Customer Reviews

A First Course in Statistical Programming with R

5 star:		(2)
4 star:		(1)
3 star:		(2)
2 star:		(2)
1 star:		(0)

 

 

It's nice in that it gives you problems and solutions to the problems, so you can teach yourself the language. But it is not nearly comprehensive enough to be a reference book. If you can afford it, buy this much more comprehensive, but still good for beginners, book instead:The R Book

Now if you just want to explore R some and are looking for a lower cost book with which to do so, or want a structured book with problems and exercises, then this Braun and Murdoch book is fine.

Despite its title, this book approaches teaching R from less of a statistical perspective than most other introductory texts on R. There are other texts that I would suggest (such as Verzani or Dalgaard) for those looking to learn how to do statistical analyses with R.
As some of the other reviewers have noted, this book is not comprehensive (I'd have rated it 3 and a half stars if that were possible). Also this book doesn't seem to have an accompanying website which might have added extra value.
For most potential R users, I'd recommend this book only if it were available at a lower price than most of its rivals. On the other hand, there are some important topics that are covered here in a more accessible manner than you are likely to find elsewhere. There is some useful explanation of low level graphics functions in Chapter 3. In particular, the introductions to Simulation (Chapter 5); Computational linear algebra (Chapter 6); and Numerical optimization (Chapter 7) are worth checking out. In my estimation, this book would be most useful for someone interested in introducing R to capable secondary students who might want to use R as a computational tool for a range of purposes including, but not limited to, data analysis. I think this would be a very useful enterprise, and for such a purpose I would recommend this particular book.

This is a really well written book and does everything it said it would do. The only problem was that I was looking for a book that was more geared towards learning R, than a book on learning statistics. The book is surprisingly cursory in regards to leading the reader through the structurees and approach that R takes to solving problems. I found that most of the discussions of R's syntax to be incredibly lacking and its discussion of the how the various features of R to be very disappointingly poor for someone who is trying to learn R as a novice. I have spent most of my 30 years in the workforce learning a variety of languages and am used to "sefl study" challenges...but I have yet to find anything ever remotely similar to what almost every other language provides...an introductory tutorial that focuses on the language rather than all the cool things you can do with the language. This book is the losest so far; but is still, in my opinion, not a very useful or productive approach to learning R.

Given that it is open source and easy to use, the programming package "R" is a natural choice for courses in statistics where a fundamental component is programming. Provided of course that an understandable text is available.
This book satisfies that requirement, making the fundamentals of programming as implemented in "R" accessible to the programming novice. Given that the mathematical coverage is a bit advanced, the prerequisites are mathematical in nature with precalculus a necessity and calculus very helpful.
While the basics of statistical programming are covered in sufficient detail, some more advanced topics in the area of numerical analysis are also included. Those topics are:

*) Computational linear algebra
*) Numerical optimization
*) Linear programming

If you are looking for an inexpensive computational package that your students can download on their laptops for use in a course on statistical programming, then "R" is a natural choice. With this textbook, it also becomes a practical one.

The book teaches very basics of R programming language, but despite its title, not with a statistical approach. The approach is like teaching any other general purpose programming language.

It is good to have solutions to some problems on the webpage of the book. However some exercise are irrelevant (although they are nice), like proof of infinite set of prime numbers, timing analysis of mergesort algorithm, etc.

If you are completely new in programming, the text may be OK for you. Otherwise, it is not the book for you.

A lot of people have need of means to manipulate and chart data. Some of them are professionals from various fields, some of them are students, some of them are users of computers who have outgrown the meager capabilities of Excel or OpenOffice's Calc, or simply can't stand the spreadsheet model any longer. R is a good solution to these needs.

Recognize that R is not just a programming language, not in the sense of Python or Ruby or C. It is a universe. In addition to the capability in the base language, the availability of hundreds of sophisticated packages extends its capability to all kinds of terrains, from the traditional and statistical, to GIS, spatial, image-based, and nearly research edges. Mastery of those packages would take a lifetime. There are new ones every month.

But these packages are not what the average user needs or wants. What the average user needs or wants is a gentle introduction, how to install R, how to use it for common things, getting data in and out, some graphs, some basic statistical notions. Alas, until recently, most R books were sophisticated presentations, ones giving terse, in depth illustrations of R's uses for statistics and analysis. It was difficult to find discussions on the core language, and most of those went back to S+, and Chambers. Admittedly, there are detailed discussions of these as PDF documents with installations, but these may not be reachable by the casual user.

Fortunately, today we have at one end of the beginners' path, the Venables and Smith AN INTRODUCTION TO R. That text is also available online, and was part of some installations of R, at least on Windows. At the other end of the beginners' path is Rizzo's STATISTICAL COMPUTING WITH R, a book on introductory statistics using R for illustration. And, in the middle of the path, there's this text, Braun and Murdoch's A FIRST COURSE. It's fine. It won't let you down. You won't learn how to do projection pursuit regression with R from it. But, then, many people don't need that.

Book was most defintely written for the beginning R student. I would recommend this book to someone who has never had any experience in R. There were a few examples from the book that I thought interesting. If you have had experience in R, I would suggest a different book; however, this is a great book for the beginner.