R for medicine and biology / Paul D. Lewis.

By:

Lewis, Paul D, 1969-

Material type: Text

TextSeries: Jones and Bartlett series in biomedical informaticsPublication details: Sudbury, Mass. : Jones and Bartlett Publishers, c2010.Description: xix, 399 p. : ill. ; 26 cmISBN:

9780763758080 (pbk. : alk. paper)
0763758086 (pbk. : alk. paper)

Subject(s):

DDC classification:

610.285 22

Contents:

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 -- ter 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.

Summary: 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.Summary: 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.

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Holdings
Item type	Current library	Call number	Copy number	Status	Date due	Barcode
Books	Main library General Stacks	610.285 / LE.R 2010 (Browse shelf(Opens below))	1	Available		006909

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Previous	610.285 / HO.P 2007 Project management for healthcare informatics /	610.285 /HU.B 2008 Biomedical informatics in translational research /	610.285 / KU.H 2010 Healthcare informatics :	610.285 / LE.R 2010 R for medicine and biology /	610.3 / HI.H REF 2008 Hitti’s new medical dictionary, English-Arabic :	610.3 / MA. M 199- معجم مرعشي الطبي الكبير : عربي انجليزي	610.72 BA.C / 2006 Conducting and reading research in health and human performance /	Next

Includes bibliographical references (p. 363-366) and index.

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.

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