Men out-registering Women in WA State?

Here is a first look at some point to point registration data after yesterday's VRDB drop; the first in 2 mos and 17 days.  The VRDB had net increase from 12/31/2018 to 07/17/2019 of 58,563. That increase looked liked this:

r3all[StatusCode == "A",.N]
[1] 4373910
s3all[StatusCode == "A",.N]
[1] 4432473
s3all[StatusCode == "A",.N] - r3all[StatusCode == "A",.N]
[1] 58563

Data below is for StatusCode == "A" (Active voters only and there are still some 'Pending').  Avoiding the 13K - 15K labelled U (unknown) for gender, women always outnumber men in the WA States VRDB. Women always outvote men too, but that is another story: 

m2[Gender == "F",fsum(July2019)]
[1] 2294250
m2[Gender == "M",fsum(July2019)]
[1] 2123081

m2[Gender == "F",fsum(December2018)]
[1] 2266893
m2[Gender == "M",fsum(December2018)]
[1] 2093279

And yet when I started to sort through the counties, I started seeing some unlikely  gender registration diffs for the #metoo, hyper-vigilant voting era. In some of the larger counties, new male registrations were either exceeding or holding pace with new female registrations: 

m2[Gender == "M",][order(-diff)][1:10]
    CountyCode Gender July2019 December2018 diff
 1:         PI      M   243305       237332 5973
 2:         KI      M   633168       628292 4876
 3:         SN      M   225978       222894 3084
 4:         CR      M   137178       135305 1873
 5:         KP      M    83249        81421 1828
 6:         SP      M   151545       149819 1726
 7:         TH      M    86799        85123 1676
 8:         BE      M    53353        52399  954
 9:         SK      M    36608        35848  760
10:         CZ      M    31724        31039  685

m2[Gender == "F",][order(-diff)][1:10]
    CountyCode Gender July2019 December2018 diff
 1:         PI      F   269010       262935 6075
 2:         KI      F   668101       664669 3432
 3:         SN      F   242750       240213 2537
 4:         CR      F   150771       148820 1951
 5:         TH      F    97289        95514 1775
 6:         KP      F    88548        86887 1661
 7:         SP      F   166993       165372 1621
 8:         BE      F    57230        56250  980
 9:         SK      F    40637        39975  662
10:         CZ      F    34291        33664  627

m2[,sum(diff)]

[1] 58534

m2[Gender == "F",sum(diff)]
[1] 27357

m2[Gender == "M",sum(diff)]
[1] 29802

This table below represents the increase or decrease in gender ('Male - Female)' between December 2018 and July 2019 for the top 10 counties. Red represents net female increase, black represents net male increase:

m2b[Gender == "F" | Gender == "M",dcast(.SD,CountyCode~ Gender,value.var="diff")][,.SD[,.(diff=M-F)],.(CountyCode,M,F)][order(-diff)]
    CountyCode    M    F diff
 1:         KI 4876 3432 1444
 2:         SN 3084 2537  547
 3:         KP 1828 1661  167
 4:         SP 1726 1621  105
 5:         YA  603  604   -1
 6:         WM  348  351   -3
 7:         BE  954  980  -26
 8:         CR 1873 1951  -78
 9:         TH 1676 1775  -99
10:         PI 5973 6075 -102

hmmm.... I don't like going 2.5 months without a VRDB drop...