Thank you @GregorClegane ! Have a great day ... View more

Hi @umeshgiri48  Here is one approach to do that: proc sort data=test out=test_sorted; by Outlet Category; run; proc transpose data=test_sorted out=test_tr; by Outlet; var Category; run; data want (keep=outlet); set test_tr; Category = catx(" ",of col:); if find(Category,"CSP") and find(Category,"Stills") and countw(Category)=2 then output; run; ... View more

Hi @GregorClegane  Thank you for the sample data. Could you please specify also the desired output? What is the rule to identify the 14 days ? (e.g. the first day = the first check in date, the beginning of the month, etc.?) ... View more

Hi @Ronein  The PROC REPORT proposed by @PaigeMiller would also work. You can add the missing option in the first option like below to consider missing values as valid values: proc report data=tbl missing; columns amnt pct field,factor,reduce; define amnt/group; define pct/group; define field/across; define factor/across; define reduce/analysis sum; run; ... View more

Hi @Pooja98    The %include statement allow you to bring a SAS program stored in an external file in your current program.   Here is the documentation about this statement: http://support.sas.com/documentation/cdl/en/lrdict/64316/HTML/default/viewer.htm#a000214504.htm ... View more

Hi @Ronein  So you can remove 'mean' as there is only one possibility (-> NB: it is actually strictly equivalent, as the mean will be based on only one value)   ... View more

Hi @GregorClegane    Happy new Year and welcome to the community! Could you please share a sample of your input data so that we can help you? ... View more

Hi @Pooja98    Both DATA step merges and PROC SQL joins enable to join two or more tables. Sometimes they can produce the same outputs. Here are some of the main differences: - contrary to DATA step merges, you don't need to sort your data when you use PROC SQL joins - you need to add a "create table xxx as" clause in the PROC SQL as the default output is a report - common columns need to have the same name in a DATA step merge, which is not the case for PROC SQL. - PROC SQL does not overlay common columns by default -> you need to mention one explicitly in the select clause or use the COALESCE option - Another thing is that you can use another operator that = in you joint expression. - in case your input datasets have not a unique "key" for merging, it is useful to use PROC SQL as by design, it will create the Cartesian product of input datasets as the result and then delete observations that don't meet the join expression condition(s) -> so depending on the method you choose, you can have different results.   PROC SQL can be also more efficient, especially if you have large datasets.   Hope this help!   Best, ... View more