Best R Books

Why R Programming Books?

R is a widely used language in data science technology. This is used in organised and unorganized data analysis . R is very significant in computer science. It is an interpreted language, thus users can execute the code without the need for a compiler. 

Hence, R outperforms other programming languages in terms of capacity and speed. R is an analytical programming language that has applications in health, economics and statistics.

R does not solely assist users in technical fields, but it also assists users with their commercial ideas. It’s amazing for visualization and it offers a ton of features than other programmes. The scarcity of data analysts is a major problem for data-driven firms. 

R programming is becoming more prominent as a key framework for businesses because of which R programmers are in high demand. It also helps to discover numerous R books that analysts especially recommend to find and secure a career in fields related to R. 

As a result, R has become the de facto benchmark for statistical computation. R has a number of characteristics that distinguish it from other computer languages. 

Beginners, intermediate and advanced programmers can gain adequate knowledge by reading some of the widely recognised books of R. The R books will guide everyone about the best books to study R that will help boost the knowledge of R language and make it easier to become an R expert. 

This article covers the major books that help in learning and improving knowledge about R language programming. 

Books 1: The Book of R: A first course in Programming and Statistics:

The Book Of R: A First Course In Programming And Statistics is written by Tilman M. Davies, a versatile R language programmer for the last 10-15 years. 

He teaches R programming language and statistics as a senior lecturer at the University of Otago, New Zealand. 

This book gives the readers a broad view of the basics and structure of the R programming language. 

Readers with no prior knowledge or experiences in R language can understand this book easily. 

Programmers from other programming languages can read this book to understand and cope up with the R language from scratch. 

The students who are interested in statistics, data mining, data science, data analysis and so forth can get plenty of valuable information that will help them in their career domain. 

The Book Of R gives insights about developing visuals of data through using some of the prominent packages and tools of R such as ggplot2, ggvis, rgl, etc. 

It also gives lessons on managing and writing basic coding using R. The formation and management of making variables, data frames, loops, libraries, functions and statements are exclusively covered in this book. 

Several topics of statistics are covered in this book such as the theory of probability, regression, hypothesis, etc. 

Rating: 4.5

Pros:

• The Book Of R can be understood by anyone.

• The book covers all the information and knowledge of the basics of R.

• Many topics are explained by providing examples such as graphs, charts, codes, etc. 

• There are several exercises that readers can try practically. This helps the readers to get hands-on experience in computing a program. 

• The procession from simple programmes to complex practices is very smoothly derived. 

Cons:

• There is some technical information that may be irrelevant for some readers. It can be confusing as well. 

• It is highly recommended for beginners learning R but programmers in intermediate or advanced levels would not be greatly benefited from this. 

Book 2: Python and R for the modern Data Scientist:

This book has been written to focus on the relationship between Python and R programming languages. 

The topics covered by the authors give a wide range of knowledge about programming languages that helps data scientists and data analysts to enhance and improve their skills. In order to excel in their programming and statistical capabilities. 

Data scientists and data analysts, who know the basics of programming languages like Ruby and Julia, can also get valuable information and knowledge from this R book. 

Having knowledge about data mining, deep learning, machine learning, Python and its libraries, R, etc, adds as a plus point in understanding this book better, although it is not mandatory. 

The book Python and R for the modern data scientist discusses the changing scenarios in today’s era of data science and ways to keep up to date with it easily. 

It aims to promote the ideology that irrespective of the language a code is written in, the main focus should be on the various advantages that can be delivered and ways to incorporate different languages for other benefits. 

Readers would get insights into the merits and demerits of both R and Python languages. 

They would also understand and get hold of the concepts, frameworks, structures, packages of both the languages and blend these effortlessly. 

Book 3: Machine Learning with R expert techniques:

Machine learning with R is written by Brett Lantz, a DataCamp instructor with more than a decade of experience in using machine learning.

This book focuses on the various ways machine learning can be operated and utilized to help billions of people around the world. 

With its approach to R, the book gives a broad understanding of machine learning and its application in various data sets that helps programmers to develop codes efficiently. 

Readers can see the initiation of machine learning and its evolution to date through this book. It also gives essential tips and methods to accelerate the performance of a model.

Even readers with a non-programming background can easily understand the concepts through this book. 

It discusses Spark, H2O, TensorFlow, etc and its connection to R. 

Some of the important topics covered by the book are neural networks, regression, classification, clustering, decision trees and so forth. 

Rating: 4.5

Pros:

• The book is good for beginners.

• According to reviews on Amazon, it gives an apt description about how to install R studios. The format and structure of the book are easy to understand. 

• The chapters are distinct in nature so readers can start from any chapter they are interested in. 

• Helps in applying machine learning techniques to solve real-time issues. 

Cons:

• The details covered in the book regarding deep learning don’t have sufficient information and programmers can find it not very useful. 

• The book is beneficial to beginners but the same cannot be said in regards to the advanced programmers as the main machine learning topics aren’t covered in a proper manner. 

• It doesn’t give an in-depth analysis of numeric prediction models. 

• According to some reviews on Amazon, the book is nearly the same as its 2nd edition. Fresh techniques are not discussed thoroughly in this book. 

• In-depth details statistics and mathematical computations are not discussed.

Book 4: The Art of R Programming

Published on October 15, 2011, The Art of R Programming is written by Norman Matloff. He has been researching the R programming language for a long time and is currently teaching Computer Science at the University of California. 

The Art Of R Programming covers the foundation, structures and all other major information related to the R programming language. 

Some of the topics that are covered are visualizing complex data sets and functions, applying R and vectorization to create codes easily. 

It also helps to know about the latest debugging methods to minimize bugs, utilizing packages for image manipulation and analysis, ways to increase speed by applying R with Python and C/C++, etc. 

Readers would also be able to understand about applying mathematical simulations, converting complex data into a simple format, object-oriented programming, and so forth. 

The book is highly recommended to beginners, intermediate and advanced level R programmers. 

Rating: 4.5

Pros:

• The book covers many important aspects of R such as data frames, arrays, libraries, structures, etc. 

• It concentrates on understanding the foundation of the R programming language that helps new programmers to get a direct insight into R. 

• The length of the chapters doesn’t make it much time consuming for readers that want quick solutions. 

• The book is ideal for statisticians, data analysts and developers who want to create codes easily and gain extra knowledge about R. 

Cons:

• The book doesn’t give many details about using R to execute regression analysis. 

• There’s a lack of real-time case studies on the application of R. 

• Those who are not proficient in statistics may find it difficult to understand some examples. 

• It doesn’t give much scope to understand the major differences between functions such as apply, lapply, tapply, etc. 

Book 5: Advanced R

Advanced R is written by Hadley Wickham, who is widely known for his books like R for Data Science, R Packages and ggplot2 : elegant graphics for data analysis. 

This book helps beginners to learn the functions and concepts of the programming language R in a detailed manner. 

Programmers using other programming languages can also read this book to learn about R and its application. 

Intermediate and advanced programmers can understand the importance of the distinction, vector structures, environment, improving the running of code, eliminating bottlenecks, etc. 

It gives a vivid picture about R functions and their application, the condition system, loops, the OOP systems, efficient debugging methods and so forth. 

It also covers the topic of metaprogramming, packages like rlang and purr, and coloring of code chunks and figures. 

Rating: 5

Pros:

• It can help to increase the overall productivity and efficiency of the tools in order to secure the targeted results. 

• The book helps to give a broad view of the programmer’s own syntax so that they can identify the errors and modify them effectively. 

• Advanced R is highly recommended for intermediate and advanced programmers, even if they are proficient in other coding languages. 

• There are many extensive diagrams used in the book for better understanding. 

Cons:

• The author extensively writes about paradigms and tidyverse packages which confuse some readers. 

• It is also not useful to readers who are not proficient in writing complex functions in R. 

Book 6: R in Action Data Analysis and Graphics with R:

R in Action is written by Dr Rob Kabacoff.

The book describes various techniques for managing incomplete data using traditional methods. It also helps to get insights about graphical capabilities and visualization of data using R. 

It’s a whole package that contains the detailed analysis of R language tutorial, making packages, debugging methods, OOP in R as well as data mining, report writing, forecasting, regression, classification, time series and so forth. 

It is suitable for all levels of learners (beginners, intermediate and advanced). 

Rating: 4.5

Pros:

• The book has given plenty of real-life examples with clear clarity.
• The integrated approach to describe data analysis and graphing helps the readers to know more about the functions of R. 

• Data science and statistics can be easily covered while reading R. 

• Regression, machine learning, advanced data science foundation and concepts are discussed in this book. 

• Students, researchers, and others interested in statistics will be able to get clarity about statistics, plotting and graphing. 

Cons:

• Some users found that mathematical aspects are only briefly discussed in this book. 
• The coding for the graphs is not exclusively discussed which makes it incomplete. 
• Neural networks, structural equation modeling are not covered in this book thoroughly. 

Book 7: An introduction to Statistical Learning with Applications in R:

It was written by Professor Gareth James (University of Southern California), Professor Daniela Witten (University of Washington), Professor Trevor Hastie and Professor Robert Tibshirani of Stanford University. 

This book is currently ranked #4 in the list of Mathematical & Statistical Software and Mathematical Physics books. 

An introduction to Statistical Learning lets readers get a deeper concept of statistics and its approach towards R. 

Many essential topics of R are elaborately discussed such as resampling methods, classification, modelling, prediction, linear regression, clustering, decision tree and so forth. 

There are individual lessons in the book that cover different tutorials on exercising the implementation of R. 

This book uses several examples based on practical experiences. Colour graphics are widely used to demonstrate different methods.

Rating: 4.8

Pros:

• People from a statistical background as well as non-statistical backgrounds can also easily understand the concepts.
• There are self-analysis questions at the end of each chapter that lets the readers know their level of understanding. 

• A wider understanding of statistical computation using machine learning. 

• Useful as an R programming guide for beginners who want to explore R and its numerous functions and applications. 

Cons:

• This is not very beneficial in learning elements of statistical learning (ESL).

• Advanced programmers will not find this relatively informing. 

• Some readers may find the book to be mathematically complex. 

Book 8: Practical Machine Learning in R:

Practical machine learning in R is written by Dr. Fred Nwanganga and Dr. Mike Chapple. Both of them are professors at the Mendoza College of Business. 

The concept of the book is built on applying machine learning in R to solve many problems that users face while developing artificial intelligence. 

Some of the topics covered by the book are techniques to manage data, foundation and application of supervised and unsupervised learning, nearest neighbor, decision tree, etc. 

It also guides the programmers to increase the efficiency of XGBoost,  Random Forest and other ensemble methods. 

Logistic regression, clustering, association rules, etc are exclusively explained in this book. Data analysts, statisticians, mathematicians, and so forth are the targeted audience for this book. 

It emphasizes the need for machine learning in business organizations, healthcare facilities, financial services, etc. 

It is ideal for students or anyone who is interested to learn more about machine learning while using R.  

Rating: 4.7

Pros:

• Anyone, even without a mathematical or statistical background, would easily grasp the concept. 

• It gives hands-on experience in understanding machine learning. 

• The author has used several coloured illustrations for better understanding. 

Book 9: Beyond Spreadsheets with R: Guide to R and Rstudio:

Beyond Spreadsheets with R is a book based on transforming raw data into computations, graphs, tables, etc using R. 

It teaches the readers to do simple common programming such as loops & strategies to analyse data through R. 

This book is for programmers to know about ways to use R and RStudio to extract useful data from a simple one. One can also learn to do tidying, refining and plotting of data. 

Rating: 4.5

Pros:

• It covers almost all the functions and applications of R, especially in creating meaningful data in spreadsheets. 

• The viewing pane allows the readers to observe data tables, R code, output, etc simultaneously. 

• Detailed examples contain function arguments that help the readers to get a better understanding. 

• RStudio is exclusively explained. 

• Beginners are highly recommended to read this book as it doesn’t need a strong understanding of R. 

Cons:

• Beginners may get impatient in the first chapter because of its lengthy explanation on R and RStudio. 

• Advanced programmers may not find this very much useful for their studies and research. 

Book 10: R for Everyone: Advanced Analytics and Graphics:

R for Everyone is written by Jared P. Lander, who teaches statistics at Columbia University. 

New programmers or anyone who is interested to learn about R can read this book for their understanding. It gives commands on the installation of R and its packages, navigating R, data import and so forth. 

The book also teaches how to learn and apply linear and nonlinear models, data mining techniques, solving statistical problems, etc. 

Rating: 4.4

Pros:

• Gives a broad analysis of R, RStudio and R packages. 

• Application of R on mathematical computations like vectors, functions, variables, etc. 

• Application of statistical tools in real-world situations. 

• Interaction and understanding the various R programmers worldwide. 

Cons:

• Beginners learning R may find some topics difficult in this book because of its complex elaboration. 

• This may not be highly beneficial for advanced R programmers. 

Conclusion 

This is the list of 10 best R books. Though all of these R books may not be beneficial for all levels of learners, some readers may relate or get help from most of the topics that are discussed in these books. 

Some of the books like R for Everyone and An introduction to Statistical Learning with Applications in R can be difficult to understand because of their advanced topics and difficult mathematical computations. 

These R books are written by several eminent professors working in prestigious universities across the globe. 

They have decades of experience in writing and handling R Programming language because of which many professional statisticians, scientists, data analysts and developers can get reliable techniques and solutions for generating advanced tools.