title '6b. Mixing Categorical and Continuous Data Percentages';
* For numeric variables, create categories to label and group values;
proc format;
 value agec 1='Age (Years)';
 value sexc 1='Sex';
 value wgtc 1='Weight';
 value agefmt 1='0 - 15'
              2='16 - 64'
              3='>= 65';
 value sexfmt 1='Male'
              2='Female';
 value wgtfmt 1='<= 100'
              2='> 100';
run;

data class;
* Create dummy variables;
 agecol=1; racecol=1; sexcol=1; wgtcol=1;

 set sashelp.class;

 * Apply numeric grouping;
 if 0 le age le 15 then agecat=1;
 else if 16 le age le 64 then agecat=2;
 else if age ge 65 then agecat=3;

 * Apply to label Sex as Male and Female instead of M and F;
 if sex='M' then sexcat=1; else if sex='F' then sexcat=2;

 * Apply numeric grouping;
 if weight <= 100 then wgtcat=1; else if weight > 100 then wgtcat=2;
run;


proc tabulate data=class missing;
 class agecol sexcol wgtcol agecat sexcat wgtcat;
 format agecat agefmt. sexcat sexfmt. agecol agec. sexcol sexc. wgtcat
        wgtfmt. wgtcol wgtc.;
 keylabel N='N' PCTN='%';

* Age and weight groups are rows by sex values as columns;
 tables (agecol=' '*agecat=' ' wgtcol=' '*wgtcat=' ')
       , (sexcol=' '*sexcat=' ' all)*(n='N' pctn<agecat wgtcat>='%');

 * Altenative to PCTN<>;
 *tables (agecol=' '*agecat=' ' wgtcol=' '*wgtcat=' ')
       , (sexcol=' '*sexcat=' ' all)*(n='N' colpctn='%');
run;