10 Best R Books in 2022

The popularity and importance of R have been growing in recent years. The reason is its extreme importance in data science and statistical analysis. However, learning a new language is no child’s play. The best R books are the best way to learn R especially if you are a beginner.

R is a programming language that allows users to analyze, organize, and display data using objects, variables, and functions. Data analysis is done with R. R is a programming language for handling, storing, and analyzing data in data science. It may be used to analyze data and model statistics.

Considering the number of books available in the market today, it is difficult to find one that will truly help you grasp the subject. Therefore, we have done the hard work for you and curated a list of the 10 best R books for you in 2022.

Table of contents

S.No.

Book Name

Author

1.

R in Action

Robert L. Kabacoff
2.

R for data science

Hadley Wickham, Garrett Grolemund
3.

Hands-on programming with R

Garrett Grolemund
4.

R for everyone

Jared P. Lander
5.

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

Norman Matlof
6.

R for dummies

Andries de Vries, Joris Meys
7.

Practical Data Science with R

Nina Zumel and John Mount
8.

R Programming: A Step-by-Step Guide for Absolute Beginners

 Daniel Bell
9.

Machine Learning with R

Brett Lantz
10.

The R Book

Felix Alvaro

10 Best R Books in 2022

1. R in Action

R in Action

Author: Robert L. Kabacoff
Publisher: Dreamtech press
Latest edition: Second
Available formats: Paperback
Level: Beginner/ Intermediate

About the author

Dr. Rob Kabacoff is an expert in data analysis and an experienced researcher. He’s conducted university courses in statistical programming and runs the statmethods.net Quick-R site.

About the book

R in Action, Second Edition, explains to you how to use the R programming language through real-world applications for scientists, engineers, and businesspeople. The book provides a crash course in statistics, with elegant approaches for dealing with complex and imperfect data while emphasizing workable solutions. You’ll also learn how to use R’s wide graphical features for visual data exploration and presentation.

Additional chapters on forecasting, data mining, and generating dynamic reports are included in this extended second edition. This is a great book on exploratory data analysis with many graphs. This book digs into machine learning models and covers a lot of terrain in terms of data import, processing, analysis, and presentation.

We found the writing style to be quite approachable, and the range of topics covered to be quite extensive.

What you’ll learn

  • R Fundamentals
  • Making a dataset
  • How to Begin with Graphs
  • Fundamental data management
  • Data handling at its best
  • Fundamental graphs
  • Statistics for the layperson
  • Variance Regression Analysis
  • An examination of power
  • Graphs in the middle
  • Statistics from re-sampling and bootstrapping
  • Models of generalized linearity
  • Factor and principal component analysis
  • Advanced approaches for dealing with lost data Advanced graphics

You can buy this book from here.

2. R for data science

R for data science Import, Tidy, Transform, Visualize, And Model Data

Author: Hadley Wickham, Garrett Grolemund
Publisher: O’Reilly
Latest edition: First
Available formats: Paperback/ Kindle
Level: Beginner

About the author

Garrett Grolemund works at RStudio as a statistician, instructor, and R programmer. Data analysis, he believes, is a relatively unexplored source of value for both business and academia. Garrett spends all his time outside of the classroom conducting clinical trials studies, research work, and financial statement analysis. He also creates R software; he co-wrote the lubridate R package (which provides methods for parsing, manipulating, and doing arithmetic with date-times) and the ggsubplot library (which expands the ggplot2 bundle).

About the book

What is data science, and how does it differ from other disciplines? This book will provide you with a thorough understanding of this subject for identifying natural principles in data structure. You’ll also learn how to analyze data using the R programming language.

If you measure something twice, you’ll receive two different results—as long as you measure exactly enough. Conflict and possibility are created by this event. Author and RStudio Master Instructor Garrett Grolemund demonstrates how data science may assist you in dealing with uncertainty and seizing opportunities.

You’ll also discover the statistical viewpoint, a way of looking at the world that allows for knowledge in the face of ambiguity and clarity in the face of complexity, throughout the book.

What you’ll learn

  • Data Wrangling is the process of manipulating datasets in order to uncover new information
  • Data Visualization—how to make graphs and other visuals from data
  • Exploratory Data Analysis – how to look for evidence of correlations in your measurements
  • Modeling- extracting insights and forecasts from data
  • Inference—how to escape being duped by data studies that do not produce infallible results

You can buy this book from here.

3. Hands-on programming with R

Hands–On Programming with R Write Your Own Functions and Simulations

Author: Garrett Grolemund
Publisher: O’Reilly
Latest edition: First
Available formats: Paperback/ Kindle
Level: Intermediate

About the book

Hands-On Programming with R teaches you how to write user-defined functions, import data, browse the R environment, and use R’s tools. This is accomplished in a clear and comprehensible manner by the book.

It is important that learning is fun. The book Hands-On Programming with R includes three genuine data management tasks influenced by casino games. Each example includes step-by-step instructions for using R programming abilities like visualization tools and simulation.

Hands-On Programming with R was written by Garrett Grolemund, an RStudio Master Instructor and co-author of another great R platform book, R for Data Science. In addition to R, the teacher utilizes the book to teach pupils about data science and programming.

What you’ll learn

  • Hands-on experience with three real-world data analysis projects based on casino games
  • In the memory of your computer, you can store, access, and alter data values
  • Produce programs and tests that surpass those developed by average R users
  • R programming features such as if-else expressions, for loops, and S3 classes can be used
  • Discover how to develop vectorized R code that is lightning fast
  • Use R’s package structure and debugging tools to your advantage
  • Learn R programming fundamentals and put them into practice as you go.

You can buy this book from here.

4. R for everyone

R for everyone

Author: Jared P. Lander
Publisher: Pearson
Latest edition: First
Available formats: Paperback/ Kindle
Level: Beginner/ Intermediate

About the author

Lander Analytics’ Chief Data Scientist is Jared P. Lander. He is the company’s long-term visionary and serves as its Lead Data Scientist, exploring the best approach, models, and algorithms for modern data requirements. This is in addition to his consulting and training for clients. Data management, generalized linear models, machine learning, multilevel models, visualization, data management, and statistical computing are among his areas of expertise.

About the book

Jared P. Lander’s book R for Everyone: Advanced Analytics and Graphics focuses on the 20% of R capabilities required to complete 80% of modern data jobs. This book includes a lot of practice and code examples.

There are 30 chapters in this book, which is basically two books in one. The R language’s fundamentals are covered in the first 13 chapters. They’re fairly nice, and you’ll find them extremely helpful if you’re new to R. The subsequent chapters go into statistical learning approaches using R. The book includes a lot of practice exercises and code examples. The book then goes through how statisticians can use R to analyze data. Before you take up this book, you don’t need any programming knowledge.

What you’ll learn

  • R Markdown: An Overview
  • LaTex Fundamentals
  • Introduction to R Fundamentals of R Practice
  • Data Structures of the Future
  • Control Statements, Graphics Functions, and Loops
  • Manipulation of Data in Groups Data Reshaping Manipulation of Strings Probability Distributions
  • Statistics for Beginners

You can buy this book from here.

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

The Art of R Programming A Tour of Statistical Software Design

Author: Norman Matlof
Publisher: Addison-Wesley Professional
Latest edition: First
Available formats: Paperback/ Kindle
Level: Beginner/ Intermediate

About the author

Norman Matloff is a computer science professor (and used to be a statistics professor) at the University of California, Davis. Parallel processing and statistical regression are two of his academic interests, and he is the writer of a variety of frequently used Web courses on software development. He has contributed to the New York Times,  Forbes Magazine, the Washington Post, and the Los Angeles Times, as well as being the co-author of The Art of Debugging.

About the book

R is the most widely used statistical programming language on the planet: It is used by archaeologists to monitor the development of historical civilizations, pharmaceutical businesses to determine whether treatments are effective and safe, and actuaries to analyze financial risks and keep economies operating smoothly.

From basic categories and data patterns to complicated concepts like closures, recurrence, and anonymous functions, The Art of R Programming takes you on a walking tour of software creation using R. There is no requirement for statistical understanding, and your programming abilities might range from novice to expert.

You’ll master functional and entity programming, and scientific simulations, and reorganize complex data into more accessible formats along the way.

What you’ll learn

  • Make beautiful graphs to represent complex data sets and functions.
  • Create more efficient code by utilizing parallel R and vectorization.
  • Connect R to C/C++ and Python for greater speed or functionality.
  • Discover new R packages for text analysis, image editing, and more.
  • Using advanced debugging techniques, you may eliminate bothersome issues.

You can buy this book from here.

6. R for dummies

R for dummies

Author: Andries de Vries, Joris Meys
Publisher: Wiley
Latest edition: Second
Available formats: Paperback/ Kindle
Level: Beginner/ Intermediate

About the author

Joris Meys works as a statistician at the University of Ghent’s Biostatistics Department. R for Dummies is his co-authorship with Andrie de Vries.

About the book

Andries de Vries and Joris Meys’ R for Dummies is a quick and simple approach to learning all the R you’ll ever need. There are numerous practical examples, step-by-step activities, and sample code throughout the book.

The most typical syntax for writing basic R code is covered in this book. You’ll discover how to shape and process data, combine data sets, divide and integrate information, conduct vector and array computations, and much more. The book also discusses why R is the programming language of choice for statisticians and data analysts all over the world.

What you’ll learn

  • R is a programming language that may be used to analyze and process data
  • Create functions and scripts to allow for repeatable analysis
  • Make high-quality graphs and visuals
  • Conduct statistical analysis and model development

You can buy this book from here.

7. Practical Data Science with R

Practical Data Science with R

Author: Nina Zumel and John Mount
Publisher: Wiley
Latest edition: Second
Available formats: Paperback/ Kindle
Level: Beginner/ Intermediate

About the author

Nina Zumel is the co-founder of Win-Vector, a San Francisco-based data science consulting organization. She graduated from Carnegie Mellon University with a Ph.D. in robotics and worked as a content developer for EMC’s Data Science and Big Data Analytics Training Course. Nina also writes to the Win-Vector Blog, a site dedicated to statistics, chance, computer science, arithmetic, and optimization.

Win-Vector, a data science consulting firm based in San Francisco, was co-founded by John Mount. He holds a Ph.D. in computer science from Carnegie Mellon and has worked in biotech research, internet advertising, dynamic pricing, and finance for more than 15 years.

About the book

Manning Publications is known for publishing books about programming and associated technology. Practical Data Science with R, published by the publishing behemoth, delves into not only the renowned data science platform, R but also the discipline of data science.

Nina Zumel and John Mount’s book, Practical Data Science with R, teaches readers how to apply data science in real-world situations and how R might help.

It also elucidates the statistical tools needed to solve complicated business challenges in a clear and concise manner.

The book Practical Data Science with R is jam-packed with extensive examples from BI, judgment, and advertising. These are used to demonstrate the process of creating predictive models, devising appropriate tests, and catering outcomes to a wide range of audiences, including professionals of various levels as well as beginners.

You can buy this book from here.

8. R Programming: A Step-by-Step Guide for Absolute Beginners

R Programming A Step-by-Step Guide for Absolute Beginners

Author: Daniel Bell
Publisher: Wiley
Latest edition: Second
Available formats: Paperback/ Kindle
Level: Beginner/ Intermediate

About the author

Daniel Bell is an author and R developer who has been working in the field of development for several years.

About the book

Felix Alvaro’s R: Easy R Programming For Beginners- Your Step-By-Step Guide to Learning R Programming begins at the very beginning and gradually builds your knowledge of R one brick at a time. The book takes you through a logical series of classes while meticulously explaining each idea.

A lot of the basics are covered in this book. Functions, packages, arguments, and the best design principles for coding in R will all be covered. Images, examples, and other learning aids are included in the book.

Additionally, you will learn about the history of R programming and its advantages as you progress through the book. Apart from that, you will also learn how to set up R and R Studio, as well as how to use code editors Functions, and Arguments, the basics of R syntax User packages in R programming Using vectors to organize data, and so on.

What you’ll learn

  • R fundamentals
  • Types of data in R
  • Constants and variables in R
  • Operators with the letter R
  • Making decisions in R
  • Loops in R
  • R has a lot of functions
  • Objects and classes
  • R is a programming language used in data science

You can buy this book from here.

9. Machine Learning with R

Machine Learning with R Expert techniques for predictive modeling, 3rd Edition

Author: Brett Lantz
Publisher: Wiley
Latest edition: Third
Available formats: Paperback/ Kindle
Level: Beginner/ Intermediate

About the author

Brett Lantz has been studying human behavior for over ten years using cutting-edge data approaches. Brett, a trained sociologist, became interested in machine learning while working on a massive collection of teens’ social media accounts. He is a DataCamp instructor who speaks at machine learning workshops and seminars all around the world.

About the book

At its most basic level, machine learning is concerned with translating data into useful knowledge. R is a robust collection of machine learning tools that may help you acquire insight from your data quickly and effectively.

Machine Learning with R, Third Edition is a practical, easy-to-understand approach to using R to solve real-world issues. So, Brett Lantz teaches you all you need to know about uncovering critical insights, making new predictions, and visualizing your findings, whether you’re a veteran R user or new to the language.

The famous R data science book is updated to R 3.6 with new and improved libraries, guidance on moral and bias concerns in machine learning, and an intro to deep learning in this new 3rd edition. Learn machine learning using R and gain fresh insights from your data.

What you’ll learn

  • Discover how a computer learns by example and the roots of machine learning
  • With the R programming language, prepare your data for machine learning work
  • Use the closest neighbor and Bayesian approaches to classify significant outcomes
  • Decision trees, rules, and support vector machines can all be used to predict future events
  • Using regression algorithms, forecast numerical data and predict monetary values
  • Artificial neural networks, the foundation of deep learning, can be used to model complex processes

You can buy this book from here.

10. The R Book

R Easy R Programming for Beginners

Author: Felix Alvaro
Publisher: Createspace Independent Pub
Latest edition: Second
Available formats: Paperback/ Kindle
Level: Beginner/ Intermediate

About the author

Felix is a web designer, businessman, educator, and author who is enthusiastic about his work.

About the book

The R Book uses full-color text and accompanying images to teach learners everything they need to know about the R platform, from the basics to advanced topics like designing R-based solutions to solve complicated data science problems.

Aside from the wide range of topics covered by The R Book, the book on R also includes an examination of R’s evolution during the previous five years (from the date of publication of the book). The new edition contains a new chapter on Bayesian Analysis and Meta-Analysis.

What you’ll learn

  • The origins of R programming and its advantages
  • How to set up R and R Studio, as well as how to work with code editors
  • Functions and Arguments are the foundations of R syntax.
  • User packages in R programming
  • Using Vectors to organize data
  • Working with Matrices and Data-Frames
  • Making a List
  • R coding that works

You can buy this book from here.

Conclusion

Hopefully, the books mentioned in this article will help you learn R and become a pro at it. Remember, it is important to actually learn it practically because books can only do so much. So which of these are you planning to read? Let us know via the comments.

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