How to start using SAS

The topics

An overview of the SAS system

Reading raw data/ create SAS data set

Combining SAS data sets & Match merging SAS Data Sets

Formatting data

Introduce some simple regression procedure

Summary report procedures

Basic Screen Navigation

Main: Editor contains the SAS program to be submitted. Log contains information about the processing of the SAS

program, including any warning and error messages Output contains reports generated by SAS procedures and

DATA steps Side:

Explore navigate to other objects like libraries Results navigate your Output window

SAS programs

A SAS program is a sequence of steps that the user submits for execution.

Data steps are typically used to create SAS data sets

PROC steps are typically used to process SAS data sets (that is, generate reports and graphs, edit data, sort data and analyze data

SAS Data Libraries

A SAS data library is a collection of SAS files that are

recognized as a unit by SAS

A SAS data set is one type of SAS file stored in a data

library

Work library is temporary library, when SAS is closed, all

the datasets in the Work library are deleted; create a

permanent SAS dataset via your own library.

SAS Data Libraries

Identify SAS data libraries by assigning each a library reference name (libref) with LIBNAME statement

LIBNAME libref “file-folder-location”;

Eg: LIBNAME readData 'C:\temp\sas class\readData‘;

Rules for naming a libref: The name must be 8 characters or less The name must begin with a letter or underscore The remaining characters must be letters, numbers or

underscores.

Reading raw data set into SAS system

In order to create a SAS data set from a raw data file, you must

Start a DATA step and name the SAS data set being created (DATA statement)

Identify the location of the raw data file to read (INFILE statement)

Describe how to read the data fields from the raw data file (INPUT statement)

Reading external raw data file into SAS system

LIBNAME readData 'C:\temp\sas class\readData‘;

DATA readData.wa80; INFILE “k:\census\stf2_wa80.txt”; INPUT @10 SUMRYLVL $2. @40 COUNTY $3. @253 TABA1 9.0 @271 TABA1 9.0;RUN;

The LIBNAME statement assigns a libref ‘readData ’ to a data library. The DATA statement creates a permanent SAS data set named ‘wa80’. The INFILE statement points to a raw data file. The INPUT statement - name the SAS variables

- identify the variables as character or numeric ($ indicates character data) - specify the locations of the fields in the raw data - can be specified as column, formatted, list, or named input

The RUN statement detects the end of a step

Example 1

Reading raw data separated by spaces

/* Create a SAS permanent data set named HighLow1; Read the data file temperature1.dat using listing input */

DATA readData.HighLow1; INFILE ‘C:\sas class\readData\temperature1.dat’; INPUT City $ State $ NormalHigh NormalLow RecordHigh RecordLow; RUN;/* The PROC PRINT step creates a isting report of the

readData.HighLow1 data set */PROC PRINT DATA = readData.highlow1; TITLE ‘High and Low Temperatures for July’;RUN;

Nome AK 55 44 88 29

Miami FL 90 75 97 65

Raleign NC 88 68 105 50

temperature1.dat:

Example 2

Reading multiple lines of raw data per observation

/* Read the data file using line pointer, slash(/) and pount-n (#n). The slash(/) indicates next line, the #n means to go to the n line

for that observation. Slash(/) can be replaced by #2 here */

DATA readData.highlow2; INFILE ‘C:\sas class\readData\temperature2.dat’; INPUT City $ State $ / NormalHigh NormalLow #3 RecordHigh RecordLow;PROC PRINT DATA = readData.highlow2; TITLE ‘High and Low Temperatures for July’;RUN;

Nome AK

55 44

88 29

Miami FL

90 75

97 65

Raleign NC

88 68

105 50

temperature2.dat:

Example 3

Reading multiple observations per line of raw data

/* To read multiple observations per line of raw data,use double railing at signs (@@) at the end of INPUT statement */

DATA readData.highlow3; INFILE ‘C:\sas class\readData\temperature3.dat’; INPUT City $ State $ NormalHigh NormalLow RecordHigh

RecordLow @@;

PROC PRINT DATA = readData.highlow3; TITLE ‘High and Low Temperatures for July’;RUN;

Nome AK 55 44 88 29 Miami FL 90 75 97 65 Raleign NC 88

68 105 50

temperature3.dat:

Reading external raw data file into SAS system

Reading raw data arranged in columns

INPUT FILEID $ 1-5 RECTYP $ 6-9 SUMRYLVL $ 10-11 URBARURL $ 12-13 SMSACOM $ 14-15;

Reading raw data mixed in columns

INPUT FILEID $ 1-5 @10 SUMRYLVL $ 2. @253 TABA1 9.0

@271 TABA1 9.0;

/* The @n is the column pointer, where n is the number of the column SAS should move to. The $w. reads standard character data, and w.d reads standard numeric data, where w is the total width and d is the number of decimal places. */

Reading Delimited or PC Database Files with the IMPORT Procedure

If your data file has the proper extension, use the simplest form of the IMPORT procedure:

PROC IMPORT DATA FILE = ‘filename’ OUT = data-set

Type of File Extension DBMS Identifier

Comma-delimited .csv CSV Tab-delimited .txt TAB Excel .xls EXCEL Lotus Files .wk1, .wk3, .wk4 WK1,WK3,WK4 Delimiters other than commas or tabs DLM

Examples: 1. PROC IMPORT DATAFILE=‘c:\temp\sale.csv’ OUT=readData.money; RUN;

2. PROC IMPORT DATAFILE=‘c:\temp\bands.xls’ OUT=readData.music; RUN;

Reading Files with the IMPORT Procedure

If your file does not have the proper extension, or your file is of type with delimiters other than commas or tabs, then you must use the DBMS= and DELIMITER= option

PROC IMPORT DATAFILE = ‘filename’ OUT = data-set

DBMS = identifier; DELIMITER = ‘delimiter-character’; RUN;

Example:

PROC IMPORT DATAFILE = ‘C:\sas class\readData\import2.txt’ OUT =readData.sasfile DBMS =DLM;

DELIMITER = ‘&’; RUN;

Format in SAS data set

Standard Formats (selected): Character: $w. Date, Time and Datetime:

DATEw., MMDDYYw., TIMEw.d, …… Numeric: COMMAw.d, DOLLARw.d, ……

Use FORMAT statement PROC PRINT DATA=sales;

VAR Name DateReturned CandyType Profit;

FORMAT DateReturned DATE9. Profit DOLLAR 6.2;

RUN;

Format in SAS data set

Create your own custom formats with two steps: Create the format using PROC FORMAT and VALUE statement. Assign the format to the variable using FORMAT statement.

General form of a simple PROC FORMAT steps: PROC FORMAT;

VALUE name range-1=‘formatted-text-1’

range-2=‘formatted-text-2’ ……;

RUN;

The name in VALUE statement is the name of the format you are creating, which can’t be longer than eight characters, must not start or end with a number. If the format is for character data, it must start with a $.

Format in SAS data setExmaple:

/* Step1: Create the format for certain variables */ PROC FORMAT; VALUE genFmt 1 = 'Male' 2 = 'Female'; VALUE money low-<25000='Less than 25,000' 25000-50000='25,000 to 50,000' 50000<-high='More than 50,000'; VALUE $codeFmt 'FLTA1'-'FLTA3'='Flight Attendant' 'PILOT1'-'PILOT3'='Pilot'; RUN;

/* Step2: Assign the variables */

DATA fmtData.crew1; SET fmtData.crew; FORMAT Gender genFmt. Salary money. JobCode $codeFmt.; RUN;

Format in SAS data set

Permanently store formats in a SAS catalog by Creating a format catalog file with LIB in PROC

FORMAT statement Setting the format search options

Example: LIBNAME class ‘C:\sas class\Format’; OPTIONS FMTSEARCH=(fmtData.fmtvalue); RUN; PROC FORMAT LIB=fmtData.fmtvalue; VALUE genFmt 1 = ‘Male’ 2=‘Female’; RUN;

Combining SAS Data Sets: Concatenating and Interleaving

Use the SET statement in a DATA step to

concatenate SAS data sets.

Use the SET and BY statements in a DATA

step to interleave SAS data sets.

Combining SAS Data Sets: Concatenating and Interleaving

General form of a DATA step concatenation: DATA SAS-data-set;

SET SAS-data-set1 SAS-data-set2 …;

RUN;

Example: DATA stack.allEmp; SET stack.emp1 stack.emp2 stack.emp3; RUN;

Combining SAS Data Sets: Concatenating and Interleaving

General form of a DATA step interleave: DATA SAS-data-set; SET SAS-data-set1 SAS-data-set2 …; BY BY-variable; RUN;

Sort all SAS data set first by using PROC SORT Example:

PROC SORT data=stack.emp2 OUT=stack.emp2_sorted; BY Salary; RUN;

DATA stack.allEmp; SET stack.emp1 stack.emp2 stack.emp3; BY salary; RUN;

Match-Merging SAS Data Sets

One-to-one match merge

One-to-many match merge

Many-to-many match merge The SAS statements for all three types of match

merge are identical in the following form:

DATA new-data-set;

MERGE data-set-1 data-set-2 data-set-3 …;

BY by-variable(s); /* indicates the variable(s) that control

which observations to match */

RUN;

Merging SAS Data Sets: A More Complex Example

/* To match-merge the data sets by common variables - EmpID, the data sets must be ordered by EmpID */

PROC SORT data=combData.Groupsched;

BY EmpID;

RUN;

Example: Merge two data sets acquire the names of the group team that is scheduled to fly next week.

combData.employee combData.groupsched

EmpID LastName

E00632 Strauss

E01483 Lee

E01996 Nick

E04064 Waschk

EmpID FlightNum

E04064 5105

E0632 5250

E01996 5501

Merging SAS Data Sets: A More Complex Example

/* simply merge two data sets */DATA combData.nextweek;

MERGE combData.employee combData.groupsched;

BY EmpID;

RUN;

EmpID LastJName FlightNum

E00632 Strauss 5250

E01483 Lee

E01996 Nick 5501

E04064 Waschk 5105

Merging SAS Data Sets: A More Complex Example

Eliminating Nonmatches Use the IN= data set option to determine which

dataset(s) contributed to the current observation. General form of the IN=data set option:

SAS-data-set (IN=variable) Variable is a temporary numeric variable that has two

possible values: 0 indicates that the data set did not contribute to the

current observation. 1 indicates that the data set did contribute to the

current observation.

Merging SAS Data Sets: A More Complex Example/*Exclude from the data set employee who are scheduled to fly next

week. */

LIBNAME combData “K:\sas class\merge”;

DATA combData.nextweek; MERGE combData.employee combData.groupsched (in=InSched); BY EmpID; IF InSched=1; TrueRUN;

EmpID LastJName FlightNum

E00632 Strauss 5250

E01996 Nick 5501

E04064 Waschk 5105

Merging SAS Data Sets: A More Complex Example

/* Find employees who are not in the flight scheduled group. */

LIBNAME combData “K:\sas class\merge”;DATA combData .nextweek; MERGE combData .employee (in=InEmp) combData.groupsched (in=InSched); BY EmpID; IF InEmp=1; True IF InSched=0; False RUN;

EmpID LastJName FlightNum

E01483 Lee

Different Types of Merges in SAS

DATA work.three;

MERGE work.one work.two;

BY X;

RUN;

One-to-Many Merging

X Y

1 A

2 B

3 C

X E

1 A1

1 A2

2 B1

3 C1

3 C2

X Y Z

1 A A1

1 A A2

2 B B1

3 C C1

3 C C2

Work.three

Work.two

Work.one

Different Types of Merges in SAS

DATA work.three;

MERGE work.one work.two;

BY X;

RUN;

Many-to-Many Merging

X Y

1 A1

1 A2

2 B1

2 B2

X Z

1 AA1

1 AA2

1 AA3

2 BB1

2 BB2

X Y Z

1 A1 AA1

1 A2 AA2

1 A2 AA3

2 B1 BB1

2 B2 BB2

Work.three

Work.two

Work.one

Some simple regression analysis procedure

The REG Procedure

The LOGISTIC Procedure

The REG procedure

The REG procedure is one of many regression procedures in the SAS System.

The REG procedure allows several MODEL statements and gives additional regression diagnostics, especially for detection of collinearity. It also creates plots of model summary statistics and regression diagnostics.

PROC REG <options>;

MODEL dependents=independents </options>;

PLOT <yvariable*xvariable>;

RUN;

An example

PROC REG DATA=water; MODEL Water = Temperature Days Persons / VIF; MODEL Water = Temperature Production Days / VIF; RUN; PROC REG DATA=water; MODEL Water = Temperature Production Days; PLOT STUDENT.* PREDICTED.; PLOT STUDENT.* NPP.; PLOT NPP.*r.; PLOT r.*NQQ.; RUN;

The LOGISTIC procedure

The binary or ordinal responses with continuous independent variables

PROC LOGISTIC < options > ;

MODEL dependents=independents < / options > ;

RUN;

The binary or ordinal responses with categorical independent variables

PROC LOGISTIC < options > ;

CLASS categorical variables < / option > ;

MODEL dependents=independents < / options > ;

RUN;

Example

PROC LOGISTIC data=Neuralgia;

CLASS Treatment Sex;

MODEL Pain= Treatment Sex Treatment*Sex Age Duration;

RUN;

Overview Summary Report Procedures

PROC FREQ: produce frequency counts

PROC TABULATE: produce one- and two-dimensional tabular

reports

PROC REPORT: produce flexible detail and summary reports

The FREQ Procedure

The FREQ procedure display frequency counts of the data values in a SAS data set.

General form of a simple PROC FREQ steps:

PROC FREQ DATA = SAS-data-set;

TABLE SAS-variables </options>;

RUN;

The FREQ Procedure

Example: PROC FREQ DATA = class.crew ;

FORMAT JobCode $codefmt. Salary money.;

TABLE JobCode*Salary /NOCOL NOROW OUT =freqTable;

RUN;

The TABULATE Procedure

PROC TABULATE displays descriptive statistics in tabular format.

General form of a simple PROC TABULATE steps:

PROC TABULATE DATA=SAS-data-set;

CLASS class-variables;

VAR analysis-variables;

TABLE row-expression,

column-expression</options>;

RUN;

The TABULATE Procedure

Example: TITLE 'Average Salary for Cary and Frankfurt';

PROC TABULATE DATA= class.crew FORMAT=dollar12.;

WHERE Location IN ('Cary','Frankfurt');

CLASS Location JobCode;

VAR Salary;

TABLE JobCode, Location*Salary*mean;

RUN;

The REPORT procedure

REPORT procedure combines features of the PRINT, MEANS, and TABULATE procedures.

It enables you to

create listing reports

create summary reports

enhance reports

request separate subtotals and grand totals

The REPORT procedure

Example PROC REPORT DATA =class.crew nowd HEADLINE HEADSKIP;

COLUMN JobCode Location Salary;

DEFINE JobCode / GROUP WIDTH= 8 'Job Code';

DEFINE Location / GROUP 'Home Base';

DEFINE Salary / FORMAT=dollar10. 'Average Salary‘ MEAN ;

RBREAK AFTER / SUMMARIZE DOL;

RUN;