# R Books – Select, Learn & Become a Data Science Expert!

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*“Books are uniquely portable magic” *

― Stephen King

Through this R Books tutorial, you will learn about the best books to learn R that will ease your way for becoming an R expert. Moreover, you will learn about various R books that experts suggest for different roles like Data Analyst or Data Scientist. Also, you will get the best books to *learn R programming, Statistical Learning, R graphics, and RStudio.*

So, let’s start the tutorial.

## Best Books to Learn R

R is the lingua franca of statistics. More recently, it has become the go-to language for every data science operation. R is mostly used for *building robust data models, visualisation and analysis of the data.* There are several libraries, applications and techniques that are used to perform data exploration with R. We will recommend some of the popular and useful books to master R programming and implement it in everyday data science operations.

### 1. Hands-On Programming with R

With this book, you will learn various data operations like loading, data assembling and dissembling and navigation of R’s data objects as well as its environment system, writing your own functions, and usage of all R’s programming tools.

This book is for the people who are venturing into R for the first time. It is a novice-friendly book that will teach you how to perform various programming operations in R. You will learn* how to write functions, use loops, conditions and data objects in R.* With its easy to understand, lucid and concise language, this book provides a beginner-friendly explanation to its readers. This book has a concise and easy to understand language. Furthermore, the examples mentioned in this book are easily reproducible.

**You must definitely learn about the R Functions**

### 2. R for Data Science

*by Hadley Wickham & Garrett Grolemund*

Data Science is one of the most popular technologies of the present era and R is the primary tool for it. With this book, you will learn how Data Scientists use R. You will gain various skills required to become a proficient Data Scientist using R. You will master skills like *data cleaning, wrangling, visualisation,* etc. This book also provides an introduction to RStudio. Furthermore, the trending packages like tidyverse are part of this book, helping you to gain recent tools that are used in data science.

However, this book is not specifically oriented towards beginners as it requires prerequisite knowledge from the book by the same author of the book, “Hands on Programming with R”. With this book, you will learn the everyday tasks of a data scientist.

### 3. The Art of R Programming

The sole focus of this book is to teach programming in R. This book is ideal for people who want to expand their programming knowledge of R. This book also contains extended examples along with black-box packages to help you understand how R makes use of various programming constructs.

Another important and rare feature that this book provides is the **debugging principles in R**. The prerequisite knowledge of statistics is not mandatory and you can be a hobbyist or a pro-programmer.

### 4. An Introduction to Statistical Learning With Applications in R

*by Trevor Hastie and Rob Tibshirani*

The focus of this book is to perform the statistical implementation of various methodologies in R. In order to gain a comprehensive insight into the contents of this book, there is a MOOC provided by Stanford Lagunita that comprises of series of lectures that will help you along the way.

With the help of this book, you will not only gain a theoretical understanding of how various statistical methodologies work but also learn to implement them with R.

### 5. Learning RStudio for R Statistical Computing

*by Mark P.J.van der Loo & Edwin de Jonge*

If you want to make RStudio your ideal IDE for performing statistical computing in R, then this is the best book for you. The focus of this book is on the RStudio, a popular IDE for programming in R. You will learn how to use various functionalities with RStudio, perform reporting and optimise the development process.

With the various functionalities of R, you can create efficient statistical models without any hassle. You can also *manage various projects, easily import the data and plot robust visualisations.*

**Do you know – How to import & transform data through RStudio**

### 6. Practical Data Science with R

The approach of this book is very pragmatic as it teaches R through its various applications and use-cases in Data Science. It is a more application-driven book as the examples are based on R’s usage in *A/B testing, business intelligence and decision support.* The various authors of the book have an in-depth understanding of the different packages of R that are used in Data Science. In this way, they have combined the useful packages together to provide comprehensive implementation of R in data science.

For aspiring data scientists, who wish to learn all the essential tools and packages of R in one single place, this book will prove to be the best guide for them. Another important feature of this book is that in order to increase the readability, it does away with all the unnecessary. This book has laid out only the important facts and figures that are necessary while learning R programming.

### 7. R for Everyone: Advanced Analytics and Graphics

This book covers the diverse topics of Data Science in R such as *data visualisation, data manipulation, predictive modeling as well as clustering and building of R packages.* One downside of this book is its limitation in covering only the breadth of R but not its depth, due to which this book is much briefer in its approach. Furthermore, the emphasis of this book is on the implementation of various algorithms in R and their various examples. People who are much more interested in getting a hands-on approach in R without dwelling much over the details will find this book appealing.

**Don’t forget to check the Data Manipulation in R programming**

### 8. R Cookbook

With the help of this book, you will gain the knowledge of the various important techniques that are essential for performing data analysis in R. The focus of this book is mainly on the practical implementation of the fundamentals of data analysis in R.

Another feature of this book is that it covers several topics like *data pre-processing, statistics, probability, time-series analysis* etc. You will also learn the various techniques that are applied to the datasets in R. There are **200 practical recipes** using which you will be able to perform data analysis in R without any hassle.

### 9. R Packages

For R programmers, who want to write their own packages, this book provides all the necessary skills required for the job. Using the documentation of the reusable R functions, the author provides a detailed description of how to use them.

This book makes it easy to write your own customised packages which may seem daunting to the beginners. You will also gain knowledge of several components of R packages such as *unit tests and vignettes.*

**Understand the concept of R Lattice Package with Lattice Graphs**

### 10. Advanced R

As the name suggests, this book is not suitable for R programmers who wish to append additional knowledge to their arsenal of R skills. With this book, you will understand the various features that keep R ahead of other contemporary tools like SAS and SPSS. One important feature of this book is that it provides a step-by-step explanation of various topics.

These explanations are also accompanied by code snippets that can be easily reproduced through its practical implementation. This is an intermediate to advanced level book which is not for the newbies of R. This book is specifically for the R practitioners who are looking forward to expanding upon their R skills.

## Summary

In this tutorial, we have seen the best R books to master R programming. Also, these books will help both freshers and experienced programmers of R. Still, if you want to add some more information about R books, you can tell us through the comments.

**Now, you can explore the core differences between R and Python in our R vs Python tutorial.**

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