Hi @Anv_SAS , I think this is what you wanted to achieve by the code you provided, however, you did not get what you desired. Here is my suggestion, please kindly let me know if it answer your question.
(1) you applied categories to the age and sex variables through the proc sort and merge steps, the code and output are as follows (in the output table, column n is age category and column m is sex category):
proc sort data=sashelp.class
out=class0 nodupkey;
by age;
run;
data class0;
set class0;
n=_n_;
run;
proc print data=class0;title "class0";run;
proc sort data=sashelp.class
out=class;
by age;
run;
proc print data=class;title "class";run;
data class2;
merge class(in=a)
class0(in=b keep=age n);
by age;
if a;
if sex="F" then m=1;
if sex="M" then m=2;
run;
proc print data=class2;title "class2";run;
(2) you were going to produce a macro variable which is the combination of the age and sex categories, and this macro variable resolves to respective students' names in the class as its' macro variable values. However, you did not choose the simple and efficient method to do this (you applied many techniques including macro, sql, nested do loop, which made the code difficult to debug). Here is my thinking and suggestion: you can simply use a single sql or data _null_ step to produce this macro variable.
proc sql;
select name,n,m
into :name1-,:n1-,:m1-
from class2;
quit;
%put _user_;
data _null_;
set class2;
call symputx(name,cat(n,m));
run;
%put _user_;
data _null_;
set class2;
call symputx(cat('age',n,m),name);
run;
%put _user_;
86 %put _user_; GLOBAL AGE11 Joyce GLOBAL AGE12 Thomas GLOBAL AGE21 Louise GLOBAL AGE22 Robert GLOBAL AGE31 Barbara GLOBAL AGE32 Jeffrey GLOBAL AGE41 Judy GLOBAL AGE42 Henry GLOBAL AGE51 Mary GLOBAL AGE52 William GLOBAL AGE62 Philip GLOBAL ALFRED 42 GLOBAL ALICE 31 GLOBAL BARBARA 31 GLOBAL CAROL 41 GLOBAL HENRY 42 GLOBAL JAMES 22 GLOBAL JANE 21 GLOBAL JANET 51 GLOBAL JEFFREY 32 GLOBAL JOHN 22 GLOBAL JOYCE 11 GLOBAL JUDY 41 GLOBAL LOUISE 21 GLOBAL M1 1 GLOBAL M10 2 GLOBAL M11 2 GLOBAL M12 1 GLOBAL M13 2 GLOBAL M14 1 GLOBAL M15 1 GLOBAL M16 1 GLOBAL M17 2 GLOBAL M18 2 GLOBAL M19 2 GLOBAL M2 2 GLOBAL M3 2 GLOBAL M4 1 GLOBAL M5 2 GLOBAL M6 1 GLOBAL M7 2 GLOBAL M8 1 GLOBAL M9 1 GLOBAL MARY 51 GLOBAL N1 1 GLOBAL N10 3 GLOBAL N11 4 GLOBAL N12 4 GLOBAL N13 4 GLOBAL N14 4 GLOBAL N15 5 GLOBAL N16 5 GLOBAL N17 5 GLOBAL N18 5 GLOBAL N19 6 GLOBAL N2 1 GLOBAL N3 2 GLOBAL N4 2 GLOBAL N5 2 GLOBAL N6 2 GLOBAL N7 2 GLOBAL N8 3 GLOBAL N9 3 GLOBAL NAME1 Joyce GLOBAL NAME10 Jeffrey GLOBAL NAME11 Alfred GLOBAL NAME12 Carol GLOBAL NAME13 Henry GLOBAL NAME14 Judy GLOBAL NAME15 Janet GLOBAL NAME16 Mary GLOBAL NAME17 Ronald GLOBAL NAME18 William GLOBAL NAME19 Philip GLOBAL NAME2 Thomas GLOBAL NAME3 James GLOBAL NAME4 Jane GLOBAL NAME5 John GLOBAL NAME6 Louise GLOBAL NAME7 Robert GLOBAL NAME8 Alice GLOBAL NAME9 Barbara GLOBAL PHILIP 62 GLOBAL ROBERT 22 GLOBAL RONALD 52
(3) to conclude, the last data _null_ step might be the solution, which produces desired result and the macro variable &age11 was correctly resolved to a class student's name, Joyce, the code and results is as follows.
data _null_;
set class2;
call symputx(cat('age',n,m),name);
run;
%put &age11;69 data _null_;
70 set class2;
71 call symputx(cat('age',n,m),name);
72 run;
NOTE: There were 19 observations read from the data set WORK.CLASS2.
73 %put &age11;
Joyce
Learn how use the CAT functions in SAS to join values from multiple variables into a single value.
Find more tutorials on the SAS Users YouTube channel.
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