Merging the Not Missing Observation also in the final Table and transpose the final Output

[narulap]

I have 10 different variables in 10 different tables with the VARNAME and MISSING PERCENT.

Out of these 10, lets say 5 do not have the "MISSING PERCENT" and I want to include these observation with 0% Missing. For now, it eliminates this observation in the final output.

Secondly, I have already merged the different tables containing different variables(0% still needs to be merged) as shown below:

data Final_Output_All_Missing;
length VARNAME $ 30;
 merge work.Final_Output_MOLD work.Final_output_tbm_stage2 work.final_output_article7 
 work.final_output_tbm_stage1 work.final_output_bladder work.final_output_batch_id;
 by varname;
 keep VARNAME PERCENT;
run;


VARNAME              MISSING PERCENT
BLADDER               0.10
MOLD                  0.06
TBM_STAGE1            0.18
TBM_STAGE2            99.9    

After merging, I want to see the output in this format. is it possible for me to get in this format?

         BLADDER            MOLD       TBM_STAGE1         TBM_STAGE2

1.         0.10%              0.06%             0.18%                       99.9%

Appreciate your help!!!

[RW9 · Accepted solution]

You want to proc transpose the data:

http://support.sas.com/documentation/cdl/en/proc/61895/HTML/default/viewer.htm#transpose-overview.ht...

E.g.

proc transpose data=have out=want;
  var missing_percent;
  id varname;
  idlabel varname;
run;

[Astounding]

Just in case this would have saved you a ton of work, here are a couple of relevant questions.

Originally, were all these variables in just one data set?

If so, were they all numeric?

[narulap]

Yes originally these variables were in a one data set.

But I need some relevant variables(shown below in the picture) for my missing value analysis and I have created different data sets of these selected 15 variables. Now, I have merged them all in one Data Set(FINAL_OUTPUT_ALL_MISSING) with VARNAME and MISSING PERCENT.

Different Data Sets :

Data Sets

I do have numeric and character variables as well. 

[Astounding]

OK, in case it would help ... here is a method for your numeric variables only, to go directly from the original data to the final result with percent of missing values:

proc summary data=original_data;

var numeric1 numeric2 numeric3;

output out=missing_counts (drop=_type_) nmiss=;

run;

data want;

set missing_counts;

numeric1 = numeric1 / _freq_;

numeric2 = numeric2 / _freq_;

numeric3 = numeric3 / _freq_;

drop _freq_;

run;

proc print data=want;

format _numeric_ percent9.2;

run;