Sub title

SAS/Excel Tricks

ods noresults;
ods listing close;
ods html body="c:\temp\classods.xls";
proc print data=sashelp.class(obs=10);
run;
ods html close;
ods html body="c:\temp\shoesods.xls";
proc print data=sashelp.shoes(obs=10);
run;
ods html close;
ods html body="c:\temp\zipcodeods.xls";
proc print data=sashelp.zipcode(obs=10);
run;
ods html close;
ods listing;
ods results;



Macro to Combine Worksheets:



%macro many2one(in=,out=);
options noxwait;
x erase "&out";
options xwait;

data _null_;
file "c:\temp\class.vbs";
put 'Set XL = CreateObject("Excel.Application")' /
'XL.Visible=True';
%let n=1;
%let from=%scan(&in,&n," ");
%do %while("&from" ne "");
%let fromwb=%scan(&from,1,"!");
%let fromws=%scan(&from,2,"!");
put "XL.Workbooks.Open ""&fromwb""";
%if &n=1 %then
put "XL.ActiveWorkbook.SaveAs ""&out"", -4143"%str(;);
%else %do;
put "XL.Workbooks(""%scan(&fromwb,-1,'\')"").Sheets(""&fromws"").Copy ,XL.Workbooks(""%scan(&out,-1,'\')"").Sheets(%eval(&n-1))";
put "XL.Workbooks(""%scan(&fromwb,-1,'\')"").Close";
%end;
%let n=%eval(&n+1);
%let from=%scan(&in,&n, " ");
%end;
put "XL.Workbooks(""%scan(&out,-1,'\')"").sheets(1).activate";
put "XL.Workbooks(""%scan(&out,-1,'\')"").Save";
put "XL.Quit";
run;

x 'c:\temp\class.vbs';
%mend;

Example:

%many2one(in=c:\temp\classods.xls!classods
c:\temp\shoesods.xls!shoesods c:\temp\zipcodeods.xls!zipcodeods,
out=c:\temp\combined.xls);

sas-excel-tricks

Stratified Random Sampling

Stratified Random Sampling, also sometimes called proportional or quota random sampling, involves dividing your population into homogeneous subgroups and then taking a simple random sample in each subgroup. In more formal terms:

Objective: Divide the population into non-overlapping groups (i.e., strata)
N₁, N₂, N₃, ... N_i,
such that N₁ + N₂ + N₃ + ... + N_i = N.
Then do a simple random sample of
f = n/N in each strata.

There are several major reasons why you might prefer stratified sampling over simple random sampling. First, it assures that you will be able to represent not only the overall population, but also key subgroups of the population, especially small minority groups.If the subgroup is extremely small, you can use different
sampling fractions (f) within the different strata to randomly over-sample the small group although you'll then have to weight the within-group estimates using the sampling fraction whenever you want overall population estimates). When we use the same sampling raction within strata we are conducting proportionate stratified random sampling.
When we use different sampling fractions in the strata, we call this disproportionate stratified random sampling. Second, stratified random sampling will generally have more statistical precision than simple random sampling. This will only be true if the strata or groups are homogeneous. If they are, we expect that the variability within-groups is lower than the variability for the population as a whole. Stratified sampling capitalizes on that fact.

Probability Sampling

A probability sampling method is any method of sampling that utilizes some form of random selection. In order to have a random selection method, you must set up some process or procedure that assures that the different units in your population have equal probabilities of being chosen.



Some Definitions :

N = the number of cases in the sampling frame
n = the number of cases in the sample
NCn = the number of combinations (subsets) of n from N
f = n/N = the sampling fraction
Many computer programs can generate a series of random numbers.
After that you have rearrange the list in random order from the lowest to the highest random number. Then, all you have to do is take the first hundred names in this sorted list.Simple random sampling is not the most statistically efficient method of sampling and you may, just because of the luck of the draw, not get good representation of subgroups in a population

The way of creating the same distribution

Sometimes We want to create the same distribution .
We can do it in this way (The ttt limits the commulative distribution of another variable,which we can receive from proc freq )

DATA resh2;
SET halvaot.resh2;
IF TARGET =0 THEN DO;
ttt=ranuni(31311115)*100;
if ttt<=5 then kod=200502;
else if ttt<=28 then kod=200503;
else if ttt<=40 then kod=200504;
else if ttt<=52 then kod=200505;
else if ttt<=62 then kod=200506;
else if ttt<=71 then kod=200507;
else if ttt<=80 then kod=200508;
else if ttt<=88 then kod=200509;
else if ttt<=93 then kod=200510;
else if ttt<=100 then kod=200511;
end;
if target=1 then kod=100* year( Loan_Value_Date)+month(Loan_Value_Date);
run;

This is more wise way to do the same:

proc freq DATA=halvaot.new_halv;
tables Loan_Value_Date /out=outkod outcum noprint;
run;

data _null_ ; length kod_str pct_str $5000;
set outkod end=eof;
retain kod_str pct_str;
kod_str=compress(kod_str||','||Loan_Value_Date);
pct_str=compress(pct_str||','||cum_pct);
if eof then do;
call symput('a1',substr(pct_str,2));
call symput('a2',substr(kod_str,2));
call symput('nn',_n_);
end;
run;

DATA resh222;
SET halvaot.resh2;
array a1 {&nn} _temporary_ (&a1);
array a2 {&nn} _temporary_ (&a2);
IF TARGET =0 THEN DO;
ttt=ranuni(31311115)*100;
do i=1 to dim(a1);
if i=1 then do;
if ttt<=a1[i] then Loan_Value_Date=a2[i];
end;
else do;
if a1[i-1]<ttt<=a1[i] then Loan_Value_Date=a2[i];
end;
end;
end;

kod=100* year( Loan_Value_Date)+month(Loan_Value_Date);
run;

Factor Analyses with Sas

In the beginning of the process we have to transpose the data in order to receive one column to each category.
proc transpose data =jjj1 out=toz prefix=peul;
by branch_cust_ip;
id Event_Costing_Activity_Type_Co;
var count;
run;

We want fill missing values with 0:

data toz;
set toz;
array toz{*} _NUMERIC_ ;
do i = 1 to dim(toz);
if toz{i} = . then toz{i} = 0;
end;
drop i;
run;

Definition of factors:
proc factor score data=rehishot.ishit method=p rotate=orthomax nfactors=10 outstat=fact_ish;
var peul: ;
run;
Scoring of the data:
proc score data=rehishot.ishit score=fact_ish out=scores_ishit;
var peul: ;
run;

In the end we want to find the most influent (max Factor)
and the less influent (min Factor) data scores_ishit;
set scores_ishit ;
max=max(Factor1,Factor2,
Factor3,Factor4,Factor5,
Factor6,Factor7,Factor8,
Factor9,Factor10)
;
min=min(Factor1,Factor2,
Factor3,Factor4,Factor5,
Factor6,Factor7,Factor8,
Factor9,Factor10)
;
run;

data scores_ishit;
set scores_ishit;
array factor Factor1-factor10;
do i=1 to dim(factor);
if max=factor [i] then factor_max=i;
if min=factor [i] then factor_min=i;
end;
run;

proc logistic

ods trace on;

It helps for us to receive all possible outputs
for example Type3

proc logistic data = k outest=jj;
class ses race schtyp/param =glm;
model female = age ses race schtyp science write/outroc =roc lackfit;
units age=35 45 55;
ods output ParameterEstimates = model_female
Type3=chisq;
run;

I want to keep only significent variables:

data chisq1;
set chisq;
/*format ProbChiSq;*/
if ProbChiSq>0.05 then delete;
run;


PROC SQL ;
SELECT effect INTO :mm separated BY " "
from chisq1;
quit;




%put &mm;
proc logistic data = k outest=jj;
class &mm/param =glm;
model female = &mm/outroc =roc lackfit;
units age=35 45 55;
ods output ParameterEstimates = model_female
TypeIII=chisq;
run;

Export using scan

%let name=1+3+6+12;
%macro stam;
%do
i=1 %to 4;
data kim_%scan(&name,&i,+) ;
set kim;
if vetek=%scan(&name,&i,+)*1;
run;

PROC EXPORT DATA= kim_%scan(&name,&i,+)
OUTFILE= "\\c\kim_%scan(&name,&i,+).csv"
DBMS=CSV REPLACE;
RUN;
%end
;
%mend
;
%stam;

Teradata extract function

case when b.Branch_Cust_IP is null then extract(year from a.Ruler_Date)
when a.Branch_Cust_IP is null then extract(year from b.Ruler_Date)
else extract(year from a.Ruler_Date) end as shana

Sas proc mi-fill missing

proc mi data=events.no_gil seed=555536 simple nimpute=1 out=ll;
em itprint outem=outem;
var gil_max sum_PASIV sum_maskoret sum_KIZBA_YELED sum_KIZBAOT ;
run;

Sas proc tabulate

proc tabulate data=shotef.matah ;
class division_att_Desc Nalan_Cluster_Desc L_GLISHA_YESHIRA;
var schum internet emda phone pakid_all;
table ((division_att_Desc='name' all)*Nalan_Cluster_Desc='name'),L_GLISHA_YESHIRA=''*(schum='name'*(sum='name'*f=14. n)
internet='name'*(mean='name'*f=5.1 n)
emda='name'*(mean='name'*f=5.1 n)
phone='name'*(mean='name'*f=5.1 n)
pakid_all='name'*(mean='name'*f=5.1 n));
format format Nalan_Cluster_Desc $nalan. L_GLISHA_YESHIRA membr.;
keylabel all='total';
run;

Sas array demi

data a;
input id name $;
cards;
10 -11
10 222
10 -33
23 333
23 -33
76 -11
76 222
76 -33
;
run;
data b;
input names $;
cards;
-11
-33
222
333
999
888
555
666
777
;
run;
data bb; length nam_nam $200;set b end=eof;
if _n_=1 then nam_nam=' ';
retain nam_nam;
nam_nam=trim(compress(nam_nam||"','"||names));
if eof then do;
nam_nam=compress(substr(nam_nam,3)||"'");
output;
end;
call symput('name_macro',nam_nam);
run;
proc sort data=a; by id name; run;
options symbolgen mprint;
data b;
set a; by id;
array nam [9] $ _temporary_ (&name_macro);
array vars v1-v9;
retain v1-v9;
do jj=1 to dim(vars);
vars[jj]=.;
end;
do j=1 to dim(vars);
if name=nam[j] then vars[j]=1;
end;
if last.id then output;
drop jj j;
run;

Sas array transpose

data average_lag6;
set niud.ovr_lag_all_200110_200201;
array product ms_kartisim
maskorot
l_miuazim
miuazim
ms_hk
sch_hk
halvaot
misgeret_ashrai
osher
pasiv;

array name{10} $ 16;
do i=1 to dim(product);
name[i]=vname(product[i]);
schum=product [i];
varname=name[i];
month_lag=month_lag6;
output;
end;
keep schum varname month_lag;
run;


proc sort data=dug;
BY b;
run;

proc sql;
select distinct
year into: tkufa
separated by '+'
from dug;
quit;



%macro dugma;
%do i=1 %to 2;
DATA stam
(DROP=I year x y year1 year2 z);
;
ARRAY years {2} year1- year2;
ARRAY xs {2} x1-x2;
ARRAY ys {2} y1-y2;
DO I=1 TO 2 UNTIL (LAST.b);
SET dug;
BY b;
years {I}=year;
x_%scan(&tkufa,&i,+)=xs{I};

xs{I}=x;
ys{I}=y;
END;

run;
%end;
%mend;
%dugma;

Sas array combination

data zevet1.arr;
set zevet1.for_model;
array dich[15] ms_harshot_d sum_mispar_hor_keva_d arhrai_d maskorot1 mashkanta1 TIH_HAP1 TIH_TASH1 avg_sch_kranot1 avg_sch_ne1 avg_pkl_pkd1 avg_pkl_pkd_zm1 GEMEL_NZL1 halvaot1 modern1 count;

array name{15} $ _temporary_ ('ms_hars','hor_keva_d','arhrai_d',
'maskorot1','mashkanta1','TIH_HAP1','TIH_TASH1', 'sch_kranot1',
'sch_ne1','pkl_pkd1','pkl_pkd_zm1','GEMEL_NZL1','halvaot1',
'modern1', 'count' );

tzvt_activ_form=put(tzvt_activ,zev.);
kod_vetek=put(vetek_lak,vetek.);
do i=1 to 15;
category=dich [i];
prod=name[i];
num_product=i;
output;
end;
keep sd10_numerator category num_product tzvt_activ tzvt_activ_form prod kod_vetek;
run;


data b4_300;
set zevet1.for_model(where=(tzvt_activ_form='300'));
array aa ms_harshot_d sum_mispar_hor_keva_d arhrai_d maskorot1
avg_pkl_pkd1 ;
m=0;
do i=1 to 5;
do j=i+1 to 5;
do k=j+1 to 5;

comb=aa[i]+aa[j]+aa[k];
name=compress(vname(aa[i])||'_'||vname(aa[j])||'_'||vname(aa[k]));
m+1;
if comb<3 then continue;
else output;


end;
end;
end;
run;

Sas array replace missing

data toz;
set toz;
array toz{*} _NUMERIC_ ;
do i = 1 to dim(toz);
if toz{i} = . then toz{i} = 0;
end;
drop i;
run;

Sas array transpose

/***The equivalent DATA step code using arrays*/

DATA NEW (KEEP=NAME DATE1-DATE3);
SET OLD;
BY NAME;
ARRAY DATES {3} DATE1-DATE3;
RETAIN DATE1-DATE3;
IF FIRST.NAME THEN I=1;
ELSE I + 1;
DATES{I} = DATE;
IF LAST.NAME;
run;


DATA NEW (KEEP=NAME DATE1-DATE3);
ARRAY DATES {3} DATE1-DATE3;
DO I=1 TO 3;
SET OLD;
DATES{I} = DATE;
END;
/*if we have missing values:*/

PROC FREQ DATA=OLD ORDER=FREQ;
TABLES NAME / NOPRINT OUT=TEMP;
DATA _NULL_;
SET TEMP;
CALL SYMPUT('N',
COMPRESS(PUT(COUNT,3.)));
STOP;


DATA NEW (KEEP=NAME DATE1-DATE&N);
ARRAY DATES {&N} DATE1-DATE&N;
DO I=1 TO &N UNTIL (LAST.NAME);
SET OLD;
BY NAME;
DATES{I} = DATE;
END;

DATA NEW (KEEP=NAME DATE1-DATE&N);
SET OLD;
BY NAME;
ARRAY DATES {&N} DATE1-DATE&N;
RETAIN DATE1-DATE&N;
IF FIRST.NAME THEN I=1;
ELSE I + 1;
DATES{I} = DATE;
IF LAST.NAME;
IF I < &N THEN DO I=I+1 TO &N;
DATES{I}=.;
END;
DATA NEW (KEEP=NAME DATE1-DATE20);
ARRAY DATES {20} DATE1-DATE20;
OBSNO + 1;
DO I=1 TO 20 UNTIL (LAST.NAME);
SET OLD;
BY NAME;
IF FIRST.NAME THEN OBSNO=1;
DATES{I} = DATE;
END;


DATA ALL7VARS
(DROP=I DATE RESULT);
ARRAY DATES {3} DATE1-DATE3;
ARRAY RESULTS {3} RESULT1-RESULT3;
DO I=1 TO 3 UNTIL (LAST.NAME);
SET OLD;
BY NAME;
DATES {I} = DATE;
RESULTS{I} = RESULT;
END;
BY NAME;
run;


DATA DIFFER;
SET NEW;
ARRAY DATES {20} DATE1-DATE20;
ARRAY DIFFS {19} DIFF1-DIFF19;
DO I=1 TO 19;
DIFFS{I} = DATES{I+1} - DATES{I};
END;

DATA ALL7VARS
(DROP=I DATE RESULT);
ARRAY DATES {3}DATE1-DATE3;
ARRAY RESULTS {3}RESULT1-RESULT3;
DO I=1 TO 3 UNTIL (LAST.NAME);
SET OLD;
BY b;
DATES {I}=DATE;
RESULTS{I}=RESULT;

END;

Sas proc transpose

data old;
NAME DATE
Amy Date #A1
Amy Date #A2
Amy Date #A3
Bob Date #B1
Bob Date #B2
Bob Date #B3


PROC TRANSPOSE DATA=OLD OUT=NEW
PREFIX=DATE;
VAR DATE;
BY NAME;
run;

******* ;
data new;
NAME DATE1 DATE2 DATE3
Amy Date #A1 Date #A2 Date #A3
Bob Date #B1 Date #B2 Date #B3
******* ;
The PREFIX= option controls the names for the*/
transposed variables (DATE1, DATE2, etc.)*/

/**************************/

Sas macro format

текст

DATA _NULL_;
FILE PRINT;
RUN;
%MACRO FORMAT(VAL=VAL,LOW=,START=,STOP=,COUNT=);
PROC FORMAT;
VALUE &VAL
%DO X=&START %TO &STOP %BY &COUNT;
%LET Y=%SYSEVALF(&X-5/10);
&LOW<-&Y="&LOW-&Y"
%LET LOW=%SYSEVALF(&Y+5/10);
%PUT &LOW;
%END;
;
%MEND FORMAT;

%FORMAT(LOW=0,START=1,STOP=5,COUNT=1) ;

data next;
DO VALUE=1 TO 5 BY 0.05;
OUTPUT;
END;
run;

proc print;
format value val.;
RUN;

Sas format

/************* to get out format********/

proc format lib=formats
cntlout=peulot ;
select $ origact;
run;

/********* to get in format *********/

proc format cntlin=ofi
lib=formats;
run;


/********** convert numeric to character*****/
new_char=put(numeric,4.0);
/********** convert character to numeric *****/
new_num=input(char4,best4.);


DATASTEP using a CNTLIN= option.The smaller file must not have any duplicates of the key variable used for matching. The DATASET created must have these elements:

FMTNAME: name of format to create
TYPE: ‘C’ for character or ‘N’ for numeric
START: the value you want to format into a label.

If you are specifying a range,
START specifies the lower end of the range and
END specifies the upper end.*/
LABEL: the label you wish to generate.*/


proc sort data=small
out=temp
nodupkey
force;
by seqnum;
data fmt (rename=(seqnum=start));
retain fmtname ‘$key’
type ‘C’
label ‘Y’;
set temp;
proc format cntlin=fmt;
run;
data _null_;
infile bigfile;
file extract;
if put(seqnum,$key.)=’Y’
then put _infile_ ;
run;
data match;
set bigfile;
if put(seqnum,$key.)=’Y’;
run;



proc format;
value score 370 - 670 = ‘670-‘
671 - 870 = ‘671+’
other = ‘unscored’
;
proc freq data=cb;
tables score;
format score score.;
run;


1. The name of the format does not have to be the
name of the variable that it will be assigned to.
2. The assignment of the FORMAT occurs in the
PROC with a FORMAT statement.
3.The format definition ends with the ‘;’ on a new line.



/***************GENERATING NEW VARIABLES WITH PROC
FORMAT AND A VALUE REPLACEMENT FORMAT
SPECIFYING RANGES IN PROC FORMAT*********/



proc format;
value edr low-159 = ’53.4’
160-169 = ’39.3’
170-179 = ’32.3’
180-high = ’25.8’
;
data stuff;
set cb2;
edr=input(put(score,edr.),4.1);
run;


Ranges can include intervals such as:
– . means that the interval includes
both endpoints.
<- . means that the interval
includes higher endpoint, but not the lower
one.
- < means that the interval
includes lower endpoint, but not the higher
one.
<- < means that the interval
does not include either endpoint.
3. The numeric “ . “ and character ‘ “ “ ‘ missing
values can be individually assigned values.
4. Ranges can be specified with special kewords:
LOW From the least (most negative) possible
number.
HIGH To the largest (positve) possible number.
OTHER All other numbers not otherwise
specified.
5. The LOW keyword does not format missing values.
6. The OTHER keyword does include missing values
unless accounted for with a ‘.’ or ‘ ‘.*/;


SAS PICTURE FORMATS
SAS PICTURE Formats provide a template for
printing numbers .
To use the saved format in a subsequent program
without having to enter the FORMAT code, specify a
LIBNAME of LIBRARY referencing the library .


One example of using PICTURE FORMATS is to truncate numbers that represents dates from
YYMMDD display to YY/MM.
proc format;
picture dt 0-991231=’11/11’
(multiplier=.01);
run;

The ‘11/11’ specifies that all leading zeros will be displayed.
If the label was ‘01/11’ then 001213 would
be displayed as 0/12 instead of 00/12.


will print a leading 0 if the percentage is less than 1
(i.e., .67% displays as 0.67%).
proc format;
picture p8r (round)
0-100 = '0009.99%'
run;

Sas sql-examples

proc sql;
create table ff as
select Branch_Cust_Nbr,
branch_nbr ,
mask_3 format=comma10.2
from events.Gidul_maskorot_10_1 a
where not exists
(select *
from events.Gidul_maskorot_9_1 b
where A.branch_cust_ip=b.branch_cust_ip);

quit;


proc sql;
create table jjj as
select Branch_Cust_Nbr,
branch_nbr ,mask_3
from events.Gidul_maskorot_10_1
where branch_cust_ip not in (select branch_cust_ip
from events.Gidul_maskorot_9_1);
quit;

proc sql;
create table jjj as
select Branch_Cust_Nbr,
branch_nbr ,mask_3
from events.Gidul_maskorot_10_1
where mask_3 > (select avg(mask_3)
from events.Gidul_maskorot_9_1);
quit;



proc sql;
create table jjj as
select Branch_Cust_Nbr,
branch_nbr ,mask_3/3 as avg_mask
from events.Gidul_maskorot_10_1
where branch_cust_ip not in (select branch_cust_ip
from events.Gidul_maskorot_9_1)
and
2*min_mask>calculated avg_mask;
quit;



proc sql;
create table res as
select coalesce (a.branch_cust_ip,b.branch_cust_ip) label='x'
from events.Gidul_maskorot_9_1 a
left join events.Gidul_maskorot_10_1 b
on a.branch_cust_ip=b.branch_cust_ip;
quit;


proc sql;
create table jjj as
select *
from events.Gidul_maskorot_10_1
except
select *
from events.Gidul_maskorot_9_1
;
quit;
/*delete double*/

proc sql;
create table jjj as
select *
from events.Gidul_maskorot_10_1
except all
select *
from events.Gidul_maskorot_9_1
;
quit;
/*not delete double*/


proc sql;
create table jjj as
select *
from events.Gidul_maskorot_10_1
except corr
select *
from events.Gidul_maskorot_9_1
;
quit;
/*relate only to variables this the same name */


proc sql;
create table jjj as
select *
from events.Gidul_maskorot_10_1
intersect
select *
from events.Gidul_maskorot_9_1
;
quit;

proc sql;
create table jjj as
select *
from events.Gidul_maskorot_10_1
union
select *
from events.Gidul_maskorot_9_1
;
quit;


proc sql;
create table jjj as
select *
from events.Gidul_maskorot_10_1
union
select *
from events.Gidul_maskorot_9_1
union
select *
from events.Gidul_maskorot_12_1
;
quit;

proc sql;
create table jjj as
select *
from events.Gidul_maskorot_10_1
outer union
select *
from events.Gidul_maskorot_9_1
outer union
select *
from events.Gidul_maskorot_12_1
;
quit;
SAS SQL