Stat 597A Introduction to Statistical Computing

John Staudenmayer (Office LGRT 1435K, Phone 545 0999)

Office hours: Wed and Fri 11AM, or by appointment.

jstauden at math.umass.edu

www.math.umass.edu/~jstauden/stat597A.html

Textbook

The Art of R Programming (Norman Matloff) We will use the book a lot. Additional readings will be assigned during the semester.

Software & Prerequisites

This class will mostly be about using R to do modern statistical analyses and data management. (You need a computer with R on it! R studio is strongly recommended.) We will learn to use R to run existing programs, and we will also learn to write our own programs and functions. Effective programming strategies and principles will be emphasized.

This class does not assume a lot of programming background. We will cover core of ideas of programming — functions, objects, data structures, flow control, input and output, debugging, logical design and abstraction — through writing code to assist in numerical and graphical statistical analyses. If you know a lot about programming already, much of the class may be review. The class will assume that students know the basic concepts of statistical thinking (with data) and basic probability.

Classes & Assignments

Classes will consist of lectures and labs. A tentative schedule and topic list is below. I will try to post the lecture notes on the web after class. The labs will consist of group activities done on your computers during class time. Each lab will have an assignment to be handed in. There will also be approximately weekly problem sets.

All assignments must be turned in electronically, via email. All assignments will involve writing a combination of code and actual prose. You must submit your assignment in a format that allows for the combination of the two, and the automatic execution of all your code. The easiest way to do this is to use R Markdown (http://rmarkdown.rstudio.com).

Projects

There will be a midterm project (assigned 10/23 and due 10/30) and a final project (assigned 11/16 and due before end of final exams). The midterm will be done alone, and the final project will be done in small assigned groups. The last few classes will consist of project presentations.

Grading

Approximately weekly problem sets and labs: 30%

Midterm Project: 30%

Final Project: 40%

Some R Resources

There are many online resources for learning about it and working with it, in addition to the textbook:

• The official intro, "An Introduction to R", available online in HTML and PDF

• John Verzani, "simpleR", in PDF

• Google R Style Guide offers some rules for naming, spacing, etc., which are generally good ideas

• Quick-R. This is primarily aimed at those who already know a commercial statistics package like SAS, SPSS or Stata, but it's very clear and well-organized, and others may find it useful as well.

• Patrick Burns, The R Inferno. "If you are using R and you think you're in hell, this is a map for you."

• Thomas Lumley, "R Fundamentals and Programming Techniques" (large PDF)

• The website Software Carpentry is not specifically R related, but contains a lot of valuable advice and information on scientific programming.

Important Note

A lot of this class (and this information!) is based on a class at Carnegie Mellon:

Shalizi, C. R. and Thomas, A. C. (2014), "Statistical Computing 36-350: Beginning to Advanced Techniques in R", http://www.stat.cmu.edu/~cshalizi/statcomp/14

Tentative Schedule / Class Outline

Data types and data structures

Lecture 1 (Sept 9): Simple data types and structures

Lecture 1 R Markdown File

Lecture 2 (Sept 11): Bigger data structures

Lecture 2 R Markdown File

Problem set 1 (due 9/18)

Lab 1 R Markdown File (Sept 14)

Flow control and looping

Lecture 3 (Sept 16): Data Frames and Control

Lecture 3 R Markdown File

Lab 2 R Markdown File (Sept 18)