R Programming for Bioinformatics
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.
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.
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.
A note on the text
R Language Fundamentals
Managing your R session
Subscripting and subsetting
Object-Oriented Programming in R
The basics of OOP
Using classes and methods in packages
Managing S3 and S4 together
Navigating the class and method hierarchy
Input and Output in R
Basic file handling
File input and output
Source and sink: capturing R output
Tools for accessing files on the Internet
Working with Character Data
Prefixes, suffixes and substrings
Foreign Language Interfaces
Calling C and FORTRAN from R
Writing C code to interface with R
Using the R API
Using R for data manipulation
Bioinformatics resources on the WWW
Debugging and Profiling
The browser function
Debugging in R
Debugging C and other foreign code
Profiling R code
An Introduction appears at the beginning of each chapter.
"This is a very excellent book. … I think this is actually the best handbook on R programming that is currently available. … the nine chapters provide an indispensable handbook for R programmers, and an excellent textbook for a graduate course in R programming."
—Journal of Statistical Software