R Programming for Bioinformatics | Date: 28 April 2011, 05:38
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R Programming for Bioinformatics (Chapman & Hall/Crc Computer Science & Data Analysis)
By Robert Gentleman
* Publisher: Chapman & Hall/CRC
* Number Of Pages: 328
* Publication Date: 2008-07-14
* ISBN-10 / ASIN: 1420063677
* ISBN-13 / EAN: 9781420063677
Product Description:
Shows how R can be used to solve bioinformatics and computational biology problems
Due to its data handling and modeling capabilities as well as its flexibility, R is becoming the most widely used software in bioinformatics. R Programming for Bioinformatics explores the programming skills needed to use this software tool for the solution of bioinformatics and computational biology problems.
From the co-developer of R and lead founder of the Bioconductor project
Drawing on the author’s first-hand experiences as an expert in R, the book begins with coverage on the general properties of the R language, several unique programming aspects of R, and object-oriented programming in R. It presents methods for data input and output as well as database interactions. The author also examines different facets of string handling and manipulations, discusses the interfacing of R with other languages, and describes how to write software packages. He concludes with a discussion on the debugging and profiling of R code.
Hone your programming skills
With numerous examples and exercises, this practical guide focuses on developing R programming skills in order to tackle problems encountered in bioinformatics and computational biology.
Summary: Decent book
Rating: 4
My only concern is that it at various stages the book feels like a programming book on S instead of on R. It needs better editing... content wise its decent & a good starting point!! Particularly god thing is that you don't have type in all the code when you are practicing, you can download written code from the CRAN website!!
Summary: Clearly written intro to R _programming_, not general use
Rating: 5
The previous two reviewers are apparently frustrated because this book is not what they expected. In R world, however, _programming_ does not mean doing statistics or graphics in R - it means R software development. If the book was called "R Software Development for Bioinformatics", there would perhaps be less confusion - unless there was somebody who would be then led to believe that it was the book about developing core R software...
Anyway, this book is well organized and clearly written. As for bioinformatics, the author is not only a co-creator of R, he is a leading figure of the Bioconductor project. Suitable for a bioinformatics-oriented book, it has a whole big chapter on working with character data (not something discussed at great length in other R books, and a very useful chapter on Data Techniques replete with real-life Bioconductor examples. The chapter on OOP comes early in the book and, while does not go into too many details, it is enough to get you going sooner rather than later.
As a conclusion: potential buyers should be careful about whether the content of this book is what thy need. Those who do will probably be happy with the way it is written.
Summary: General R introduction, not for bioinformatics
Rating: 1
I expected that this book is designed to introduce R programming for various works of bioinformatics with bunch of practical examples.
In short, this book is another R programming introduction book with no practical bioinformatics examples. I wonder why this book is for bioinformatics. This book is just for general R users, not especially for bioinformatics purpose.
Summary: Perhaps a decent resource for R package developers, not end-users
Rating: 2
This is a strange little book in that it seems somewhat directed toward statisticians who want to develop R packages. The OOP section takes up 50 pages and discusses "S3 and S4" implementations of OOP in R in great detail, all of which is not doubt important for those few dozen accomplished statisticians who wish to write packages. However, by the time you are ready to actually write an R function that other people will use I can't imagine you wouldn't already be familiar with some of the basic commands discussed elsewhere in this book. So I am wondering who the intended audience is.
I think the majority of R users (biologists and programmers) want to run through some common statistical routines in a procedural fashion and produce reports that perform some analysis and show some graphs. The difficulty with R is learning how to massage data into a form that an existing statistical function will accept. That will invariably involve helper R-specific helper functions that do not exist in programming languages (e.g. unsplit) or that require a precise understanding of input (e.g. xtabs), and statistical routines that almost never return meaningful errors (glm). Manipulating data structures in R is not particularly intuitive (e.g. as.numeric(levels(f))[f]), so tons of examples are a must. However this book simply does not include enough R code - probably fewer than 250 lines.
In some instances commands are discussed at length in the space it would take to simply show the command. For example, a beginner would want to know how to save a data frame. Instead of providing a useful example like:
save(myDataFrame,file="myDataFrame.frame.RData",compress=TRUE)
there is a bizarre paragraph called "Working with R's binary format", in which save and load are discussed in theory as if they are planned for a distant release.
There is no chapter on using Sweave to develop pdf reports despite the book being actually written in Sweave. The author is more focused on "vignettes" which appear to be for documentation akin to POD files.
This book does include excellent sections on string manipulation, connecting to databases, and C integration. I learned some things about some neat Bioconductor functions available but a dedicated chapter would be nice.
At no point do you ever sense the author does not know what he is talking about - he just doesn't know who he is talking to. I hope in the future "R Programming For Bioinformatics" is split this into two more comprehensive books: "Developing R Packages" and "R for Biologists"
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