# Top 10 R Programming Books To Learn From

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.

There are so many libraries, applications and techniques exist to explore data in R that I’m sure even experts don’t know them all!

Aspiring data scientists who are reading this though, fear not, for you are well on your way to understanding these secrets.

, from the basics of data analysis to the most complex R libraries. The links provide the ability to download the pdfs of the books.

**Authored by:** Trevor Hastie and Rob Tibshirani, recognized Stanford professors and authors of “The Elements of Statistical Learning”

**What you’ll learn: **Implementation of statistical and machine learning techniques in R

This book will teach you what you need to know, without harassing you much about the math behind it all. 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.

**Authored by: Jeff Leek**

**What you’ll learn: **Methods of analyzing data

**Why:** Data analysis is a process of inspecting, cleaning, transforming and modeling data with the goal of finding useful information, suggesting conclusions and supporting decision-making. This book explores data analysis methods that sometimes fall through the cracks in traditional statistical methods.

The book is based on the authors’ blog posts, lecture materials, and tutorials on simply stats and Coursera.

**Authored by**: Roger D.Peng

**What you’ll learn: **The basics of R, for a non-programmer

**Why:** This is the ideal book for someone with no prior programming experience. It doesn’t talk about statistics or machine learning. It is solely dedicated to the fundamentals of R programming.

Below are some concepts you’ll familiarize yourself with, over the course of the book:

- How to install R on your computer
- How to clean messy data sets
- How to manage data frames with dplyr packages
- The coding standards for R

There is even a case study at the end of the book in which the author explains how to process and analyze a raw data set using R. The book is based on the famous data science specialization course on Coursera.

**Authored by:** Roger D.Peng

**What you’ll learn: **Data visualization

**Why:** Data visualization is a must-have skill for a data scientist, and this book will walk you through some crucial techniques to visualize data in R.

*“
*

*” – Carly Fiorina*

Topics discussed in this book include plotting systems in R, basic principles of constructing informative data graphics, making exploratory and analytical graphs, clustering methods and visualizing high dimensional data.

#### Authored by: Mark P.J.van der Loo

**What you’ll learn: **Quickly and efficiently create and manage statistical analysis projects, import data, develop R scripts, and generate reports and graphics

**Why:** This book is different from the others in the list in the sense that it teaches you how to user on the popular IDE R Studio rather than on the standard R software. The book is aimed at R developers and analysts who wish to do R statistical development while taking advantage of RStudio functionality.

**Authored by:** Robert Kabacoff

**What you’ll learn: **An introduction to R.

**Why:** The book gives a complete picture of all basic elements needed by an R user. The author uses small sample data sets to explain the concepts of R, and statistics which makes it easy for a beginner to grasp the concepts better. Ggplot2 (most widely used visualization library) is explained in depth in this book.

Authored by: Winston Chang

**What you’ll learn: **How to generate high-quality graphics using ggplot2, and that too quickly.

**Why:** This R cook book makes the task of finding best practices for ggplot2 much easier, like

- Do I set fill or color?
- What exactly does grouping do?
- How do I rearrange factors?
- How do I remove legends and grid lines?

R graphics cook book and elegant graphics for data analysis book makes a powerful combination.

**Authored by:** Paul Teetor

**What you’ll learn**: 200 practical recipes that helps you to do data analysis using R

**Why:** This book may not offer a tutorial on R, but is definitely recommended for beginners, for it offers you the techniques to quickly and efficiently analyze data in R. It clearly explains how a particular technique is applied to a data set.

I would recommend you use this book as a supplement, and not as your only source to learn R.

**Authored by:** Hadley Wickham

**What you’ll learn**: How to write your own R packages.

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

**Authored by:** Hadley Wickham

**What you’ll learn**: How to understand how R works.

**Why:** This book is about how R language works. The book gives a step by step explanation, with little code snippets that you can easily try yourself as you read.

It’s not for R beginners nor any readers new to programming. It is for the reader who want to advance their skills and one who already has command of sub-setting, vectorization, and R data structures.

**Conclusion**

I hope you now have a clearer idea as to which book would fulfil your requirements in the journey to learn and understand this beautiful programming language.

What other R programming books have you read and loved and feel should be in this list? Let me know in a comment below so that we can share it with our readers.

You can also learn R through our courses: **Certified Business Analytics Professional **and **Certified R Programmer**

### Aatash Shah

Aatash is passionate about combining education with technology to create a new paradigm of learning which is relevant, timely, cost-effective and focused on fulfilling the end goal of education, which is meeting the needs of the employers.

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