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R Programming for Bioinformatics (Chapman & Hall/CRC Computer Science & Data Analysis) 1st Edition

3.1 out of 5 stars 7 customer reviews
ISBN-13: 978-1420063677
ISBN-10: 1420063677
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Product Details

  • Series: Chapman & Hall/CRC Computer Science & Data Analysis
  • Hardcover: 328 pages
  • Publisher: Chapman and Hall/CRC; 1 edition (July 14, 2008)
  • Language: English
  • ISBN-10: 1420063677
  • ISBN-13: 978-1420063677
  • Product Dimensions: 6.2 x 0.9 x 9.3 inches
  • Shipping Weight: 1.2 pounds (View shipping rates and policies)
  • Average Customer Review: 3.1 out of 5 stars  See all reviews (7 customer reviews)
  • Amazon Best Sellers Rank: #1,583,087 in Books (See Top 100 in Books)

Customer Reviews

Top Customer Reviews

By Jeremy Leipzig on August 6, 2008
Format: Hardcover
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.
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Format: Hardcover
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.
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Format: Hardcover
The book is not designed to teach you R or programming and offers little about using R for bioinformatics (apparently Chapter 5, for "Working with Character Data", and Chapter 8, about "Data Technologies", account for the "bioinformatics" part). The text is riddled with writing errors -- the author writes badly in English, however expert he may be in R -- and looks as though it has not been copy-edited at all, with all the typos, extra words, misspellings, and awkward or wrong syntax. Concepts are not introduced sequentially or systematically defined; for one example (p43) "expr" is defined as "any valid R expression" -- but "expression" is never defined.

Chapter 3, on "Object-Oriented Programming in R", I find effectively unreadable. In 3.2 appears "Inheritance allows new classes to extend, often by adding new slots...". Aside from the misuse of the transitive verb and the dangling participle, the author nowhere bothers to define "slot", but continues to use it thereafter. If he had decided for whom he is writing the book and that it was an audience advanced enough to have used an OOP language, he might have said "In R, "slot" refers to what is called a "member" in Java or C++." Then he starts talking about "dispatch", only some time later casually "defining" it two or three times. The trouble with this approach is that you never work out what you're supposed to know already, and finally decide that the author himself doesn't know or care. It would be far better to have a book of which the first third is elementary, but systematic, lead-in that you can skip if sufficiently advanced, and the second two thirds is useful stuff that refers back to the earlier material.
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Format: Hardcover
Personally, I have found this book to be very useful. Chapter 9, which covers the specifics of debugging in R really helped me get a sound knowledge of how to debug in R. It also does a nice job of explaining the lapply() family of functions in the context of BioInformatics. The purpose of the book is pretty specific - "To provide a reference for Scientists and programmers working on problems in the bioinformatics and computational biology" - in that context I feel it did and continues to be a good reference. The chapters on debugging (9) and data technologies (8) at least for me was super helpful.

Nb. I purchased a paper copy of this book and have access to it online via a subscription service.
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