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This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression.

Each recipe tackles a specific problem with a solution you can apply to your own project, and includes a discussion of how and why the recipe works.

Most of the recipes use the ggplot2 package, a powerful and flexible way to make graphs in R. Devtools encapsulates best practices that Hadley has learned from years of working with this programming language.

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.

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The Art of R Programming is a simple approach to R programming from a generalists viewpoint. Any data scientist or statistician should be able to follow along with ease.

The early lessons cover the basics of R with variable scope and some basic functions. 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. If you already have some experience or if you need a real technical book then this is not for you.

Advanced coders may feel left out since many of these books target beginners. But Efficient R Programming is one of the few great books made for skilled R programmers.

It spans pages teaching efficiency from your initial R setup to writing your code and debugging. This book is for intermediate-to-advanced developers who can already code in R but might want to improve their coding techniques.

With R you can build packages that contain functions and data compiled together into sharable formats. R Packages covers the fundamentals of building and sharing your own packages using R.

This book teaches you the R package methodology using 3rd party tools and common workflows. Since this book delves into more complex topics it is not meant for beginners.

This book teaches the best practices for building packages and how to submit them into the CRAN archive. Many statisticians use Excel to organize numbers and generate formulas that save time in the day-to-day.

R can also connect into Excel documents to pull out information, organize it, and even graph it using visualization. The book has plenty of screenshots and each chapter has a series of step-by-step tutorials to help you learn through example.

Everything in this book centers around data manipulation with the connection between spreadsheets and R programming.

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