*First we read in a test data set for you;
data mydata;
 input BAD	VAR2	VAR3	VAR4	VAR5	VAR6	VAR7	VAR8	VAR12	ALPHA $;
cards;
1	20	22	1	55	1	1	9	0.791533	ABCDEF
0	20	33	5	44	0	1	9	0.58006		ABCDEF
1	30	55	2	55	1	0	9	0.030765	ABCDEF
0	10	29	3	32	0	0	9	0.728529	ABCDEF
1	30	65	2	25	1	1	3	0.855352	ABCDEF
1	30	99	2	25	1	1	1	0.186514	ABCDEF
1	30	66	1	43	1	1	2	0.435062	ABCDEF
0	10	22	2	32	0	0	9	0.926317	ABCDEF
0	20	29	4	32	0	0	9	0.819711	ABCDEF
1	30	67	3	25	1	1	9	0.755185	ABCDEF
1	20	66	1	25	1	1	1	0.82157		ABCDEF
0	20	22	5	27	0	0	9	0.381826	ABCDEF
0	30	30	3	32	0	1	9	0.69663		ABCDEF
0	30	22	2	32	0	0	9	0.805265	ABCDEF
0	20	29	3	32	0	0	9	0.873597	ABCDEF
1	10	49	2	38	0	0	9	0.109114	ABCDEF
0	30	24	4	32	0	0	9	0.042165	ABCDEF
0	20	21	4	54	1	0	9	0.090542	ABCDEF
0	10	29	4	32	0	0	9	0.161341	ABCDEF
0	30	23	5	27	0	0	9	0.217645	ABCDEF
1	10	28	2	32	0	0	9	0.937066	ABCDEF
0	30	49	3	44	0	0	9	0.554767	ABCDEF
1	30	49	2	32	1	0	9	0.184839	ABCDEF
0	20	29	3	32	0	1	9	0.51089		ABCDEF
1	20	66	1	25	1	1	9	0.214085	ABCDEF
0	30	33	2	34	0	0	9	0.476002	ABCDEF
0	20	29	4	32	0	0	9	0.687496	ABCDEF
1	10	46	1	43	1	0	9	0.601898	ABCDEF
0	10	30	3	32	0	0	9	0.746762	ABCDEF
1	30	65	1	25	1	1	9	0.813671	ABCDEF
1	20	44	3	25	1	1	1	0.577398	ABCDEF
1	10	59	2	55	0	1	2	0.291862	ABCDEF
0	10	24	5	27	0	0	9	0.235653	ABCDEF
0	10	22	5	27	0	1	9	0.279852	ABCDEF
0	20	22	3	37	1	0	9	0.740561	ABCDEF
1	10	77	1	25	1	1	1	0.761611	ABCDEF
0	20	30	3	32	0	0	9	0.134736	ABCDEF
0	10	33	4	32	0	0	9	0.351236	ABCDEF
1	10	55	1	25	1	1	4	0.350707	ABCDEF
1	30	56	1	26	1	1	2	0.554283	ABCDEF
0	30	68	1	43	1	0	9	0.751453	ABCDEF
1	20	55	2	55	1	0	9	0.55211		ABCDEF
1	30	77	2	24	1	1	9	0.714435	ABCDEF
0	30	49	3	44	0	0	3	0.692118	ABCDEF
1	30	59	2	55	0	0	9	0.09301		ABCDEF
0	10	29	3	32	0	0	9	0.840332	ABCDEF
0	30	49	2	38	0	1	9	0.169412	ABCDEF
1	20	49	3	44	0	0	3	0.635838	ABCDEF
1	20	59	2	55	0	1	1	0.871939	ABCDEF
0	30	49	2	44	0	0	9	0.45459		ABCDEF
1	20	55	1	27	1	1	9	0.653539	ABCDEF
0	30	19	6	46	1	0	9	0.95832		ABCDEF
0	10	29	3	32	0	0	9	0.293503	ABCDEF
0	10	29	3	32	0	0	9	0.893453	ABCDEF
1	30	66	1	43	1	0	1	0.816852	ABCDEF
1	20	55	2	55	1	0	9	0.385023	ABCDEF
1	10	55	2	55	1	0	3	0.585017	ABCDEF
1	10	28	2	32	0	0	9	0.112736	ABCDEF
0	30	22	3	32	0	0	9	0.958921	ABCDEF
1	20	59	2	55	0	0	9	0.966912	ABCDEF
0	30	45	3	32	0	0	9	0.0473		ABCDEF
1	10	49	2	38	0	0	9	0.620112	ABCDEF
1	20	28	2	38	0	0	9	0.68911		ABCDEF
0	10	33	2	32	0	1	9	0.837598	ABCDEF
1	10	22	2	37	1	0	9	0.428998	ABCDEF
0	30	33	5	27	0	1	9	0.455931	ABCDEF
0	30	33	4	32	0	0	9	0.731894	ABCDEF
1	10	28	2	38	0	0	9	0.789909	ABCDEF
1	20	59	2	55	0	0	1	0.290774	ABCDEF
0	30	33	4	37	1	0	9	0.547194	ABCDEF
1	20	33	1	25	1	0	9	0.896589	ABCDEF
1	10	55	2	55	1	0	9	0.689193	ABCDEF
0	30	78	2	43	1	1	9	0.061569	ABCDEF
1	20	67	1	43	1	0	9	0.03787		ABCDEF
1	20	49	2	32	1	0	9	0.746467	ABCDEF
0	10	30	3	32	0	1	9	0.71046		ABCDEF
1	10	66	1	25	1	1	9	0.150983	ABCDEF
1	10	49	2	38	0	1	9	0.676569	ABCDEF
1	20	56	1	43	1	0	1	0.978532	ABCDEF
1	20	78	1	25	1	1	9	0.185021	ABCDEF
1	30	55	2	55	1	0	9	0.153698	ABCDEF
0	30	22	2	37	1	0	9	0.100587	ABCDEF
1	30	59	2	55	0	0	9	0.977505	ABCDEF
1	20	55	2	55	0	0	3	0.781166	ABCDEF
0	30	23	3	37	0	1	9	0.171081	ABCDEF
1	10	49	3	44	0	0	5	0.267002	ABCDEF
1	10	49	3	38	0	0	9	0.935018	ABCDEF
0	10	43	5	24	0	0	9	0.602167	ABCDEF
0	10	23	4	32	0	0	9	0.74257		ABCDEF
1	20	49	3	44	0	0	5	0.941777	ABCDEF
0	30	31	2	32	0	0	9	0.783983	ABCDEF
0	30	33	5	27	0	1	9	0.100045	ABCDEF
0	20	22	3	37	0	0	9	0.027626	ABCDEF
1	30	77	5	22	1	0	9	0.839419	ABCDEF
1	30	59	2	55	0	1	2	0.579814	ABCDEF
0	20	22	5	32	0	0	9	0.512515	ABCDEF
1	30	55	1	23	1	1	9	0.511528	ABCDEF
0	30	30	3	32	0	0	9	0.965713	ABCDEF
1	10	49	2	44	0	0	1	0.021495	ABCDEF
0	20	22	3	32	0	0	9	0.012336	ABCDEF
1	10	68	1	43	1	0	9	0.240498	ABCDEF
1	10	49	2	55	1	0	1	0.162414	ABCDEF
1	20	49	2	55	1	0	1	0.332437	ABCDEF
0	20	29	3	32	0	0	9	0.879268	ABCDEF
0	10	24	4	27	0	0	9	0.724304	ABCDEF
1	20	36	4	37	1	0	9	0.484362	ABCDEF
0	30	32	2	37	1	1	9	0.8418		ABCDEF
1	10	57	1	25	1	1	2	0.008626	ABCDEF
0	10	21	2	37	1	0	9	0.679064	ABCDEF
1	20	59	2	55	0	1	2	0.923857	ABCDEF
0	30	22	2	32	0	0	9	0.56039		ABCDEF
0	10	31	2	32	0	0	9	0.863525	ABCDEF
0	30	29	4	32	0	0	9	0.587931	ABCDEF
1	20	55	1	25	1	1	1	0.283987	ABCDEF
1	10	89	1	22	1	0	9	0.937773	ABCDEF
0	30	22	3	66	1	0	9	0.567971	ABCDEF
0	10	22	5	27	0	0	9	0.393349	ABCDEF
0	30	34	5	23	0	0	4	0.904269	ABCDEF
0	20	29	4	32	0	0	9	0.720225	ABCDEF
1	20	34	1	33	1	1	2	0.22209		ABCDEF
0	10	45	3	32	0	0	9	0.154999	ABCDEF
0	10	20	5	47	1	0	9	0.487965	ABCDEF
1	10	33	3	37	1	0	9	0.149062	ABCDEF
0	20	30	3	32	0	0	9	0.184226	ABCDEF
0	10	45	2	32	0	0	9	0.746296	ABCDEF
0	30	28	3	38	0	0	9	0.496312	ABCDEF
0	20	21	4	37	1	0	9	0.29882		ABCDEF
1	30	76	1	21	1	1	9	0.625453	ABCDEF
0	30	29	3	32	0	0	9	0.420142	ABCDEF
0	30	49	2	38	0	1	9	0.245023	ABCDEF
0	20	55	4	36	1	0	9	0.290797	ABCDEF
1	10	55	1	27	1	1	9	0.207295	ABCDEF
0	10	29	4	32	0	0	9	0.29183		ABCDEF
0	20	24	5	27	0	0	9	0.474352	ABCDEF
1	10	28	3	32	0	0	9	0.31753		ABCDEF
0	30	22	5	27	0	0	2	0.495858	ABCDEF
0	30	29	3	32	0	0	9	0.519436	ABCDEF
1	10	97	2	18	1	1	9	0.536148	ABCDEF
1	30	67	1	25	1	1	9	0.7249		ABCDEF
1	30	55	2	55	1	0	2	0.993585	ABCDEF
0	30	25	3	37	1	0	9	0.757854	ABCDEF
1	10	22	3	37	1	0	9	0.774445	ABCDEF
0	30	28	2	32	0	1	9	0.725221	ABCDEF
0	20	33	2	32	0	1	9	0.566063	ABCDEF
0	30	49	2	44	0	0	1	0.803126	ABCDEF
1	20	22	1	25	1	1	1	0.620885	ABCDEF
1	10	77	2	24	1	1	9	0.185034	ABCDEF
0	10	29	4	32	0	0	9	0.591525	ABCDEF
0	30	55	4	36	1	0	9	0.818192	ABCDEF
0	10	32	2	37	1	1	9	0.615924	ABCDEF
1	20	59	2	55	0	1	2	0.452439	ABCDEF
0	20	29	3	32	0	0	9	0.794314	ABCDEF
1	10	28	3	38	0	0	9	0.59481		ABCDEF
1	20	88	2	43	1	1	1	0.9138		ABCDEF
0	20	29	3	32	0	0	9	0.902852	ABCDEF
0	20	23	4	30	0	0	9	0.463839	ABCDEF
1	30	55	1	25	1	1	1	0.877583	ABCDEF
1	20	88	1	43	1	0	9	0.864896	ABCDEF
1	10	49	2	44	0	0	9	0.179285	ABCDEF
1	30	54	2	25	1	1	2	0.718789	ABCDEF
1	20	55	2	55	1	0	9	0.68318		ABCDEF
0	30	28	2	32	0	0	9	0.330888	ABCDEF
1	20	88	2	23	1	0	9	0.456294	ABCDEF
0	20	44	3	37	1	0	9	0.554051	ABCDEF
0	20	21	4	46	1	0	9	0.19539		ABCDEF
1	10	49	2	38	0	1	9	0.344412	ABCDEF
1	20	55	1	23	1	1	9	0.29784		ABCDEF
0	10	29	3	32	0	0	9	0.322278	ABCDEF
1	10	44	3	25	1	1	1	0.706533	ABCDEF
0	20	31	2	32	0	0	9	0.977669	ABCDEF
0	30	29	3	32	0	0	9	0.095139	ABCDEF
0	30	33	5	24	0	0	9	0.696932	ABCDEF
0	10	33	5	23	0	0	9	0.204931	ABCDEF
0	10	31	2	32	0	0	9	0.882071	ABCDEF
1	30	59	2	55	0	0	1	0.694104	ABCDEF
0	10	22	5	27	0	0	2	0.114065	ABCDEF
0	10	22	2	32	0	0	9	0.007479	ABCDEF
1	20	55	2	55	1	0	9	0.273739	ABCDEF
1	10	43	3	37	1	0	9	0.920749	ABCDEF
0	30	22	5	32	0	0	9	0.826653	ABCDEF
0	10	25	5	27	0	1	9	0.439581	ABCDEF
0	20	31	2	32	0	0	9	0.258062	ABCDEF
0	20	29	4	32	0	0	9	0.01674		ABCDEF
1	20	44	1	43	1	0	9	0.800906	ABCDEF
0	20	45	3	32	0	0	9	0.070814	ABCDEF
0	10	29	3	32	0	0	9	0.565107	ABCDEF
1	30	55	1	23	1	1	9	0.453773	ABCDEF
0	20	30	5	32	0	0	9	0.824868	ABCDEF
0	30	30	3	32	0	1	9	0.573673	ABCDEF
1	10	49	2	44	0	0	9	0.6281		ABCDEF
0	20	30	5	32	0	0	9	0.799247	ABCDEF
0	30	25	4	27	0	0	1	0.38251		ABCDEF
0	10	29	3	32	0	1	9	0.583994	ABCDEF
0	20	33	5	24	0	0	9	0.415387	ABCDEF
1	20	55	2	55	1	0	2	0.187298	ABCDEF
0	30	44	1	43	1	0	9	0.98875		ABCDEF
0	10	29	3	32	0	0	9	0.450917	ABCDEF
1	10	36	4	37	1	0	9	0.366209	ABCDEF
0	30	21	5	27	0	0	9	0.375269	ABCDEF
1	20	66	1	43	1	1	2	0.662063	ABCDEF
0	30	49	3	44	0	0	5	0.361138	ABCDEF
0	30	36	4	37	1	0	9	0.718363	ABCDEF
0	30	30	3	32	0	0	9	0.431675	ABCDEF
1	20	88	1	23	1	1	1	0.143696	ABCDEF
1	20	49	2	38	0	0	9	0.821813	ABCDEF
0	30	45	2	32	0	0	9	0.246392	ABCDEF
1	20	46	1	43	1	0	9	0.551396	ABCDEF
0	20	21	3	44	1	0	9	0.279447	ABCDEF
1	20	66	1	22	1	0	9	0.923456	ABCDEF
0	30	23	4	32	0	0	9	0.67066		ABCDEF
1	20	59	2	55	0	0	2	0.453754	ABCDEF
0	10	22	3	45	1	0	9	0.034456	ABCDEF
0	20	30	3	32	0	0	9	0.674866	ABCDEF
0	10	33	5	24	0	0	9	0.473507	ABCDEF
0	10	29	3	32	0	0	9	0.813951	ABCDEF
0	30	29	3	32	0	1	9	0.953593	ABCDEF
0	10	31	2	32	0	0	9	0.754322	ABCDEF
1	10	78	3	43	1	0	9	0.227924	ABCDEF
0	20	33	4	37	1	0	9	0.985491	ABCDEF
1	20	78	1	21	1	0	9	0.100222	ABCDEF
1	10	88	1	23	1	1	1	0.87848		ABCDEF
1	20	59	2	55	0	0	9	0.367176	ABCDEF
1	30	59	2	55	0	1	1	0.174795	ABCDEF
0	30	43	5	24	0	0	9	0.701943	ABCDEF
1	30	78	1	25	1	1	9	0.073317	ABCDEF
1	30	88	1	23	1	1	1	0.360491	ABCDEF
1	30	66	1	22	1	0	9	0.270685	ABCDEF
1	20	54	2	25	1	1	2	0.441605	ABCDEF
0	20	45	2	32	0	0	9	0.219868	ABCDEF
1	20	49	2	44	0	1	2	0.070118	ABCDEF
0	20	24	5	55	1	0	9	0.843591	ABCDEF
1	10	66	1	43	1	0	1	0.626606	ABCDEF
1	20	44	1	22	1	1	2	0.272602	ABCDEF
1	10	55	2	55	1	0	9	0.280851	ABCDEF
1	30	59	2	55	0	0	9	0.402514	ABCDEF
0	20	30	3	32	0	0	9	0.571997	ABCDEF
0	10	22	3	32	0	0	9	0.400661	ABCDEF
1	20	49	2	44	0	0	9	0.696127	ABCDEF
0	30	66	1	43	1	0	9	0.680299	ABCDEF
1	30	68	1	25	1	1	9	0.297079	ABCDEF
1	30	59	2	55	0	1	1	0.673944	ABCDEF
0	20	28	2	38	0	1	9	0.934191	ABCDEF
1	10	59	2	55	0	0	9	0.659987	ABCDEF
0	20	29	4	32	0	0	9	0.063341	ABCDEF
1	10	55	2	55	1	0	9	0.333197	ABCDEF
0	30	25	5	27	0	1	9	0.873578	ABCDEF
0	10	29	4	32	0	0	9	0.645273	ABCDEF
0	10	55	4	36	1	0	9	0.647001	ABCDEF
0	30	21	4	37	1	0	9	0.310514	ABCDEF
0	30	28	2	38	0	0	9	0.632472	ABCDEF
;

*Note: this macro writes a couple scratch files to your hard drive;
%macro getlist1(data,textfile,xname);
%global nbrvars;
*We get the names of the numeric variables;
data myvars (keep=name);
 length name $32;
 set &data;
 array x{*} _numeric_;
 do i=1 to dim(x);
 call vname(x{i},name);
 output;
 end; 
 stop;
run;

*We write these names out to a text file with macro code embedded;
*Two lists are needed.;
data _null_;
 file &textfile;
 set myvars end=last;
 if _n_=1 then put '%macro ' &xname";";
 put name;
 if last then put '%mend ' &xname";";
 if last then call symput('nvar',_n_);
run;
data _null_;
 file './names_2.txt';
 set myvars end=last;
 if _n_=1 then put '%macro names_2;';
 put "'" name "'";
 if last then put '%mend names_2;';
 if last then call symput('nbrvars',_n_);
run;
%mend getlist1;


%macro correlation(data,depv);
OPTIONS NOTES SOURCE SOURCE2 MPRINT MLOGIC MERROR SYMBOLGEN;

*First we write out the correlation matrix to a file;
proc corr data=&data outp=p noprint;
*Read into an array starting with the 4th observation representing correlations;
*for only numeric variables;
%getlist1(p,'./names_1.txt','names_1');
*Write out to a file the first list of variables;
*Now you have some macro variables for the names;
*These files were placed in a default WIN directory;
%inc './names_1.txt';
%inc './names_2.txt';

*Extracting information from correlation matrix - upper right corner;
data getr (keep=VAR_1_LABEL VAR_2_LABEL p_corr abscorr);
set p (firstobs=4);
* First read the correlations into the array;
   array pcorr {&nbrvars} %names_1;
* Next read the variable names into a string array;
   array varnames {&nbrvars} $32 (%names_2);
   if _TYPE_='CORR' then
   do;
      do i=_n_ to dim (pcorr);
	  /* the if statement below excludes correlations of 1 derived from self-correlations*/
        if _n_^=i then 
         do;
           p_corr=pcorr{i};
           VAR_1_LABEL=_NAME_;
           VAR_2_LABEL=varnames{i};
           abscorr=abs(p_corr);
           output;
         end;
      end;
   end;
run;

*Creating Bivariate data (Getting rid of Pairwise);
data bivariate;
RETAIN VAR_1_LABEL VAR_2_LABEL P_CORR ABSCORR;
 set getr;
 if VAR_1_LABEL ^="&depv" AND VAR_2_LABEL ^="&depv" then delete;
run;

*Sorting by absolute value;
proc sort data=bivariate out=bivariate(drop=abscorr); by descending abscorr;
run;

TITLE BIVARIATE CORRELATIONS SORTED BY ABSOLUTE VALUE;
PROC PRINT DATA=BIVARIATE;

*Creating Only Pairwise table (not including depend. var);
data getr2;
RETAIN VAR_1_LABEL VAR_2_LABEL P_CORR ABSCORR;
 set getr;
if VAR_1_LABEL ="&depv" then delete;
if VAR_2_LABEL ="&depv" then delete;
run;

*Sorting by absolute value;
proc sort data=getr2 out=getr2(drop=abscorr); by descending abscorr;
run;
TITLE PAIRWISE CORRELATIONS SORTED BY ABSOLUTE VALUE;
PROC PRINT DATA=getr2;
quit;
%mend correlation;

%correlation(mydata,BAD);