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Macro Interface Functions In SAS

Macro Interface Functions

Interface functions are 2 types.

  1. Dataset (or) datastep interface functions
  2. Interface functions.
  3. Dataset Interface Function: Using interface functions, we can call required macro functions during data step execution,
  4. Call symput function: It is a call routine (function). Using this function, we can create macrovariables from the dataset variables during during datastep execution.

Syntax: call symput (“Macrovariable”(name), dataset var name);

Eg:        data one;

group =100;

drug catalog;

run;

data_null_;

set one;

call symput (‘var1’, group);

call symput (‘var2’,drug);

run;

opitons symbolgen;

% put  &var1  &var2;

Symget function

Using this function, we can create dataset variables from the macrovariables during datastep execution.

Syntax: Symget9″Macrovariable”);

Eg:

data medi;

Gid-symget (“var1”);

Ndrug=symget(“var2”);

run;

proc print data=medi;

run;

Eg:

data medi2;

input pid drug &;

cards;

100   col5mg

101   col10mg

102   col15mg

;

data_null_;

set medi2;

call symput (‘var3’, drug);

run;

% put   7var3;

These core tutorials helps you to learn the Macro Interface Functions In SAS. For in-depth knowledge and practical experience explore SAS Online Training.

Note: If dataset has multiple values call symput function default stores last data value in macrovariable

Symexist function

Using this function, we can report required macrovariable existed or not. This function we can run by using condition.

Syntax: symexist (‘Macrovariable’);

Eg:      

data_null_;

if symexist (‘var6’); then

put ‘Macrovariable is existed’;

else

put ‘Macrovariable does not exist’;

run;

rawdata (sheet1)

center      pid      age      drug          date           group

Appolo    100      27        5mg      12/23/2005   G100

Appolo    109      23        5mg      12/23/2005   G100

Appolo    108      34        10mg    12/24/2005   G100

Appolo    109      22        5mg      12/23/2005   G100

Appolo    118      45        15mg    12/25/2005   G100

Appolo    134      49        15mg    12/25/2005   G100

(sheet2)

center          group          Age          pid

Appolo         G200            26           190

Appolo         G200            22           136

Appolo         G200            37           156

Appolo         G200            49           189

Appolo         G200            43           123

Appolo         G200            42           150

1st Import the data

data_null_;

set medi1;

if age >=20 and age <=30 then do;

call symput (‘var1’, drug);

call symput (‘D1’, date);

end;

else if age > 30 and age <=40

then do;

call symput (‘var2’, drug);

call symput (‘D2’, date);

end;

else do;

call symput (‘var3’, drug);

call symput (‘D3’, date);

end;

run;

% put  &var1  &var2 &var3;

% put  &d1  &d2  %d3;

data medi3;

set medi2;

if age >=20 and age <=30 then do;

drug = symget (‘var1’);

date = symget (‘D1’);

end;

else if age > 30 and age <=40

then do;

drug = symget (‘var2’);

date = symget (‘D2’);

end;

else do;

drug = symget (‘var3’);

date = symget (‘D3’);

end;

date1 = input (date, best12);

drop date;

rename date1 = date;

run;

proc print data = medi3;

format date date9;

footnote.”Drug information &var1-&var2-&var3″;

run;

call execute: Using call execute function, we can call required catalog (macrocall) from th dataset block.

Syntax: call execute (‘%<macroname>’);

Interface functions

%sysfunC: Using this function, we can call dataset functions in macros.

% global varB;

% let varB=89.67;

% global varB1;

% let varB1=%sysfnc(int(&varB));

% put  &varB1;

Dataset Functions

a) exist: Using this function, we can report required SAS file is existed or not. If it is existed it returns ‘1’ otherwise ‘Zero’.

Syntax: Exist (‘Datasetname’);

b) file exist: Using this function, we can report pc files are existed or not. If it is existed it returns ‘1’ otherwise ‘zero’.

Eg:

 data_null_;

of exist (‘medi1’)=1 then

put ‘dataset is existed’;

else put ‘dataset does not exist’;

run;

c) Open Function: Using this function, we can open the required dataset internally.

Syntax: open (‘datsetname’);

d) Attrn: Using this function, we can count no of variables and no of observations using open results.

Syntax: Attrn (open result, ‘nvars’ or ‘nobs’);

e) close: Using this function, we can close the dataset.

Syntax: close (open result);

Eg:

data_null_;

if exist (‘sasuser.demo’)=1

then do;

OP=open(‘sasuser.demo’);

NV=attrn(OP, ‘NVARS’);

NO=attrn(OP, ‘NOBS’);

CL=close(OP);

put ‘dataset is existed’;

put ‘   ‘;

put ‘   ‘;

put ‘No of variables _ _’NV;

put ‘No of Obs _ _ _’NO;

end;

else put ‘dataset does not exist’;

run;

(same coding in macros)

% macro dex(dname);

% if  % sysfunC(exist(&dname))=1

% then   % do;

% local op nv nob cl;

% let op=%sysfunc(open(&dname));

% let nv=%sysfunc(attrn(&op, nvars));

% let nob=%sysfunc(attrn(&op, nobs));

% let cl=%sysfunc(close(&op));

% put  &dname dataset is existed with  7nv variables  &nob observations;

% end;

% else;

% put  &dname dataset is not existed;

% mend;

% dex(sasuser.lab);

% dex(sasuser.demo);

/* To create a macrovariable with sql block */

Query (or) select statement and into clause: Using these 2 options, we can create macrovariable from the dataset variable.

Here SAS system default stores 1st data value (or) first occurence in macrovariable.

Eg:  

data medi;

input group & visit drug &;

cards;

G100      1       col5mg

G200      1       col10mg

G300      1       col15mg

G100      2       col5mg

G200      2       col10mg

G300      2       col15mg

;

proc sql noprint;

select drug into : med 1

from medi

quit;

% put  & med1;

/* To create multiple macrovariables */

Eg: 

proc sql noprint;

select distinct(drug) into : medicine1-:medicine3

from medi;

quit;

% put &medicine1 &medicine2 &medicine3;

titile “Drug information &medicine1 – &medicine2 – &medicine3”;

proc sql;

select * from medi;

quit;

Eg:

 proc sql noprint;

select distinct(group) into : G1-: G3 from medi;

quit;

title1 “Group information is &G1 – &G2 – &G3”;

title2 “Drug information &medicine1 – &medicine2 – &medicine3”;

proc sql;

select * from medi;

quit;

To report list of macrovariables

_Global_: Using this statement, we can report list of the macrovariables (global macrovariables) with values. This statement can be written anywhere in SAS application.

Eg: 

% global var1  var2  var3;

% let var1 = 100;

% let var2 = col5mg;

% let var1 = 23;

% put _global_;

_Local_: Using this statement, we can report list of the local macrovariables. This statement can be written inside of the macroblock.

Eg:

% local pid age center;

% let pid = 200;

% let age = 45;

% let center = appolo;

% put _local_;

% mend;

%mvar;

_User_: Using this statement, we can report user defined macrovariable. If we write inside of the macrobloxk, it reports both global; and local macrovariables. If we write outside of the macroblock, it report only global macrovariables.

Eg:         

% put _User_;

_Automatic_:

Using this statement, we can report list of automatic macrovariable.

Eg:         

% put _automatic_;

Sysdate: It report current operating system date.

Eg:          options nodate;

title1 ‘Drug information’;

title2 “Report date-&sysdate9-&systime”;

proc print data = saashelp.class;

run;

Systime: It report current operating system time (24hrs format)

Syslast: It report recently existed dataset. 

Eg:          data demo;

x = 78;

run;

% put  &syslast;

– Default value of the syslast is _null_

Sysdsn: It report recently existed dataset & current working library.

Eg:          % put  &sysdsn;

Sysncpu: It report number of cpu’s connected to pc.

Eg:          % put  &sysncpu;            

Sysuserid: It report user id.

Eg:          % put  &sysnuserid;       

Sysver: It report current SAS version.

Eg:          % put  &sysver;

Sysvlong: It report full of information for current SAS version.

Eg:          % put  &sysnvlong;         

Note: If we use same name for global and local macrovariables, inside it works like a local macrovariable and outside it works like a global macrovariable.

Eg:          % global pid;

% let pid = 100;

% macro loc;

% local pid;

% let pid = 200;

% put &pid;

% mend;

% loc;

% put  & pid;

 

/* To reassign global macrovariable */

Eg:          % global pid;

% let pid = 100;

% macro loc;

% let pid = 200;

% put  &pid;

% mend;

% loc;

% put  & pid;

Sysmax: Using this statement, we can delete macrovariable from the SAS environment.

Eg:          % global pid age center;

% let pid = 200;

% let age = 34;

% let center = appolo;

% put _global_;

% symdel age;

% put  _global_;

Sysexec: using this function, we can manage operating system environment.

To store macros permanently

For storage: mstored SASMstore=<Autocall library>;

Using these 2 options, we can store macro catalog in required library. Here SAS Mstore option indicate autocall library.

Eg:          Opitons mstored

sasmstore = sasuser;

% macro print (fname)/store;

proc print data = &dname;

run;

% mend;

Calling:

SAS Autos: Using this options, we can indicate autocall library for calling.

/* Calling */

Options sasautos = sasuser;

% print (sasuser.demo);

SAS Base

Arrays: Using arrays concept, we can run same action for required variables.

– Array statement starts with array keyword & requires name of the array, length of the array, variables list.

Eg:          data demo;

input pid age gender & race & height weight;

cards;

100   23   female   Asian   4.5   56

101   23     male     Asian   5.6   56

102   56   female   Asian   5.6   56

103   23   female   African   5.6   56

;

data demo1;                      <– one dimensional explicit array

set demo;

array apple(3) age height weight;

do i = 1 to 3;

if apple(i) = then apple(i) = 0;

end;

array orange(2) & gender race;

do i = 1 to 2;

if orange(i) = ‘   ‘ then

orange(i) = ‘Miss’;

end;

drop i;

run;

proc print;

run;

ARRAYS

– Arrays are 2 types

  1. One Dimensional Array
  2. Two Dimensional Array

1) One Dimensional Array: is of 2 types

a)Explicit Array

b)Implicit Array

a) Explicit Array: It is working based on length of the array & loop variables.

Syntax: Array <Array name. <length of array> <data type> <variable list>;

b) Implicit Array: It is working based on array names with do over statement.

Syntax: Array <Array name> <data type> <variable list>;

Eg:          data demo2;

set demo;

array apple age height weight;

do over apple;

if apple = then apple = 0;

end;

array orange & gender race;

do over orange;

if orange = ‘   ‘ then

orange = ‘Miss’;

end;

run;

proc print data = demo2;

run;

– Explicitly array is more efficient than implicity array.

– To run array, we should use special numeric and character varaibles.

Dim Function

It requires only one argument, the argument must be array name & it returns length of the array.

Eg:          data demo3;

set demo;

array apple(*) _numeric_;

do i = 1 to dim(apple);

if apple(i) = then apple(i) = 0;

end;

array orange(*) _char_;

do i = 1 to dim(orange);

if orange(i) = ‘   ‘ then

orange(i) = ‘Miss’;

end;

import the data into the table (sheet2)

/* Extraction */

proc import

data file = ‘d:\ condata.xls’

out = two  dbms = excel replace;

sheet = ‘sheet2&’;

run;

proc sql;

describe table two;

quit;

To create a data set with not null constraint

/* Transformation */

proc sql;

create table demo1 (pid num not null, age num, gender char, race char);

quit;

proc sql;

describe table demo1;

quit;

/* Loading */

proc append base = demo1

data = two force;

run;

Check:                                  Sheet3

                Eg:          pid     age     gender     race

100     56      female     Asian

101     45      female     African

102     46      female     African

103     34        male       Asian

Step1: Importing the data into SAS dataset name3

Step2: To create a table demo2 with check constraint.

/* Transformation */

Eg:          proc sql;

create table demo2 (pid num, age num, gender char, check(gender = ‘female’), race char);

quit;

proc sql;

describe table demo2;

quit;

/* Loading */

proc append base = demo2

data = three force;

run;

Multiple constraints for multiple variable in table

Note: If we use multiple constraints in table, in loading time each and every observation must be satisfied all constraints in table.

Raw data (sheet4):

pid     age     gender     race

100     56      female     Asian

101     45        male     African

101     46      female     African

102     34        male       Asian

103                female     Asian

104     56                        Asian

104                 male     African

105     45       female       Asian

Step1: Importing the data into SAS dataset name4

Step2: To create a table with multiple constraint for multiple variable.

Eg:          Step1 /* Extraction */

step2 /* transformation */

proc sql;

create table demo4(pid num unique, age num not null, gender char not null, race char);

                quit;

proc sql;

describe table demo4;

quit;

drop i;

run;

proc print data = demo3;

run;

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