It is doing what you asked. If you want it only exclude cases with 1 and John then change to: where not (dept=1 and name='John'); or where dept^=1 or name^='John' ; ... View more

The flags created by the IN= dataset option will be boolean flags indicating whether the dataset contributed to the current observation. For 1-to-1 and 1-to-N matches the meaning is clear. But when you have N-to-M matches the logic is a little more complex.  SAS will basically match the records 1 to 1 until it runs out of records for that BY group from one of the sources.  In that case the result is that SAS will carry forward the variables for the last observation from that dataset for the rest of the BY group.  This includes the IN= flag.  Resetting the flag (either at the end of the step or before the MERGE statement at the beginning of the step) will cause it to not be set until SAS reads another observation from that dataset.  You might want to also add a CALL MISSING statement to force the variables to missing as they will also retain their values. You do not need that in this program for the variables coming from the STORES dataset, but you might need it for the variables coming from the MODELIDS dataset if it is possbile to have more newstores than modelids for a group. ... View more

I think you missed my point.  An autocall macro is written as a MACRO where as a stored process is written as a PROGRAM. When writing a MACRO you typically want to define the parameters into the macro definition.  But when writing a PROGRAM you will have to treat your parameters as externally defined macro variables. Am not sure how stored processes can access macro such as the %MYMACCALL in your example program.  I assume that they are either defined in some setup field that defines what directories on the server to search or are perhaps %INCLUDED during the execution of the %STPBEGIN macro. ... View more

If you cannot get it any other way then direct the print out to another file. For example: filename sudaan temp; proc printto print=sudaan ; run; proc sudaan .... .... proc printto print=print; run; ... View more

You did not include any SAS datasets in the Zip file.  There is just a DLL file, which I decline to expand as I have no idea what it might do to my computer. 1) To limit the observations (rows) included in an analysis you could use a WHERE statement. 2) You could use PROC SUMMARY to create a new files that has the SUM of variables. Use a BY or CLASS statement to make it group be the variables (columns) that uniquely identify the groups.  To fill in the zeros it depends one what is missing.  If you have an observation already and are just missing a value then use program logic to make the variable be zero instead.  If x=. then x=0; or x=coallese(x,0); 3) Not sure what you are talking about here. Normally you would refer to matching of observations (rows) from one dataset with another by referencing the variables (columns) that need to match.  So if there are variables that indicate row1 of dataset A should be matched with row2 of dataset B then you can merge be those.  But it really sounds like what you want to do is more like your second question. In that case just concatenate the datasets using a DATA step with a SET statement and then use PROC SUMMARY as in the second question. ... View more

Not that I actually have an installation that can create or run stored processes, but to me the main difference is how the parameters are passed. In an autocall macro you are allowed to define parameters in the %MACRO statement. And you specify the values when you write the code to call the macro.  The parameters appear as local macro variables. With a stored process the parameter values are assigned to global macro variables and then the stored process program is called. This is similar to other system for calling SAS programs such as SAS/Intrnet or Oracle's Life Sciences Hub. Calling an autocall macro: %mymacro(parm1=Fred,parm2=George) Calling a stored process: %let parm1=Fred; %let parm2=George; %include mystoredprocess; ... View more

Your original data seems to have all new stores matched to all model ids. If this is true then you can use a data step many-to-many merge on GROUP to assign stores to models. data have ;   input Group Newstore Modelid ; cards; 10549 6453 5582  10549 6453 5217  10549 6453 2871  10549 2001 5582  10549 2001 5217  10549 2001 2871 10548 9648 6453  10548 9648 3251  10548 9648 2205  10548 9453 6453  10548 9453 3251  10548 9453 2205  run; proc sort data=have(keep=group modelid) out=modelids nodupkey;   by group modelid; run; proc sort data=have(keep=group newstore) out=stores nodupkey;   by group newstore; run;   * Need to reset the IN flag to keep it from matching the last store to all of the remaining models ; data want ;   in1=0;   merge stores(in=in1) modelids;   by group ;   if in1;   put group newstore modelid ; run; If you want to introduce some randomness into the issue then sort the stores and models randomly within the groups before the merge. ... View more

If you only want the ones that are in both datasets then do not use a LEFT JOIN. proc sql;   create table a1 as    select a.*, b.purchase_total    from customer a          , purchase_summary b    where a.customer_id = b.customer_id       and a.customer_account = b.customer_account   ; quit; Or translated into normal SAS programming: data a1;    merge customer (in=in1) purchase_total(in=in2 keep=customer_id customer_account purchase_total) ;    by customer_id customer_account ;    if in1 and in2; run; ... View more

You need to create a numeric variable. data fixed_onhand;    set onhand (rename=(onhand = char_onhand));    onhand = input(char_onhand,best.); run; ... View more

Can you check if the default settings for buffers have changed? ... View more