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Data Analysis Using R Programming

  • Bertram K. C. Chan
Chapter
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 1082)

Abstract

Beginning R

R is an open-source, freely available, integrated software environment for data manipulation, computation, analysis, and graphical display. The R environment consists of
  • *a data handling and storage facility,

  • *operators for computations on arrays and matrices,

  • *a collection of tools for data analysis

  • *graphical capabilities for analysis and display, and

  • *an efficient, and continuing developing programming algebra-like programming language which consists of loops, conditionals, user-defined functions, and input and output capabilities.

Many R programs are available for biostatistical analysis in Genetic Epidemiology. Typical examples are shown.

Keywords

R environment R as a calculator R graphics R in statistics R in data analysis in human genetic epidemiology Function data.entry() Function source() Spreadsheet interface in R plot() function 

Special References

  1. Aragon TJ (2011) Applied epidemiology using R (epir). UC Berkeley School of Public Health, and San Francisco Department of Public Health, BerkeleyGoogle Scholar
  2. BMI Notes (2012) Body mass index. http://en.wikipedia.org/wiki/Body_mass_index
  3. Centers for Disease Control and Prevention (2005) Antiretroviral postexposure Prophylaxis after sexual, injection-drug use, or other nonoccupational exposure to HIV in the United States: recommendations from the U.S. Department of Health and Human Services. MMWR Recomm Rep 54(RR-2):1–20 Available from: http://www.cdc.gov/mmwr/preview/mmwrhtml/rr5402a1.htmGoogle Scholar
  4. CRAN, The comprehensive R archive network: http://cran.r-project.org/
  5. Dalgaard P (2002) Introductory statistics with R, Springer statistics and computing series, Springer, New YorkGoogle Scholar
  6. Daniel WW (2005) Biostatistics – a foundation for analysis in the health sciences. Wiley, New YorkGoogle Scholar
  7. Everitt BS, Hothorn T (2006) A handbook of statistical analysis using R. Chapman & Hall/CRC, Boca RatonCrossRefGoogle Scholar
  8. Teetor P (2011) R Cookbook. O’Reilly Media, SebastopolGoogle Scholar
  9. Venables WN, Smith DM, and the R Development Core Team (2004) An introduction to R. Network Theory, Ltd., BristolGoogle Scholar
  10. Virasakdi C (n.d.) Analysis of epidemiological data using R and Epicalc. Epidemiology unit, Prince of Songkla University, Thailand: cvirasak@medicine.psu.ac.thGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Bertram K. C. Chan
    • 1
  1. 1.Epidemiology and BiostatisticsLoma Linda University School of Medicine and Public HealthSunnyvaleUSA

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