Great!!! Thanks Pgstat and Reeza. Below is the code with your inputs reflected. One last thing, because chart is getting large in size, I'd like to have ROWHEADERPOS=both COLHEADERPOS=both options for easire eyeballing. Currently, row and col headers I guess are interfered by axis titles at left and bottom. Is there anyway to have row/col headers stick to the body of the chart? not split by axis titles?  Like in the image?   proc sort data=b.plotdata; by race agegroup bf year; run; ods graphics / height=1200 width=4000; proc sgpanel data=plotdata1; panelby race agegroup/ novarname onepanel LAYOUT=LATTICE ROWHEADERPOS=both COLHEADERPOS=both; series y=pr_obs x=year / group=bf lineattrs=(pattern=solid); keylegend/  title="Categories" position=bottom; *format race race. agegroup agegroup. bf bf.; colaxis label='YEARS' fitpolicy=thin valuesformat=best4.0 values=(2002 to 2015 by 1); rowaxis label='PREVALENCE OF DISEASE' grid; title "TITLE"; run; ... View more

Reeza, the variable 'bf' which has 6 levels. Legend with 3 levels in my post wasn't meant to be exact. ... View more

@Reeza   Hi Reeza, Thanks for pointing me out to the Census documentation where I found correct data layout in 2011-2015 Documentation, page 19. Now I have dimensions of fixed columns such as below. However, I have a log saying that "variables named ... contains more than 32 characters" yet I'd like to keep variable names as long as they're informative especially when data is shared among multiple people. Any suggestions to solve this problem please? proc printto log= 'D:\log1.txt'; run; filename myfile URL 'https://www2.census.gov/programs-surveys/demo/tables/geographic-mobility/2015/county-to-county-migration-2011-2015/county-to-county-migration-flows/MCDxMCD_ager_US.txt'; data myfile; infile myfile length=len dlm=' ' dsd obs=5 truncover; input record $varying500. len; if _n_>=0 then do; put record $varying500. len; end; run; proc printto; run; data temp; infile myfile lrecl=29000 obs=100 truncover; input var_cur_res_State_Code 1-3 var_cur_res_cty_Code 4-6 var_cur_res_MCD_Code 7-11 var_res_1_Year_Ago_State_US_Island_Foreign_Region_Code 12-14 res_1_Year_Ago_cty_Code 15-17 res_1_Year_Ago_MCD_Code 18-22 Age 24-25 cur_res_State_Name $ 26-55 cur_res_cty_Name $ 56-90 cur_res_MCD_Name $ 91-135 Population_cur_cty_MCD_Est 137-144 Population_cur_cty_MCD_MOE 146-153 Nonmovers_cur_cty_MCD_Est 155-161 Nonmovers_cur_cty_MCD_MOE 163-169 Movers_within_United_States_cur_Municipio_Est_ 171-177 Movers_within_United_States_cur_Municipio_MOE 179-185 Movers_within_Same_cur_cty_MCD_Est 187-193 Movers_within_Same_cur_cty_MCD_MOE 195-201 "Movers_from_a_diff_cty_MCD_in_the_Same_State_for_cur cty_MCD__Est" 203-209 "Movers_from_a_diff_cty_MCD_in_the_Same_State_for_cur cty_MCD_MOE" 211-217 Movers_from_a_diff_State_for_cur_cty_MCD_Est 219-225 Movers_from_a_diff_State_for_cur_cty_MCD_MOE 227-233 Movers_from_Abroad_Est 235-241 Movers_from_Abroad_MOE 243-249 res_1_Year_Ago_State_Name_US_Island_Foreign_Region $ 251-280 res_1_Year_Ago_cty_Name $ 281-315 res_1_Year_Ago_MCD_Name $ 316-360 Population_Lived_in_cty_MCD_1_Year_Ago__Est 362-369 Population_Lived_in_cty_MCD_1_Year_Ago_MOE 371-378 Nonmovers_cty_MCD_of_res_1_Year_Ago_Est 380-386 Nonmovers_cty_MCD_of_res_1_Year_Ago_MOE 388-394 Movers_within_the_US_for_cty_MCD_of_res_1_Year_Est 396-402 Movers_within_the_US_for_cty_MCD_of_res_1_Year_MOE 404-410 "Movers_within_the_Same_cty_MCD_for_cty_MCD_of_res_1_Year Ago_Est" 412-418 "Movers_within_the_Same_cty_MCD_for_cty_MCD_of_res_1_Year Ago_MOE" 420-426 Movers_to_a_diff_cty_MCD_in_the_Same_State_for_cty_MCD_of res_1_Year_Ago__Est 428-434 "Movers_to_a_diff_cty_MCD_in_the_Same_State_for_cty_MCD_of res_1_Year_Ago_MOE" 436-442 Movers_to_a_diff_State_for_cty_MCD_of_res_1_Year_Ago_Est 444-450 Movers_to_a_diff_State_for_cty_MCD_of_res_1_Year_Ago_MOE 452-458 Movers_to_PR_Est 460-466 Movers_to_PR_MOE 468-474 Movers_in_cty_MCDtocty_MCD_Flow_Est 476-482 Movers_in_cty_MCDtocty_MCD_Flow_MOE 484-490 ; run;     ... View more

the paper you referred to is really good, solved the problems for me!!! thank you. ... View more

@AhmedAl_Attar   Thank you very much for your inputs!! I truly appreciate it. I now got the logic behind your do loop and I'd like to use this approach for final data preparation for a chart. May I have a question please? The resulting data 'want' is correct except row 11 and 13 has wrong values for a variable 'category'. What went wrong in modifying your do loop? I will summarize data for a chart after 'category' variable is created. Thanks zillions!   data have; input id bf1 bf2 bf3 bf4 bf5 bf6 bf7 bf8 bf9 bf10 bf11 bf12 ever denom obs years; datalines; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2002 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 2003 2 1 1 1 1 1 1 1 1 1 1 1 0 1 3 0 2002 3 1 1 1 1 1 1 1 1 0 0 0 0 1 4 1 2003 4 1 1 1 1 1 0 0 0 0 0 0 0 1 5 1 2004 5 0 0 0 0 0 0 0 0 0 0 0 0 0 6 1 2004 6 1 0 0 0 0 0 0 0 0 0 0 0 0 6 1 2004 ; data want; SET have; length category $13; if min(of bf1--bf12)=1 and max(of bf1--bf12)=1 then category='BF 12M+ (Yes)'; else do; if min(of bf1--bf12)=0 and max(of bf1--bf12)=0 then category='BF 12M+ (No)'; else do; if min(of bf1--bf11)=1 and max(of bf1--bf11)=1 then category='BF 12M (Yes)'; else do; if min(of bf1--bf11)=0 and max(of bf1--bf11)=0 then category='BF 12M (No)'; else do; if min(of bf1--bf8)=1 and max(of bf1--bf8)=1 then category='BF 9M (Yes)'; else do; if min(of bf1--bf8)=0 and max(of bf1--bf8)=0 then category='BF 9M (No)'; else do; if min(of bf1--bf5)=1 and max(of bf1--bf5)=1 then category='BF 6M (Yes)'; else do; if min(of bf1--bf5)=0 and max(of bf1--bf5)=0 then category='BF 6M (No)'; else do; if min(of bf1--bf2)=1 and max(of bf1--bf2)=1 then category='BF 3M (Yes)'; else do; if min(of bf1--bf2)=0 and max(of bf1--bf2)=0 then category='BF 3M (No)'; else do; if min(of bf1--bf12)=0 and max(of bf1--bf12)=0 and ever=1 then category='BF 1Mo (Yes)'; else category='BF 1Mo (No)'; end; end; end; end; end; end; end; end; end; end; output; if (ever=1) then category='BF Ever (Yes)'; else category='BF Ever (No)'; output; run;      ... View more

@AhmedAl_Attar This is good ! The guy who created the data is not here anymore. However, I have just learned that variable 'category' is inclusive which is meaning: BF_1: individual received treatment for more than one month. BF_3 means that individual received treatment for more than 3 months instead equal to 3 months., so forth so on. So below simple array is correct even though I still need complementary categories of (No) for each category created exactly by the manner you demonstrated in your last do loop. Please note that the logic underlying "if EVER=1 and x(i)=0 then bf='BF 3M (Yes)'; " is true given internal data log note.   Now question becomes: If I modify your code to ...min(of bf3--bf5) instead min(of bf1--bf5) do we still need 'else' logic there? Sorry, if it's only confusing. Would you mind to show me how your do loop would be modified given 'category' was inclusive as I described above?     array x (12) BF_1-BF_12; do i=1 to 12; if EVER=1 and x(i)=0 then cat='BF 3M (Yes)'; end; if EVER =0 then cat='BF Ever (No)'; if BF_1=1 or BF_2=1 then cat='BF 3M (Yes)'; if BF_3=1 or BF_4=1 or BF_5=1 then cat='BF 6M (Yes)'; if BF_6=1 or BF_7=1 or BF_8=1 then cat='BF 9M (Yes)'; if BF_9=1 or BF_10=1 or BF_11=1 then cat='BF 12M (Yes)'; if BF_12=1 then cat='BF 12M+ (Yes)'; where 'cat' in above code means: BF Ever (No) vs (Yes)=never vs ever received treatment BF 3M (Yes)=received BF treatment for 3 months BF 6M (Yes)=received BF treatment for 6 months BF 9M (Yes)=received BF treatment for 9 months BF 12M (Yes)=received BF treatment for 12 months BF 12M+ (Yes)=received BF treatment beyond 12 months ... View more

@AhmedAl_Attar Q1: Please see attached image below. I'm awfully sorry for providing you with a screenshot for an idea of how my real data looks like. My SAS on laptop had expired.    Q2: What does your categories, such as "BF 12M+ (Yes)" mean? - "BF 12M+ (Yes)" means that given individual had received BF treatment for longer than 12 months vs "BF 12M+ (No)"  means "else, then less than 12 months". - Yes, 1==Yes, 0==No. - While 12M+ means 12 months or More? Yes, you're right. - Are your bf1 -- bf12 represent months? Yes, they're number of months that treatment BF continued. - As shown in the data screen shot, id=1 invidual volunteered for measurements at his/her age of 1,2,3 and 4 and known to have BF treatment for more than 12 months. Thus also takes value 1 for "ever" variable. Individual id=2 volunteered for only one measurement at age category of 1 and known to have BF treatment for only 2 months.  Individual id=3 had never received a BF treatment thus took "0" for "ever" variable and 0 for across all BF dummy variables. Same logic applies for id=4 who has 3 measurements but treatment continued for only 1 month.     Thank you very much Ahmed! ... View more