## Table of Contents

**R and Python** are currently the top programming languages preferred for accomplishing data science and so they have a fair share of advantages and disadvantages over one another.

Python is preferred for **Machine Learning** owing to its production-ready built and the ease of integrating data analysis with web apps. Whereas R is preferred by data miners and statisticians for developing statistical **computing software** by virtue of its inherent statistical nature.

R is a complete platform for **statistical programming** and not only a programming language. In this article, we are going to focus on the best R books that will help you realize your dream of working in the lucrative field of data science or make you better at it if you’re already living the dream.

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**Best R Books**

Before we begin, take note that this is the list of 10 best R books in a general way i.e. we aren’t comparing these books among themselves. The book detailed at number 1 need not be better than the book mentioned on number 2 and others. All of them are worthy - in our opinion - to be on the list.

**1. R in Action**

By - Robert L. Kabacoff

Latest Edition - Second

Formats Available - Paperback

Publisher - Dreamtech Press

Reading Level - Beginner/Intermediate

**Ratings:**

- Amazon - 4.5/5(141 ratings)
- Goodreads - 4.2/5 (233 votes)

Aside from maintaining the popular Quick-R website, Dr. Robert L. Kabacoff has penned several great books on R, including R in Action. The book detailing R, now in its second edition, presents readers with detailed real-world examples belonging to business, science, and technology.

In addition to detailing real data science scenarios and practical R-based solutions, the R in Action book offers a crash course in statistics. This primarily involves detailing powerful methods for making sense out of incomplete, unclear, vast amounts of data.

R in Action also details out the graphical capabilities of R for exploring, managing, and solving data visualization challenges. The latest edition of the R in Action book adds more chapters that detail data mining, dynamic report writing, and forecasting.

**Topics covered:**

- Basics of ggplot2.
- Data mining.
- Data visualization.
- EDA (Exploratory Data Analysis).
- Graphics in R.
- Machine learning models.

You can buy this book from here.

**2. R for Data Science**

By - Hadley Wickham and Garrett Gorlemund

Latest Edition - First

Formats Available - Kindle and Paperback

Publisher - O’Reilly

Reading Level - Beginner

**Ratings:**

- Amazon - 4.7 (666 ratings)
- Goodreads - 4.6 (640 votes)

Hadley Wickham is a reputed writer when it comes to writing about the inner (and outer) workings of the R programming language, and also data science. The R for Data Science is a complete package for readers interested in dissecting and digesting both data science and R served from a single plate.

The R for Data Science book begins with developing a holistic understanding of data science, the implementation of the discipline, and the science behind it as well. Sooner from the early chapters, the book picks up the pace on leveraging the R platform for accomplishing various data science tasks and operations.

The (credited) main author of the book - Garrett Grolemund - is a Master Instructor at RStudio - takes on the mantle to explain the practical, real-world implementation of the synergy among R and data science in a way that is both captivating and motivating to do and know more.

**Topics covered:**

- Data wrangling.
- Data visualization.
- Exploratory data analysis.
- Fundamentals of R.
- Fundamentals of data science.
- Implementation of R and data science.

You can buy this book from here.

**3. The Art of R Programming - A Tour of Statistical Software Design**

By - Norman Matloff

Latest Edition - First

Formats Available - Kindle and Paperback

Publisher - No Starch Press

Reading Level - Beginner

**Ratings:**

- Amazon - 4.4/5 (200 ratings)
- Goodreads - 4.1/5 (533 votes)

Another book that secures its place among the best R books is The Art of R Programming by Norman Matloff. The author of the book is the creator of several popular software packages as well as serves as a Professor of Computer Science at the University of California. So, learning from him, is obviously, great.

The Art of R Programming doesn’t require any statistical knowledge and will work for you even if you’re having a low-level/beginner-level competency in programming. Hence, it is a perfect fit for beginners. The R book offers a thorough understanding of software development using R.

Other than R and software development, The Art of R Programming also covers object-oriented and functional programming paradigms, complex data rearrangement, and running mathematical simulations.

**Topics covered:**

- Complex functions.
- Data visualization.
- Fundamentals of R.
- Statistical programming.
- Statistical software development.

You can buy this book from here.

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

By - Garrett Grolemund

Latest Edition - First

Formats Available - Kindle and Paperback

Publisher - Shroff/O’Reilly

Reading Level - Intermediate

**Ratings:**

- Amazon - 4.4/5 (131 ratings)
- Goodreads - 4.4/5 (129 votes)

The Hands-On Programming with R book details assembling and disassembling data objects, loading data, navigating the R environment, using the tools available for R, and developing user-defined functions. The book does so using an easy-to-comprehend language.

To make learning fun, The Hands-On Programming with R book features 3 casino-games-inspired practical data analysis projects. Each includes comprehensive examples that involve using a range of R programming skills, such as data visualization and modeling.

Hands-On Programming with R is written by Garrett Grolemund, the RStudio Master Instructor who is also the co-author of another superb book based on the R platform, R for Data Science. Aside from focusing on R, the instructor uses the book to educate readers about data science and the art of programming.

**Topics covered:**

- Basics of R.
- Data modeling.
- Data visualization.
- Fundamentals of data science.
- Complementary R tools and software.

You can buy this book from here.

**5. R Graphics Cookbook: Practical Recipes for Visualizing Data**

By - Winston Chang

Latest Edition - Second

Formats Available - Kindle and Paperback

Publisher - Shroff/O’Reilly

Reading Level - Advanced

**Ratings:**

- Amazon - 4.6/5 (33 ratings)
- Goodreads - 4.2/5 (261 votes)

For readers seeking a book that vividly explains R but stays focused on its graphical capabilities, an excellent read is the R Graphics Cookbook. It features 150+ illustrations, called recipes, for generating high-quality graphics quickly using the R platform.

Each recipe takes on a specific problem with a detailed solution. The why and how of the so-called recipe is also explained in detail so as to develop a robust understanding of the related concepts among the readers. Most examples leverage the extensively popular ggplot2 package.

The R Graphics Cookbook is written by Winston Chang, a software engineer at RStudio. Interestingly, the R Graphics Cookbook is an updated version of the author’s past project, the Cookbook for R. It was a website detailing programs for efficiently handling the usual tasks in R.

**Topics covered:**

- Data visualization.
- Graphics in R.
- Solutions to common/redundant tasks.
- The visual design of graphics.

You can buy this book from here.

### 6. R Packages: Organize, Test, Document, and Share Your Code

By - Hadley Wickham

Latest Edition - First

Formats Available - Kindle and Paperback

Publisher - Shroff/O’Reilly

Reading Level - Advanced

**Ratings:**

- Amazon - 4.7/5 (43 ratings)
- Goodreads - 4.5/5 (126 votes)

The R Packages: Organize, Test, Document, and Share Your Code is meant for learners looking to develop a robust understanding of the R packages. In addition to explaining the underlying concepts of R packages, the book details the process of creating and sharing your very own R packages.

The book on R programming lets readers know and work with dev tools and oxygen, two of the most popular R tools. Throughout the R Packages book, the readers will test, study, and develop an understanding of how R packages automate the common development tasks.

R Packages is written by Hadley Wickham, an Adjunct Professor of Statistics at Rice University, a renowned author - having authored R for Data Science - , and the Chief Scientist at RStudio. Learning assured as Wickham has also many contributions in developing and perfecting the R platform as well as teaching the complexities of R with equal ease as clearing the fundamentals.

**Topics covered:**

- Basics of R programming.
- R packages: working, developing, implementation, and optimization
- Adding documentation.
- Reusability of R functions.
- Uploading R packages.
- Data sampling.
- Devtools and oxygen.
- The importance of documentation in R.

You can buy this book from here.

**7. Practical Data Science with R**

By - Nina Zumel and John Mount

Latest Edition - First

Formats Available - Paperback

Publisher - Dreamtech Press/Manning Publications

Reading Level - Beginners

**Ratings:**

- Amazon - 3.9/5 (12 ratings)
- Goodreads - 4.1/5 (81 votes)

Manning Publications has a sparkling reputation for publishing books detailing programming and related technologies. The publication giant brings the immensely useful Practical Data Science with R that not only details the popular data science platform i.e. R but the field of data science as well.

Practical Data Science with R, authored by Nina Zumel and John Mount, helps readers develop a robust understanding of the practical applications of data science and how R can help in accomplishing the same. It also explains, beautifully, the statistical techniques required for solving complex business problems.

The Practical Data Science with R book is replete with detailed examples from the fields of business intelligence (BI), decision-making, and marketing. These are leveraged for vividly demonstrating the process of building predictive models, designing suitable tests, and catering results to an audience of variety, spanning professionals of different levels as well as beginners.

**Topics covered:**

- Basics of data science.
- Fundamentals of R programming.
- Graphics in R.
- Implementation of the R platform.
- Predictive modeling.

You can buy this book from here.

**8. R for Everyone: Advanced Analytics and Graphics**

By - Jared P. Lander

Latest Edition - Second

Formats Available - Kindle and Paperback

Publisher - Pearson Education

Reading Level - Beginners

**Ratings:**

- Amazon - 4.3/5 (97 ratings)
- Goodreads - 4.1/5 (167 votes)

As the title of the book suggests, R for Everyone is an R reference for everybody. It starts with the absolute basics of the R programming language and then works on to advance into the expert R tasks, like adding rich documentation, advanced analytics, and making your own packages. It does so in a total of 30 self-contained chapters full of exhaustive hands-on example code.

R for Everyone makes it easy for professionals or even beginners to start with R despite having no prior exposure to statistical programming. This is something that the author of the book - Jared P. Lander, a reputed data scientist - has been doing for so many years of his professional life. So, this book is just an implementation of his teaching technique/narrative that is both easy and fun to understand.

The R book covers everything about R, from installing and setting up the R environment to developing your own, optimized R packages. All you need is just the will to do it.

**Topics covered:**

- Basics of R.
- Basics of statistics.
- Data modeling.
- Data visualization.
- Interactive dashboard using Shiny.
- R packages development.
- Rich documents using RMarkdown.
- Statistical programming.

You can buy this book from here.

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

By - Tilman M. Davies

Formats Available - Kindle and Paperback

Publisher - No Starch Press

Reading Level - Beginner

**Ratings:**

- Amazon - 4.5/5 (136 ratings)
- Goodreads - 4.2/5 (35 votes)

The Book of R: A First Course in Programming and Statistics is one of the best noob-friendly books available on R out there. Aside from a basic understanding of mathematics and the determination to learn R, readers require nothing else to benefit from The Book of R. It is a lengthy book not because of the content but because of the detailed, tons of examples.

The R book features a galore of extensive examples that will help the reader better ingest the concepts of R. The Book of R is a shining example of how combining detailed topics explanations with rich, real-world examples significantly ups the degree, and ease of, of learning.

In the advanced R section, The Book of R covers developing and performing optimized statistical tests and models, production of statistical summaries, and developing publication-ready graphics. The book on R is penned by Tilman M. Davies, a Ph.D. holder and statistics lecturer at the University of Otago (New Zealand). The book is inspired by Introduction to R, an annual three-day workshop hosted by the author.

**Topics covered:**

- Basics of statistics.
- Data visualization using ggplot2, ggvis, and rgl packages.
- Fundamentals of R.
- Graphics in R.
- Implementation of the R platform.

You can buy this book from here.

**10. The R Book**

By - Michael J. Crawley

Latest Edition - Second

Formats Available - Kindle, Hardcover, and Paperback

Publisher - Wiley

Reading Level - Beginner

**Ratings**

- Amazon - 4.4/5 (103 ratings)
- Goodreads - 4.1/5 (121 votes)

The R Book leverages full-color text and extensive graphics to educate learners about the R platform, from the fundamentals to all the way up to the advanced topics that involve implementing R-based solutions for solving complex data science problems.

In addition to all the vast array of topics covered by The R Book, the book on R also covers an analysis of the evolution of R over the previous 5 years (from the date of publication of the book). The new edition brings in a novel chapter detailing Bayesian Analysis and Meta-Analysis.

The R Book is authored by Michael J. Crawley an FRS at the Department of Biological Sciences, Imperial College of Science, Technology, and Medicine. The writer is well-versed in building interest around data science, the R platform, and solving complex, real-world problems.

**Topics covered:**

- Basics of R.
- Bayesian Analysis and Meta-Analysis.
- Data science fundamentals.
- Statistical programming.
- The evolution of the R platform.

You can buy this book from here.

**Other Honorable R Books**

There are hundreds to thousands of books available for studying the R programming language. So, choosing only 10 among them might make it unfair for some other brilliant titles. So, aside from the 10 listed above, here are 10 more honorable best R books mentions:

- Advanced R from Hadley Wickham
- An Introduction to Statistical Learning: With Applications in R form Daniela Witten
- Data Analysis and Graphics Using R: An Example-Based Approach form John Braun
- Learning R: A Step-by-Step Function Guide to Data Analysis form Richard Cotton
- R Cookbook from Paul Teetor
- R in a Nutshell form Joseph Adler
- Software for Data Analysis: Programming with R form John Chambers
- Statistics: An Introduction Using R form Michael J. Crawley
- Text Mining with R: A Tidy Approach form Julia Silge
- Using R for Introductory Statistics form John Verzani

Other than this list, there are still many other, mostly new, books on R that are alluding and great. Share your favorite R Books in the comments section below.

**Conclusion**

That sums up our list of the best 10 R programming books. No matter where your competency stands in the usage of the R programming language meter, you will find one - or more - of these 10 best R books helpful for your future R, and data science, endeavors.

Don’t agree with our picks? What’s your favorite R books? Share with the community via the comments section below or learn R better with some superb tutorials, filtered by the hackr.io team.

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