25446 - Demonstrates relationship between Data Step and SQL

Sample 25446: Demonstrates relationship between Data Step and SQL

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 /****************************************************************/
 /*          S A S   S A M P L E   L I B R A R Y                 */
 /*                                                              */
 /*    NAME: SQLFUN10                                            */
 /*   TITLE: Demonstrates Relationship Between Data Step and SQL */
 /* PRODUCT: BASE                                                */
 /*  SYSTEM: ALL                                                 */
 /*    KEYS: SQL DATMAN WHERE TRANSLATE LIKE CREATE TABLE        */
 /*   PROCS: SQL                                                 */
 /*    DATA:                                                     */
 /* SUPPORT:                             UPDATE:                 */
 /*     REF:                                                     */
 /*    MISC: This example was contributed by Howard Schreier     */
 /*          of the US Dept. of Commerce, via BITNET             */
 /*                                                              */
 /****************************************************************/


 /* A User asks:                                                     */
 /* I have two sas data sets, which need to be merged by social      */
 /* security number (SSN). The variable SSN in DATA 1 is clean       */
 /* and non-missing. But some SSN in DATA 2 have some digits         */
 /* missing, but the remaining ones ARE still in the RIGHT columns.  */
 /* For instance, the correct one in DATA 1 is 123456789. But the    */
 /* missing one in DATA 2 is 1 345 789. By the way, both variables   */
 /* are character variables. How can I still merge the two data sets */
 /* based on the remaining figures, which are in the RIGHT columns?  */
 
 /* I think PROC SQL  is  the  tool  of  choice  here,  for  two */
 /* reasons.   First  of all, it is necessary to compare each of */
 /* the corrupted SSN values with all of the clean ones  in  the */
 /* other  data  set;  this  is  essentially a "join" operation, */
 /* which is pretty much characteristic of SQL.  Secondly,  PROC */
 /* SQL  offers  the LIKE operator (pattern matching *with* wild */
 /* cards), which is exactly what is needed here.                */

 /* Here is a sample test.  After the two data sets are created, */
 /* a special data set containing *only* the corrupted values is */
 /* created (in  actual  applications,  this  should  enormously */
 /* reduce   the   volume   of  comparisons  to  be  made);  the */
 /* single-character wildcard, an underscore, is substituted for */
 /* each  blank.   Note  that  this preparation could readily be */
 /* done by a statement within PROC  SQL.   The  LIKE  condition */
 /* serves as a screen, so that the resulting data set lists all */
 /* possible matches found for each of the corrupted SSN values. */

 data clean;
   input ssn $ 1-9;
   cards;
123456789
234567890
345678901
345678902
;

 data dirty;
   input ssn $ 1-9;
   cards;
12 4567 9
456 89012 no match
34567890  ambiguous
234567890 clean
;

 data withwild;
   set dirty;
   length withwild $ 9;
   
 /* Substitute underscores for blanks */
   withwild = translate(ssn,'_',' ');
   
 /* Get rid of observations having all nine digits */
   if index(withwild,'_');
   run;

 proc sql;
   create table possible as
    select withwild.ssn, clean.ssn as possible
     from withwild, clean
     where clean.ssn like withwild.withwild;

   title2 'Possible Matches';
   select * from possible;

 quit;

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Possible Matches

ssn        possible
--------------------
12 4567 9  123456789
34567890   345678901
34567890   345678902

Type:	Sample
Topic:	SAS Reference ==> Procedures ==> SQL

Date Modified:	2005-08-24 16:06:33
Date Created:	2005-05-23 13:54:20

Product Family	Product	Host	SAS Release
			Starting	Ending
SAS System	Base SAS	All	n/a	n/a

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Sample 25446: Demonstrates relationship between Data Step and SQL

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