The Book of R: A First Course in Programming and Statistics

by Tilman M. Davies

The Book of R is a comprehensive, beginner-friendly guide to R, the world’s most popular programming language for statistical analysis. Even if you have no programming experience and little more than a grounding in the basics of mathematics, you’ll find everything you need to begin using R effectively for statistical analysis.You’ll start with the basics, like how to handle data and write simple programs, before moving on to more advanced topics, like producing statistical summaries of your data and performing statistical tests and modeling. 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 –The fundamentals of programming in R, including how to write data frames, create functions, and use variables, statements, and loops–Statistical concepts like exploratory data analysis, probabilities, hypothesis tests, and regression modeling, and how to execute them in R–How to access R’s thousands of functions, libraries, and data sets–How to draw valid and useful conclusions from your data–How to create publication-quality graphics of your resultsCombining detailed explanations with real-world examples and exercises, this book will provide you with a solid understanding of both statistics and the depth of R’s functionality. Make The Book of R your doorway into the growing world of data analysis.

Genres: Programming, Nonfiction, Coding, Mathematics, Technology, Reference, Computers

833 pages, Kindle Edition

First published November 25, 2015

Reviews

[Mikhael Hayes · Rating 4 out of 5]

So glad to be done with this. It does the job, but it makes me feel like it's 2008 and I'm learning to type or something

[Mike Harris · Rating 3 out of 5]

This book has one of better over views of R and statistical modeling for people new to R and statistical modeling. Maybe if I did not read it cover to cover and just read the middle part I would have given it a 4 but I felt that the last section on graphs just dragged on.

I did almost all of the exercises and found them to be okay for re-enforcing the content.

The diamond data 404s now but if you use the diamond data set that comes with ggplot2 you’ll be able to follow along with the examples and do the exercises which use the diamond data set.

[Kelsey Edwards · Rating 5 out of 5]

Best book about R I’ve read so far. Loved it enough to purchase this instead of continuing to rent from the library.

[Jaime · Rating 5 out of 5]

A great approach to learning R and how to implement it into a working environment of Data Analysis and database management.

[Nathaniel Inman · Rating 5 out of 5]

It's rare to see an author that not only covers a language breadth but also isn't afraid to reach depths in practical applications. This book is tailored towards a computer or data scientist, although the layperson with a good foundation in discrete math, calculus and statistics will feel comfortable. The first third covers the language itself, slowly introducing language semantics and common patterns before sliding into statistical analysis such as p values, linear regression, etc. If I had to nitpick, from an engineering perspective I would have enjoyed digging into the S4 structure. My page tabs are on 48, 84, 97, 130, 171, 176, 207, 227, 242, 251, 274, 278, 298, 326, 369, 378, 387, 391, 405, 460 & 463.

[Christopher Hall · Rating 5 out of 5]

Excellent book. This took me from no knowledge of R and relatively little knowledge of programming to being conversant in R. And I got a job offer partly as a result of my skills in R!

The book covers the basic aspects of programming all the way to introducing complex statistical concepts and creating full-color 3D plots. Each section (about 4 per chapter) has exercises to practice the concepts covered.

I know I'll be using this a reference for years to come.

[Lime Street Labrador · Rating 2 out of 5]

poorly explained