Introduction to SAS System Where Expressions

Introduction to Where
Expressions
Mark Tabladillo, Ph.D.
Software Developer, MarkTab Consulting
Associate Faculty University of Phoenix
Faculty,
January 30, 2007

Introduction
• WHERE expressions allow for processing
subsets of observations
• WHERE expressions can be used in the
DATA step or with PROC (procedure)
statements
• This presentation will contain a series of
features and examples of the WHERE
p
expression
• We end with some intensive macros

WHERE-expression Processing
WHERE expression
• Enables us to conditionally select a subset
of observations, so that SAS processes
only the observations that meet a set of
specified conditions.

http://support.sas.com/onlinedoc/913/getDoc/en/lrcon.hlp/a000999253.htm

Work Sales Dataset
Work.Sales
data work.sales (drop=i randomState);
length state $2 sales 8 randomState 3;
do i = 1 to 2500;
randomState = round(rand('gaussian',3,1)+0.5);
if randomState in (1,2,3,4,5) then do;
( )
select(randomState);
when(1) state='TN';
when(2) state='AL';
when(3) state= GA ;
state='GA';
when(4) state='FL';
when(5) state='MS';
end;
sales = int(rand('gaussian',1000000,500000));
output work.sales;
end;
end;
run;

Data Set Option or Statement
data work.highSales;
set work.sales (where=(sales>1500000));
run;

data work highSales;
work.highSales;
set work.sales;
where sales>1500000;
run;

proc means data=work.sales;
where sales>1500000;
run;
;

Data Set Option or Statement
data work.lowSales;
set work.sales (where=(sales<0));
run;

data work lowSales;
work.lowSales;
set work.sales;
where sales<0;
run;

proc means data=work.sales (where=(sales<0));
run;

Multiple Comparisons
data work.highFloridaSales;
set work.sales (where=(sales>1500000 and state = 'FL'));
run;

data work highFloridaSales;
work.highFloridaSales;
set work.sales;
where sales>1500000 and state = 'FL';
run;

proc freq data=work.sales;
tables state;
where sales>1500000 and state = 'FL';
;
run;

SAS Functions
data work.highFloridaSales;
set work.sales (where=(sales>1500000 and substr(state,1,1) = 'F'));
run;

data work highFloridaSales;
work.highFloridaSales;
set work.sales;
where sales>1500000 and substr(state,1,1) = 'F';
run;

proc means data=work.sales;
where sales>1500000 and substr(state,1,1) = 'F';
run;
;

Comparison Operators
Priority Order of Symbols Mnemonic
Evaluation Equivalent
Group I right to left **
+
-
ˆ¬~ NOT
>< MIN
<> MAX


Group II left to right *
/
Group left to right +
III
-
Group left to right || ¦¦ !!
IV


Group left to right < LT
V
<= LE
= EQ
¬= NE
>= GE
> GT
IN


Group left to right & AND
VI
Group left to right |¦! OR
VII


data work.extremeNonGeorgia;
set work.sales
(where=((sales<0 | sales>1500000) and state in ('TN','AL','FL','MS')));
run;

set work.sales;
where (sales<0 | sales>1500000) and state in ('TN','AL','FL','MS');
run;

set work.sales;
;
where ^ (0 <= sales <= 1500000) & state ne 'GA';
run;


“Between And”
Between And
data work.boundedNonGeorgia;
set work.sales (where=((sales between 1000000 and 1500000) &
state in ('TN','AL','FL','MS')));
run;

set work.sales;
where (sales between 1000000 and 1500000) &
state in ('TN','AL','FL','MS');
t t i ('TN' 'AL' 'FL' 'MS')
run;


Contains ?
data work.LStates;
set work.sales (where=(state contains 'L'));
run;

data work LStates;
work.LStates;
set work.sales;
where state contains 'L';
run;

data work.LStates;
set work.sales;
where state ? 'L';
;
run;


Is Null/Is Missing
data work.nullStates;
set work.sales (where=(state is null));
run;

data work.missingStates;
se o sa es (where=(state s ss g));
set work.sales ( e e (s a e is missing));
run;

data work.nullSales;
set work.sales;
work sales;
where sales is missing;
run;

data work.nonNullSales;
set work.sales;
where sales is not missing;
run;

Like
data work.likeL;
set work.sales (where=(state like '%L'));
work sales
run;

data work.likeL;
set work.sales (where=(state like quot;%Lquot;));
run;

data work likeL;
work.likeL;
set work.sales (where=(state like quot;%%Lquot;));
run;

data work.notLikeG;
set work.sales;
where state not like 'G_';
run;


Sounds Like (Soundex)
data work.soundsLikeFill;
set work.sales (where=(state =* 'fill'));
run;

data work notSoundsLikeTin;
work.notSoundsLikeTin;
set work.sales;
where state not =* 'tin';
run;


“Same And”
Same And
set work.sales (where=((sales between 1000000 and 1500000) &
state in ('TN','AL','FL','MS')));
run;

set work.sales;
where (sales between 1000000 and 1500000);
where same and state i ('TN' 'AL' 'FL' 'MS')
h d t t in ('TN','AL','FL','MS');
run;

g ;
set work.sales;
where same and (sales between 1000000 and 1500000);
where same and state in ('TN','AL','FL','MS');
run;

WHERE vs. Subsetting IF
vs
Task Method

Make the selection in a procedure without using a WHERE expression
preceding DATA step
Take advantage of the efficiency available with an indexed WHERE expression
data set
Use one of a group of special operators, such as WHERE expression
BETWEEN-AND, CONTAINS, IS MISSING or IS
NULL, LIKE, SAME-AND, and Sounds-Like
B th l ti thi th th i bl l b tti
Base the selection on anything other than a variable value subsetting IF
that already exists in a SAS data set. For example, you
can select a value that is read from raw data, or a
value that is calculated or assigned during the course
of the DATA step
f th t
Make the selection at some point during a DATA step subsetting IF
rather than at the beginning
Execute the selection conditionally subsetting IF


Intensive Dataset Generation
%macro OurCentury();
%local year interest;
y ;
%do year = 2001 %to 2100;
%let interest = %sysfunc(compound(1,.,0.05,%eval(&year.-2001)));
data work.sales&year. (drop=i randomState index=(state sales));
length state $2 stateName $20 sales 8 randomState 3;
g ;
do i = 1 to 2500;
randomState = round(56*rand('uniform')+0.5);
if randomState <= 56 and randomState not in (3,7,14,43,52) then do;
state = fipstate(randomState);
p ( )
stateName = fipnameL(randomState);
sales = int(rand('gaussian',1000000*&interest.,500000*&interest.));
output work.sales&year.;
end;
end;
run;
%end;
%mend OurCentury; y
%OurCentury;

Year/State Datasets
%macro SalesByYearState();
%local year stateCode state;
%do year = 2001 %to 2100;
%do stateCode = 1 %to 56;
%if &stateCode ne 3 & &stateCode ne 7 & &stateCode. ne 14 &
&stateCode. &stateCode. &stateCode
&stateCode. ne 43 & &stateCode. ne 52 %then %do;
%let state = %sysfunc(fipstate(&stateCode.));
data work.sales&year.&state.;
set work.sales&year.;
t k l &
where state = quot;&state.quot;;
run;
%end; ;
%end;
%end;
%mend SalesByYearState;
%SalesByYearState;

Year/State High Sales Datasets
%macro HighSalesByYearState();
%local year stateCode state interest keepDataset;
%do year = 2001 %to 2100;
%let interest = %sysfunc(compound(1,.,0.05,%eval(&year.-2001)));
%do stateCode = 1 %to 56;
%if &stateCode. ne 3 & &stateCode. ne 7 & &stateCode. ne 14 & &stateCode. ne 43 &
&stateCode. ne 52 %then %do;
%let state = %sysfunc(fipstate(&stateCode.));
%let keepDataset = 0;
data work.sales&year.&state.high;
set work.sales&year.;
where state = quot;&state.quot; and sales > 2000000*&i t
h t t quot;& t t quot; d l 2000000*&interest.;
t
call symput('keepDataset',left('1'));
run;
%if not(&keepDataset.) %then %do;
p
proc datasets lib=work nolist;
delete sales&year.&state.high;
run; quit;
%end;
%end;
%end;
%end;
%mend HighSalesByYearState;
%HighSalesByYearState;

Conclusion
• The WHERE expression allows for
efficient observation processing in the
DATA step and the PROC statements
• The SAS System Documentation provides
specific details on the syntax
• Using macros increases the processing
power of WHERE expressions
f i

Contact Information
• Mark Tabladillo
MarkTab Consulting
http://www.marktab.com/
http://www marktab com/

Introduction to SAS System Where Expressions

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