Thank you very much sir. I was able to resolve. I did proc freq for <65 and >=65 and another one for <75 and >=75.   ... View more

I did as you said using if else condition to create a new variable agegroup; <65 >=65 as agroupx then proc freq <75 >=75  as agegroupy then proc freq. Thank you very much. Very helpful. ... View more

Thank you.   <65 : 55 57 63 64 64. i.e <65 should appear 5 times in the column >=65 : 65 66 67 67 68 68 68 69 70 71 71 71 71  72 72 73 74 76 76 77 77 78.      >=65 should appear 22 times <75 : 55 57 63 64 64 64 65 66 67 67 68 68 69 70 71 71 71 72 72 73 74.     <75 should appear 21 times >=75 :  75 76 76 77 77 78     >=75 should appear 6 times.     That is what I expect for each category if that is possible at all.     ... View more

Hi, I actually added a length statement before running the code. "<65" ">=65" "<75" ">=75"  Is this possible to group like that at all?     ... View more

Ok sir. Thank you. ... View more

Thank you very much sir, I will the proc format. This is a brief on what I'm trying to achieve; I'm working on a sdtmig dataset, I have the Age variable; I'm supposed to generate age_group; <65 >=65 <75 >=75 and then generate the count and percent using proc freq. ... View more

I'm working on a sdtmig dataset, I have the Age variable; I'm supposed to generate age_group; <65 >=65 <75 >=75 and then generate the count and percent of the age_group using proc freq. It is a case study.   ... View more

Thank you, sir. It is a class work, I will research the %tableN. Thanks for your response. ... View more

Thank you.     data dm; input pat active $gender age placebo; datalines; 101   .    Male      37   0 102  1    Female  40    . 103  1    Male      50    . 104  .     Female  60   0 ; run; Gender was initially 1 and 2, I formated it to male and female. dmx1_dmy1 appear like this; Pat     Active    Gender    Age      Placebo 101       .            Male       37           0 102       1          Female    40           . 103       1          Male        50           . 104       .           Female    60           0 ... View more

  Thank you.     data dm; input pat active $gender age placebo; datalines; 101   .    Male      37   0 102  1    Female  40    . 103  1    Male      50    . 104  .     Female  60   0 run; Gender was initially 1 and 2, I formated it to male and female. dmx1_dmy1 appear like this; Pat     Active    Gender    Age      Placebo 101       .            Male       37           0 102       1          Female    40           . 103       1          Male        50           . 104       .           Female    60           . ... View more

  Thank you for your response. Any idea of what I can do from the begining to arrive at B? This is my code below and how I arrived at table A; data dmed; set dmx1_dmy1; keep gender active; run; proc freq data=dmed; table gender active/ nopercent out=dm_freq; run; data cat_freq1; set dm_freq; length count 7 percent 6; male= catx("(", put(count, 2.), put(percent, 3.), "%)"); female = catx("(",put(count, 2.), put(percent, 3.), "%)"); where active ne .; drop count percent; run; data dfrem; set dmx1_dmy1; keep gender placebo; run; proc freq data=dfrem; table gender placebo/ nopercent out=dm_freq2; run; data cat_freq2; set dm_freq2; length count 7 percent 6; male= catx("(", put(count, 2.), put(percent, 3.), "%)"); female = catx("(",put(count, 2.), put(percent, 3.), "%)"); where placebo ne .; drop count percent; run; data cat_freq1_freq2; set cat_freq1 cat_freq2; run; proc sort data= cat_freq1_freq2 out=sorted_cat_freq; by active; run; proc transpose data= sorted_cat_freq out=transpose_freq; *by active; var male female; run; data my_freq(rename=(_name_=Gender COL1=placebo col2=active)); set transpose_freq; *drop active; run; proc sql; create table my_freq_rear as select gender, active, placebo from my_freq; quit; data Ade_lib.demog; set my_freq_rear mymeans; run;     ... View more

I expect to have the sex(gender) and aval(lab_group result)to be nexted on the column, PS lab_group result = lgr, sum=S and pctn=P. I expect the column to look like this;     gender(count)           gender(%) F    M     All             F      M     All lgr   lgr   lgr             lgr     lgr   lgr S      S    S              P       P      P ... View more