I have 18 separate datasets that contain similar information: patient ID, number of 30-day equivalents, and total day supply of those 30-day equivalents. I've output these from a dataset that contains those 3 variables plus the medication class (VA_CLASS) and the quarter it was captured in (a total of 6 quarters).
Here's how I've created the 18 separate datasets from the snip of the dataset shown above:
%macro rx(class,num);
proc sql;
create table dm_sum&clas._qtr&num as select PatID,
sum(equiv_30) as equiv_30_&class._&num
from dm_qtrs
where va_class = "HS&class" and dm_qtr = &qtr
group by 1;
quit;
%mend;
%rx(500,1);
%rx(500,2);
%rx(500,3);
%rx(500,4);
%rx(500,5);
%rx(500,6);
%rx(501,1);
and so on...
I then need to merge all 18 datasets back together by PatID and what I'd like to do is iteratively add the next dataset created to the previous, as in, add dataset dm_sum_500_qtr3 to a file that already contains the results of dm_sum_500_qtr1 & dm_sum_500_qtr1.
Thanks for looking, Brian
In the macro append the created data set to it an accumulator data set. Be sure to delete it before starting so there is a fresh accumulation. If the process is run at different times (like weekly or monthly) you may want to incorporate a unique index to prevent repeated appendings. If you are stacking all these sums, the create table should also select va_class and dm_qtr
%macro (class, num, stack=perm.allClassNumSums);
proc sql; create table dm_sum&clas._qtr&num as … ;
proc append force base=perm.allClassNumSums data=dm_sum&clas._qtr#
run;
%mend;
proc sql;
drop table perm.allClassNumSums;
%rx(500,1)
%rx(500,2)
%rx(500,3)
%rx(500,4)
%rx(500,5)
…
A better approach might be a single query with an larger where, and leave the class and qtr as categorical variables. Your current approach is moving data (class and qtr) into metadata (column names). Such a transformation makes additional downstream processing more difficult.
Proc TABULATE or REPORT can be use a CLASS statement to assist the creation of output having category based columns. These procedures might even be able to work directly with the original data set and not require a preparatory SQL query.
proc sql;
create table want as
select
PatID, va_class, dm_qtr,
sum(equiv_30) as equiv_30_sum
from dm_qtrs
where catx(':', va_class, dm_sqt) in
(
'HS500:1'
'HS500:2'
'HS500:3'
…
'HS501:1'
)
group by PatID, va_class, dm_qtr;
quit;
Related
I have a dataset with 20 columns all starting with the name morb_, which are all 1 or 2, coded as No and Yes. There is an additional column called Pat_TNO which is the patient reference number. Patients have more than one row.
I wish to create a new dataset which summarises whether each patient has had at least one of each type of event. So far the code I have written works perfectly, but is there a way to simplify it using an array?
proc sql;
select
Pat_TNO,
max(morb_1) as morb_1 format yn.,
max(morb_2) as morb_2 format yn. /* etc etc */
from morbidity
group by Pat_TNO;
quit;
COumn names aren't morb_1 and morb_2, rather morb_amputation, morb_mi, morb_tia, etc.
proc summary data=morbidity nway missing;
class pat_tno;
output out=max max(morb_:) = ;
run;
Hi,
Can someone explain to me what a given code sequence does step by step?**
I must describe it in detail what is happening in turn
%macro frequency_encoding(dataset, var);
proc sql noprint;
create table freq as
select distinct(&var) as values, count(&var) as number
from &dataset
group by Values ;
create table new as select *, round(freq.number/count(&var),00.01) As freq_encode
from &dataset left join freq on &var=freq.values;
quit;
data new(drop=values number &var);
set new;
rename freq_encode=&var;
run;
data new;
set new;
keep &var;
run;
data dane(drop = &var);
set dane;
run;
data dane;
set dane;
set new;
run;
The SQL is first finding the frequency of each value of the variable. Then it divides those counts by the total number of non-missing values and rounds that percentage to two decimal places (or integers when you think of the ratio as a percentage).
This could be done in one step with:
proc sql noprint;
create table new as
select *,round(number/count(&var),0.01) as freq_encode
from (select *,&var as values,count(&var) as number
from &dataset
group by &var
)
;
quit;
It is not clear what the DANE dataset is supposed to be. If &DATESET does not equal DANE then those last four data steps make no sense. If it does then it is a convoluted way to replace the original variable with the percentage.
The first one is basically trying to rename the calculated percentage as the original variable and eliminate the original variable and the other two intermediate variables used in calculating the percentage.
The second one is dropping all of the variables except the new percentage.
The third one is dropping the original variable from "dane".
The last one is adding the new variable back to "dane".
Assuming DANE should be replaced with &DATASET then those four data steps could be reduced to one:
data &dataset;
set &dataset(drop=&var);
set new(keep=freq_encode rename=(freq_encode=&var));
run;
It is probably best not to overwrite your original dataset in that way. So perhaps you should add an OUT parameter to your macro to name to new dataset you want to create.
You could have avoided all of those data steps by just adding the DROP= and RENAME= dataset options to the dataset generated by the SQL query.
So perhaps you want something like this:
%macro frequency_encoding(dataset, var,out);
proc sql noprint;
create table &out(drop=&var number rename=(freq_encode=&var)) as
select *,round(number/count(&var),0.01) as freq_encode
from (select *,count(&var) as number
from &dataset
group by &var
)
;
quit;
%mend ;
%frequency_encoding(sashelp.class,sex,work.class);
How do i start this??
I have two data sets.
For the output you will deliver:
It should be an excel or XML format
Each query logic/programmed check should be on each tab
Columns should be
Subject #,
Visit Date (You will need the Visit Date Listing also attached)
Visit Name (Visit date from the file_34422 must match Visit name in the Blood Pressure File)
Date of Assessment (From the BP Log), VSBPDT_RAW, VSTPT, BP results.
A column for SYBP1. SYBP2, SYBP3, DIABP1, DIABP2, DIABP3
Findings/query text.
Below are Specification for BP:
For same SUBJECT and same FOLDERNAME, where VSTPT is Blood Pressure 1.
if VSBPYN is No, then all must be null or =0 (VSBPDT_RAW, VSBPTM1, SYSBP1, DIABP1, VSBPND2, VSBPTM2, SYSBP2, DIABP2, VSBPND3, VSBPTM3, SYSBP3, DIABP3)
This is what i have started with and
proc sql;
select
f.subject,
f.SVSTDT_RAW, f.FolderName,
b.FolderName,
VSBPDT_RAW, VSTPT,
SYSBP1, SYSBP2, SYSBP3,
DIABP1, DIABP2, DIABP3
FROM first_data as f, bp_data as b
group by subject, foldername
where f.subject = b.subject
having VSTPT is Blood Pressure set 1,
VSBPYN is No;
quit;
I just need to be pointed towards the right direction. I know this can't be right.
I do not know the exact structure of your data, so the solution below may need to be modified by you to select the right columns.
From the descritpion, this looks like it might be a good situation for SQL and a data step. You have a lot of columns to merge with the bp table. It will be easy to do merge all of these columns with first_data in SQL.
When you have lots of by-row conditionals, a data step will be easier to work with and read than many CASE statements in SQL. We'll do a two-stage approach in which we use SQL and a data step.
Step 1: Merge the data
proc sql noprint;
create table stage as
select t1.*
, t2.VSBPYN
from bp_data as t1
INNER JOIN
first_data as t2
ON t1.subject = t2.subject
AND foldername = t2.foldername
where t1.VSTPT = 1
;
quit;
Step 2: Conditionally set values to missing
Next, we'll do a data step for our conditional logic. call missing() is a useful function that will let you set the value of many variables to missing all in a single statement.
data want;
set stage;
if(upcase(VSBPYN) = 'NO') then call missing(VSBPDT_RAW, VSBPTM1, SYSBP1, DIABP1,
VSBPND2, VSBPTM2, SYSBP2, DIABP2,
VSBPND3, VSBPTM3, SYSBP3, DIABP3
);
run;
Step 3: Output to Excel
Finally, we sent the output to Excel.
proc export
data=want
file='/my/location/want.xlsx'
dbms=xlsx
replace;
run;
I have a do loop in which I do calculation on new variable and results are stored as additional column, this column-s (at each iteration) should be attached to the output table defined by macro.
Here on SO something similar has been asked but the answer is not acceptable, the last answer is not compatible with sas command but very close, getting incomplete script with following:
proc sql;
update &outlib..&out.
set var._iqr = b.&var._iqr
from &outlib..&out. as a
left join cal_resul as b
on a.id_client=b.id_client
and a.reference_date=b.reference_date;
quit;
Here is my attempt which works but very slow:
proc sql; create table &outlib..&out. as select * from &inlib..&in.; quit; /* the input is as a basis for output table */
proc sql; alter table &outlib..&out. add &var._iqr numeric; quit; /* create empty column to be filled at each iteration */
proc sql;
update &outlib..&out. as a
set &var._iqr=(select b.&var._iqr from cal_resul as b
where a.id_client=b.id_client
and a.reference_date=b.reference_date
and a.data_source=b.data_source);
quit;
Attempt 2:
This is somewhat faster:
proc sort data=cal_resul; by id_client reference_date data_source; run;
data &outlib..&out.;
update &outlib..&out. cal_resul;
by id_client reference_date data_source;
run;
Simple left join (adding new column into existing table is way faster) but with left join I did not figure out how I can update (always retain the same dataset) the &outlib..&out. at each iteration. Many thanks for any help;
If you want to ADD a variable to a dataset you will have to make a new dataset. (Your ALTER TABLE statement will create a new dataset and copy over all of the observations.)
Looks like your data has three key variables. So use those in merging the new data to the old.
For example to make a new variable in HAVE named EXAMPLE_IQR using the variable EXAMPLE in the dataset NEW you could use code like this. I have used macro variables to show how you might use those macro variables as the parameters to a macro. It sounds like you don't want the process to add new observations to the existing dataset so I have added a check for that using the IN= dataset option.
%let base=work.have;
%let indata=work.new;
%let var=example;
data &base ;
merge &base(in=inbase)
&indata(keep=id_client reference_date data_source &var
rename=(&var=&var._iqr)
)
;
by id_client reference_date data_source;
if inbase;
run;
I have to join 2 tables on a key (say XYZ). I have to update one single column in table A using a coalesce function. Coalesce(a.status_cd, b.status_cd).
TABLE A:
contains some 100 columns. KEY Columns ABC.
TABLE B:
Contains just 2 columns. KEY Column ABC and status_cd
TABLE A, which I use in this left join query is having more than 100 columns. Is there a way to use a.* followed by this coalesce function in my PROC SQL without creating a new column from the PROC SQL; CREATE TABLE AS ... step?
Thanks in advance.
You can take advantage of dataset options to make it so you can use wildcards in the select statement. Note that the order of the columns could change doing this.
proc sql ;
create table want as
select a.*
, coalesce(a.old_status,b.status_cd) as status_cd
from tableA(rename=(status_cd=old_status)) a
left join tableB b
on a.abc = b.abc
;
quit;
I eventually found a fairly simple way of doing this in proc sql after working through several more complex approaches:
proc sql noprint;
update master a
set status_cd= coalesce(status_cd,
(select status_cd
from transaction b
where a.key= b.key))
where exists (select 1
from transaction b
where a.ABC = b.ABC);
quit;
This will update just the one column you're interested in and will only update it for rows with key values that match in the transaction dataset.
Earlier attempts:
The most obvious bit of more general SQL syntax would seem to be the update...set...from...where pattern as used in the top few answers to this question. However, this syntax is not currently supported - the documentation for the SQL update statement only allows for a where clause, not a from clause.
If you are running a pass-through query to another database that does support this syntax, it might still be a viable option.
Alternatively, there is a way to do this within SAS via a data step, provided that the master dataset is indexed on your key variable:
/*Create indexed master dataset with some missing values*/
data master(index = (name));
set sashelp.class;
if _n_ <= 5 then call missing(weight);
run;
/*Create transaction dataset with some missing values*/
data transaction;
set sashelp.class(obs = 10 keep = name weight);
if _n_ > 5 then call missing(weight);
run;
data master;
set transaction;
t_weight = weight;
modify master key = name;
if _IORC_ = 0 then do;
weight = coalesce(weight, t_weight);
replace;
end;
/*Suppress log messages if there are key values in transaction but not master*/
else _ERROR_ = 0;
run;
A standard warning relating to the the modify statement: if this data step is interrupted then the master dataset may be irreparably damaged, so make sure you have a backup first.
In this case I've assumed that the key variable is unique - a slightly more complex data step is needed if it isn't.
Another way to work around the lack of a from clause in the proc sql update statement would be to set up a format merge, e.g.
data v_format_def /view = v_format_def;
set transaction(rename = (name = start weight = label));
retain fmtname 'key' type 'i';
end = start;
run;
proc format cntlin = v_format_def; run;
proc sql noprint;
update master
set weight = coalesce(weight,input(name,key.))
where master.name in (select name from transaction);
run;
In this scenario I've used type = 'i' in the format definition to create a numeric informat, which proc sql uses convert the character variable name to the numeric variable weight. Depending on whether your key and status_cd columns are character or numeric you may need to do this slightly differently.
This approach effectively loads the entire transaction dataset into memory when using the format, which might be a problem if you have a very large transaction dataset. The data step approach should hardly use any memory as it only has to load 1 row at a time.