I have a SAS dataset that looks like this:
id | dept | ...
1 A
2 A
3 A
4 A
5 A
6 A
7 A
8 A
9 B
10 B
11 B
12 B
13 B
Each observation represents a person.
I would like to split the dataset into "team" datasets, each dataset can have a maximum of 3 observations.
For the example above this would mean creating 3 datasets for dept A (2 of these datasets would contain 3 observations and the third dataset would contain 2 observations). And 2 datasets for dept B (1 containing 3 observations and the other containing 2 observations).
Like so:
First dataset (deptA1):
id | dept | ...
1 A
2 A
3 A
Second dataset (deptA2)
id | dept | ...
4 A
5 A
6 A
Third dataset (deptA3)
id | dept | ...
7 A
8 A
Fourth dataset (deptB1)
id | dept | ...
9 B
10 B
11 B
Fifth dataset (deptB2)
id | dept | ...
12 B
13 B
The full dataset I'm using contains thousands of observations with over 50 depts. I can work out how many datasets per dept are required and I think a macro is the best way to go as the number of datasets required is dynamic. But I can't figure out the logic to create the datasets so that they have have a maximum of 3 observations. Any help appreciated.
Another version.
Compared to DavB version, it only processes input data once and splits it into several tables in single datastep.
Also if more complex splitting rule is required, it can be implemented in datastep view WORK.SOURCE_PREP.
data WORK.SOURCE;
infile cards;
length ID 8 dept $1;
input ID dept;
cards;
1 A
2 A
3 A
4 A
5 A
6 A
7 A
8 A
9 B
10 B
11 B
12 B
13 B
14 C
15 C
16 C
17 C
18 C
19 C
20 C
;
run;
proc sort data=WORK.SOURCE;
by dept ID;
run;
data WORK.SOURCE_PREP / view=WORK.SOURCE_PREP;
set WORK.SOURCE;
by dept;
length table_name $32;
if first.dept then do;
count = 1;
table = 1;
end;
else count + 1;
if count > 3 then do;
count = 1;
table + 1;
end;
/* variable TABLE_NAME to hold table name */
TABLE_NAME = catt('WORK.', dept, put(table, 3. -L));
run;
/* prepare list of tables */
proc sql noprint;
create table table_list as
select distinct TABLE_NAME from WORK.SOURCE_PREP where not missing(table_name)
;
%let table_cnt=&sqlobs;
select table_name into :table_list separated by ' ' from table_list;
select table_name into :tab1 - :tab&table_cnt from table_list;
quit;
%put &table_list;
%macro loop_when(cnt, var);
%do i=1 %to &cnt;
when ("&&&var.&i") output &&&var.&i;
%end;
%mend;
data &table_list;
set WORK.SOURCE_PREP;
select (TABLE_NAME);
/* generate OUTPUT statements */
%loop_when(&table_cnt, tab)
end;
run;
You could try this:
%macro split(inds=,maxobs=);
proc sql noprint;
select distinct dept into :dept1-:dept9999
from &inds.
order by dept;
select ceil(count(*)/&maxobs.) into :numds1-:numds9999
from &inds.
group by dept
order by dept;
quit;
%let numdept=&sqlobs;
data %do i=1 %to &numdept.;
%do j=1 %to &&numds&i;
dept&&dept&i&&j.
%end;
%end;;
set &inds.;
by dept;
if first.dept then counter=0;
counter+1;
%do i=1 %to &numdept.;
%if &i.=1 %then %do;
if
%end;
%else %do;
else if
%end;
dept="&&dept&i" then do;
%do k=1 %to &&numds&i.;
%if &k.=1 %then %do;
if
%end;
%else %do;
else if
%end;
counter<=&maxobs.*&k. then output dept&&dept&i&&k.;
%end;
end;
%end;
run;
%mend split;
%split(inds=YOUR_DATASET,maxobs=3);
Just replace the INDS parameter value in the %SPLIT macro call to the name of your input data set.
Related
I have a SAS data set t3. I want to run a data step inside a loop through a set of variables to create additional sets based on the variable value = 1, and rank two variables bal and otheramt in each subset, and then merge the ranks for each subset onto the original data set. Each rank column needs to be dynamically named so I know what subset is getting ranked. I know how to do proc rank and macros basically but do not know how to do this in the most dynamic way inside of a macro. Can you assist?
ID
bal
otheramt
firstvar
secondvar
lastvar
444
581
100
1
1
555
255
200
1
1
1
666
255
300
--------------
1
--------------
%macro dog();
data new;
set t3;
ARRAY Indicators(5) FirstVar--LastVar;
/*create data set for each of the subsets if firstvar = 1, secondvar = 1 ... lastvar = 1 */
/*for each new data set, rank by bal and otheramt*/
/*name the new rank columns [FirstVar]BalRank, [FirstVar]OtherAmtRank; */
/*merge the new ranks onto the original data set by ID*/
%mend;
%dog()
The Proc rank section would be something like this, but I would need the rank columns to have information about what subset I am ranking.
proc rank data=subset1 out=subset1ranked;
var bal otheramt;
ranks bal_rank otheramt_rank;
run;
Instead of using macro, use data transformation and reshaping that allows simpler steps to be written.
Example:
Rows are split into multiple rows based on flag so group processing in RANK can occur. Two transposes are required to reshape the results back a single row per id.
data have;
call streaminit(20230216);
do id = 1 to 100;
foo = rand('integer', 50,150);
bar = rand('integer', 100,200);
flag1 = rand('integer', 0, 1);
flag2 = rand('integer', 0, 1);
flag3 = rand('integer', 0, 1);
output;
end;
run;
data step1;
set have;
/* important: the group value becomes part of the variable name later */
if flag1 then do; group='flag1_'; output; end;
if flag2 then do; group='flag2_'; output; end;
if flag3 then do; group='flag3_'; output; end;
drop flag:;
run;
proc sort data=step1;
by group;
run;
proc rank data=step1 out=step2;
by group;
var foo bar;
ranks foo_rank bar_rank;
run;
proc sort data=step2;
by id group;
run;
* pivot (reshape) so there is one row per ranked var;
proc transpose data=step2 out=step3(drop=_label_);
by id foo bar group;
var foo_rank bar_rank;
run;
* pivot again so there is one row per id;
proc transpose data=step3 out=step4(drop=_name_);
by id;
var col1;
id group _name_;
run;
* merge so those 0 0 0 flag rows remain intact;
data want;
merge have step4;
by id;
run;
Since we don't have much sample data, I created test data from sashelp.class with some indicator variables like yours.
data have;
set sashelp.class;
firstvar=round(rand('uniform',1));
secondvar=round(rand('uniform',1));
thirdvar=round(rand('uniform',1));
drop sex weight;
run;
Partial output:
Name Age Height firstvar secondvar thirdvar
Alfred 14 69 1 0 1
Alice 13 56.5 0 1 1
Barbara 13 65.3 1 0 0
Carol 14 62.8 0 0 0
To dynamically rank data based on indicator variables, I created a macro that accepts a list of indicators and rank variables. The 2 lists help to create the specific variable names you requested. Here's the macro call:
%rank(indicators=firstvar secondvar thirdvar,
rank_vars=age height);
Here's part of the final output. Notice the indicators in the sample output above coincide with the ranks in this output. Also note that Carol is not in the output because she had no indicators set to 1.
Name Age Height firstvar_age_rank firstvar_height_rank secondvar_age_rank secondvar_height_rank thirdvar_age_rank thirdvar_height_rank
Alfred 14 69 8 11 . . 6.5 10
Alice 13 56.5 . . 3.5 2 4.5 2
Barbara 13 65.3 6.5 8 . . . .
Henry 14 63.5 . . 5.5 5 . .
The full macro is listed below. It has 3 parts.
Create a temp data set with a group variable that contains the number of the indicator variable based on the order of the variable in the list. Whenever an indicator = 1 the obs is output. If an obs has all 3 indicators set to 1 then it will be output 3 times with the group variable set to the number of each indicator variable. This step is important because proc rank will rank groups independently.
Generate the rankings on the temp data set. Each group will be ranked independently of the other groups and can be done in one step.
Construct the final data set by essentially transposing the ranked data into columns.
%macro rank(indicators=, rank_vars=);
%let cnt_ind = %sysfunc(countw(&indicators));
%let cnt_vars = %sysfunc(countw(&rank_vars));
data temp;
set have;
array indicators(*) &indicators;
do i = 1 to dim(indicators);
if indicators(i) = 1 then do;
group = i; * create a group based on order of indicators;
output; * an obs can be output multiple times;
end;
end;
drop i &indicators;
run;
proc sort data=temp;
by group;
run;
* Generate rankings by group;
proc rank data=temp out=ranks;
by group;
var &rank_vars;
ranks
%let vars = ;
%do i = 1 %to &cnt_vars;
%let var = %scan(&rank_vars, &i);
%let vars = &vars &var._rank;
%end;
&vars;
run;
proc sort data=ranks;
by name group;
run;
* Contruct final data set by transposing the ranks into columns;
data want;
set ranks;
by name;
* retain statement to declare new variables and retain values;
retain
%let vars = ;
%do i = 1 %to &cnt_ind;
%let ivar = %scan(&indicators, &i);
%do j = 1 %to &cnt_vars;
%let jvar = %scan(&rank_vars, &j);
%let vars = &vars &ivar._&jvar._rank;
%end;
%end;
&vars;
if first.name then call missing (of &vars);
* option 1: build series of IF statements;
%let vars = ;
%do i = 1 %to &cnt_ind;
%let ivar = %scan(&indicators, &i);
%str(if group = &i then do;)
%do j = 1 %to &cnt_vars;
%let jvar = %scan(&rank_vars, &j);
%let newvar = &ivar._&jvar._rank;
%str(&newvar = &jvar._rank;)
%end;
%str(end;)
%end;
if last.name then output;
drop group
%let vars = ;
%do i = 1 %to &cnt_vars;
%let var = %scan(&rank_vars, &i);
%let vars = &vars &var._rank;
%end;
&vars;
run;
%mend;
When constructing the final data set and transposing the rank variables, there are a couple of options. The first option shown above is to dynamically build a series of if statements. Here is what the code generates:
MPRINT(RANK): * option 1: build series of IF statements;
MPRINT(RANK): if group = 1 then do;
MPRINT(RANK): firstvar_age_rank = age_rank;
MPRINT(RANK): firstvar_height_rank = height_rank;
MPRINT(RANK): end;
MPRINT(RANK): if group = 2 then do;
MPRINT(RANK): secondvar_age_rank = age_rank;
MPRINT(RANK): secondvar_height_rank = height_rank;
MPRINT(RANK): end;
MPRINT(RANK): if group = 3 then do;
MPRINT(RANK): thirdvar_age_rank = age_rank;
MPRINT(RANK): thirdvar_height_rank = height_rank;
MPRINT(RANK): end;
The 2nd option is to use an array and mathematically calculate the index into the array by the group number and variable number. Here is the snippet of macro code to replace the if series code:
* option 2: create arrays and calculate index into array
* by group number and variable number;
array ranks(*) &vars;
array rankvars(*)
%let vars = ;
%do i = 1 %to &cnt_vars;
%let var = %scan(&rank_vars, &i);
%let vars = &vars &var._rank;
%end;
&vars;
%str(idx = dim(rankvars) * (group - 1);)
%str(do i = 1 to dim(rankvars);)
%str(ranks(idx + i) = rankvars(i);)
%str(end;)
Here is the generated code:
MPRINT(RANK): * option 2: create arrays and calculate index into array * by group number and variable number;
MPRINT(RANK): array ranks(*) firstvar_age_rank firstvar_height_rank secondvar_age_rank secondvar_height_rank thirdvar_age_rank
thirdvar_height_rank;
MPRINT(RANK): array rankvars(*) age_rank height_rank;
MPRINT(RANK): idx = dim(rankvars) * (group - 1);
MPRINT(RANK): do i = 1 to dim(rankvars);
MPRINT(RANK): ranks(idx + i) = rankvars(i);
MPRINT(RANK): end;
It takes a minute to understand the array option, but once you do, it is preferable over generating if statments. As the number of variables increases, the code generated by the array option is the same and operates more efficiently.
I am having a SAS dataset where I want to keep, let's say, the firt 2 columns and last 4 columns, so to speak. In other words, only columns from beginning and from end.
data test;
input a b c d e f g h i j;
cards;
1 2 3 4 5 6 7 8 9 10
;
Initial output:
What I want is the following -
Output desired:
I checked on the net, people are trying something with varnum, as shown here, but I can't figure out. I don't want to use keep/drop, rather I want an automated way to solve this issue.
%DOSUBL can run code in a separate stream and be part of a code generation scheme at code submit (pre-run) time.
Suppose the requirement is to to slice the columns of a data set out based on meta data column position as indicated by varnum (i.e. places), and the syntax for places is:
p:q to select the range of columns whose varnum position is between p and q
multiple ranges can be specified, separated by spaces ()
a single column position, p, can be specified
negative values select the position downward from the highest position.
also, the process should honor all incoming data set options specified, i.e. keep= drop=
All the complex logic for implementing the requirements could be done in pure macro code using only %sysfunc and data functions such as open, varnum, varname, etc... That code would be pretty unwieldy.
The selection of names from meta data can be cleaner using SAS features such as Proc CONTENTS and Proc SQL executed within DOSUBL.
Example:
Macro logic is used to construct (or map) the filtering criteria statement based on varnum. Metadata retrieval and processing done with Procs.
%macro columns_slice (data=, places=);
%local varlist temp index p token part1 part2 filter joiner;
%let temp = __&sysmacroname._%sysfunc(monotonic());
%do index = 1 %to %sysfunc(countw(&places,%str( )));
%let token = %scan(&places,&index,%str( ));
%if NOT %sysfunc(prxmatch(/^(-?\d+:)?-?\d+$/,&token)) %then %do;
%put ERROR: &sysmacname, invalid places=&places;
%return;
%end;
%let part1 = %scan (%superq(token),1,:);
%let part2 = %scan (%superq(token),2,:);
%if %qsubstr(&part1,1,1) = %str(-) %then
%let part1 = max(varnum) + 1 &part1;
%if %length(&part2) %then %do;
%if %qsubstr(&part2,1,1) = %str(-) %then
%let part2 = max(varnum) + 1 &part2;
%end;
%else
%let part2 = &part1;
%let filter=&filter &joiner (varnum between &part1. and &part2.) ;
%let joiner = OR;
%end;
%put NOTE: &=filter;
%if 0 eq %sysfunc(dosubl(%nrstr(
options nonotes;
proc contents noprint data=&data out=&temp(keep=name varnum);
proc sql noprint;
select name
into :varlist separated by ' '
from &temp
having &filter
order by varnum
;
drop table &temp;
quit;
)))
%then %do;&varlist.%end;
%else
%put ERROR: &sysmacname;
%mend;
Using the slicer
* create sample table for demonstration;
data lotsa_columns(label='A silly 1:1 merge');
if _n_ > 10 then stop;
merge
sashelp.class
sashelp.cars
;
run;
%put %columns_slice (data=lotsa_columns, places=1:3);
%put %columns_slice (data=lotsa_columns, places=-1:-5);
%put %columns_slice (data=lotsa_columns, places=2:4 -2:-4 6 7 8);
1848 %put %columns_slice (data=lotsa_columns, places=1:3);
NOTE: FILTER=(varnum between 1 and 3)
Name Sex Age
1849 %put %columns_slice (data=lotsa_columns, places=-1:-5);
NOTE: FILTER=(varnum between max(varnum) + 1 -1 and max(varnum) + 1 -5)
Horsepower MPG_City MPG_Highway Wheelbase Length
1850 %put %columns_slice (data=lotsa_columns, places=2:4 -2:-4 6 7 8);
NOTE: FILTER=(varnum between 2 and 4) OR (varnum between max(varnum) + 1 -2 and max(varnum) + 1
-4) OR (varnum between 6 and 6) OR (varnum between 7 and 7) OR (varnum between 8 and 8)
Sex Age Height Make Model Type MPG_City MPG_Highway Wheelbase
Honoring options
data have;
array x(100);
array y(100);
array z(100);
run;
%put %columns_slice (data=have(keep=x:), places=2:4 8:10 -2:-4 -25:-27 -42);
1858 %put %columns_slice (data=have(keep=x:), places=2:4 8:10 -2:-4 -25:-27 -42);
NOTE: FILTER=(varnum between 2 and 4) OR (varnum between 8 and 10) OR (varnum between max(varnum)
+ 1 -2 and max(varnum) + 1 -4) OR (varnum between max(varnum) + 1 -25 and max(varnum) + 1 -27) OR
(varnum between max(varnum) + 1 -42 and max(varnum) + 1 -42)
x2 x3 x4 x8 x9 x10 x59 x74 x75 x76 x97 x98 x99
If you don't know number of variables, you can use this macro(you should specify num of first variables and num of last variables to keep in data set, libname and name of dataset):
%macro drop_vars(num_first_vars,num_end_vars,lib,dataset); %macro d;%mend d;
proc sql noprint;;
select sum(num_character,num_numeric) into:ncolumns
from dictionary.tables
where libname=upcase("&lib") and memname=upcase("&dataset");
select name into: vars_to_drop separated by ','
from dictionary.columns
where libname=upcase("&lib") and
memname=upcase("&dataset") and
varnum between %eval(&num_first_vars.+1) and %eval(&ncolumns-&num_end_vars);
alter table &lib..&dataset
drop &vars_to_drop;
quit;
%mend drop_vars;
%drop_vars(2,3,work,test);
Dataset before macro execution:
+---+---+---+---+---+---+---+---+---+----+
| a | b | c | d | e | f | g | h | i | j |
+---+---+---+---+---+---+---+---+---+----+
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
+---+---+---+---+---+---+---+---+---+----+
Dataset after macro execution:
+---+---+---+---+----+
| a | b | h | i | j |
+---+---+---+---+----+
| 1 | 2 | 8 | 9 | 10 |
+---+---+---+---+----+
If the names follow a pattern just generate the list using the pattern. So if the names look like month names you just need to know one month to generate the other.
%let last_month = '01JAN2019'd ;
%let first_var = %sysfunc(intnx(month,&last_month,-12),monyy7.);
%let last_var = %sysfunc(intnx(month,&last_month,-0),monyy7.);
data want;
set have(keep= id1 id2 &first_var -- &last_var);
run;
If you cannot find a SAS function or format that generates the names in the style your variables use then write your own logic.
data _null_;
array month_abbr [12] $3 _temporary_ ('JAN' 'FEB' 'MAR' 'APR' 'MAY' 'JUN' 'JUL' 'AUG' 'SEP' 'OKT' 'NOV' 'DEK' );
last_month=today();
first_month=intnx('month',last_month,-12);
call symputx('first_var',catx('_',month_abbr[month(first_month)],year(first_month)));
call symputx('last_var',catx('_',month_abbr[month(last_month)],year(last_month)));
run;
hello am trying access columns from library with specific date format and using year function on the columns in my macro code but it produces duplicate values... but the year function displays duplicate values and does not provide desired results. my code should return only the year from the input dates.
%macro dteyear(lib=,outdsn=);
proc sql noprint;
select distinct catx(".",libname,memname), name
into :dsns separated by " ", :varname separated by " "
from dictionary.columns
where libname = upcase("&lib") and format=('YYMMDD10.')
order by 1;
quit;
%put &dsns;
%put &varname;
%local olddsn curdsn curvbl i;
data &outdsn.;
set
%let olddsn=;
%do i=1 %to &sqlobs;
%let curdsn=%scan(&dsns,&i,%str( ));
%let curvbl=%scan(&varname,&i,%str( ));
%if &curdsn NE &olddsn
%then %do;
%if &olddsn NE
%then %do;
)
%end;
%let olddsn=&curdsn.;
&curdsn (keep=&curvbl
%end;
%else %do;
&curvbl
%end;
%end;
);
%do i=1 %to &sqlobs;
%scan(&varname,&i,%str( ))=year(&varname.);
%end;
run;
proc print data=&outdsn;run;
%MEND;
%dteyear(lib=dte3,outdsn=dtetst);
the input data is as follows
1975-12-04
1977-11-03
1989-09-15
1998-06-17
1999-05-31
2000-08-14
2001-03-11
2007-03-11
2007-12-28
2008-10-07
2009-12-03
duplicate output from my code is-->
Obs RFDTC
1 1965-05-19
2 1965-05-19
3 1965-05-19
4 1965-05-19
5 1965-05-19
6 1965-05-19
7 1965-05-19
8 1965-05-19
9 1965-05-19
10 1965-05-19
11 1965-05-19
12 1965-05-19
13 1965-05-19
The basic problem is that the YEAR() function returns a 4-digit number, and the variable's format is YYMMDD10., so the result is formatted as a SAS date very close to 1960 (SAS's beginning of all time).
What I did in the code below was change the format to 4.0, so it displays as a 4-digit number.
If you want to have access to the original date variable, you'll have to create a new variable for the year. I'll leave that to you.
There was an additional problem--that is, YEAR(&varname.) inserts the entire list of variables, not just the one you're working with. It works if there is only one date variable, but not if there are more than one. I fixed this, too.
%macro dteyear(lib=,outdsn=);
proc sql noprint;
select distinct catx(".",libname,memname), name
into :dsns separated by " ", :varname separated by " "
from dictionary.columns
where libname = upcase("&lib") and format=('YYMMDD10.')
order by 1;
quit;
%put &dsns;
%put &varname;
%local olddsn curdsn curvbl i;
data &outdsn.;
set
%let olddsn=;
%do i=1 %to &sqlobs;
%let curdsn=%scan(&dsns,&i,%str( ));
%let curvbl=%scan(&varname,&i,%str( ));
%if &curdsn NE &olddsn
%then %do;
%if &olddsn NE
%then %do;
)
%end;
%let olddsn=&curdsn.;
&curdsn (keep=&curvbl
%end;
%else %do;
&curvbl
%end;
%end;
);
%do i=1 %to &sqlobs;
%let curvbl=%scan(&varname,&i,%str( ));
&curvbl=year(&curvbl.);
format &curvbl 4.0;
%end;
run;
proc print data=&outdsn;run;
%MEND;
data have;
input datevar yymmdd10.;
format datevar yymmdd10.;
cards;
1975-12-04
1977-11-03
1989-09-15
1998-06-17
1999-05-31
2000-08-14
2001-03-11
2007-03-11
2007-12-28
2008-10-07
2009-12-03
run;
options mprint;
%dteyear(lib=work,outdsn=want)
The result, then, is:
Obs datevar
1 1975
2 1977
3 1989
4 1998
5 1999
6 2000
7 2001
8 2007
9 2007
10 2008
11 2009
To convert a date value to just a year you can use the YEAR() function, but you also need to change the format attached to the variable since you will have essentially divided the value stored in it by 365 to convert it from the number of days to the number of years.
rfdtc = year(rfdtc);
format rfdtc 4. ;
Your macro is attempting to read many variables from many datasets and generate a single output dataset. I am not sure the resulting dataset will be of much value to you since it will look like a checker board of missing values. Also if the same variable name appears in more than one input dataset you will get corrupted values because of applying the YEAR() function to value that has already been converted from a date value to a year value.
For example you could end up generating a data step like this:
data WANT ;
set ds1 (keep=datevar1)
ds1 (keep=datevar2)
ds2 (keep=datevar3)
ds3 (keep=datevar3)
;
datevar1=year(datevar1);
datevar2=year(datevar2);
datevar3=year(datevar3);
datevar3=year(datevar3);
format datevar1 datevar2 datevar3 datevar3 4.;
run;
Since both input datasets DS2 and DS3 have a variable named DATEVAR3 you will be applying the YEAR() function to the value twice. That will convert everything to the year 1965.
To eliminate the problem with running the YEAR() function on the same value multiple times and losing the actual year perhaps you just want to apply the YEAR. format instead of converting the stored value.
format datevar1 datevar2 datevar3 datevar4 year. ;
That would still leave the underlying different date values. If you really need to values to be identical perhaps you could convert the value to the first day of the year? You could use INTNX() function
datevar1 = intnx('year',datevar1,0,'b');
or the MDY() function
datevar1 = mdy(1,1,year(datevar1));
My initial Dataset has 14000 STID variable with 10^5 observation for each.
I would like to make some procedures BY each stid, output the modification into data by STID and then set all STID together under each other into one big dataset WITHOUT a need to output all temporary STID-datsets.
I start writing a MACRO:
data HAVE;
input stid $ NumVar1 NumVar2;
datalines;
a 5 45
b 6 2
c 5 3
r 2 5
f 4 4
j 7 3
t 89 2
e 6 1
c 3 8
kl 1 6
h 2 3
f 5 41
vc 58 4
j 5 9
ude 7 3
fc 9 11
h 6 3
kl 3 65
b 1 4
g 4 4
;
run;
/* to save all distinct values of THE VARIABLE stid into macro variables
where &N_VAR - total number of distinct variable values */
proc sql;
select count(distinct stid)
into :N_VAR
from HAVE;
select distinct stid
into :stid1 - :stid%left(&N_VAR)
from HAVE;
quit;
%macro expand_by_stid;
/*STEP 1: create datasets by STID*/
%do i=1 %to &N_VAR.;
data stid&i;
set HAVE;
if stid="&&stid&i";
run;
/*STEP 2: from here data modifications for each STID-data (with procs and data steps, e.g.)*/
data modified_stid&i;
set stid&i;
NumVar1_trans=NumVar1**2;
NumVar2_trans=NumVar1*NumVar2;
run;
%end;
/*STEP 3: from here should be some code lines that set together all created datsets under one another and delete them afterwards*/
data total;
set %do n=1 %to &N_VAR.;
modified_stid&n;
%end;
run;
proc datasets library=usclim;
delete <ALL DATA SETS by SPID>;
run;
%mend expand_by_stid;
%expand_by_stid;
But the last step does not work. How can I do it?
You're very close - all you need to do is remove the semicolon in the macro loop and put it after the %end in step 3, as below:
data total;
set
%do n=1 %to &N_VAR.;
modified_stid&n
%end;;
run;
This then produces the statement you were after:
set modified_stid1 modified_stid2 .... ;
instead of what your macro was originally generating:
set modified_stid1; modified_stid2; ...;
Finally, you can delete all the temporary datasets using stid: in the delete statement:
proc datasets library=usclim;
delete stid: ;
run;
The character variable in dataset never matches with the macro variable. The %IF loop never comes true. Kindly advice.
I am trying to match by months and accordingly trying to create array and put counts only for specific months. Not working because the month macro variable never matches with dataset variable having month.
/*create dummy data*/
data datefile;
input tran_date date9. cnt 3.;
datalines;
13feb2015 5
10feb2015 4
11feb2015 3
05feb2015 8
08feb2015 5
01jan2015 1
20dec2014 1
31jan2015 2
23dec2014 2
12jan2015 1
;
/*calculate month*/
data datefile11;
set datefile;
tran_mon=year(tran_date)*100+month(tran_date);
run;
/*select distinct month*/
proc sql;
create table datefile12 as select distinct(tran_mon)
from datefile11 order by tran_mon;
quit;
/*convert month from numeric to character*/
data datefile11(drop=tran_mon);
informat tran_mon2 $6.;
set datefile11;
tran_mon2=tran_mon;
run;
/*create macro variables through datastep*/
data datefile13;
set datefile12;
monum = cat('mnth',_N_);
run;
data _null_;
set datefile13;
call symput(monum,trim(left(tran_mon)));
run;
/*use array to make separate column for each month and
put split count for each month to each colunms*/
%macro c;
proc sql noprint;
select count(1) into :nrow from datefile13;
quit;
%let nrow = &nrow;
data datefile14;
set datefile11;
array mon{*} mon_1 - mon_&nrow;
%do i=1 %to &nrow;
%if tran_mon2 = &&mnth&i %then %do; %put tran_mon2;
mon_&i = cnt; %end;
%else %do; mon_&i = 0 ; %end;
%end;
run;
%mend c;
%c
Your macro %if %then %do check executes while the data step is still being compiled - by the time the data step has begun to execute, there is no further opportunity to use macro logic like that.
Try doing it the other way round - write your loop using if then do data step logic instead.