations, and rearranging complex data into simpler, more useful formats.”

Statistical Analysis with R For Dummies

OUR TAKE: This easy-to-follow guide focuses on the foundational statistical concepts that R addresses, as well as step-by-step guidance that shows you how to implement them in the R programming language.

“People are becoming more aware of R every day as major institutions are adopting it as a standard. Part of its appeal is that it’s a free tool that’s taking the place of costly statistical software packages that sometimes take an inordinate amount of time to learn. Plus, R enables a user to carry out complex statistical analyses by simply entering a few commands, making sophisticated analyses available and understandable to a wide audience.”

The Book of R: A First Course in Programming and Statistics

OUR TAKE: The Book of R is a gentle yet informative introduction to the statistical software environment R. It is for anyone interested in programming, statistics, and data analysis, and is especially well-suited for students and instructors of statistics courses.

“The Book of R is a comprehensive, beginner-friendly guide to R, the world’s most popular programming language for statistical analysis. Even if you have no programming experience and little more than a grounding in the basics of mathematics, you’ll find everything you need to begin using R effectively for statistical analysis. Combining detailed explanations with real-world examples and exercises, this book will provide you with a solid understanding of both statistics and the depth of R’s functionality.”

R in Action: Data Analysis and Graphics with R

OUR TAKE: This book teaches you how to use the R language by presenting examples relevant to scientific, technical, and business developers. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats.

“R in Action, presents both the R language and the examples that make it so useful for business developers. Focusing on practical solutions, the book offers a crash course in statistics and covers elegant methods for dealing with messy and incomplete data that are difficult to analyze using traditional methods. You’ll also master R’s extensive graphical capabilities for exploring and presenting data visually. And this expanded second edition includes new chapters on time series analysis, cluster analysis.”

Machine Learning with R: Expert techniques for predictive modeling

OUR TAKE: The new 3rd edition updates the classic R data science book to R 3.6 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning.

“Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data. Machine Learning with R, Third Edition provides a hands-on, readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings.”

Hands-On Programming with R: Write Your Own Functions and Simulations

OUR TAKE: Author Garrett Grolemund is a statistician, teacher and R developer who currently works for RStudio. The book shows you how to work hands-on with three practical data analysis projects based on casino games.

“Learn how to program by diving into the R language, and then use your newfound skills to solve practical data science problems. With this book, you’ll learn how to load data, assemble and disassemble data objects, navigate R’s environment system, write your own functions, and use all of R’s programming tools. RStudio Master Instructor Garrett Grolemund not only teaches you how to program, but also shows you how to get more from R than just visualizing and modeling data.”

R for Everyone: Advanced Analytics and Graphics

OUR TAKE: This book shows you how to write R programs and construct both linear and nonlinear models using some data mining techniques. Author Jared P. Lander is the owner of Lander Analytics, a statistical consulting firm based in New York.

“Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you’ll need to accomplish 80 percent of modern data tasks. Lander’s self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code.”

The R Book

OUR TAKE: This book received rave reviews from leading minds in the field and looks at the evolution of R over the past five years. It also features a new chapter on Bayesian Analysis and is supported by an accompanying website.

“The R language is recognized as one of the most powerful and flexible statistical software packages, enabling users to apply many statistical techniques that would be impossible without such software to help implement such large data sets. R has become an essential tool for understanding and carrying out research. This edition features full colour text and extensive graphics throughout and introduces a clear structure with numbered section headings to help readers locate information more efficiently.”

Learning R: A Step-by-Step Function Guide to Data Analysis

OUR TAKE: Author Richard Cotton has worked extensively on tools to give non-technical users access to statistical models. He is the author of the R packages “assertive” for checking the state of your variables and “sig” to make sure your functions have a sensible API.

“Learn how to perform data analysis with the R language and software environment, even if you have little or no programming experience. With the tutorials in this hands-on guide, you’ll learn how to use the essential R tools you need to know to analyze data, including data types and programming concepts. The second half of Learning R shows you real data analysis in action by covering everything from importing data to publishing your results. Each chapter in the book includes a quiz on what you’ve learned.”

Advanced R

OUR TAKE: This second edition acts as a comprehensive guide and includes new foundational chapters. The title also offers comprehensive coverage of object-oriented programming with chapters on S3, S4, R6, and how to choose between them.

“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.”

Beginning R: The Statistical Programming Language

OUR TAKE: This book delves into the R language by featuring simple data examples. It also learn how to carry out a range of commonly used statistical methods like Analysis of Variance and Linear Regression.

“While R is very flexible and powerful, it is unlike most of the computer programs you have used. In order to unlock its full potential, this book delves into the language, making it accessible so you can tackle even the most complex of data analysis tasks. Simple data examples are integrated throughout so you can explore the capabilities and versatility of R. Along the way, you’ll also learn how to carry out a range of commonly used statistical methods, including Analysis of Variance and Linear Regression.”

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