Contents

- 1. Objective
- 2. Best Books to Learn R
- a. A Handbook of programming with R by Garrett Grolemund
- b. The Art of R programming by Norman Matloff
- c. An Introduction To Statistical Learning With Applications in R by Trevor Hastie and Rob Tibshirani
- d. Learning RStudio For R Statistical Computing by Mark P.J.van der Loo
- e. Practical Data Science with R by Nina Zumel & John Mount
- f. R for Everyone: Advanced Analytics and Graphics by Jared P. Lander
- g. R Cookbook by Paul Teetor
- h. R Graphics Cookbook by Winston Chang
- i .R Packages by Hadley Wickham
- j. Advanced R by Hadley Wickham

## 1. Objective

Through this tutorial, you will learn about the best books to learn R that will help you in becoming R expert. You will learn about various R books being suggested by experts for various roles like Data Analyst or Data Scientist. You will get the best books to learn R programming, Statistical Learning, R graphics, and RStudio.

## 2. Best Books to Learn R

R is probably every data scientist’s preferred programming language (besides Python and SAS) to build prototypes, visualize data, or run analyses on data sets. Many libraries, applications and techniques exist to explore data in R programming language. So here is our recommendation for the best Book to learn R and become a master of the technology.

### a. A Handbook of programming with R by Garrett Grolemund

It is best suited for people new to R. This book teaches you to learn how to load data, assemble and disassemble data objects, navigate R’s environment system, write your own functions, and use all of R’s programming tools. Here you will understand how to write functions & loops in R, rather than just juggling with packages. The book language is simple to understand and examples can be reproduced easily.

### b. The Art of R programming by Norman Matloff

This book teaches how to do software development with R, from basic types and data structures to advanced topics. No statistical knowledge is required, and your programming skills can range from hobbyist to pro.

### c. An Introduction To Statistical Learning With Applications in R by Trevor Hastie and Rob Tibshirani

Even if you’re a novice at machine learning and don’t know R, I’d highly recommend reading this book from cover to cover, to gain both, a theoretical and practical understanding of many important machine learning and statistical techniques.

### d. Learning RStudio For R Statistical Computing by Mark P.J.van der Loo

This book is different from the others in the list in the sense that it teaches you how to use R on the popular IDE RStudio rather than on the standard R software. The book is for R developers and analysts who want to do R statistical development using RStudio functionality. So you can Quickly and efficiently create and manage statistical analysis projects, import data, develop R scripts, and generate reports and graphics.

### e. Practical Data Science with R by Nina Zumel & John Mount

It focuses on data science methods and their applications in real world. It’s different in itself. None of the books listed above, talks about real world challenges in model building, model deployment, but it does. The author focusses on establishing a connection between ML and its impact on real world activities. It’s a must read for freshers who are yet to enter analytics industry.

### f. R for Everyone: Advanced Analytics and Graphics by Jared P. Lander

It’s a decent book covering all aspects of data science such as data visualization, data manipulation, predictive modeling, but not in as much depth. You can understand as it covers a wide breadth of topic and misses out on details of each. Precisely, it emphasizes on the usage criteria of algorithms and one example each showing its implementation in R. This books is meant for people who are more inclined towards understanding the practical side of algorithms.

### g. R Cookbook by Paul Teetor

This book offers you the techniques to quickly and efficiently analyze data in R. It focuses on practical aspects of concepts in R. It covers a wide range of topics such as probability, statistics, time series analysis, data pre-processing etc. It explains how different techniques are applied to a data set. This book provides you 200 practical recipes that help you to do data analysis using R.

### h. R Graphics Cookbook by Winston Chang

Data visualization enables a person to express and analyze their findings using shapes & colors, not just in tables. Having a solid understanding of charts, when to use which chart, how to customize a chart and make it look good, is a key skill of a data scientist. This book focuses on building concepts in R using sample data sets. It focuses on the ggplot2 package to undertake all visualization activities.

### i .R Packages by Hadley Wickham

This book is for advanced R programmers who are looking to write their own R Packages. The author dives deep into documentation on R packages. The author also explains the components of R package, including unit tests and vignettes.

### j. Advanced R by Hadley Wickham

This book is about how R language works that creates a difference between top 3 analytical tool – R vs SAS vs SPSS. It gives a step by step explanation, with code snippets that you can easily try yourself as you read. It’s neither for R beginners nor any readers new to programming. It is for the readers who want to advance their skills and one who already has command of sub-setting, vectorization, and R data structures.