7 Best R Books To Teach Yourself From Scratch

7 Best R Books for R Programmers
7 Best R Books for R Programmers

Introduction to R Programming

Let us review the basics before we start reading about Best R Books. R and Python are the best programming languages for solving data science queries and they share a wide set of advantages and disadvantages among each other.

Python is production-ready built and allows easy integration of data analysis with web applications. In contrast, R is preferred by statisticians and data miners for building statistical computing sites owing to its statistical nature.

R is not just a programming language, it is a complete package for statistical programming.

In this article, we are going to deal with the best R books, this will help budding programmers to make their way through the promising field of data science.

7 Best R Books for R Programmers

Before we start learning R, let’s go through 7 Best R Books for R Programmers, it is not necessary that you have to pick any one of these books, you can refer to one or more among them. The correct books depend on the topic that you have to study and the content of the book.

1.R in Action

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Written By – Robert L. Kabacoff
Latest Edition – Second
Available in Formats – Paperback
Published by – Dreamtech Press
Recommended For – Beginner/Intermediate

Apart from creating and maintaining a handful of informative Quick-R websites, Dr Robert L. Kabacoff has penned down his knowledge of R in various books, such as R in Action. The second edition of the book detailing R contains diverse real-life examples from science, business and technology.

On top of the informative details about real-data science and real-life R-based solutions, it also offers a crash course in statistics. R in Action mainly focuses on strategies that involve deriving sense from incomplete, unstructured, vague, and huge amounts of data.

R in Action also brings out the details representing the graphical capabilities of R for managing, exploring, and solving data visualisation challenges. In the latest edition of the book, a few more chapters have been included that deal with data mining, dynamic report writing, and prediction.

Essential Content:

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

2. R for Data Science

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Written By – Hadley Wickham and Garrett Gorlemund
Latest Edition – First
Available in Format- Kindle and Paperback
Published By – O’Reilly
Recommended For – Beginner

Hadley Wickham is a remarkable author, his works on R programming and data science are noteworthy. The R for Data Science is a complete package for enthusiasts keen on dissecting both data science and R coming from a single source.

The R for Data Science book initiates the concept of data science and implementation of this discipline. Post the early chapters, the book shifts towards leveraging the R platform for performing various data science tasks and operations.

The accredited author of this book – Garrett Grolemund, is one of the Master Instructors at RStudio, goes on to explain the real-life implementation of the synergy between R and data science, in a really captivating way. 

Topics covered:

  • Data wrangling
  • Data visualisation
  • Exploratory data analysis
  • Fundamentals of R
  • Fundamentals of data science
  • Implementation of R and data science

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

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Written By – Norman Matloff
Latest Edition – First
Available in Format – Kindle and Paperback
Published By – No Starch Press
Recommended For – Beginner

The Art of R Programming by Norman Matloff is another book that has made to the list of the 7 best books for R programming. The author is well-versed in R programming, as he comes from the Department of Computer Science at the University of California and has created several widely used software packages.

On top of R and software development, The Art of R Programming also includes concepts related to object-oriented and functional programming paradigms, complex data rearrangement, and running mathematical simulations.

The Art of R Programming doesn’t come with any pre-requisite statistical knowledge and you can refer to it even if you have low-level/beginner-level competency in R programming. Hence, it is highly recommended for beginners. The R Programming book induces a deep understanding of software development with R.

Essential Content:

  • Complex functions
  • Data visualisation
  • Fundamentals of R
  • Statistical programming
  • Statistical software development

4. R Graphics Cookbook: Practical Recipes for Visualizing Data

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Written By – Winston Chang
Latest Edition – Second
Available in Format – Kindle and Paperback
Published by – Shroff/O’Reilly
Recommended For – Advanced

The R Graphics Cookbook is authored by Winston Chang, a well-versed software engineer at RStudio. The current R Graphics Cookbook is a modified version of one of his popular early works, the Cookbook for R. It was not a book, rather it was a website with programming details for efficiently managing the common tasks in R.

The R Graphics Cookbook is an excellent reader for learners seeking a book that explains the graphical capabilities of R. It comes with more than 150 illustrations, also known as recipes, used for generating dense graphics using the R platform.

Each recipe deals with a specific problem and its detailed solution. A complete understanding of the so-called recipe is given by going to and fro around the origin of the recipe. The commonly used examples demonstrate the ggplot2 package.

Essential Content:

  • Data visualisation
  • Graphics in R
  • Solutions to common/redundant tasks
  • The visual design of graphics

5. Practical Data Science with R

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Written By – Nina Zumel and John Mount
Latest Edition – First
Available in formats – Paperback
Published by – Dreamtech Press/Manning Publications
Recommended for – Beginners

Manning Publications are renowned for their works on detailed programming and related software technologies. This publication has launched the extremely useful  Practical Data Science with R, apart from the popular data science platforms, this also includes data science concepts.

The Practical Data Science with R was written by Nina Zumel and John Mount. It gives us a proper understanding of the practical use cases of data science and its relationship with R programming. This also deals with the statistical techniques required for solving complex business problems. The practical applications of data science can be very distinctly understood and implemented with the help of R programming.

The Practical Data Science with R book comes with a wide range of examples from the field of decision-making, marketing and sales, business intelligence (BI). These concepts are used for vividly demonstrating the designing of suitable tests, building predictive models, catering results to a varied audience, spanning experts as well as beginners.

Essential Content:

  • Basics of data science
  • Fundamentals of R programming
  • Graphics in R
  • Implementation of the R platform
  • Predictive modelling

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6. R for Everyone: Advanced Analytics and Graphics

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Written By – Jared P. Lander
Latest Edition – Second
Available in Formats- Kindle and Paperback
Published by – Pearson Education
Recommended for – Beginners

We can infer from the title of the book itself that it is written assuming that any person with absolutely zero knowledge of R programming can refer to this book. It is one of the best books for R programming that explains the basics of R programming in detail and then gradually moves towards the advanced R tasks.

Some of the critical R tasks include creating own packages, performing advanced analytics and maintaining rich documentation. It contains 30 distinct chapters that come with a wide range of hands-on example codes.

R for Everyone is primarily designed for beginners that don’t even have minimal knowledge of statistical programming. This has been the main agenda of the professional life of the author of the book – Jared P. Lander, a remarkable data scientist. 

This book includes his narrations that are fun to understand and easy to learn. This book includes everything around R, from installation and set up to the development of your own R packages.

Essential Content:

  • Basics of R
  • Basics of statistics
  • Data modelling
  • Data visualisation
  • Interactive dashboard using Shiny
  • R packages development
  • Rich documents using RMarkdown
  • Statistical programming

7. The R Book

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Written By – Michael J. Crawley
Latest Edition – Second
Available in Formats – Kindle, Hardcover, and Paperback
Published by – Wiley
Recommended for – Beginners

Authored by Michael J. Crawley, an FRS from the Department of Biological Sciences, Imperial College of Science, Technology, and Medicine, the R book remarkably builds interest in the content around the R platform, data science and solving complex real-life problems. It is one of the best books for R as it comes with a full-colour text and extended graphics for educating programmers about the R fundamentals to advanced R concepts for complex data science problems.

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Essential Content:

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

In addition to the above-mentioned stack of topics, the R book also includes a detailed analysis of the evolution of R over the last five years. The latest edition of the book also includes a novel chapter describing Bayesian Analysis and Meta-Analysis. It is being updated regularly to give the best results to programmers.

Frequently Asked Questions

What are some books on R programming that you read?

Some the books that I refer for learning R are:
1. Beginning R
2. Getting Started with RStudio
3. R Graphics Cookbook
4. Practical Data Science with R
5. R for Data Science
6. Machine Learning for Hackers
7. Social Media Mining with R
8. Data Mining and Business Analytics with R
9. Data Analysis with R
10. Web Application Development with R Using Shiny
11. Mastering Scientific Computing with R
12. Learning Predictive Analytics with R

What are the best R programming books for beginners?

Apart from the above-mentioned books, beginners can even read:
1. R for Everyone: Advanced Analytics and Graphics
2. The Book of R: A First Course in Programming and Statistics
3. Machine Learning with R: Expert techniques for predictive modeling, 3rd Edition
4. Hands-On Programming with R: Write Your Own Functions And Simulations
5. R Packages
6. Learning R
7. Advanced R (Chapman & Hall/CRC The R Series)
8. Text Mining with R: A Tidy Approach
9. R in a Nutshell
10. Software for Data Analysis: Programming with R (Statistics and Computing)

What are some good books to learn Machine Learning?

Best Machine Learning Books for Beginners:
1. Machine Learning For Absolute Beginners: A Plain English Introduction (2nd Edition)
2. Machine Learning (in Python and R) For Dummies (1st Edition)
3. Machine Learning for Hackers: Case Studies and Algorithms to Get You Started (1st Edition)
4. Machine Learning: The New AI (The MIT Press Essential Knowledge Series)

Best Machine Learning Books for Experts:
1. Pattern Recognition and Machine Learning (1st Edition)
2. Fundamentals of Machine Learning for Predictive Data Analytics
3. Machine Learning: The Art and Science of Algorithms that Make Sense of Data (1st Edition)
4. Programming Collective Intelligence: Building Smart Web 2.0 Applications (1st Edition)


That was our list of the 7 Best R Books for R Programmers. Even if you are an R programming expert you will surely find something very fruitful and unique in any of these books.

The references and source codes given in these books will surely help you in your future data science and machine learning projects. You can pick out one or multiple books for getting a proper idea about R. 

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Always go through the content page before you start reading any book, you must be aware that the contents of the book are relevant for you. Apart from R programming, there are numerous books written about Python, Machine Learning and Data Science, you can go through them as well so that you can create a complete real-time project.

By Vanshika Singolia