This document provides an introduction and outline for using SAS software. It covers basic SAS windows and rules, loading and viewing data, manipulating data by selecting subsets, adding or deleting variables, sorting, summarizing data with procedures, and creating plots and outputting results to Word. Examples are provided for common procedures like SORT, MEANS, UNIVARIATE, FREQ, CORR and PLOT. Practice exercises are included to try these skills on a sample dataset.

1. Introduction to SAS
BIO 226 – Spring 2011

2. 2
Outline
• Windows and common rules
• Getting the data
– The PRINT and CONTENTS Procedures
• Basic SAS procedures
– The SORT Procedure
– The MEANS Procedure
– The UNIVARIATE Procedure
– The FREQ Procedure
– The CORR Procedure
– The PLOT Procedure
• Manipulating the data, e.g., creating new
variables
• Libraries
• Output in Word document
• References
• Practice
Slides 3-7
Slides 8-10
Slide 9
Slide 13
Slides 14-15
Slide 15
Slide 16
Slide 16
Slide 17
Slide 11-12
Slide 18
Slide 19
Slide 20
Slides 21-22

3. 3
The different SAS windows
• Explorer: contains SAS files and libraries
• Editor: where you can open or type SAS programs
• Log: stores details about your SAS session (code run,
dataset created, errors...)
• Results: table of contents for output of programs
• Output: printed results of SAS programs

4. 4
Basic SAS rules (1)
• Variable names must:
– be one to 32 characters in length
– begin with letter (A-Z) or underscore (_)
– continue with any combination of number, letters or underscores
• A variable’s type is either character or numeric
• Missing values:
– missing character data is left blank
– missing numeric data is denoted by a period (.)

5. 5
Basic SAS rules (2)
• Two ways to make comments:
– * write comment here;
– /* write comment here */
• SAS is insensitive to case

6. 6
Basic programming rules (1)
• SAS programs are composed of statements: these are
organized in DATA steps and PROC steps
– DATA step: gives dataset a name, manipulates dataset
– PROC step: procedure or analysis you want SAS to carry out
• SAS reads code line by line and the end of a line is
marked by a semicolon.
• All SAS programs end with RUN;
• Quotes can be single or double.

7. 7
Basic programming rules (2)
• SAS statements are free-format:
– Can begin and end in any column
– One statement can continue over several lines
– Several statements can be on one line
• To submit program, highlight the code to run and click on
the submit button (running silhouette)

8. 8
Loading data
• If you have SAS data set (sasintro.sas7bdat) you can double
click on it and it will load itself.
• If you don’t have SAS data set (sasintro.txt), and the first row
of your dataset contains the variable names, you can import it
using File > Import Data… and specify the directory.
• Or you can use the following code:
DATA mydata;
INFILE ‘g:sharedbio226sasintro.txt’;
INPUT weight bmi id age activity education smoking;
RUN;
• Setting your current directory: on the bottom line of the main
SAS window, you should see it set to
C:WINDOWSsystem32. Double click on it to change it.

9. 9
How to view the loaded data?
• Go in the Explorer window, double click on Libraries,
then Work and sasintro.sas7bdat
• To view general information about the data set, like
variables’ name and type:
PROC CONTENTS DATA=mydata;
RUN;
• Use the PRINT procedure to view the first 25 records:
PROC PRINT DATA=mydata (OBS=25);
RUN;

10. 10
Variables from sasintro.txt
# Variable Type Unit
5 activity Num kcal/week
4 age Num years
2 bmi Num kg/m2
6 education Num years
3 id Num
7 smoking Num 1:current smoker, 0:non-smoker
1 weight Num lbs

11. 11
Manipulating data (1)
• selecting a subset of rows
DATA mydata_s;
SET mydata;
IF smoking=1;
RUN;
• deleting a column (or columns)
DATA mydata2;
SET mydata;
DROP weight education;
RUN;

12. 12
Manipulating data (2)
• adding a column (or columns)
DATA mydata3;
SET mydata;
weight_kg=weight*0.453;
IF age <= 60 THEN agegroup=1;
ELSE IF age<=70 THEN agegroup=2;
ELSE agegroup=3;
/*drop age;*/
RUN;

13. 13
Sorting data
PROC SORT DATA=mydata OUT=mydata4;
BY ID age weight;
PROC PRINT DATA=mydata (OBS=5);
PROC PRINT DATA=mydata4 (OBS=5);
RUN;

14. 14
Summarizing data (1)
• Summarizing weight:
PROC MEANS DATA=mydata;
VAR weight;
RUN;
• Summarizing weight in the youngest agegroup:
PROC MEANS DATA=mydata3;
VAR weight;
WHERE agegroup=1;
RUN;

15. 15
Summarizing data (2)
• Summarizing weight by smoking status (two possible codes):
PROC SORT DATA=mydata OUT=mydata5;
BY smoking;
PROC MEANS DATA=mydata5;
VAR weight;
BY smoking;
RUN;
PROC MEANS DATA=mydata;
CLASS smoking;
VAR weight;
RUN;
• All these summarizing measures can be obtained with PROC
UNIVARIATE also.

16. 16
Categorical data and correlation
• Summarizing categorical data
PROC FREQ DATA=mydata3;
TABLES smoking*agegroup /chisq exact;
RUN;
• Examining correlation
PROC CORR DATA=mydata;
VAR weight;
WITH bmi age;
RUN;

17. 17
Basic procedures: plots
• Barcharts
PROC CHART DATA=mydata3;
VBAR agegroup /DISCRETE;
RUN;
• Scatterplot
PROC PLOT DATA=mydata3;
PLOT bmi*weight='*';
RUN;
• Histogram, Boxplot, Normal Probability Plot
PROC UNIVARIATE DATA=mydata3 PLOT;
VAR weight;
RUN;

18. 18
/* Libraries */
• A library is the directory where your SAS dataset is stored.
• The default library is named Work and stores your SAS
datasets temporarily: they will be deleted when you end
your SAS session
• If you want to save your SAS datasets and use them again
later, create your own library:
LIBNAME SAS_Lab 'p:BIO226SAS';
DATA SAS_Lab.mydata;
INFILE ‘g:sharedbio226sasintro.txt’;
INPUT weight bmi id age activity education
smoking;
RUN;

19. 19
SAS output and Word
• To send you SAS output to a Word document:
ODS RTF FILE=‘p:output.RTF’ style=minimal;
PROC CORR DATA =mydata;
VAR weight;
WITH bmi age;
RUN;
ODS RTF CLOSE;
• Other styles: Journal, Analysis, Statistical

20. 20
For further references
• SAS9 Documentation on the Web:
http://support.sas.com/onlinedoc/913/docMainpage.jsp
• Applied Statistics and the SAS Programming Language
(5th Edition) Ron P. Cody and Jeffrey K. Smith
• The Little SAS Book, L.D. Delwiche and S.J. Slaughter
• See SAS_help.doc on course website

21. 21
Try your own
• Find the summary statistics (mean, mode, standard
deviation,…) for education with PROC UNIVARIATE, as
well as a histogram for years of education.
• Create a new variable educ_group which breaks years of
education into four groups (0-10, 10-15,15-18,18-25). Put
this new variable in a new data set and drop the education
variable, as well as weight, bmi and age.
• Find the number of smokers per education group.
• Find the mean physical activity in each education group.

22. 22
Data name Description
mydata original imported data
mydata_s only smokers
mydata2 dropped weight, education
mydata3 added weight_kg, agegroup,
dropped age
mydata4 sorted original data by age and weight
mydata5 sorted original data by smoking status
Recap of different datasets created