Read R Programming for Bioinformatics (Chapman & Hall/CRC Computer Science & Data Analysis) by Robert Gentleman Online
Read [Robert Gentleman Book] ! R Programming for Bioinformatics (Chapman & Hall/CRC Computer Science & Data Analysis) Online * PDF eBook or Kindle ePUB free. R Programming for Bioinformatics (Chapman & Hall/CRC Computer Science & Data Analysis) Coolgard said Weak writing, poor editing, uncertain audience. 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, extr
| Title | : | R Programming for Bioinformatics (Chapman & Hall/CRC Computer Science & Data Analysis) |
| Author | : | |
| Rating | : | 4.60 (755 Votes) |
| Asin | : | 1420063677 |
| Format Type | : | paperback |
| Number of Pages | : | 328 Pages |
| Publish Date | : | 2016-02-17 |
| Language | : | English |
It presents methods for data input and output as well as database interactions. R Programming for Bioinformatics explores the programming skills needed to use this software tool for the solution of bioinformatics and computational biology problems. 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. 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. Due to its data handling and modeling capabilities as well as its flexibility, R is becoming the most widely used software in bioinformatics. He concludes with a discussion on the debugging and profiling of R code.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.
Coolgard said Weak writing, poor editing, uncertain audience. 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 examp. Perhaps a decent resource for R package developers, not end-users 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 SPerhaps a decent resource for R package developers, not end-users Jeremy Leipzig 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 in. " 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 in. bmaverick said Clearly written intro to R _programming_, not general use. 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 softwareAnyway, this book is well organized and clearly written. As for bioinformatics, the author is not only a co-creator of R, he is a leadi
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