Instructor

Syllabus

Office Hours -- Fall 2019

Class Meeting Time

Purpose: To help students learn computing skills that will be useful to them both in academic endeavors and as working statisticians. To introduce students to programming using the free software package R and the widely used commercial software SAS. Programming skills, rather than straightforward data analyses, are emphasized in this course.

Current Textbook: The Little SAS Book: A Primer (5th edition), by Delwiche and Slaughter (earlier editions are probably OK too).

Required Supplementary Material: Supplementary material available for download:
Basics of R: A Primer, by Don Edwards.
Click here for the Basics of R: A Primer (as Word document) or Basics of R: A Primer (as pdf).
Introduction to R, Comprehensive R Archive Network (CRAN).
Available at CRAN home page (click "Manuals" at left side of page; then choose the first manual, "Introduction to R").

Required Computing Resources: Access to a computer with R (available as a free download from the CRAN home page) and SAS (students who want SAS can either buy a copy from USC Computer Services, or use SAS Studio via SAS OnDemand for free (see below)). Help in using R can be found on the CRAN home page.

Downloading Instructions

• Instructions for Downloading R

Access Instructions for SAS Studio

• SAS OnDemand Entry Page

• SAS OnDemand Control Center

SAS Resources

• SAS Resources (PowerPoint)

• SAS Certification Information

Useful Links

• A Nice RStudio Tutorial from Oscar Torres-Reyna at Princeton

• Another Good RStudio Tutorial from Muhammad Ali Gulzar at UCLA

• A Good Quick R Tutorial from Rob Kabacoff

Course Outline

Topics Covered	Text	Time
Objects in R; Inputting and Outputting Data in R	Edwards	1.5 weeks
R Graphics, Functions, Arithmetic, Logicals, Conditional Execution, Subsetting, Sorting, Iteration	Edwards	2 weeks
Random Variables and Simulation Studies	TBA	1.5 weeks
Introduction to SAS; Reading and Writing Data	Delwiche and Slaughter	2 weeks
Working with Your Data; Sorting, Printing, Summarizing Data	Delwiche and Slaughter	2 weeks
Modifying and Combining Data Sets	Delwiche and Slaughter	1 week
MACRO programming in SAS; ODS output; PROC SGPLOT	Delwiche and Slaughter	2.5 weeks

 

Slides for Class Notes

• Class Notes: Edwards Sections 1-6 (R)

• Class Notes: Edwards Sections 7-12 (R)

• Class Notes: Random Numbers and Simulation

• Class Notes: Delwiche/Slaughter Chapter 1 (SAS)

• Class Notes: Delwiche/Slaughter Chapter 2 (SAS)

• Class Notes: Delwiche/Slaughter Chapter 3 (SAS)

• Class Notes: Delwiche/Slaughter Chapter 4 (SAS)

• Class Notes: Delwiche/Slaughter Chapter 6 (SAS)

• Class Notes: Delwiche/Slaughter Chapter 7 (SAS)

• Class Notes: Delwiche/Slaughter Chapter 5 (SAS)

• Class Notes: Delwiche/Slaughter Chapter 8 (SAS)

Example R Code from Class

• monarchDGE.Rdata file

• Examples from Class: Section 2

• Example function code: vecnorm.txt

• Examples from Class: Section 3

• Examples from Class: Section 4

• Excel file with Presidents data

• Examples from Class: Section 5

• Example data file: brainbod.txt

• Example data file: brainbod.csv

• Example data file: brainbod_rownames.txt (with row names, tab-delimited)

• Example data file: brainbod_nonames.txt (with no column names, tab-delimited)

• Example data file: garden.txt

• Example data file: simplefixedwidth.txt

• Examples from Class: Section 6

• Examples from Class: Section 7

• Examples from Class: Section 8

• Example R code: conditional.txt

• Examples from Class: Section 9

• Examples from Class: Section 10

• Examples involving Dates

• Examples from Class: Section 11

• Examples from Class: Some 3-D plots

• football_data.txt file from class example

• Examples from Class: Section 12

• Example function code: twosamp.txt

• Example code: Simple Stochastic Search optimization

• Examples from Class: The optim() function

• Example 1: Hit-or-Miss integration

• Example 1: Classical Monte Carlo integration

• Example 2: Classical Monte Carlo integration

• Example code: Expected Values of Functions of r.v.'s

• Example code: Approximating Sampling Distributions

• Example code: Bootstrap method

SAS Code: Examples from Class

• Example of a Very Simple SAS Program

• hair_data.txt

• Spreadsheet to read in using Import Wizard or PROC IMPORT

• reading baseball data

• reading from external file

• Scores data file and reading in the simple scores

• More complicated scores data file and reading in the complicated scores

• Messy dog data and reading in the messy dog data

• Garden data (multiple lines per observation) and reading in garden data (multiple lines per observation)

• Garden data (multiple observations per line) and reading in garden data (multiple observations per line)

• reading in part of hair data

• scores data file (options) and reading in the scores with options

• scores data file (comma-delimited), scores data file (2 commas indicate missing value) and reading in the comma-delimited file (DLM and DSD)

• McEntire rain data (.xlsx file) and reading in McEntire data with PROC IMPORT

• Example with PROC CONTENTS

• Creating new variables in the DATA step

• Conditional (IF-THEN-ELSE) statements and Dogs data

• Selecting a subset of data

• Working with dates

• RETAIN and sum statements

• ARRAY statements and Shortcut variable references and soil data set

• Subsetting with the WHERE statement (plus options for TITLE and FOOTNOTE)

• Sorting the baseball data

• Specialized printing with PROC PRINT and FORMAT

• PROC FORMAT for working with coded data and sas_roll.txt

• Simple custom reports and sas_roll2.txt

• Summary Statistics with PROC MEANS

• Counting Data with PROC FREQ

• NFL franchise data and using PROC TABULATE

• Expanded garden data and using PROC REPORT

• Modifying data sets with SET statement

• Merging data sets: temperature data and student data

• Examples with the OUTPUT statement

• Basic data set options and the IN= option

• Example of WHERE= option

• baseball_data_wide.txt

• PROC TRANSPOSE examples: baby data and converting "wide" to "long" data

• NFL players data (very long) and using SAS automatic variables

• Macro Variables and %LET

• Simple Macros and Macros with Parameters and Macros with Conditional Logic

• Extension of Macros with Parameters Example

• Example of DO WHILE and DO UNTIL loops

• ODS examples: Plotting with ODS command and ODS with TRACE and SELECT

• Customizing the Appearance of Titles, Footnotes, and Output

• Examples with PROC SGPLOT

Homework

• STAT 540 Homework 1, Fall 2019 (due Thursday, September 5 by 5:00 p.m.)
(Instructions for uploading via Blackboard) (NOTE: Please use Chrome or Firefox when uploading assignments in Blackboard, not Safari!)

• STAT 540 Homework 2, Fall 2019 (due Tuesday, September 24 by 5:00 p.m.)

• STAT 540 Homework 3, Fall 2019 (due Friday, October 18 by 5:00 p.m.)

• STAT 540 Homework 4, Fall 2019 (due Thursday, October 31 by 5:00 p.m.)

• STAT 540 Homework 5, Fall 2019 (due Friday, November 15 by 5:00 p.m)

• STAT 540 Homework 6, Fall 2019 (due Tuesday, December 3 by 5:00 p.m)

Data Files for Homework

• tennisplayers2019simple.txt

• tennisplayers2019.txt

• tshirt.txt

• districtdata.txt

• champ36pres.txt

• baseballroster.txt

• moviereview.txt

• beatles_songlengths.txt

• nfl_season_data.txt

• cake_data_duncan.txt

• cake_data_betty.txt

Example Homework Solutions

• Example Solution Code, Homework 1, Fall 2019 (This may not be the only way to solve the problem)

• Example Solution Code, Homework 2, Fall 2019 (This may not be the only way to solve the problem)

• Example Solution Code, Homework 3, Fall 2019 (This may not be the only way to solve the problem)

• Example Solution Code, Homework 4, Fall 2019 (This may not be the only way to solve the problem)

• Example Solution Code, Homework 5, Fall 2019 (This may not be the only way to solve the problem)

• Example Solution Code, Homework 6, Fall 2019 (This may not be the only way to solve the problem)

Information about the Graduate Student Project

Exams

• Exam 1: Tuesday, October 1

• Exam 2: Tuesday, November 5

• Final Exam: Tuesday, Dec. 10, 12:30 p.m.