Statistical computing is a big area and it’s growing rapidly thanks to the big data movement. This leads to more jobs in languages like R, and open source project made for statisticians and data scientists.

Learning R can be tough without prior programming experience. But it’s not impossible.

With the right book you can pick up R in a month or less and be building your own projects from scratch. In this post I’ve curated the best R books to help beginners get started and to help intermediate-to-advanced coders expand their knowledge.

There are usually two types of beginners: those looking for an easy-to-understand intro and those looking for a technical intro.

If you want a simple intro to R then The Book of R is absolutely perfect. It’s over 800 pages long but the writing style is easy to follow. If you want a more technical intro then R in Action has some brilliant examples. Both are quality books for R beginners and they’re general enough to cover all aspects of the language.

This is the clearest book to help you understand R programming. The author doesn’t assume any experience with programming or data science.

The Art of R Programming is a simple approach to R programming from a generalists viewpoint. Over 400 pages you’ll learn the basics of R from compiling to debugging, visualization and image manipulation techniques.

Any data scientist or statistician should be able to follow along with ease. The early lessons cover the basics of R with variable scope and some basic functions.

This will take a while to complete if you’re brand new to programming. However this is also the best intro for beginners who may be intimidated at the idea of learning to write code.

What I like most about this book is how it teaches through example. Learning R introduces many different concepts all from a beginner’s point of view.

However it doesn’t just throw theory at you. Each chapter has many tutorials and exercises to help you practice writing code in a practical way.

The first chapter teaches you about the basics of R. How to setup, pick an IDE, and how to write your first program. Later chapters get into the nitty-gritty of R with sample exercises to help you learn.

This book covers loops, arrays, packages, unit testing, and common workflow techniques for data analysis. It doesn’t get into super advanced topics so this book is fantastic for a complete beginner.

By far one of the largest books on R you can find. R in Action spans 600+ pages with a wide variety of exercises for beginners and more experienced coders alike.

This book was also updated in its 2nd edition to include more exercises and some expanded passages. You’ll learn the basics of R through example along with related libraries like ggplot2. You’ll learn about graphs, charting, cluster analysis, and dozens of other topics built around real exercises.

I really like the writing style and how this book doesn’t target one specific person. It doesn’t matter if you’re a complete beginner or why you want to learn R.

This book covers so much ground that it can take you from a novice to at least a semi-experienced R programmer.

Be warned this is a very thick book, both in length and content. The writing style is clear but heavy and technical. You may need to reread some chapters to fully digest the info.

Thankfully there is a lot of info here making this book one of the best learning tools for all experience levels.

Here we have a more theory-based book covering R from the perspective of data science. There are still plenty of exercises in this book but it also covers the idea of data science and how R fits into the equation.

In Practical Data Science with R you’ll learn about the process of data science and how to analyze data. The author frequently shares workflow tips and wraps up each chapter with bullet points as takeaways.

The information is very simple to digest and this book can vastly improve your knowledge of data science.

However it doesn’t feel like a detailed look at R beyond simple data analysis. This works as a fine intro for beginners into that field, but it won’t satisfy someone learning R for others reasons.

This is one of the newest books on the market and it covers R in a very positive light. The Book of R totals a massive **832 pages** which is huge for an intro programming book.

The writing style is fantastic and the author clearly wrote this to help beginners dive into R programming. It doesn’t focus on any single area, but rather spans the gamut of everything R can do.

Each chapter moves in a logical progression starting with the basics of R, the syntax, and finally techniques for building with R. These later chapters cover data analysis, statistics, and visualization with R graphics/charts.

Throughout this process you’ll learn many different functions and proper techniques for writing great R code. This is one of the best titles for learning R from a generalist’s perspective.

You can pick this up with absolutely zero programming knowledge and by the end you should feel confident writing an R program from scratch.

If you’re looking to use R for statistics then this is your book. R for Everyone covers the basics of R for beginners, then delves into how R can be used by statisticians for data analysis.

The early chapters hold your hand introducing R, RStudio, and the basic mathematical functions. From there you’ll get into user-defined functions and more complex(yet specific) tutorials.

You should already have some knowledge of statistics before picking up this book. However you don’t need any prior programming experience so this is the one-stop guide for R in statistics.

Later chapters can get complex since they cover probability and mathematical models graphed using ggplot2. As long as you take it slow you should have no trouble finishing this book.

If you’re looking for a much simpler intro guide to R and RStudio then you might like R For Dummies. It tackles the basics of the language from setup to initialization and creating new projects.

But there’s one common factor I find in the dummies series of programming books: they always skim the details. And R For Dummies is no exception.

This book is a nice little intro to clarify the purpose of R, how it works, and why you’d use it. But this book will not truly help you understand the programming language.

It’s definitely great at explaining the basics but if you’re looking for more practical exercises you’ll be happier with a book like R in Action.

I’d recommend this title to people who simply have no idea what R is or why it’s used in data analysis. Easy R Programming for Beginners starts from the very bottom and slowly builds your knowledge one brick at a time.

It’s a pretty short book at only 156 pages but it does cover a lot of the fundamentals. The writing style is technical but also accurate.

Every chapter in this book is a guide to R programming. You’ll learn about functions, arguments, packages, and the best design patterns for coding in R.

If you already have some experience or if you need a real technical book then this is not for you.

But if you’re a complete beginner with no idea where to turn then this book should answer all your questions and give you a solid foundation to keep learning.

Advanced coders may feel left out since many of these books target beginners. But Efficient R Programming is one of the few great books made for skilled R programmers.

This O’Reilly title covers advanced techniques and workflows to write quality R code. It spans 220 pages teaching efficiency from your initial R setup to writing your code and debugging.

Efficient programming can be considered subjective but the results don’t lie. This book teaches you how to write code that’s easy to read, easy to compile, and easy to share with others. It’ll take your existing knowledge and morph that into a powerhouse workflow with R.

This book is for intermediate-to-advanced developers who can already code in R but might want to improve their coding techniques. It’s one of the few optimization books that will make a difference in your workflow.

With R you can build packages that contain functions and data compiled together into sharable formats. R Packages covers the fundamentals of building and sharing your own packages using R.

This book teaches you the R package methodology using 3rd party tools and common workflows. You’ll learn about vignettes and unit testing and how these features fit into the overall picture of R programming.

Automation is also a huge part of this process and it’s something you’ll learn over time. Since this book delves into more complex topics it is not meant for beginners.

I’d recommend this title to R programmers who know enough about packages and want to learn to create their own. This book teaches the best practices for building packages and how to submit them into the CRAN archive.

Many statisticians use Excel to organize numbers and generate formulas that save time in the day-to-day. R can also connect into Excel documents to pull out information, organize it, and even graph it using visualization.

With R for Excel Users you’ll learn everything about R and Excel. The book has plenty of screenshots and each chapter has a series of step-by-step tutorials to help you learn through example.

Everything in this book centers around data manipulation with the connection between spreadsheets and R programming. The author has an excellent writing style and he knows how to teach R well.

However this is not a beginner’s book and it’s certainly not an intro to R. You should already have some knowledge of R programming and plenty of experience using Excel. This book bridges the gap between these two tools with clear writing and plenty of examples.

There’s no limit to what you can do with R and this book offers just one more path to walk. Mastering Social Media Mining with R is the pinnacle of API development and data analysis.

Over 250 pages you’ll learn the basics of social media mining and connecting into different APIs. The author covers a variety of social sites including Facebook, Twitter, Wikipedia, Instagram, LinkedIn, and Github(among others).

R has tons of packages with pre-built API functions that can save you time. This book doesn’t force you to learn everything from scratch. Instead it teaches you how to be smart about mining data, analyzing that data, and presenting that data with R.

You do not need to know API development to pick up this book. However you do need experience with R and preferably some experience with machine learning as well.

One of the most valuable R packages is ggplot2, a graphics package for customizing data charts. And one of the best books to study this package is ggplot2: Elegant Graphics for Data Analysis.

This book assumes prior knowledge of R and maybe some understanding of ggplot2. But you don’t need any experience with this graphics library to work through the book.

Every chapter is easy to read and full of live examples. You’ll learn how to customize ggplot2 with themes, multiple layers, and automated legends/axes.

The syntax is incredibly simple to understand so I can’t imagine any R programmer would have trouble with this book.

Just make sure you understand the basics of R and data analysis before diving into ggplot2.

There’s always more to learn with R programming and this book is proof of that. R for Data Science is the best book for newcomers learning R specifically for data science & analysis.

The authors Hadley Wickham and Garrett Grolemund are both renowned R developers who have built dozens of packages and programs. This book teaches you how to write R code, but also how to explore datasets and test your ideas with custom R programs.

It’s a fairly large book totaling 520 pages with 24 chapters. Each chapter covers a different skillset using functions, vectors, ggplot2, and other R techniques/packages.

The authors approach each chapter from the perspective of a curious data scientist.

Thankfully this is a beginner-friendly book and it’s a great choice for any data scientist hoping to learn R. It’s well written and clear enough to understand without any programming experience.

If you’re more of a generalist then Advanced R is an excellent book covering a wide array of problems and how to solve them.

It should be obvious from the title that you need R experience before nabbing this book. It’s written by Hadley Wickham who has authored many different R programming books. He’s a skilled practitioner with an excellent writing style that’s easy to read.

Each chapter delves into a different area of expertise and you’ll learn how to solve typical problems with R and RStudio. But you’ll also learn about tools and workflows like metaprogramming, functional programming, and unit testing.

The author doesn’t hold your hand so you should be able to follow technical code samples and Google when you’re confused.

Once you’ve gone beyond the basics and want to learn more then Advanced R would be an excellent resource for any programmer.

There’s a whole branch of modern programming dedicated to machine learning. These algorithms are becoming much more popular for both enterprise and consumer applications.

Machine Learning with R is a great introduction to this methodology. The writing is very technical and a bit terse so don’t expect much hand holding.

However the topics covered are very beginner-friendly. You learn through example with source codes and explanations of the code in each chapter.

And while you don’t need much(or any) programming knowledge to get started, you will need a statistics background. Refresh your brain on statistics and probability before diving into this book.

Here’s another Packt title that covers a much more detailed look at data analysis. Mastering Predictive Analytics with R teaches predictive modeling through datasets and machine learning.

This book is pretty complex but the writing is approachable from any semi-experienced coder. You should have a background in R before picking up this title, although you don’t need to be an expert.

Early chapters actually cover the basics of predictive modeling and how it all works. You’ll learn about linear regression, neural networks and machine learning techniques that all stem from R fundamentals or free packages.

This is a tough book to recommend because it is broad enough that anyone could pick it up. But only data analysts and/or machine learning aficionados would really appreciate the entire book cover to cover.

Over 430 pages you’ll get a brilliant selection of theory and practice for statistical learning in R. This book may feel like a college textbook yet it reads like a relaxed programming book.

An Introduction to Statistical Learning focuses on R using examples of statistics and big datasets. This book is very heavy in math so it helps to have a background in statistics.

If you can work through this book you’ll learn the basics of statistical learning for machine learning. It’s a huge undertaking so it is wise to have some knowledge of statistics and R programming before you dive in.

But the writing style is exceptionally clear and the examples perfectly illustrate everything the authors are trying to convey.

There isn’t too much R code to call this a programming book. But there’s also not too much jabber to call this a theory-based intro book.

It strikes the perfect balance of code and theory to help anyone get up to speed with machine learning in R. This is a must-read for anyone just getting into the topic of R machine learning.

I’m a huge fan of reference guides because many times you’ll find more complete solutions in a book vs on the Internet. The R Cookbook is a fantastic reference guide and teaching tool for R programmers.

With over 200 recipes you’ll learn all about different aspects, techniques, and features in the R programming languages(and associated packages). The recipes span the gamut of big data analysis to statistics/probability and visualization with graphics.

You can skim the index to find specific recipes or look through to find a relevant problem/solution. Each recipe starts with an example problem and uses R to show how it can be solved.

The best thing about this book is how it reaches such a wide audience. A beginner can learn a lot from studying these recipes and an expert coder can use this as a powerful reference guide.

R Cookbook is one of the most valuable programming cookbooks regardless of why you’re learning R or what you need it for.

If you’ll be using R to create graphs with ggplot2 then you’ll benefit from the R Graphics Cookbook.

The ggplot2 syntax isn’t too difficult to learn. However it is a very complex package and it can be like learning a brand new library(like jQuery vs JavaScript). This cookbook teaches you the fundamentals of the syntax first.

Early chapters cover the basics of R but quickly move into recipes for various solutions like bar charts, scatter plots, annotations, legends, axes, and custom colors/styles.

This book comes with **over 150 different recipes** all teaching the intricacies of visualization & charting with R. ggplot2 is the standard graphics package but there are some other tools mentioned as well.

I’d recommend this book for any semi-experienced R programmer who needs to master graphics and data visualization. The recipes may seem easy at first but the difficulty rises quickly and by the end you’ll feel like an R graphics expert.

This is one whopper of a collection and you may feel overwhelmed with so many choices. But remember these R books target different people at different stages of their learning.

Complete beginners will do best with The Book of R or R in Action. I personally prefer R in Action because Manning’s books are formatted so well and they have high editorial standards. But you cannot go wrong with either book since they’re both broad enough to cover statistics, data analysis, big data, or anything else you wanna learn.

More advanced coders can benefit from books that focus on specific areas. Efficient R Programming is a must-read if you want to build clean R programs & packages.

I also highly recommend the R Cookbook to any serious R programmer. It also takes a generalist approach with code snippets for many different scenarios.

Take another look over this list to see if any titles grab your attention. R is a big language with many practical uses. If you know what you’re trying to learn it’ll be that much easier to find the right book for your needs.

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