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You'll even learn how to create impressive data visualizations with R's basic graphics tools and contributed packages, like ggplot2 and ggvis, as well as interactive 3D visualizations using the rgl package.

Dozens of hands-on exercises with downloadable solutions take you from theory to practice, as you learn:. Skip to main content. Start your free trial.

The Book of R by Tilman M. However this is also the best intro for beginners who may be intimidated at the idea of learning to write code. What I like most about this book is how it teaches through example.

Each chapter has many tutorials and exercises to help you practice writing code in a practical way. The first chapter teaches you about the basics of R.

How to setup, pick an IDE, and how to write your first program. Later chapters get into the nitty-gritty of R with sample exercises to help you learn.

This book covers loops, arrays, packages, unit testing, and common workflow techniques for data analysis.

By far one of the largest books on R you can find. This book was also updated in its 2nd edition to include more exercises and some expanded passages.

This book covers so much ground that it can take you from a novice to at least a semi-experienced R programmer. Be warned this is a very thick book, both in length and content.

The writing style is clear but heavy and technical. You may need to reread some chapters to fully digest the info. Thankfully there is a lot of info here making this book one of the best learning tools for all experience levels.

Here we have a more theory-based book covering R from the perspective of data science. There are still plenty of exercises in this book but it also covers the idea of data science and how R fits into the equation.

The author frequently shares workflow tips and wraps up each chapter with bullet points as takeaways. The information is very simple to digest and this book can vastly improve your knowledge of data science.

This is one of the newest books on the market and it covers R in a very positive light. The Book of R totals a massive pages which is huge for an intro programming book.

The writing style is fantastic and the author clearly wrote this to help beginners dive into R programming. Each chapter moves in a logical progression starting with the basics of R, the syntax, and finally techniques for building with R.

You can pick this up with absolutely zero programming knowledge and by the end you should feel confident writing an R program from scratch.

R for Everyone covers the basics of R for beginners, then delves into how R can be used by statisticians for data analysis. The early chapters hold your hand introducing R, RStudio, and the basic mathematical functions.

You should already have some knowledge of statistics before picking up this book. Later chapters can get complex since they cover probability and mathematical models graphed using ggplot2.

As long as you take it slow you should have no trouble finishing this book. It tackles the basics of the language from setup to initialization and creating new projects.

And R For Dummies is no exception. But this book will not truly help you understand the programming language. Easy R Programming for Beginners starts from the very bottom and slowly builds your knowledge one brick at a time.

The writing style is technical but also accurate. Every chapter in this book is a guide to R programming. Each chapter includes a brief account of the relevant statistical background, along with appropriate references.

This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more.

You'll apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support.

Views: Tags: Like. Comment You need to be a member of Data Science Central to add comments! People interested in applying functional programming concepts to data analysis might be interested in my book, "Modeling data with functional programming in R", due out next year.

Add Videos View All. Please check your browser settings or contact your system administrator. Upcoming DSC Webinar Data Science Leadership Exchange: Best Practices for Driving Outcomes Despite an increasing awareness of the role data science plays in successful business outcomes, data science leaders still struggle to organize, implement and communicate effective data science initiatives.

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