R Programming for Data Science

by Roger D. Peng

Data science has taken the world by storm. Every field of study and area of business has been affected as people increasingly realize the value of the incredible quantities of data being generated. But to extract value from those data, one needs to be trained in the proper data science skills. The R programming language has become the de facto programming language for data science. Its flexibility, power, sophistication, and expressiveness have made it an invaluable tool for data scientists around the world. This book is about the fundamentals of R programming. You will get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code. With the fundamentals provided in this book, you will have a solid foundation on which to build your data science toolbox.

Genres: Programming, Computer Science, Textbooks, Computers, Nonfiction, Ebooks, Science

132 pages, ebook

First published April 13, 2015

Reviews

[Amin]

This is the worst R book you could imagine, especially after considering the fact it is actually accompanying an online course. So, simply, the book doesn't add anything to the course, which by the way, is a very bad-instructed and terrible course. It is not clear who the audience is, since the chapters are not in the same level. For instance, from very basic packages and tools, it jumps to very narrow topic of how to use multi-cores to speed up the calculations!

I think if you want to benefit the most from the whole material provided in this course, simply install the swirl package, which is a complementary source for practicing what you are supposed to learn during the course, or read in the book. I think it is worth using that if you are a beginner, and then you've exploited the whole course!

[Biblioworm]

Очень небольшого объема, практически методичка.
На 99.99% совпадает со слайдами курса R Programming by Roger D. Peng, PhD, Jeff Leek, PhD, Brian Caffo, PhD(https://class.coursera.org/rprog-013).
Для меня, как знающего другие языки программирования, было совершенно невозможно выполнить задания на курсере, без изучения в других источниках самых основ этого языка. Лекции и соответственно эта "книга" дают очень мало информации. Причем Роджер практически читает этот текст, не говоря ничего, кроме того, что есть в слайдах. Я не против того, чтобы для выполнения упражнений было нужно поискать информацию самому, но в данном случае не дается вообще базовых основ.
В итоге я прочитал R inferno и это дало мне на порядок больше информации в том же объеме.

Изумительно, что Роджер еще и денег хочет за эту существующую только в электронном виде книгу.

Кстати, и упражнения на курсере в этом курсе тоже какие-то сомнительные. Например, как образец дается крайне неряшливый интерфейс для кэширования, api которого позволяет привести его в неконсистентное состояние, а то, что там воткнули closures вообще трудно понять, зачем.

[Ala]

A nice introduction to R. It definitely makes you curious to further explore R language and its capabilities in transforming data.

[Chris]

I completed the Coursera course that this book is associated with. I think it's a good introduction, but really the course and the book deliver the same information in slightly different formats. I did not find much additional value in reading the book.

[Richard Latham]

Peng does a great job of exploring the entire R ecosystem without too much focus on the tidyverse. It's an interesting perspective (an academic who was an early adopter of R over S-PLUS) to read from. I, generally, have learned R from only the tidyverse mindset with some base R thrown in. The book's core has very little outdated content and I would recommend it to someone who is new to R and maybe wants a good grasp of base R before navigating into tidyverse and reading 'R for Data Science'. It's an amazing overview of a number of core components that I've been exposed to but never explicitly taught, and will definitely serve as a resource for me in the future.

The author has published the book online for free, via bookdown.org.

[Aina]

This is an excellent practical book on how to use R effectively doing data science and analysis. Loved the advanced sections showing how to use R with regular expressions, parallel programming and code profiling. This book is a recommended textbook for the 'R for data science' course with Coursera and a great way to keep notes after the end of the course.

[John Mihelic]

This book was the accompaniment for the Data Science with R course in Coursera. It was a good supplement to the course lectures, and I have a feeling it will be handy as a reference book as I go forward in learning data science.

[Chaitanya]

Very good way to start off learning R by knowing the fundamentals through examples illustrating many useful concepts , which by far provides a strong understanding of the language.

[LIUF]

Textbook for the same course on Coursera.
Good intro / reference for R beginners, you can finish it very quickly if you already have some programming experience.

[Matt]

Fine, informative text. However, there were loads of spelling and grammar mistakes. Mistakes that absolutely should have been caught by the simplest spell-check program.

[Ayas]

The course was amazing so was this book.
Look the best way to learn is to simply "Dive into the think of things"
And this is what this does, you start applying stuff from start to finish.

[Douglas Mendes]

Good introduction

[Miguel Veliz]

Is an excellent book for begginers so you can get all the basic information about R programming, the best of all is that this is a free ebook that can be downloaded from the author's website

[Vlad]

I was advised this book by an instructor on an online course https://skillcombo.com/topic/r/ and it is generally a good book for a beginner with a basic knowledge of the language.

[Andrew]

A good, quick intro to R.

[Nathan]

Excellent introduction to R and companion to the Coursera course (https://www.coursera.org/course/rprog) for which it serves as the textbook.

[David]

This is a good book on the basics of R. Roger Peng does a good job explaining the simple programming theories in layman's terms.

[Avishek Das]

Peng has a very definitive way of working on problem statement, he also emphasizes the aspect of step by step approach.

[Bogdan]

Nice accompaniment to the Coursera R course. Helped me remember/review some things but no real new information than that from the course.

But its a good and easy read. I'd recommend to have it.

[Tomasz Jaskula]

Very good support for the accompanying Coursera course

[Pooia]

It is a nice book to start R