Syllabus: CSSS 508 – Winter 2014

INTRODUCTION TO R FOR SOCIAL SCIENTISTS

Instructor:

Elena Erosheva  

C 14C, Padelford Hall (CSSS)

elena at stat.washington.edu

 
  • Class time: Wednesday 4-4:50
  • Optional lab: Wednesday 4:50-5:40
  • Class and lab location: SAV 117
  • Office hour:  by appointment
  • Web: follow the class link from my homepage at http://www.stat.washington.edu/elena
  • Questions by e-mail are welcome. They will often be answered quite quickly, but this is not guaranteed. In particularly, I don't always check e-mail over weekends.

Course description

This course familiarizes students with the R environment for statistical computing. R is a freely available, multi-platform, and powerful program for analysis and graphics similar to S-PLUS.  We will cover the basics of organizing, managing, and manipulating social science data; basic applications; introduction to programming; graphics, linear regression, logistic regression, and links to other major statistical packages.

Course Text

·         Introductory Statistics with R (2008), second edition, by Peter Dalgaard. (Available electronically in the UW library.)

Optional Text

·         Software for Data Analysis: Programming with R (2008), by John Chambers.

Course objectives

·         To introduce a number of basic concepts and techniques in R.

·         To gain familiarity with the R statistical computing environment that should allow students to get started with practical statistics.

Labs, homework assignments and grades

·         There will be a total of five homework assignments.

·         There will be no final exam, but the final homework will include review of the entire curriculum.

·         The course will be graded credit / no credit. You must obtain at least 50% of points on each assignment in order to receive credit for this class.

·         The optional lab session is an open, supportive environment where you may explore R’s capabilities at your own pace with assistance from the instructor as needed. During lab, you are welcome to work on homework, on optional practice problems related to that day’s lecture, or ask questions.

·         I encourage you to work on homework assignments with each other in small groups, although it is generally advisable to grapple with the problems alone for a while before discussing them with others. Each student is required to prepare and submit their own solution and write-up.

·         Hand in a hard copy of your homework. Late homework will be penalized by 25% for each day after the due date, except in cases of documented emergency or an advance agreement with the instructor.

·         Please type up your homework assignments using a text editor. Insert relevant graphs and appropriate parts of the code and output into your write-up.

·         Unless specifically requested, always include both your code and the relevant output in your write-up.

·         Please read instructions carefully and answer all questions. It is not enough to copy and paste output that contains key numbers for the answers – you have to provide answers to all questions in words.

·         Instructions for asking computing questions: If you are having a problem getting some code to run, follow the question format as in "I did X. I expected Y to happen, but Z happened." I need to be able to replicate your problem in order to help you.

Students with Disabilities

If you would like to request academic accommodations due to a disability, please contact Disabled Student Services, 448 Schmitz, 543-8924 (V/TTY).  If you have a letter from Disabled Student Services indicating you have a disability that requires academic accommodations, please present the letter to me so we can discuss the accommodations you might need for this class.