[Plsgs] FW: ECOL 596W Programming for Data Analysis in R- new course for Spring 2016

[Lambert, Georgina M - (glambert)]

ECOL 596W -- Programming for Data Analysis in R (Spring 2016) – 2 units
Ty Taylor and Scott Saleska
Initial meeting/proposed course time: Friday 10-11:30, BSW 302
Starting January 15

Contacts and enrollment
For all inquiries about the course, please send an email to both Ty Taylor (tytaylor at email.arizona.edu<mailto:tytaylor at email.arizona.edu>) and Scott Saleska (saleska at email.arizona.edu<mailto:saleska at email.arizona.edu>).

If you are interested in registering for the course, then please contact EEB’s Grad Coordinator, Pennie Rabago, at pliebig at email.arizona.edu<mailto:pliebig at email.arizona.edu>.
The course will be offered for 2 units. Registration is open to graduates and advanced undergraduates with permission.

Course summary
This course will teach the fundamentals of programming for data analysis in R. The course emphasis is practical, providing skills for more effective research and encouraging immediate application to students’ own work.

The course is based on a unique set of tutorials that provide a complete foundation of techniques in R, which are broadly applicable across research disciplines. We start from the very beginning, and build toward powerful techniques such as fast iteration of custom functions. We will apply a variety of packaged and custom statistical tests.  The latest iteration of course tutorials and outline are available at the following link, but note that these will be updated throughout the course: http://www.saleskalab.org/teaching/tutorials/r-tutorials/

What is R?
R should be thought of as a language for analyzing data. Creative and thorough data analysis involves not only statistical tests, but complex manipulation of datasets, e.g., merging multiple data sources, assessing nested structures, interactions, alternative conditions, data gaps, etc. Very few datasets are simple enough to fully explore and understand by manual manipulation in spreadsheet programs like Excel. Programming solves these problems. An R “script” records all analysis steps, both in English (via comments) and in R language (via code), while also performing the analysis.

This course teaches a complete foundation of analysis techniques in R. We will alternate between practical examples of exploratory analysis of complex datasets, and specific statistical tests.

Teaching method
R is a foreign language. As other introductory programming and foreign language courses are taught, this course uses ‘repeat after me’ class exercises. We will teach the principles—the meanings of the terms and the syntax that links terms into functional sentences—and students will follow along in typing in the code corresponding to each example in a neatly formatted R script. This will build muscle memory as students practice ‘speaking the language’, allowing observation of the results on their own screens. The course provides practical exercises, done in class in groups, and/or independently as homework. Students are expected to work toward a final project that creatively applies lessons of the course to unique questions in real datasets (including from students’ own research).



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A. Elizabeth (Betsy) Arnold
School of Plant Sciences
The University of Arizona
Tucson, AZ 85721

http://arnoldlab.net
barnoldaz at gmail.com<mailto:barnoldaz at gmail.com>
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