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R for Everyone: Advanced Analytics and Graphics (Addison-Wesley Data and Analytics) 1st Edition

4.3 out of 5 stars 82 customer reviews
ISBN-13: 978-0321888037
ISBN-10: 0321888030
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Editorial Reviews

About the Author

Jared P. Lander is the owner of Lander Analytics, a statistical consultanting firm based in New York City, the organizer of the New York Open Statistical Programming Meetup and an adjunct professor of statistics at Columbia University. He is also a tour guide for Scott’s Pizza Tours and an advisor to Brewla Bars, a gourmet ice pop startup. With an M.A. from Columbia University in statistics, and a B.A. from Muhlenberg College in mathematics, he has experience in both academic research and industry. His work for both large and small organizations spans politics, tech startups, fund raising, music, finance, healthcare and humanitarian relief efforts. He specializes in data management, multilevel models, machine learning, generalized linear models, visualization, data management and statistical computing
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Product Details

  • Series: Addison-Wesley Data and Analytics
  • Paperback: 464 pages
  • Publisher: Addison-Wesley Professional; 1 edition (December 29, 2013)
  • Language: English
  • ISBN-10: 0321888030
  • ISBN-13: 978-0321888037
  • Product Dimensions: 6.9 x 0.8 x 9.1 inches
  • Shipping Weight: 1 pounds (View shipping rates and policies)
  • Average Customer Review: 4.3 out of 5 stars  See all reviews (82 customer reviews)
  • Amazon Best Sellers Rank: #28,195 in Books (See Top 100 in Books)

Customer Reviews

Top Customer Reviews

Format: Kindle Edition Verified Purchase
To get the negatives out of the way: it's unfortunate that, having invested in appealing graphic design - the book looks just so much nicer than the spartan O'Reilly titles - Addison Wesley have not provided the author with similarly solid editorial support, resulting in a book that definitely feels rushed. There are typos, cosmetic blemishes (one regular annoyance is a table that's too wide for a page - one could fit it using a smaller font, but instead the table ends up split across twice as many rows), a couple of statistical blunders (on pp. 172 and 263), things that could have been left out, things that should have been included (oddly, the chapter on joins never mentions outer joins, and, in fact, does not explain what a join is) - and, finally, time and again, things that should have been explained better. I do not feel that "R for Everyone" is the best available introduction to R, and continue to endorse Robert Kabacoff's high-quality "R in Action" in that capacity.

Where "R for Everyone" differs from "R in Action" - and, coming to the positives, where it wins out - is in intermediate-R territory. One important example is coverage of "ggplot2". Whereas "R in Action" discusses the "old school" R graphics, "R for Everyone" goes with "ggplot2", becoming the second popular book (after Winston Chang's "R Graphics Cookbook") to discuss the package - and although its explanation of "ggplot2" syntax is sketchy, the samples found throughout the book do build into a useful "ggplot2" gallery that actually brought me over the fence. "plyr" package, an important data-manipulation aid, is another example, and another "R in Action" no-show. So is "data.table".
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Format: Paperback Verified Purchase
This was probably the hardest book to rate of any I have rated on Amazon.
For what it's worth, I am an R user and I like to pick up books on R to see how other people do things. The fact that I was exposed to packages I have never used was a plus and definitely make the book worthwhile.

This book is basically 2-distinct books: The first 13-chapters are the basics of R. They are quite good and if you are new to R you will find them extremely useful.

Virtually all the remainder of the book is using R for various statistical techniques. This is where I had my problem. If you get this book with the assumption that you will learn statistics at the same time, then you will be disappointed. The problem is that while the book does tell you HOW to do the test, that's about it. There isn't much in terms of explaining what it is you did or how to interpret the results. I suppose if you look at it as a book to show you how to use the various R commands to run a t-test or an ANOVA, then that's OK, but I don't see value if you do something, get a value and not understand what it's for. But, if you are already statistically savvy, then this might not be an issue.

One thing I did not like though is the use of ggplot. Now, I fully appreciate that ggplot will in fact generate far better graphics than the core plot routines in R. No question. But, ggplot in itself is a book, and in many cases, I just cut-and-pasted the code into R to see what happens. There wasn't really a whole bunch of explanations as to why you were doing what you were doing. Given that this is more an intro book (given the initial chapters of R that gives me this impression), I would have considered using the core plot routines instead. More work and less attractive I know, but if your audience are people who are new to R, then why not stay with the core routines?
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Format: Paperback Verified Purchase
In spite of the good reviews, the reality is very different. This is a bad R book with many shortcomings that range from bad explanations of the basics (functions, data types) to no explanation at all when showing the use of R in data analysis.

The book has a nice layout in color, but this is misleading. Long R output without proper formatting for a textbook is always displayed because the author wrote the book directly in the code as he himself states and printed it out as it is. And it feels like. Most of the text looks just like comments in a program code. The treatment of functions is very poor (they are also very rarely used in the book) and the explanation of the different R data types lacks depth and is misguided. Silly examples are used to show the basics as in printing the author's name. The later chapters get even worse, literally damaging all the more interesting parts, where the book leaves the very basics and moves on to data handling and then to advanced data analytics in R.

The part of the book that deals with data analytics is sincerely a bit of a tragedy. Rushed text with no clear or sometimes whatsoever explanations of what is actually being done, with just little text and lots of code output and charts taking most of the space. Ironically the book that is "for everyone" makes hard for "everyone" to understand anything that uses statistics, about 60% of the book!.
It is harder even for those trained on statistics or related "hard" sciences.

For example, In chapter 22, right in the beginning the author uses a value for the predicted number of clusters in the data under analysis. This value is taken out of the blue and only later it is shown how this value can be found using two methods.
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