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R for Medicine and Biology

Author(s): Paul D. Lewis, PhD, Swansea University
Details:
  • ISBN-13: 9780763758080
  • Paperback    399 pages      © 2010
Price: $143.95 US List
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Part of the JONES AND BARTLETT SERIES IN BIOMEDICAL INFORMATICS

R is quickly becoming the number one choice for users in the fields of biology, medicine, and bioinformatics as their main means of storing, processing, sharing, and analyzing biomedical data.  R for Medicine and Biology is a step-by-step guide through the use of the statistical environment R, as used in a biomedical domain. Ideal for healthcare professionals, scientists, informaticists, and statistical experts, this resource will provide even the novice programmer with the tools necessary to process and analyze their data using the R environment.  Introductory chapters guide readers in how to obtain, install, and become familiar with R and provide a clear introduction to the programming language using numerous worked examples. Later chapters outline how R can be used, not just for biomedical data analysis, but also as an environment for the processing, storing, reporting, and sharing of data and results. The remainder of the book explores areas of R application to common domains of biomedical informatics, including imaging, statistical analysis, data mining/modeling, pathology informatics, epidemiology, clinical trials, and metadata usage. R for Medicine and Biology will provide you with a single desk reference for the R environment and its many capabilities.

Click on the Sample Materials tab to download data files and scripts.

 

Introduction  
Chapter  1  R installation and getting help
Chapter  2  The R environment and packages
Chapter  3  Basic Fundamentals of R
Chapter  4  Plotting data
Chapter  5  Example datasets
Chapter  6  Importing and exporting data in R
Chapter  7  R, SQL and database connectivity
Chapter  8  Using R to build a biomedical database in MySQL
Chapter  9  Creating heterogeneous datasets for analysis in R
Chapter  10  Descriptive statistics in R
Chapter  11  R and Basic Inferential Statistical Analysis
Chapter  12  Writing functions in R
Chapter  13  Multivariate analysis in R
Chapter  14  Survival analysis
Chapter  15  Data mining and predictive modeling with R and WEKA
Chapter  16  Surveillance of infectious disease
Chapter  17  Medical imaging and R
Chapter  18  Retrieving public microarray datasets
Chapter  19  Working with microarray data
Chapter  20  Annotating microarray gene lists
Chapter  21  Array CGH analysis
Chapter  22  XML for storing and sharing data
References
Appendix

Paul D. Lewis, PhD-Swansea University