Thank you for the input. I remain torn between the simple bar chart versus the original intent to visually map the biopsy frequency to an intuitive cartoon-like map of the tubular organ biopsied. I note that gradar can produce bar-like graphs by choosing the "spoke" formatted chart (see first two images below). However, I think your suggestion but as a vertical bar, in which the order from left-to-right might be enough intuitive orientation, especially if I change the position numbers to their corresponding positions on a clock face (see third image). That'll make left to right sideness intuitive and only lose the intuitiveness of the up versus down positions. But I think I most agree that some form of a bar graph will be the most easily created AND understood. Just got to get the axis straightened out. Thank you for the suggestion and code example. Very helpful! Dave ... View more

I'm liking the representation using the proc gradar. Here is an image closing in on something acceptable: Here the vertices correspond to the biopsy position of the tubular organ with position "1" at 12 o'clock high. The problems are that this procedure doesn't seem to map a zero frequency and so I've had to change these to "1" in the count in order for the spoke position to show up in the graph. Suggestions still appreciated. I realize I've gotten away from my original intent on how to graph but still appreciative of any advice. Here is the code: data bx_position ; input specie $ position count ; datalines; goat 1 1 goat 2 5 goat 3 65 goat 4 27 goat 5 1 goat 6 18 goat 7 37 goat 8 2 sheep 1 1 sheep 2 6 sheep 3 78 sheep 4 32 sheep 5 2 sheep 6 14 sheep 7 45 sheep 8 1 ; run; proc gradar data=bx_position; chart position /   freq=COUNT   overlay=specie   startype=wedge   lstar=1 1 1   cstar=(red blue black)   ; run; quit; ... View more

Thank you. That is very helpful. I was struggling to even get myself to go down this path. I think I can modify this to satisfy my needs. I also started looking at radar/polar graphs as another means to my end. Dave ... View more

Here are two possibilities. I suspect the first is the simplest and would suffice. The numbers are the frequency on which color choice and/or shading would depend. It is actually supposed to be a map of biopsy regions of a tubular organ. So the following would be a more optimal graphic. Thanks for all suggestions. Dave ... View more

I was unfamiliar with 'nodupkey' but it works very well. Thank you for the suggestion. Dave ... View more

Thank you. I'm not familiar with Proc SQL but was successful in applying your code. I did need to remove the comma in the class statement of Proc Tabulate. Other than that, this solution worked great. Thank you, Dave ... View more

Reeza, The table produced by proc freq would be fine. So rows would be specie (goat of sheep) and columns would be number of females, intact males, and castrated males. the cells of the table would then tell me the number or proportion of each specie*gender pairing. Only trouble is the GLM format of the imported data over represents each animal with too many rows. I'm working on the transformed dataset now (it always takes me a while to figure out how to do this) but I was wondering if there was a more elegant procedure or command within proc freq that might allow me to identify subjects. Hope that answers your question. Dave ... View more

Thank you for those suggestions and link. That's very helpful. I will give these a try as I spruce up this figure for a pub. I'll give them a try and re-post if I have some lingering issues. Dave ... View more

So I gave the sgplot solution a try, too. Here is the code and the output for any that are interested in an example. The SUGI paper was quite helpful in getting this done. Thank you for bringing this to my attention. Dave /* The following uses code instruction and example from PharmSUG-2014-DG07.pdf */ data timeline ; input specie $ subjn Bx1 Bx2 Bx3 Bx4 Bx5 Bx6 Bx7 BxP ARF Cx Nx ; datalines; goat 1 194 384 452 . . . . 564 648 994 1057 goat 2 194 384 452 . . . . 564 648 995 1057 goat 3 194 384 452 564 662 721 . . 648 . 748 goat 4 193 383 451 563 . . . 661 647 1032 1056 sheep 5 198 387 417 452 485 583 . . . . 747 sheep 6 198 . . . . . . 387 779 849 908 sheep 7 196 385 415 450 483 581 752 . 777 . 815 sheep 8 192 381 411 446 479 577 748 . 773 . 811 ; run; footnote  'open circle = negative biopsy'; footnote2 'closed circle = postive biopsy'; footnote3 'closed star = begin clinical signs'; footnote4 'plus sign = moved locations'; proc sgplot data=timeline ; /* subject timelines as bars colored by specie */ hbarparm category=subjn response=Nx / group=specie barwidth=0.5 ; yaxis type=discrete display=(novalues noticks) label="Inoculates" ; xaxis type=linear label="Post-Inoculation (days)" values=(0 to 1200 by 60) ; /* location move */ scatter x=ARF y=subjn / markerattrs=(symbol=plus size=12 color=black) ; /* start clinical signs (filled star) */ scatter x=Cx y=subjn / markerattrs=(symbol=starfilled size=12 color=black) ; /* NEG Biopsies (open circles) */ scatter x=Bx1 y=subjn / markerattrs=(symbol=circle size=9 color=black) ; scatter x=Bx2 y=subjn / markerattrs=(symbol=circle size=9 color=black) ; scatter x=Bx3 y=subjn / markerattrs=(symbol=circle size=9 color=black) ; scatter x=Bx4 y=subjn / markerattrs=(symbol=circle size=9 color=black) ; scatter x=Bx5 y=subjn / markerattrs=(symbol=circle size=9 color=black) ; scatter x=Bx6 y=subjn / markerattrs=(symbol=circle size=9 color=black) ; scatter x=Bx7 y=subjn / markerattrs=(symbol=circle size=9 color=black) ; /* POS Biopsies (filled circles) */ scatter x=BxP y=subjn / markerattrs=(symbol=circlefilled size=9 color=black) ; run; ... View more

Quentin, What timing! I followed the link and you are right that the swimmer plot is very nice. Thank you for passing that information along. Between the two replies I've had already, I think I should be able to produce what I am looking for. Thank you, Dave ... View more

Thank you, ballardw! Below is the output image that I get with your code. It is very close to what I am interested in. I will study the code you've used and see if I can understand the elements and modify as need arises. Thanks again. Very helpful. Dave ... View more

Thank you for your suggestion, Quentin. While certain aspects of this data could be viewed as a survival plot (time to becoming "POS"), the need is to show when the subject becomes positive in relation to the other events (all biopsy events, necropsy event, a move from one facility to another, and when/if becomes clinical). So I think my need is more a project timeline than a time-to-specific-event (like in survival analysis). Yes, I think color coding would be very effective, as you describe, the time the subject becomes positive would be either filled and/or red regardless if that through a specific biopsy or at necropsy. All the best, Dave ... View more