Hi,   How about using sql?   proc sql; select id,year(test_date) as year, avg(test_value) as avg_test_value format=8.2 from have group by id,calculated year; quit; ... View more

Hi,   Try this   data want; if _n_=1 then set SRM; set sample; run; ... View more

Hi,    Please try this   proc stdize data=Dataset1(drop=var12) reponly out=dataset2 missing=0; run; ... View more

Hi,   This is because your proc freq syntax is not creating a cross tab. In order to get a cross tabulation in a data set then you have to generate a cross tab. Please try this to see the output data set   proc freq data=eq; tables id*y; where z ne . ; ods output CrossTabFreqs=CrossTabFreqs; run;   proc print data = CrossTabFreqs; run; ... View more

Hi,   I am glad you have the solution. I am also  learning from @Reeza's posts    ... View more

Hi,   Something like this may produce the desired output.   data have; input colx coly colz; datalines; 0 1 0 0 1 1 0 1 0 ;   data want(keep=name value); set have; array v(*) col:; do i = 1 to dim(v);     name=vname(v(i));    value=v(i);    output; end; run; proc sql; select name,value,count(*) as count from want group by name, value; quit; ... View more

Hi,   Missing values can be replaced with various statistics using proc stdize. Below is an example replacing missing values with median. Defining a cap would be based on your analysis. You can flag variables containing a certain percentage of missing values for imputation.       proc stdize data=have reponly method=median out=imputed; var a b c; /* Assuming a, b and c are 3 numeric variables */ run; ... View more

Hi,   Something like this? I am using a sample data set and you can try on the same pattern on your data set.   data have ; input var1-var4 ; n = _n_; cards; 0.5 1 1.2 0.4 0.9 1 1.5 0.2 0.7 1 1.1 0.3 1 1 1 1 3 1 3 1 4 1 6 7 ; proc reg data=have(where = (n>3)); model var1=var2; quit; ... View more