As others have mentioned, the problem lies with your instructions to read in the data.  While @FreelanceReinh has shown some of the many choices you have available, the simplest (yet intuitive) way to read the data would be like this: data weather_japan; input station $ 1-17 location $ 18-40; datalilnes; ... same datalines that you posted ... ; You would have to do the work of telling SAS where to find the fields you are reading.  SAS will pull the data from the columns you specify.     Also note, this style of inputting the data will left-hand justify whatever it finds so it won't matter whether LOCATION begins in column 18 or column 19.   Finally, this can't be your final data set.  There are no variables suitable for practicing the INTNX or INTCK functions ... View more

On the surface, this seems like a horrible idea. You would make your data incredibly difficult to program with, and create the situation where col3 means something different depending on which data set you are working with. Perhaps you could describe what happens next,, after the names have been changed. We might be able to suggest a less destructive path to take to your goal. ... View more

You are missing the "n" following one of the references to 'CY_Actuals' ... View more

Will AGE always be consistent, or could the same ID have two different ages? ... View more

The most important concept is, I'm sure, covered in @SASJedi 's link.  Just to isolate it, there is a difference between variables read in, vs. variables saved.  Compare:   data want; set have (keep=a b c); run; data want (keep=a b c); set have; run; The top DATA step reads in just the variables needed so the PDV contains only a, b, and c.  The bottom data step reads in all the variables, which could be hundreds of variables.  Then it saves only a few.  All variables from HAVE would be in the PDV, but only three would be written to WANT. ... View more

Probably this is a bad idea. It will complicate your life immeasurably. For example, consider a simple task like computing the mean value of a variable. Without splitting, a simple program does the trick: proc means data=raw_data; var varname; run; To calculate the same statistics from split data, you have to jump through many hoops. First, process each subset. For the mean value, calculate the Sum and N statistics. Then add those statistics across all the subsets. Finally, calculate the overall Mean from the aggregated Sum / N. This sort of process can be done for other statistics as well such as minimum value and standard deviation. However, it would be easier to learn to work with the "unsplit" data in a more efficient way. ... View more

Is it possible that a StudyID has all its observations saying "No Text"?  If so, the proposed solution will exclude that StudyID entirely.  To guarantee selecting an observation for each StudyID, a slight variation would do the trick: data want; set have (where=(firstResult ne 'See Text')) have (where=(firstResult eq 'See Text')); by studyID; if first.studyID; run; ... View more

Such a format may not exist. But if it does, it would be worth trying MONYYD6. ... View more

Have you tried turning on MLOGIC to see how the statements are being interpreted? ... View more

The answer is actually more complex than you would think.  It depends in part on whether you have a one-to-one match or a many-to-one match.  If you are interested in reading and learning, here is a link to an old paper than explains the process:   https://www.lexjansen.com/nesug/nesug99/ad/ad155.pdf   ... View more

The easiest way: include the semicolon when replacing RESULT1. The 3rd parameter in CATX would become: 'Hello world:' Do you have some conditions that would make this a bad idea? ... View more