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R for Everyone: Advanced Analytics and Graphics (Anglais) Broché – 19 décembre 2013

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Descriptions du produit

Présentation de l'éditeur

Statistical Computation for Programmers, Scientists, Quants, Excel Users, and Other Professionals

Using the open source R language, you can build powerful statistical models to answer many of your most challenging questions. R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone is the solution.

Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you’ll need to accomplish 80 percent of modern data tasks.

Lander’s self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You’ll download and install R; navigate and use the R environment; master basic program control, data import, and manipulation; and walk through several essential tests. Then, building on this foundation, you’ll construct several complete models, both linear and nonlinear, and use some data mining techniques.

By the time you’re done, you won’t just know how to write R programs, you’ll be ready to tackle the statistical problems you care about most.



• Exploring R, RStudio, and R packages

• Using R for math: variable types, vectors, calling functions, and more

• Exploiting data structures, including data.frames, matrices, and lists

• Creating attractive, intuitive statistical graphics

• Writing user-defined functions

• Controlling program flow with if, ifelse, and complex checks

• Improving program efficiency with group manipulations

• Combining and reshaping multiple datasets

• Manipulating strings using R’s facilities and regular expressions

• Creating normal, binomial, and Poisson probability distributions

• Programming basic statistics: mean, standard deviation, and t-tests

• Building linear, generalized linear, and nonlinear models

• Assessing the quality of models and variable selection

• Preventing overfitting, using the Elastic Net and Bayesian methods

• Analyzing univariate and multivariate time series data

• Grouping data via K-means and hierarchical clustering

• Preparing reports, slideshows, and web pages with knitr

• Building reusable R packages with devtools and Rcpp

• Getting involved with the R global community


Biographie de l'auteur

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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Amazon.com: 44 commentaires
80 internautes sur 82 ont trouvé ce commentaire utile 
Really useful 25 janvier 2014
Par Dimitri Shvorob - Publié sur Amazon.com
Format: Format Kindle
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". So is "knitr", used to produce reports. So is "rcpp", used to interface R and C++. So is R package-building. (You will notice that the topics become more advanced. These are introductions rather than substantial explorations, but awareness is a valuable thing). In the book's second half, when discussion moves from R to statistics-with-R, the author continues to manage to find original material; statistical explanations may be brief - this is not a textbook - but examples, and pointers to useful R utilities, are much appreciated.

I own just one R book - literally, "The R Book", by Crawley - but "R for Everyone" will be joining it; this has got to be a compliment. Kudos to Jared Lander for writing an original, substantial, useful book.
32 internautes sur 33 ont trouvé ce commentaire utile 
Good book, but….. 27 janvier 2014
Par JoeT - Publié sur Amazon.com
Format: Broché Achat vérifié
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?
23 internautes sur 23 ont trouvé ce commentaire utile 
great to get you going in R 29 janvier 2014
Par Kathy W. Chiang - Publié sur Amazon.com
Format: Broché Achat vérifié
I have several R books that I have started but had to set aside because I got busy and the book got complicated. I am about 60% through this one and still going strong. It is written with just the right amount of detail to keep you learning and moving without getting bogged down. It's like one book sized tutorial, by following along with the book, you can get started actually doing things in R, with great code examples to come back to. After going through this book, I will be able to make much better use of my other R books.
34 internautes sur 38 ont trouvé ce commentaire utile 
Current / Readable / Well-paced 4 janvier 2014
Par Edward E. Rigdon - Publié sur Amazon.com
Format: Broché Achat vérifié
I am still a novice in R, though I have used it for a while. I ordered this book because I need a more solid background in R for a major project. This book feels like exactly what I was looking for--like an experienced user sitting beside me helping me along. It is definitely *not* just a rehash of online pdf guides (which can be hard to parse). I am already marking pages that tell me things I never knew but needed to know. And the pacing of information "feels right"--no more than what I need to know, about when I need to know it. Just-released, its time references are as recent as September 2013, and that is important, because the R experience has evolved from something crude but full of possibilities to something much more rich, with an ecosystem of supporting tools. If you are like me, then I think you will really enjoy Jared lander's R for Everyone.
12 internautes sur 13 ont trouvé ce commentaire utile 
Very good introductory book for R 24 janvier 2014
Par Vedette Bianciotti - Publié sur Amazon.com
Format: Format Kindle
I consume tech books at a fast rate. I love to dip my toe in everything. Coming from a Ruby/Node background I have found the R language/environment something that takes getting used to and this book is a great example of how a learning book should approach something important but out of the ordinary (in terms of OOP languages).

Mixing learning data analytics and the language itself, I found myself being more able to pick up R. It is likely that my struggles with R come from a lack of background in traditional computer science or Maths and this helped bridge the gap as well as teach me R.

It might be that anyone with a serious background in Matlab or Octave might find this a little introductory but as a beginner it hit the spot. I haven't finished the book but I truly recommend this for anyone looking into moving into data science/statistical programming or the R language.
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