Introduction to SAS

Introduction to SAS

Ista Zahn

Harvard MIT Data Center

March 15th, 2013

The Institute
for Quantitative Social Science
at Harvard University

Ista Zahn (Harvard MIT Data Center) Introduction to SAS March 15th, 2013 1 / 38

Outline

1 Introduction

2 Data Import and Export

3 Descriptive Statistics

4 Variable and Value Labels

5 Data Manipulation

6 Wrap-up


Introduction

Topic

1 Introduction




5 Data Manipulation

6 Wrap-up


Introduction

Documents for Today

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PASSWORD: dataclass
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Copy materials to your desktop!


Introduction

Workshop Description

This is an INTRODUCTION to SAS – assumes no knowledge of SAS!
Not appropriate for people already well familiar with SAS
Learning Objectives:
Import and export data in a varity of formats
Create and use variable and value labels
Perform basic data manipulation
Compoute descriptive statistics
Wrap-up


Introduction

Why SAS?

If you know SAS, it is likely you will not need any other software
packages
Among the most powerful statistical software packages available
Great user community: macros, websites, etc.
Free online documentation:
http://support.sas.com/documentation/93/ind ex.html


Introduction

SAS history
SAS was ﬁrst developed in the 1970’s. The world was a lot diﬀerent then!
(Image source: http://en.wikipedia.org/wiki/Moore’s_law)


Introduction

The SAS Environment

Five basic SAS windows:
Results
Explorer
Editor
Log
Output
Set up your SAS window so it ﬁts your own preferences
Viewing options (Windows only):
Window > Tile Vertically


Introduction

SAS Basics

SAS has point-and-click interfaces, but most users write
command-driven SAS programs
Command syntax is not case sensitive, but ﬁle names may be
Commands can run on multiple lines (but you can’t split words across
lines)
Follow every command with RUN ;
Comments can be written as:
/* comment comment comment */ OR
* comment comment comment;


Data Import and Export

Topic

1 Introduction




5 Data Manipulation

6 Wrap-up



Working With SAS Data

SAS data sets are stored in ﬁles on your hard drive, usually with the
extension “.sas7bdat”
Under Unix/Linux SAS data ﬁle names must be all lowercase–best to
observe this restriction on Windows as well
In SAS there is no concept of “opening” a data set–Instead we use
libraries to point to directories on the hard drive that contain SAS data
sets
Unless you specify otherwise, SAS copies your data temporarily in a
library called “work”
The work library is temporary, so
use the work library for temporary data sets
create and use your own library for any data you wish to save



Libraries and Datasets
Tell SAS where our data is on our hard drive by creating a library – I’m going
to name my library introSAS and point SAS to the dataSets folder

/* initialize a SAS library in the dataSets folder */
LIBNAME introSAS "C:UsersdataclassDesktopSASintrodataSets";
RUN;
/* list sas datasets in the introSAS library / dataSets folder */
PROC DATASETS library=introSAS;
RUN;
/* initialize a SAS library in the SASintroLib folder */
LIBNAME mylib "C:UsersdataclassDesktopSASintroSASintroLib";
RUN;
/* copy pubschool to the "work" library */
DATA pubschool;
SET "C:UsersdataclassDesktopSASintrodataSetspubschool";
RUN;

The DATA command is actually saving our dataset as a new ﬁle called
“pubschool” in the “work” library
The SET command is just telling SAS what dataset to save


What if my file is not a SAS file?
In SAS “import” means “convert to SAS format, copy to library/folder”
Import Stata files with dbms=dta

/* import from stata format */
PROC IMPORT out = pubschool
DATAFILE = "C:UsersdataclassDesktopSASintrodataSetspubschool.dta"
DBMS = dta replace;
RUN;

Importing ASCII files with (e.g.) dbms=csv

/* import csv file */
PROC IMPORT out = pubschool
DATAFILE = "C:UsersdataclassDesktopSASintrodataSets/pubschool.csv"
DBMS = csv replace;
GETNAMES = yes;
DATAROW = 2;
RUN;



Where is my data?

You can “view” you data in a couple of ways:
Proc contents

/* list contents of pubschool data */
PROC CONTENTS data = pubschool;
TITLE "Public school contents";
RUN;

Data viewer
1 Go to explorer
2 Select your “work” library
3 Click on your dataset (opens in SAS Universal Viewer)
Your dataset is named in the library as “pubschool” because that’s what
you named it when you originally opened the dataset



How do I get my data out of SAS?
In SAS “export” means “convert to a non-SAS format”
Exporting CSV ﬁles:

/* export to .csv */
PROC EXPORT data = introSAS.ntcs
OUTFILE = "C:UsersdataclassDesktopSASintroSASintroLibNeighCrime_NEW
DBMS = csv;
RUN;

Exporting tab delimited ﬁles:

/* export to tab delimited */
PROC EXPORT data = introSAS.ntcs
OUTFILE = "C:UsersdataclassDesktopSASintroSASintroLibNeighCrime_NEW
DBMS = tab;
RUN;



Exercise 1: Importing Data

1 Create a library named “mylib” in the SASintroLib folder if it doesn’t
already exist
2 Import the Stata ﬁle, “ntcs.dta” to the mylib library
3 Import the ASCII ﬁle, “ntcs.csv” to the mylib library
4 Use “proc datasets” to list the datasets in the mylib libary
5 Use “proc contents” to review the data you imported in step 2


Descriptive Statistics

Topic

1 Introduction




5 Data Manipulation

6 Wrap-up



Means, standard deviations, etc.
Compute averages for q1 and q2 using proc means

/* means of vars q1 and q2 */
PROC MEANS data = pubschool;
VAR q1 q2;
TITLE "Public school means";
RUN;

Compute averages for q1 separately by timezone

/* means separatly by timezone */
/* need to sort first */
PROC SORT data = pubschool;
by timezone;
RUN;

PROC MEANS data = pubschool;
by timezone;
VAR q1;
TITLE "Public school means by timezone";
RUN;


Frequency Tables

Frequency tables for q1 and q2 using proc freq

/* counts of responses to q3, q4, and q5 */
PROC FREQ data = pubschool;
TABLE q3 q4 q5;
TITLE "Public school frequencies";
RUN;

Frequency tables for q1 by timezone

/* counts by timezone */
TABLE q3*timezone;
TITLE "Public school frequencies";
RUN;



Correlation and Regression

We’re interested in looking at the relationship between City Crime Rate
(C_CRIMRT) and Percent of High School Grads in the City (C_HSGRAD)

/* Scatterplot of relationship between high school
graduation rate and crime rate */
PROC GPLOT data = introSAS.ntcs;
PLOT C_HSGRAD * C_CRIMRT;
TITLE "Percent of High School Graduates and Crime Rates";
RUN;
/* correlation between graduation and crime rates */
PROC CORR data = introSAS.ntcs;
VAR C_HSGRAD C_CRIMRT;
RUN;
/* Regression predicting crime rate */
PROC REG data = introSAS.ntcs;
MODEL C_CRIMRT = C_HSGRAD C_PERCAP C_POVRTY;
RUN;



Exercise 2: Correlation and regression

Use the ntcs data set
1 Take a look around the ntcs dataset and identify an outcome you’d like
to predict and few variables (4-6) that you believe would serve as
relevant predictor variables
2 Run relevant descriptive statistics on your variables and look at
histograms and scatterplots
3 Test correlations leading up to ultimately testing a regression
4 Run and interpret a regression using your selected variables


Variable and Value Labels

Topic

1 Introduction




5 Data Manipulation

6 Wrap-up




Variable labels refer to the titles associated with each variable
Value labels refer to the titles you assign to the diﬀerent levels (i.e.,
values) of each variable
EXAMPLE:
Variable name: Marital
Variable label: Marital status of participant
Value labels: 1 = Married, 2 = Separated, 3= Divorced, 4 = Single, etc.



Variable Labels

Adding variable labels is a data step command:

/* copy pubschool to pubschool2 and label resp and status */
DATA pubschool2;
SET pubschool;
LABEL
resp = "Participant Identifier"
status = "Did participant complete survey?"
RUN;
/* Check output */
PROC CONTENTS data = pubschool2;
TITLE "pubschool2 contents";
RUN;



Creating Value Labels

Creating value labels is a proc command
Start by creating the label format

/* create value label named q1label */
PROC FORMAT;
VALUE q1label
1 = "best"
2 = "top 5"
3 = "top 10"
4 = "top 20"
5 = "Bottom 80"
9 = "Don’t know";
RUN;



Using Value labels
Now we can use this value scheme creating tables,output, etc.

/* display fequencies, using value labels */
TABLES q1;
FORMAT q1 q1label.;
RUN;
/* NOTE: There is a "." after q1label. This alerts SAS
that you’re referring to a value scheme Saving Value
Labels in your Dataset */

We can also save a value label in a data set,

/* add value label to SAS data set */
PROC DATASETS library = work;
MODIFY pubschool2;
format q1 q1label.;
RUN;
/* confirm that our formats were correctly applied: */
PROC FREQ data = pubschool2;
TABLES q1 q3;
RUN;


Dropping and Renaming Variables
Keeping a subset of variables is simple–use the “drop” or “keep” command:

/* create new dataset "pubschoolKeep" with only q1-q5 */
DATA pubschoolKeep (keep = q1 q2 q3 q4 q5);
SET pubschool;
RUN;
/* create new dataset pubschoolDrop, excluding q1-q5 */
DATA pubschoolDrop (drop = q1 q2 q3 q4 q5);
SET pubschool;
RUN;

Renameing variables is done in a data step: just put the rename syntax right
after your data command

/* change the name of q2 to q2newName */
DATA pubschool2 (rename = (q2 = q2newName));
SET pubschool2;
RUN;
/* View the dataset: */
PROC CONTENTS data = pubschool2;
RUN;


SAS variable names

Must be <= 32 characters in length
Must start with a letter or underscore
Can contain only numbers, letters or underscores
No special characters: @#$%^&*
Can contain upper and lowercase letters


Data Manipulation

Topic

1 Introduction




5 Data Manipulation

6 Wrap-up


Data Manipulation

Logic Statements Useful For Data Manipulation

= (EQ) equal to
=
(NE ) not equal to
> (GT) greater than
< (LT) less than
>= (GE) greater than or equal to
<= (LE) less than or equal to
(AND) and
| (OR) or


Data Manipulation

Generate New Variables
A data command - simply put new variable name followed by variable
condition.

DATA pubschool2;
SET pubschool;
/* create variable "myvar" equal to q1 */
myvar = q1;
/* create variable newvar2 equal to 1 */
newvar2 = 1;
RUN;

Create new variable based on values of existing variable

/* generate newvar4 based on q1 */
DATA pubschool2;
SET pubschool;
if q1=1 then newvar3=1;
else if q1=2 then newvar3=2;
else newvar3=.;
RUN;

Data Manipulation

Saving Subsets of Data
Subsets are created with in a data step
Create a subset of data including only participants who had a child
attending a public school

/* keep only rows where q10 is 1 */
DATA CurrentPublic;
SET pubschool;
if q10=1;
RUN;

Create a subset of data including participants in timezone 1 or 2

/* keep only rows where timezone is 1 or 2 */
DATA CurrentPublic;
SET pubschool;
if timezone=1 | timezone=2;
RUN;


Data Manipulation

Missing Values

SAS’s symbol for a missing value is “.”
SAS interprets “.” as a small value
Need to be aware of this when you are manipulating data!
What will happen when you use the < or <= commands?


Data Manipulation

Exercise 3: Data Manipulation
Use the ntcs.sas7bdat
1 Attach variable labels using the following codebook: // REGION =
“Region in United States”, CITY = “Name of City and State”
2 Create formats in SAS using the codebook below:
C_SOUTH: 1 = Southern City, 0=Non-Southern City
C_WEST: 1=Western City 0=Non-Western City
3 Run “proc freq” on C_SOUTH and C_WEST and use formats from
step 2
4 Assign the formats for C_SOUTH and C_WEST permanently in your
ntcs dataset
5 Conﬁrm with “proc freq” that your labels were correctly assigned
6 Generate new variables based on the city-level crime variables
“C_MURDRT” and “C_ROBBRT”. Choose values on which to
dichotomize each variable and create new variables that have a score of
“1” if the original variable is above that value, and a score of “0”
otherwise.
7 Conﬁrm that your new variables were properly created using “proc freq”

Wrap-up

Topic

1 Introduction




5 Data Manipulation

6 Wrap-up


Wrap-up

Help us make this workshop better!

Please take a moment to ﬁll out a very short feedback form
These workshops exist for you – tell us what you need!
http://tinyurl.com/akyvzle


Wrap-up

Other Services Available

Institute for Quantitative Social Science
www.iq.harvard.edu
Computer labs
www.iq.harvard.edu/facilities
Research Technology Consulting
www.iq.harvard.edu/researchconsulting
Training
http://projects.iq.harvard.edu/rtc/filter_by/workshops


Wrap-up

Additional Resources

How do I get SAS?
Your Department IT
HMDC labs
RCE (Research Computing Environment)
Buy it: educational or grad plan
The RCE
Research Computing Enviroment (RCE) service available to Harvard &
MIT users
www.iq.harvard.edu/research_computing
Supplies persistent desktop environment accessible from any computer
with an internet connection
Includes SAS, Stata, R etc.


Introduction to SAS

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