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The Art of R Programming - A Tour of Statistical Software Design (Anglais) Broché – 5 octobre 2011

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Book by Matloff Norman

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Format: Broché Achat vérifié
Le livre est d'un abord facile. Les exemples et exercices sont d'une compléxité limitée au début pour se complexifier ensuite. Tout est très lié, dès le début, aux applications pratiques de R à des problemes statistiques
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Un très bon livre pour comprendre les bases de la programmation avec R, mais qui devient vite limité quand l'expérience arrive et que de nouvelles questions se posent.
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Amazon.com: 4.4 étoiles sur 5 122 commentaires
216 internautes sur 221 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 Excellent guide to the R language 4 novembre 2011
Par Sitting in Seattle - Publié sur Amazon.com
Format: Broché Achat vérifié
There are hundreds of R books, but this is the best one to address the core problem of learning to *program* in R. As reviewer Jason notes, R is used by several audiences with varying needs, but anyone who uses R for long must come to terms with learning to program it. This is the book for that.

What Matloff does is to lay out the essentials of the R language (or S, if you prefer) in depth but in a readable fashion, with well-chosen examples that reinforce learning about the language itself (as opposed to focusing on statistics or data analysis).

I'm a long-time (12 years) R user, which is my platform for analytics every day, and I have programmed in a variety of languages from C to Perl. I have long missed the fact that there is nothing for R comparable to Kernighan & Ritchie ("K&R", The C Programming Language) or similar programming classics; finally there is. Matloff is not quite as beautiful and elegant as K&R (and to be fair, is not in their position as the language creator) but this book has similar goals and comes reasonably close.

I think there are two primary audiences for this book: those who are learning R from a computer science or programming background; and statisticians and others who use the programming language and want a thorough exposition. In my case, for instance, despite having written perhaps 100k lines of R code over the years, there remained areas where I was uneasy (e.g., exactly how do lists relate to data frames). Matloff sets it all straight, in friendly, readable fashion. Even in rudimentary chapters, I learned shortcuts and miscellaneous functions that are quite useful. The examples throughout are more "CS-like" than statistical, which is highly advantageous for this topic.

In addition to the tutorial content, it is well-suited as a quick reference. It doesn't aim to be comprehensive from a function point of view (which is almost impossible, and what R Help is for), but it is comprehensive from a programming conceptual point of view.

In short, if you program R, and unless you're a member of R-Core, then I believe you'll enjoy this, will learn something, and will refer back to it repeatedly.
4 internautes sur 4 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 The BEST book on R Programming out there. 12 mars 2015
Par Tarik3001 - Publié sur Amazon.com
Format: Broché Achat vérifié
Anyone seeking to learn R faces two major challenges: (1) learning how to swim in the sea of information: R packages, books, websites, blog posts, message boards etc. that threatens to drown a newbie and (2) and coming to grips with the structure, syntax and features of the language itself. Having some idea of what one wants to do with R is clearly an important first step that will set the path of learning. R, an open source computer language, is the premier software system for statistical computing. Not only can any statistical idea be expressed in R, it is likely that someone in the open source community has already written a function to accomplish or at least facilitate any statistical analysis a working statistician or data scientist might be contemplating.

R functions are organized into libraries or packages that usually relate to some particular statistical task. Assuming something like an average of 20 functions per package, the 3400 available contributed packages[1] offer over 68,000 routines to read in data, manipulate it analyze it and visualize the results. No one could possibly become familiar with all of these. But, because R is an interpreted (instant feedback) language that encourages experimentation, some serious, sophisticated statistical analyses can be accomplished by stringing together the appropriate functions into a script. If interest in R is to only perform some particular analysis then a beginner’s best bet might be to select one of 100 or so books or blogs on doing statistics with R that provides relevant sample code and cut and paste to get a workable script. There is no shame in this. That is why all the open source authors went to the trouble of packaging up their work.

However, if a person really wants to be able to speak the R language and become a competent R programmer then, at the present time, one can find no better guide than Norman Matloff’s The Art of R Programming. Professor Matloff is a statistician and a computer scientist with a considerable amount of teaching experience. His book is no mere programming reference guide. It is a carefully crafted sequence of lessons that start at the beginning and work up to some fairly advanced topics including a lucid account of object-oriented programming in R, a presentation of the rudiments of TCP/IP operations and a discussion of R programming for the internet, examples of parallel programming with R, and a discussion spanning several chapters of how to write production-level R code that includes methods and advice on debugging R code, writing efficient R code, and interfacing R with other languages. Other distinguishing features of the book are brief examples showcasing a large number of functions (including rare gems such as D() for symbolic differentiation) that indicate the power and scope of R, and over thirty “Extended Examples” each of which is a credible study in writing careful, professional code. The most captivating aspect of the book, however, is Matloff’s thoughtful manner of exposition. R’s rich, compact syntax can be challenging the first time around. Matloff knows where the difficulties are. His presentations of R’s various features and functions begin from a point of view that anticipates obstacles that likely to confound someone going down the R path for the first time and guides the novice around them. I expect that The Art of R Programming will appeal to diverse audience of aspiring R programmers.
15 internautes sur 16 ont trouvé ce commentaire utile 
3.0 étoiles sur 5 Great at times, terrible at others 28 janvier 2014
Par Dim Dandy - Publié sur Amazon.com
Format: Broché Achat vérifié
I came to this book knowing next to nothing about R. I'm an experienced programmer, but my knowledge of statistics is not as deep as it should be, and rusty.

The book does a great job at times of explaining how the various R functions work, as well as concepts such as "vectorized" functions. A bit of code is shown, and then there is a lot of explanation that describes what it does, and why. Sometimes, the phrasing could use improvement, and I found myself perhaps struggling to master a concept longer than I should have, but it was enough to get the job done.

Then I got about a quarter of the way through the book and hit an extended example of applying logistic regression. First, the code included a tilde operator, which had not been mentioned anywhere the book before that. Next, it called a function, glm, without explaining what it does, and it showed the results, and said, "Sure enough, we get a 2-by-8 matrix, with the jth column given the pair of estimated B[i] values obtained when we do a logistic regression using the jth explanatory variable."

In effect, the book suddenly shifted from an explain-it-all-as-we-go text to a we-assume-you-know-statistics-as-well-as-exotic-R-operators-and-functions text. I am completely unable to understand this example until and unless I dig into both the related concepts in statistics, and the R-related syntax. I can't blame the book too much for my lack of knowledge in statistics, but I can say that it was careful to provide explanations on some much simpler statistical concepts earlier. As far as the R syntax, I don't think there is any excuse for that. It also turns out that the caret operator in this context is not at all what a programmer would expect it to be--no coverage of that either.

Somewhat later was a very long example on a Discrete Event Simulator. Here, as in so many other places, the author likes cryptic variable names such as rw, evntty, inspt and appin. If you were to study the code long enough, you would eventually understand what all of these meant. But it's sloppy and irritating and makes the job of understanding the code much harder.

Not long after this, he makes a comment on recursion that made me burst out laughing:

"It's fairly abstract. I knew that the graduate student [who had asked him for advice on writing a function], as a fine mathematician, would take to recursion like a fish to water.... But many programmers find it tough."

What I, a mere dim-as-a-20-watt-bulb programmer, find tough, is a plethora of cryptic variable names. Recursion, not so much. I followed his example with ease. Maybe if I were a math graduate student I could understand those variables!

I've also been disappointed with how little attention the book gives to the fundamental differences between some of R's "families" of functions, such as apply, lapply, sapply, and tapply, or lm and glm. There is a brief hand-waving comment and then off we go. This is unfortunate especially since, in my view, the builtin R help is often impenetrable and written more as a technical spec then a clear explanation.

I have pushed on to subsequent chapters, and learned more from the book. But be forewarned that it has a tendency to shift suddenly and without warning from a from-the-ground-up perspective to a we're-all-experienced-R-users perspective.

One other comment, as others have noted here, the publisher really should have included data files so that readers could play along with the examples.
3 internautes sur 3 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 Really helpful for taking the Coursera.org R Programming class. 18 mars 2015
Par J. Yoon - Publié sur Amazon.com
Format: Broché Achat vérifié
I am taking the R Programming class from Coursera.org. R was really strange to me. I thought MatLab was much easier for learning matrix and vector manipulation. Just using lecture videos and handout, even searching StackOverflow, really did not cut it. This book is a must for clearly understanding what is going on. It also seem to list all the really helpful functions and gives good examples of usage (usually 2-3 different ones). I think the book is best for someone with a little programming experience. I only have taken 2-3 classes on Python, and a C++ and a Pascal class really long time ago. The book points out similarities to other languages (C, Python) which was very helpful to my understanding. The book helped me see how it is not so strange when it is well explained.
16 internautes sur 18 ont trouvé ce commentaire utile 
1.0 étoiles sur 5 Why so many five-star ratings for this book? 18 mars 2015
Par G.A.J. HOPPENBROUWERS - Publié sur Amazon.com
Format: Broché Achat vérifié
Why did this book receive so many 5-star ratings?
Being new to R and having worked through the first five chapters I was struggling with the data files that are referenced in the book. Normally, when learning a new programming language working the examples works fine for me, but for this book it proved a nightmare: 1) does not explain where the data files can be found. 2) After searching the internet, I found a link to "the data files" on the publishers web site, only to be disappointed even more: many files are missing or have different names from the ones used in the book. Some are corrupt and/or contain different values from those shown in the book.
It really made me wonder where all the five star ratings for this book were based on. I cannot belief that these reviewers used the book intensively.
This problem is not new although only few reviewers mention it: if you google "missing data files art of r programming" you will find many other people that encountered the same problem.
A second problem is that the code fragments often have errors that are really hard to solve for beginners. One example being the mount rushmore code on page 65 and another one the code for the words frequency problem on page 98. On the web I found some solutions/corrections by other readers.
Then why did this book earn so many five-star ratings? It probably has to do with the fact that it could be a very good introduction to R, if only the author (and editorial staff at No Starch Press) had payed more attention to detail and had spent some extra work in providing correct data files.
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