The Art of R Programming: A Tour of Statistical Software Design

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.

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.

Nice and clean book.

No starch press, the editor of this book, is a leading publishing house in the field of statistics and R. This was my first book on the subject; clearly written, it teaches all one needs to know to get started. The treatment of the subject is rather complete. Thoroughly enjoyable and strongly recommended.

Excelente libro, llego en excelente estado

Eine gute Einführung in die Programmierung mit R. Das Buch geht weniger auf einzelne Packete für statistische Berechnungen ein - vielmehr werden die Eigenheiten (z.B Vektorisierung, Verzweigung) von R vermittelt. Um zu verstehen wir R als Programmiersprache funktioniert genau das Richtige.

Although there is an ebook version available for all, I would highly recommend every statistician or a developer taking the journey of data analytics domain to buy the printed version of this too.Its not too difficult to state that this is perhaps the best all-round R programming book in the market. There are others too available, but I find the text here to be very comprehensive and practical. Norman has provided numerous examples on complex functions that passionate and serious R developers will find it enjoying to imitate in their own computers. I haven't read every page of the book but only the required sections and I can conclude with full surety that his book is a mix of a programming manual with a beginner's touch to it. Norman succinctly explains every technical detail of the R language without going into too much depth that the reader will find confusing and yet maintains a newbie approach to most of the stuff. Some sections might seem confusing but I always recommend to practise the exact code provided in the book to get a proper understanding of it.