Rating:
(4 reviews)
Author: Richard Cotton
ISBN : B00F2ZO8Z6
New from $15.39
Format: PDF
Download Free Learning R [Kindle Edition] from mediafire, rapishare, and mirror link
Learn how to perform data analysis with the R language and software environment, even if you have little or no programming experience. With the tutorials in this hands-on guide, you’ll learn how to use the essential R tools you need to know to analyze data, including data types and programming concepts.
The second half of Learning R shows you real data analysis in action by covering everything from importing data to publishing your results. Each chapter in the book includes a quiz on what you’ve learned, and concludes with exercises, most of which involve writing R code.
- Write a simple R program, and discover what the language can do
- Use data types such as vectors, arrays, lists, data frames, and strings
- Execute code conditionally or repeatedly with branches and loops
- Apply R add-on packages, and package your own work for others
- Learn how to clean data you import from a variety of sources
- Understand data through visualization and summary statistics
- Use statistical models to pass quantitative judgments about data and make predictions
- Learn what to do when things go wrong while writing data analysis code
Download latest books on mediafire and other links compilation Free Learning R
- File Size: 4202 KB
- Print Length: 400 pages
- Simultaneous Device Usage: Unlimited
- Publisher: O'Reilly Media; 1 edition (September 9, 2013)
- Sold by: Amazon Digital Services, Inc.
- Language: English
- ASIN: B00F2ZO8Z6
- Text-to-Speech: Enabled
X-Ray:
- Lending: Not Enabled
- Amazon Best Sellers Rank: #145,665 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
- #24
in Books > Professional & Technical > Professional Science > Biological Sciences > Bioinformatics - #71
in Books > Computers & Technology > Databases > Database Design - #94
in Kindle Store > Kindle eBooks > Computers & Technology > Software
- #24
in Books > Professional & Technical > Professional Science > Biological Sciences > Bioinformatics - #71
in Books > Computers & Technology > Databases > Database Design - #94
in Kindle Store > Kindle eBooks > Computers & Technology > Software
Free Learning R
I am getting annoyed by O'Reilly, who, in my opinion, fail to provide their authors with due editorial support - plus some pushing, if needed - and save money on graphic design, producing undercooked, plain-looking books, and let down the writer and the reader. Not all publishers are like that: take Manning, who published what is still my favorite R book, Robert Kabacoff's "R in Action".
I had expected "Learning R" to be a very beginner-friendly introduction in the vein of Paul Teetor's "R Cookbook", but "Learning R" challenges the reader a bit more, on par with "R in Action", and even gets, with topics like OOP and package design, into the territory of Norman Matloff's "Art of R Programming". Kabacoff's and Cotton's books are of similar size, but Kabacoff devotes half of his book to statistics, whereas in Cotton's book, statistics content is limited to a look at the linear regression, giving him much more space to discuss "R proper".
This advantage has not been effectively exploited. Graphics is one area where, I think, the publisher has undermined the author, and despite "Learning R" featuring a nice - if short - introduction to "ggplot2" package, "R in Action" has stayed ahead. Also, "Learning R"'s advantage was more visible on the more arcane topics - custom environments, anyone? reading JSON? - and not with more effective discussion of the more bread-and-butter things. On the other hand, to give two examples, "Learning R" got brownie points for its discussion of "plyr" and "lubridate" packages; also valuable are quizzes and short programming exercises, which come with solutions.
I will be staying on Team Kabacoff, but if you are not especially interested in statistics-in-R - I mean, fancier statistics than summary stats and linear regression - "Learning R" is a good bet.
By Dimitri Shvorob
With a background as a developer, I found this book very approachable having not touched R before this book. It remained close to the R language itself and did not veer too deep into statistics. For a programmer new to the language and the topic, I was fine with the subject matter depth. I really liked that the end of each chapter included a quiz as well as exercise portions. The answers are included as part of the Appendix and I think are a good reminder to readers to try out what they just learned. Part of learning new languages is finding about different concepts or how data structures are implemented with different names, functions, or packages. Newer topics to me were data frames and factors. These concepts are explained very well and allowed for me to quickly apply existing knowledge from other languages to make the concepts clear. In my opinion, I really liked the chapters involving getting data and on cleaning and transforming. These chapters are very practical and apply to anyone who has had to parse data in one format to put it into another. Topics like loading packages and data and time coverage are sections that you would expect to be in a book for beginners new to the language and hint at the audience this book is geared toward. It is a good starting point for those totally new to the language who will use this as a foundation to dig deeper for specific tasks later.
Overall conclusion: Good for the programmer interested in learning the R language over in-depth statistics.
Disclaimer: I got a copy of this book for review as part of O'Reilly blogger program.
By IADev