Biomedical Engineering Theory And Practice/Introduction to Biostatistics with R for Bioengineers

Biomedical engineers today collect all kinds of data from patients, animals, cell counters, microassays, imaging systems, pressure transducers, bedside monitors, manufacturing processes, material testing systems, and other systems that supports research, design and manufacturing process. The problems of health professionals today can involve some part of device and system analysis, and their design and application, and as such are of extreme importance to engineers and scientists. As many aspects of engineering and scientific practice are related to nondeterministic outcomes, statistics is important to any engineer and scientist. Statistics is a guide to the unknown. It is a science that treats designing experimental protocols, collecting, summarizing, and presenting data, and making decisions in the presence of variability and uncertainty. For example, R.A. Fisher and R. Race proposed a model for the evolution of the RH (rhesus) genes based on a statistical analysis of the experimental data[1].

R is a popular statistical computer program, made available through the Internet under the General Public License (GPL)[2][3] . It exists for Microsoft Windows 95 or later, for various Unix and Linux platforms, and for the Apple Macintosh (OS versions newer than 8.6). This chapter introduce how to use R for Bioengineering briefly.

Section 1.Introduction to R gives information about where to set up and understand R environment and R studio which is the most popular graphic user interface for R. In addition, how to use the package which is a library of prewritten code for some task, how to enter data or import data could be introduced. Basic specific statistical variable type which is important but can be igonored sometimes, would be discussed.

Section 2.R language:

Section 3.R programming:

Section 4.R graphics:

Reference edit

  1. R.Race and R.A. Fisher, 1948. The Rh blood groups. Ph.D. Thesis, Cambridge University Library, Cambridge, UK.
  2. Fox, John and Andersen, Robert (January 2005). "Using the R Statistical Computing Environment to Teach Social Statistics Courses" (PDF). Department of Sociology, McMaster University. Retrieved 2006-08-03. {{cite journal}}: Cite journal requires |journal= (help)CS1 maint: multiple names: authors list (link)
  3. Vance, Ashlee (2009-01-06). "Data Analysts Captivated by R's Power". New York Times. Retrieved 2009-04-28. "R is also the name of a popular programming language used by a growing number of data analysts inside corporations and academia. It is becoming their lingua franca..."