Want to learn R, the open source statistics software?

Below is an message from Paul Geissler (USGS) announcing a free webinar series that is excellent for getting the basics behind R, the open-source (i.e., free) statistical package.  I highly recommend it if you are planning on using R.

Learn R, a free software environment for statistical computing and graphics

The webinar course is designed for natural resource managers and is open to all who are interested without charge. R (the open-source/free version of S) provides a comprehensive environment for statistical analysis and graphics that is unrivaled in the availability of new, cutting-edge applications. It runs under most operating systems. Audio of the presentations is available either using your computer speakers and optional microphone or headset or by calling a phone bridge long distance. Live video of the presenter's computer screen is available over the web. You can also share your computer's screen with other participants when asking a question or making a point. An audio and video recording of the presentations and discussion will be available on our FTP site after the presentations.  There are no specific prerequisites but some knowledge of statistics would be helpful. A basic knowledge of computers and the internet will be assumed.

Please forward this message to those who may be interested.

This course (http://www.fort.usgs.gov/brdscience/learnR.htm) will start June 7, 2011 and will meet for about an hour on Tuesdays and Thursdays.  The times will be: Hawaii 8:00 AM, Alaska 10:00 AM, Pacific 11:00 AM, Mountain 12:00 Noon, Central 1:00 PM, Eastern 2:00 PM, UTC 18:00.  The course will continue until we finish the outline.  We will use Michael J. Crawley, 2007, The R Book, Wiley, 942 pages, list price $110.00, but available at discount for $81 (We do not recommend book sellers. compare prices using a sites such as BizRate, or NexTag, or PriceGrabber.)  It is a relatively comprehensive reference on R, although no text can cover the over 2,900 packages available in R. The course will cover the R procedures, not the statistics, which can be learned from the text book. The course will primarily use the command approach, using the Tinn_R editor.
It will present a more in-depth introduction to R than the Learn R by Example course (http://www.fort.usgs.gov/brdscience/LearnRE.htm), which uses Brian S. Everitt and Torsten Hothorn (2010, A Handbook of Statistical Analyses Using R, Second Edition, CRC Press) and primarily uses menus.  The Learn R by Example course will be presented again in the fall but it is not a prerequisite for this course.

Register at https://www1.gotomeeting.com/register/655473105

The course is presented by the US Geological Survey, Status and Trends Program (Paul Geissler, Paul_Geissler@usgs.gov). Tom Philippi (Tom_Philippi@nps.gov, the National Park Service, Inventory and Monitoring Program) assists with this course.  Please contact us for more information.

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/ .
• 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 2,900 contributed packages (extensions) available for R to perform a great variety of statistical and graphical 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.
• Although SAS is the most common statistical package in general use, R (or S) is the most popular with statistical researchers (Faraway 2005: x).
• 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.