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

This book is written very clearly with examples that don't assume a vast data science or statistics knowledge and will take you a long way to understanding and mastering R programming. The emphasis is on the programming and understanding R, rather than how you might utilise R with your own data. Which I feel is a better approach - I want to understand R with simple, clear example code, like this. Once I've gained the R programming skills I'll be able to use R for what I specifically want - i.e. time-series analysis and forecasting.

I have to do some R programming for work. This book has good extended examples which help I understand how R works and demonstrate what R can do. I have learnt some other programming languages before and learnt a little bit of R back in uni. I wish I had this book when I was doing my uni course! It is not for total beginners, and it helped me to go deeper and use R in different ways. Some people say one can learn R from the Internet. That's true but this book provides a much more structured way for understanding each "class" and the associated attributes. Have almost finished the book and I think it is a keeper. I have bought other programming books before which I found totally useless.

I needed some advanced statistical software and R came highly recommended. Having downloaded the software it became rapidly apparent I would need a guide. I have no background in computer program and the Amazon.co.uk reviews indicated this was the book for me. While I am finding the book useful as I slowly get to grips with how R runs, I would disagree about not needing previous computer programming experience. While the author explains some terms used, eg %%, I am left baffled by others and have to resort to online guides (which I was trying to avoid) in order to make sure I understand everything. If the author had included a simple glossary so I could understand why terms such as "==" are required, then I probably would have been all the way through the book by now and a competent R user. These may be simple for those with basic computer programming, but they are baffling to a newbie! This all said I would recommend the book because it has increased my knowledge. The layout is good and the level of usage builds up gradually. However external sources are also required, hence my four star rating.

Clearly doing what it states in the title, the book introduces the developer to the art of R programming. It is written with a clear vision to address developers (C++, Pearl, etc) it fully succeeds not only to provide a smooth introduction to R, but also to be used as a reference book afterwords, for quick checking during development. Through the examples, the book manages to give the reader the flavor of developing algorithms in R, turning the learning process into an easy adaptation of programming skills to the comfortable R environment.

I've had this book on my shelf for 4 years now and dip into it for reference now and then. It's very well written, comprehensive and clear. So why only 3 stars? There are no end of chapter exercises, which for me is a serious flaw in a book meant to teach programming. Programming expertise can only be obtaIned by practice, practice, practice.

The book requires no background in programming in general, and does a solid work explaining the basics of R data structures, data manipulations and so forth. Beginning with simple things, the book discusses some of the more advanced subjects as well. I have read a few books on R already, but this one has had the largest impact in terms of understanding the language. My programs became better and faster for sure.

This book is an excellent short introduction to programming in R, explains clearly several of the key concepts that are unlike 'normal' languages - eg data frames. However, don't expect any discussion of statistics - that's not what this book is about.

Excellent introduction of R for computer scientist. Deepened knowledge of computer-sciency bits of R for the rest of us. Definitely classic. One of a set of books that every R data scientist / bioinformatician should own (and read).

Comprei o livro que foi a indicação de um amigo que trabalha a tempo com o R. Sou biólogo e trabalho com grandes volumes de dados, para isso preciso de um software que seja maleável para minhas análises. O livro é realmente bom para quem está começando, tem muito mais conteúdo do que muito material que já li e cursos que fiz. Indico para todos que estiverem procurando material sobre o assunto.

Nice and clean book.

Es una guía de programación, pero no profundiza en aspectos de data science, o en estadística o en gráficos que es donde R es potente.

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

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.