The table is ordered in decreasing frequency from top to bottom, but the plot is ordered in increasing frequency from bottom to top. I understand this is probably b/c the y axis value is increasing in that direction, but it still just looks suboptimal viewing the table and plot together. Is there any way to make the ordering between table and plot consistent? As a side question, even though I set a label, is there any way to not have the original variable name be used (and especially so prominently in the plot title) - there could be times when the original variable name is even uglier (eg, from ADaM/SDTM data). Here is my code ods graphics on; proc freq data=pg1.storm_final order=freq; tables StartDate / plots=freqplot(orient=horizontal scale=percent); format StartDate monname.; label StartDate='Storm Month'; run; Example data comes from this SAS course:  https://learn.sas.com/course/view.php?id=118   ... View more

I'm having trouble adding blank lines in proc report. I'd like to have a blank line everytime the Make changes for improved readability.  From googling I thought it's just the compute block part of my code below. proc report data=sashelp.cars out=test_out(drop=_BREAK_); column Make Cylinders n; define Make / group; define Cylinders / group; define n / 'Count'; compute after Make; line=' '; endcomp; run; Below is what the 1st few rows of what's in the report. I'd like blank rows where I indicated with <  And here is test_out. It seems extra rows are added (highlighted) but they are not blank. As a side note, note how row 2 repeats 'Acura' but above only has each Make 1x. This part is also confusing to me. Does any1 know how to actually get blank rows? Thx in advance for any help 🙂 ... View more

I'm a beginner to SAS. I'm trying to have nested counts presented cleanly as follows.   This is code to generate toy data, as well as 3 attempts I tried (from hours of googling how to do this) that give the correct info, but none are as clean as the above. Imagine how awful they'd look when I need more than 2 layers of nesting. and multiplying together the total number of categories per variables is large, but the actual number of their combinations in the data is small. data toy; input A $ B $; datalines; A1 B1 A1 B2 A1 B2 A2 B1 A2 B1 A2 B1 A2 B2 A2 B2 A2 B2 A2 B2 ; proc tabulate data=toy; class A B; table A*(B all) all; run; proc summary data=toy print; class A B; run; proc freq data=toy; tables A*B / nopercent norow nocol; run; ... View more