Hi All,
I have the following code which generate a table call 'employee'. I want to create country specific table where the table name will be the country name itself. In this case there will be 3 tables (Asia, Europe and America).
My real database has 90 countries and I don't want to use IF, Else IF statement.
Can someone help me with a better/shot and efficient code to solve my problem.
Thank You,
DATA Employee;
INPUT Country_Name $ Country_Code Region $;
DATALINES;
Japan 107 Asia
USA 109 America
France 103 Europe
Canada 111 America
India 105 Asia
Nepal 115 Asia
Italy 102 Europe
;
RUN;
Output Table:
Table Name: Asia
Japan 107 Asia
India 105 Asia
Nepal 115 Asia
Table Name: America
USA 109 America
Canada 111 America
Table Name: Europe
France 103 Europe
Italy 102 Europe
Please try and search your questions before you post.
Don't.
This is what BY group processing is for, if absolutely required split the dataset at the last step.
That being said, this is an FAQ since it's how people tend to think if they haven't worked with SAS a lot.
Here's several posts with sample code and examples on how to automate this so you don't need to know the number of levels prior to running the program.
http://blogs.sas.com/content/sasdummy/2015/01/26/how-to-split-one-data-set-into-many/
http://www.sascommunity.org/wiki/Split_Data_into_Subsets
https://gist.github.com/statgeek/4bfb7574713bedf4e011
For info on how BY group processing works:
https://support.sas.com/documentation/cdl/en/lrcon/69852/HTML/default/viewer.htm#n138da4gme3zb7n1nif...
Create four region datasets from one master dataset
1. Hash method Martin, Vincent (STATCAN) via listserv.uga.edu
2. Dosubl method
3 Collection of other solutions;
HAVE
====
Up to 40 obs WORK.EMPLOYEE total obs=7
COUNTRY_
Obs NAME COUNTRY_CODE REGION
1 Japan 107 Asia
2 USA 109 America
3 France 103 Europe
4 Canada 111 America
5 India 105 Asia
6 Nepal 115 Asia
7 Italy 102 Europe
WANT
===
Member Obs, Entries File
# Name Type or Indexes Vars Label Size Last Modified
1 AMERICA DATA 2 3 131072 02/02/2017 12:43:52
2 ASIA DATA 3 3 131072 02/02/2017 12:43:52
3 EMPLOYEE DATA 7 3 131072 02/02/2017 12:43:52
4 EUROPE DATA 2 3 131072 02/02/2017 12:43:52
WORKING CODE
============
HASH do while(hi.next()=0);
h.output(dataset: region||"(where=(region="||quote(region)||"))" );
end;
DOSUBL call symputx('region',region);
rc=dosubl('
data ®ion;
set employee(where=(region="®ion"));
run;quit;
');
HASH SOLUTION
=============
* create some data;
DATA Employee;
INPUT Country_Name $ Country_Code Region $;
cards4;
Japan 107 Asia
USA 109 America
France 103 Europe
Canada 111 America
India 105 Asia
Nepal 115 Asia
Italy 102 Europe
;;;;
run;quit;
Martin, Vincent (STATCAN) via listserv.uga.edu
data _null_;
if 0 then set employee;
declare hash h(dataset: "work.employee");
h.definekey("country_name");
h.definedata(all: "y");
h.definedone();
declare hash outctrl(dataset: "work.employee(keep=region)");
declare hiter hi("outctrl");
outctrl.definekey("region");
outctrl.definedata("region");
outctrl.definedone();
do while(hi.next()=0);
h.output(dataset: region||"(where=(region="||quote(region)||"))" );
end;
run;
DOSUBL SOLUTION
===============
proc datasets lib=work kill;
run;quit;
proc sort data=employee out=have;
by region;
run;quit;
data _null_;
set have;
by region;
if last.region then do;
call symputx('region',region);
rc=dosubl('
data ®ion;
set employee(where=(region="®ion"));
run;quit;
');
end;
run;quit;
proc contents data=work._all_;
run;quit;
Member Obs, Entries File
# Name Type or Indexes Vars Label Size Last Modified
1 AMERICA DATA 2 3 131072 02/02/2017 12:43:52
2 ASIA DATA 3 3 131072 02/02/2017 12:43:52
3 EMPLOYEE DATA 7 3 131072 02/02/2017 12:43:52
4 EUROPE DATA 2 3 131072 02/02/2017 12:43:52
/* T007960 SAS and R solutions for Splitting datasets
SAS and R solutions for Splitting sashelp.class into
two datasets, one with females and one with males.
The code below will work for any dataset and
splitting variable.
LARGE DATASET AND SMALL DATASET SOLUTIONS ARE GIVEN
1. R soluton is one line of code. This splits class
into dataframes res$F and res$F where res is a list
res<-split(class, list(class$SEX), drop = TRUE);
2. SAS HASH solution. Like R this is an in memory solution
good for smaller datasets. First the female records
are loaded into memory and then sent out to the
F dataset, then the males areloaded and output.
3. This is a solution for large datasets.
First we create a macro string containing all
the levels. Just male and female for outr example.
The we use a datastep to build the following program.
Only one line has to changed on the program to do another dataset
data F M;
set sashelp.class;
select (sex);
when ('F') output F;
when ('M') output M;
otherwise;
end;
run;
/* change dataset and sex for other data */
sas=';set sashelp.class; select (sex);';
*********************************
*********************************
* LARGE DATASET SOLUTION *
*********************************
*********************************;
* put the levels of sex into &sec = "F" "M";
proc sql;
select distinct quote(strip(sex)) into :sex separated by ' ' from sashelp.class
;quit;
%put &=sex; /* the levels "F" "M";
%put &=sqlobs; /* then number of levels */
data _null_;
length sas $200;
array arysex[&sqlobs] $32 (&sex);
sas='data ';
call execute(sas);
do i=1 to &sqlobs;
sas=arysex[i];
call execute(sas);
end;
/* change dataset and sex for other data */
sas=';set sashelp.class; select (sex);';
call execute(sas);
do i=1 to &sqlobs;
sas=catx(' ',cats("when ('",arysex[i],"')"),'output',arysex[i],';');
call execute(sas);
put sas;
end;
sas='otherwise;end;run;quit;';
call execute(sas);
run;quit;
/* Generated code
NOTE: CALL EXECUTE generated line.
1 + data
2 + F
3 + M
4 + ;set sashelp.class; select (sex);
5 + when ('F') output F ;
6 + when ('M') output M ;
7 + otherwise;end;run;
*.
*********************************
*********************************
* SMALL DATASET HASH SOLUTION *
*********************************
*********************************;
* for small datasets this should be a very fast way to split
a dataset. However the data needs to be indexed or
sortted on the split variable;
proc sort data=sashelp.class out=class noequals;
by sex;
run;quit;
data _null_;
* establist the PDV fr both males and females;
if 0 then set class;
declare hash h();
* name is the primary key;
* if you do not have a primary key you can;
* use the method below;
h.definekey('name');
h.definedata('name','sex','age','height','weight');
h.definedone();
* note this is loading the records into memory -
but can consume memory;
do until ( last.sex ) ;
set class;
by sex ;
h.add () ;
end ;
* output dataset will change with next sex;
h.output (dataset: sex) ;
run ;quit;
**************
**************
* R SOLUTION *
**************
**************
data "c:\temp\class.sas7bdat"(outrep=WINDOWS_32 compress=no);
set sashelp.class;
run;quit;
%utl_submit_R
(%nrbquote
(
library(sas7bdat);
class<-read.sas7bdat("c:\\temp\\class.sas7bdat");
/* res is a list of two dataframes */
res<-split(class, list(class$SEX), drop = TRUE);
res$F;
res$M;
/* if you want just the names */
res$F$NAME;
res$F[1];
)
);
/*
RES$F
NAME SEX AGE HEIGHT WEIGHT
2 Alice F 13 56.5 84.0
3 Barbara F 13 65.3 98.0
4 Carol F 14 62.8 102.5
7 Jane F 12 59.8 84.5
8 Janet F 15 62.5 112.5
11 Joyce F 11 51.3 50.5
12 Judy F 14 64.3 90.0
13 Louise F 12 56.3 77.0
14 Mary F 15 66.5 112.0
RES$M
NAME SEX AGE HEIGHT WEIGHT
1 Alfred M 14 69.0 112.5
5 Henry M 14 63.5 102.5
6 James M 12 57.3 83.0
9 Jeffrey M 13 62.5 84.0
10 John M 12 59.0 99.5
15 Philip M 16 72.0 150.0
16 Robert M 12 64.8 128.0
17 Ronald M 15 67.0 133.0
18 Thomas M 11 57.5 85.0
19 William M 15 66.5 112.0
*/
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