viernes, 3 de septiembre de 2010

Learn the R Statistical Package by Example


Dr. Paul Geissler (US Geological Survey, Status and Trends of Biological Resources Program)

Tuesdays and Thursdays 12:00 – 2:00 PM, Mountain Time, starting October 12, 2010

Website: http://www.fort.usgs.gov/brdscience/learnRE.htm

Register at: https://www1.gotomeeting.com/register/327005065


R is a very powerful system for statistical computations and graphics, which runs on Windows, UNIX and Mac computers. You can think of it as a combination of a statistics package and a programming language. It can be downloaded for free from http://www.r-project.org/

Advantages:

* With the increasing cost of commercial statistical package, a free package

is very attractive. However, free does not imply second rate. R is a high quality package that is better than commercial package in many respects.

* There are over 2,400 contributed packages (extensions) available for R to perform a great variety of statistical and graphical procedures.

* An easy to use menu system is available for common procedures.

* R includes a powerful programming language for selecting, manipulating and transforming data.

* R is interactive and supports data analysis, which should be interactive and exploratory.

* New statistical methods often are available first in R. For example, GRTS analyses are only available in R at this time to my knowledge.

* R can easily import and export data to and from Microsoft Access and Excel as well as text files.


This course introduces R by working through a series of practical examples, showing how to use R analyses for common problems. It provides an overview, organized by problem situation, without going into all the options available with each procedure. We will use Brian S. Everitt and Torsten Hothorn, 2010, A Handbook of Statistical Analyses Using R, Second Edition, CRC Press, 355 pages, list price $54.95, but available at discount for $37.08. R Commander (a graphical user interface to R) will be used to provide menu access to R, supplemented with commands when necessary. The course will cover the R procedures, not the statistics, which can be learned from the text book.