Download Ebook R in a Nutshell: A Desktop Quick Reference (In a Nutshell (O'Reilly))
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R in a Nutshell: A Desktop Quick Reference (In a Nutshell (O'Reilly))
Download Ebook R in a Nutshell: A Desktop Quick Reference (In a Nutshell (O'Reilly))
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Book Description
A Desktop Quick Reference
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About the Author
Joseph Adler has many years of experience in data mining and data analysis at companies including DoubleClick, American Express, and VeriSign. He graduated from MIT with an Sc.B and M.Eng in Computer Science and Electrical Engineering from MIT. He is the inventor of several patents for computer security and cryptography, and the author of Baseball Hacks. Currently, he is a senior data scientist at LinkedIn.
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Product details
Series: In a Nutshell (O'Reilly)
Paperback: 636 pages
Publisher: O'Reilly Media; 1 edition (January 14, 2010)
Language: English
ISBN-10: 059680170X
ISBN-13: 978-0596801700
Product Dimensions:
6 x 1.2 x 9 inches
Shipping Weight: 1.6 pounds (View shipping rates and policies)
Average Customer Review:
3.7 out of 5 stars
55 customer reviews
Amazon Best Sellers Rank:
#463,242 in Books (See Top 100 in Books)
Back in school, I was introduced to using SPSS for use in statistical analysis. While I liked SPSS, it was too expensive for me to procure a copy for my own personal use. A friend suggested that I try R. I was a little nervous about R, because being more enthusiastic about than talented with mathematics, and I was most comfortable with a point and click program. So, before I began, I bought "R in a Nutshell" to learn more. I'm glad that I did.Adler's book begins with a basic tutorial for R and an introduction to R language. It explains how to use R to draw graphs, statistical analysis and even some bio stuff. All I needed to do was to load in my data, draw a couple charts and compute some t tests and chi-squared statistics.The book was great, multi-faceted as a teaching tool, and - unexpectedly (and atypically for such works) - entertaining to read. I'm looking forward to using R next time I need to fit a regression model, or do factor analysis. The rare mathematics tutorial that will engage academics, financial traders and baseball stat wonks alike. Nice job.
Before buying, look at the book description at the publisher's website (oreilly.com) and click on the "errata" link. Check to see how long that list is. If you can live with cross-referencing this list with the book, then buy it. Otherwise, there are probably better books out there.As it is, I am somewhat proficient in R and bought this book as a crash course for a better understanding of the basics, especially the graphics and statistics. After barrelling through roughly half of the book, I found many references to functions or parameters which were never explained or were explained later in the book (without saying so at the first reference). For someone who is hoping for a quick read through most of what R has to offer, this is like hitting a brick wall.The book helps the reader understand a lot of what R is capable of, but it seems to be done in a more slip-shod manner than I was hoping for. I get the feeling the author was rushed in getting this to print. Or, they didn't pay the editor enough.As an aside, the formatting for the kindle edition has been working pretty well. I've actually been reading it on the cloud reader without problems (be sure to download a local copy for offline reading).
If you have programming experience and you want to understand how R works at a fairly deep level this is a wonderful book. If you are a novice that is just trying to learn how to visualize and analyze data there are better choices like A Beginner's Guide to R (Use R) or search my Amazon profile to see the learning R listmania list called "Learning to use R for statistics and graphics" or Amazon's "So you'd like to..." guide called "Learn R for statistical programming and graphics" (sorry they will not let me post the hyperlink here).I teach programming, so I found the references to how R differs from languages like LISP, C and JAVA very useful. However, those distinctions will be at best distracting or more likely horribly confusing to programming novices. With that distinction in mind, this book is exceptionally well written and has great clear explanations on things that are missing from practically every other R book (like the distinction between if else and ifelse. Other sections, like the coverage of graphics can be found elsewhere but require you to distill a LOT of other books.If you want to really understand R start here and then go for John Chambers books (especially Software for Data Analysis: Programming with R (Statistics and Computing) ). If you want to learn how to use R for data analysis get Zuul's book then follow my learning R guide.
I bought the 1st edition and this one is just as good. Generally he uses straightforward examples and provides enough detail to "make it work." So, for example, with just a few pages of reading I can understand how to technically get decision trees to work in R. For the general reader I think Adler spends way too much time on object programming concepts and the behind the scenes structure of R. It would be nice to have time for theoretical detours like he uses but for those of us who are busy and in the commercial world, such material should be relegated to an appendix. I really don't care how R does it, in the same sense that I do not car how my car optimizes its gas/air ratio -- I just want it to go.That being said, Adler's range of knowledge is astounding and I certainly trust what he says. With just a slight tweak to meet the needs of his less academic readers, the book would be perfect.
This book is an introductory guide to the R statistical language. R is of course an increasingly popular open-source program that can handle everything from regression to time series analysis and neural networks. The book is written more as a tutorial than a reference manual, and this makes it surprisingly readable. The book is organized in a very intuitive way, starting with basic functions, then describing the general approach to writing algorithms, and finally going through a series of mathematical functions. It is outstanding for readers who have some familiarity with other statistical programs -- SAS, Matlab, RATS, etc., and who need to familiarize themselves with R code. The treatment of advanced mathematical techniques is rather cursory, so readers who want to use R for more sophisticated types of programming may need to consult other sources. Still, as an introduction, this is very clear and well written.
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