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

That is not that hard to read using a data step.  Read it into variable/value pairs. Here is a program that can read your data.  Using delimiter of both a space and a colon will make it easy to read the variable number and its value without having to worry about any string manipulation. Not sure what that first value represents, so I just named it variable zero.  I added ROW variable uniquely identify the rows. Then you can use PROC TRANSPOSE to convert it back to a rectagular format. The last step will both order the variables in the data vector and also creates all of the variables not actually named.  The example data seems to mainly be variable 1 to 11 but there is also others such as 26 and 43. data vert ; retain maxvar 0; infile cards truncover dlm=' :'; length var value 8 ; input value @ ; row +1 ; var=0; output; do until (var= .);    input var value @;    if var ne . then output;    if var > maxvar then do ;       maxvar=var;       call symputx('MAXVAR',maxvar);    end; end; cards; 2 1:0.392696 2:0.347222 3:0.30303 4:0.0479599 5:0.255814 6:0.241534 7:0.980315 8:0.870079 9:0.354331 10:0.72048 11:1 43:1 2 1:0.377189 2:0.102778 3:0.0757576 4:0.154617 5:0.267442 6:0.213433 7:0.866142 8:0.901575 9:0.574803 10:0.671685 11:1 26:1 2 1:0.535268 2:0.988889 3:0.181818 5:0.223514 6:0.808908 7:0.791339 8:0.862205 9:0.610236 10:0.560017 11:1 38:1 2 1:0.454227 2:0.852778 3:0.181818 4:0.0214746 5:0.23385 6:0.392862 7:0.73622 8:0.917323 9:0.732283 10:0.863237 11:1 43:1 1 1:0.566283 2:0.136111 3:0.19697 4:0.0608447 5:0.223514 6:0.871013 7:0.885827 8:0.830709 9:0.46063 10:0.503694 11:1 38:1 2 1:0.54027 2:0.155556 3:0.272727 4:0.0680029 5:0.249354 6:0.78165 7:0.901575 8:0.787402 9:0.385827 10:0.588875 11:1 38:1 2 1:0.33917 2:0.0194444 3:0.181818 5:0.223514 6:0.222425 7:0.807087 8:0.850394 9:0.582677 10:0.628607 11:1 30:1 2 1:0.630315 2:0.330556 3:0.106061 4:0.115963 5:0.236434 6:0.935788 7:0.917323 8:0.92126 9:0.523622 10:0.356336 11:1 43:1 run; proc transpose data=vert out=horiz prefix=var;   by row;   id var ;   var value ; run; data horiz;   array var var0 - var&maxvar ;   set horiz ;   drop _name_; run; ... View more

Can you verify that it was moved as BINARY rather then ASCII?  Does it have any PIB or other fields that would contain binary data? Was it originally fixed length records on the mainframe? Do you get messages in the SAS log about SAS moving to another record to satisfy the input statements?  Does it say that it read the same number of lines as the observations created.  Does the data appear to get out of align with the specifications? I like to use the LIST statement to look at the data. You might try : data _null_;    infile ....... ;    if _n_ <= 10 or 6200000 < _n_ ;   input ;    list; run; Where "62000000" is a small number of records before the point where it is stopping. ... View more

Is it  6.2 out of 8.2 lines in the flat file or observations in the output file? How did you count the lines in the flat file? How did you move the file from the mainframe? ... View more