I'm slowly learning macros and seem to have some issues along the way that I don't quite know how to resolve yet. I have different graphs that I need to create and want to save so each PDF is named with the state abbreviation followed by '_Origpop_RSEGraph.pdf'. I have an example below with the first state; however, when I run this, my PDF gets saved as &stateabb._Origpop_RSEGraph.pdf. I want it to be saved as 'FL_Origpop_RSEGraph.pdf'. (FYI - I have it saved to a network drive, so I just put desktop in front of the path.) Thanks!   %macro RSE_origpop(stateabb, state); options orientation=landscape nodate nonumber; ods pdf file = 'desktop\&stateabb._Origpop_RSEGraph.pdf' dpi=300; ods graphics on / antialias=on antialiasmax=9500 width=2500 height=1800; proc sgplot data=&stateabb.outcome.&stateabb._all_stats;     format Threshold Threshold.;     scatter x=cases y=rse_rate;     xaxis label='Cases';     yaxis label='Relative standard error';     title1 'RSE as a function of the number of cases';     title2 '&state';     refline 30 / axis=y lineattrs=(pattern=shortdash color=red);     where Threshold=0; run; title1; title2; ods pdf close; %mend RSE_origpop; %RSE_origpop(FL, Florida); ... View more

I'm creating a series of scatterplots using proc sgpanel. I want them to be organised by my population categories, so I created a new variable to use a number so they're in the proper order. The thing I want to change is the heading in each cell. I took out the variable name, so now it just shows '1', '2', etc. However, I want them to say 'Total population, 5k', 'Total population, 10k', etc. Is there a way to do this? Or do I have to set it up a certain way before doing proc sgpanel? Thanks!   data want;     set want;         if series ='tot5k' then nseries=1;         else if series='tot10k' then nseries=2;         else if series='tot15k' then nseries=3; run; proc format;     value nseries     1='Total population, 5k'                                2='Total population, 10k'                                3='Total population, 15k'; run; ods graphics / antialias=on antialiasmax=4300 subpixel=on; proc sgpanel data=want;     panelby nseries / novarname columns=4;     scatter x=case y=measure;     colaxis label='Cases';     rowaxis label='Measure';     refline 30 / axis=y label=('Cut-point'); run; ... View more

I have two datasets that I am merging - one with population data and one with case count data. The population dataset has data for all counties by gender and age group. The case dataset has cases for counties by gender and age group only where there were cases (does not include all states (or age/gender stratifications in population dataset). When I combine these datasets (by state, county, gender, and age), cases is set to missing anywhere there were no cases. This is fine for states where no case data were available (I'd want to delete those where all cases missing), but I want to change those missing values to 0s only for states where case data are available.   Example of merged dataset:   State          County             Gender             Age_group       Pop          Cases 01               01111                  M                     1                     1500            . 01               01111                  M                     2                      2000           . 01               01111                  M                     3                      2300           . 01               01111          Through M ages 1-18, F ages 1-18                   . 08               08244                 M                     14                    1498           28 08               08244                 M                     15                    1247           21 08               08244                 M                     16                     2736           . 08               08244                 M                     17                    1932            . 08               08244                 M                     18                     1140          11   So for the example above - all cases under state 01 were set to missing because the population dataset had data, but the case dataset had no data for that state. If I could somehow delete observations where all values for case are missing for all values within a state, that would be good (or have them so they don't merge). Then I would want to insert 0s where a state has data, but values are missing (e.g., above I'd want 0s for state 08 where they are missing as I know the state has data, so 0 cases for those stratifications).   I'd want to end up with:   State          County             Gender             Age_group       Pop          Cases 08               08244                 M                     14                    1498          28 08               08244                 M                     15                    1247          21 08               08244                 M                     16                     2736          0 08               08244                 M                     17                    1932           0 08               08244                 M                     18                     1140         11   Not sure if my data step with merge or proc sql code can be modified to include the above, but here is my code for merging (data step returns missing values as shown in example 1 above, proc sql just returns non-missing obs, but that's not what I want as I want to set missing to 0 where a state has case data):   data casespop;     merge final.pop final.case (drop=YEAR);     by State County Gender Age_group; run;   proc sql;     create table merged_casepop as     select     case.State, case.County, case.Gender, pop.county, pop.Age_group, cases, pop     from   final.case, final.pop     where    case.State eq pop.State AND case.County eq pop.County AND case.Age_group eq pop.Age_group             AND case.Gender eq pop.Gender; quit;   Thanks! ... View more

I have two datasets and need to manipulate some of the data. The first one is for an outcome I'm interested in and has different county IDs, age groups, gender, and cases. An example is as follows:   County_ID                        Age_Group      Gender      Cases County1                                 1                     F               15 County1                                 1                    M                3 County1                                 3                    F                 2 County1                                 3                    M               20 County1                                 4                    F                 7 County1                                 4                    M                8 County2                                 1                    F                13 County2                                 1                    M               12   The second dataset contains population data by census tract (~70k rows) and I need to get the data so that I can have it arranged like above (each CT_ID repeated to cover all age groups and genders). It is currently organised as follows because of the original file:   CT_ID      Geography      M0_4    M5-9   M10_14  M15_19....through M85up (plus same age group variables for females(F0_4, etc.)) 111111      CT1_AL           40          56          68           85 111112      CT2_AL           46          60          74           91 111113      CT3_AL           30          42          55           62 111114      CT4_AL           50          65          79           93   So I want to transform the population data (second example) to look like this so I can later these aggregate census tracts into the counties and then merge so the population would be an additional column in the outcome table (first example):   CT_ID      Geography  Age_Group  Gender  Pop 111111      CT1_AL             1               M        40         111111      CT1_AL             2               M        56 111111      CT1_AL             3               M        68 111111      CT1_AL             4               M        85 111112      CT2_AL             1               M        46 111112      CT2_AL             2               M        60         111112      CT1_AL             3               M        74 111112      CT1_AL             4               M        91 111113      CT3_AL             1               M        30 111113      CT3_AL             2               M        42         111113      CT3_AL             3               M        55 111113      CT3_AL             4               M        62 111114      CT4_AL             1               M        50 111114      CT4_AL             2               M        66         111114      CT4_AL             3               M        79 111114      CT4_AL             4               M        93   I'm not sure if this is something an array would be good for or if I need to make use of @ or @ @  (or something totally different). Any help is appreciated! Thanks 🙂 ... View more