RESENT
INITIAL EMAIL, TOO BIG
ATTACHMENTS REPLACED WITH LINKS
I created a dataset, linked.
Had to manually copy and paste from the NY Times website.
> head (data, 3)
STATE EQCOUNTY RMARGIN_2016 RMARGIN_2020 NVOTERS_2020 SUB_STATEVAL_2016
1 Alabama Mobile 13.3 12 181783 0
2 Alabama Dallas -37.5 -38 17861 0
3 Alabama Tuscaloosa 19.3 15 89760 0
> tail (data, 3)
STATE EQCOUNTY RMARGIN_2016 RMARGIN_2020 NVOTERS_2020 SUB_STATEVAL_2016
4248 Wyoming Uinta 58.5 63 9400 0
4249 Wyoming Sublette 63.0 62 4970 0
4250 Wyoming Johnson 64.3 61 4914 0
> head (data [data [,1] == "Alaska",], 3)
STATE EQCOUNTY RMARGIN_2016 RMARGIN_2020 NVOTERS_2020 SUB_STATEVAL_2016
68 Alaska ED 40 14.7 -24.0 82 1
69 Alaska ED 37 14.7 -1.7 173 1
70 Alaska ED 38 14.7 -0.4 249 1
EQCounty, is the County or Equivalent.
Several states, D.C., Alaska, Connecticut, Maine, Massachusetts, Rhode
Island and Vermont are different.
RMargin(s) are the republican percentages minus the democrate
percentages, as 2 or 3 digit numbers between 0 and 100.
The last column is 0s or 1s, with 1s for Alaska, Connecticut, Maine,
Massachusetts, Rhode Island and Vermont, where I didn't have the 2016
margins, so the 2016 margins have been replaced with state-levels
values.
Then I scaled the margins, based on the number of voters.
i.e.
wx2016 <- 1000 * x2016 * nv / max.nv
(Where x2016 is equal to RMARGIN_2020, and nv is equal to NVOTERS_2020).
There may be a much better way.
And came up the following plots (linked) and output (follows):
---INPUT---
PATH = "<PATH TO FILE>"
data = read.csv (PATH, header=TRUE)
#raw data
x2016 <- as.numeric (data$RMARGIN_2016)
x2020 <- as.numeric (data$RMARGIN_2020)
nv <- as.numeric (data$NVOTERS_2020)
subs <- as.logical (data$SUB_STATEVAL)
#computed data
max.nv <- max (nv)
wx2016 <- 1000 * x2016 * nv / max.nv
wx2020 <- 1000 * x2020 * nv / max.nv
diffs <- wx2020 - wx2016
OFFSET <- 500
p0 <- par (mfrow = c (2, 2) )
#plot 1
plot (wx2016, wx2020,
main="All Votes\n(By County, or Equivalent)",
xlab="Scaled Republican Margin, 2016", ylab="Scaled Republican Margin, 2020")
abline (h=0, v=0, lty=2)
#plot 2
OFFSET <- 200
plot (wx2016, wx2020,
xlim = c (-OFFSET, OFFSET), ylim = c (-OFFSET, OFFSET),
main="All Votes\n(Zoomed In)",
xlab="Scaled Republican Margin, 2016", ylab="Scaled Republican Margin, 2020")
abline (h=0, v=0, lty=2)
OFFSET <- 1000
#plot 3
J1 <- order (diffs, decreasing=TRUE)[1:400]
plot (wx2016 [J1], wx2020 [J1],
xlim = c (-OFFSET, OFFSET), ylim = c (-OFFSET, OFFSET),
main="400 Biggest Shifts Towards Republican",
xlab="Scaled Republican Margin, 2016", ylab="Scaled Republican Margin, 2020")
abline (h=0, v=0, lty=2)
abline (a=0, b=1, lty=2)
#plot 4
J2 <- order (diffs)[1:400]
plot (wx2016 [J2], wx2020 [J2],
xlim = c (-OFFSET, OFFSET), ylim = c (-OFFSET, OFFSET),
main="400 Biggest Shifts Towards Democrat",
xlab="Scaled Republican Margin, 2016", ylab="Scaled Republican Margin, 2020")
abline (h=0, v=0, lty=2)
abline (a=0, b=1, lty=2)
par (p0)
#most democrat
I = order (wx2020)[1:30]
cbind (data [I,], scaled.dem.vote = -1 * wx2020 [I])
#biggest move toward democrat
head (cbind (data [J2,], diffs = diffs [J2]), 30)
---OUTPUT---
#most democrat
> cbind (data [I,], scaled.dem.vote = -1 * wx2020 [I])
STATE EQCOUNTY RMARGIN_2016 RMARGIN_2020
NVOTERS_2020 SUB_STATEVAL_2016 scaled.dem.vote
229 California Los Angeles -49.3 -44
3674850 0 44000.000
769 Illinois Cook -53.1 -47
1897721 0 24271.164
4073 Washington King -48.8 -53
1188152 0 17135.953
3092 Pennsylvania Philadelphia -67.0 -63
701647 0 12028.725
215 California Alameda -63.5 -64
625710 0 10897.163
227 California Santa Clara -52.1 -49
726186 0 9682.875
238 California San Diego -19.7 -23
1546144 0 9676.942
2683 New York Brooklyn -62.0 -49
693937 0 9252.871
2162 Minnesota Hennepin -34.9 -43
753716 0 8819.350
2074 Michigan Wayne -37.1 -37
863382 0 8692.908
2673 New York Manhattan -76.9 -70
446861 0 8511.986
221 California San Francisco -75.2 -73
413642 0 8216.898
3495 Texas Dallas -26.1 -32
920772 0 8017.934
1741 Maryland Prince George's -79.7 -80
365857 0 7964.559
510 Florida Broward -34.9 -30
959418 0 7832.303
3057 Oregon Multnomah -56.3 -61
458395 0 7609.044
3563 Texas Travis -38.6 -45
605034 0 7408.882
565 Georgia DeKalb -62.9 -67
369341 0 6733.839
3942 Virginia Fairfax -35.8 -42
578931 0 6616.624
492 D.C. D.C. -86.4 -87
279152 0 6608.766
562 Georgia Fulton -40.9 -46
522050 0 6534.770
230 California Contra Costa -43.0 -48
498340 0 6509.196
2674 New York Queens -53.6 -39
597928 0 6345.617
257 Colorado Denver -54.8 -64
350606 0 6106.041
2677 New York Bronx -79.1 -66
329638 0 5920.271
3530 Texas Harris -12.3 -13
1633671 0 5779.208
1718 Maryland Montgomery -55.4 -57
369405 0 5729.781
2888 Ohio Cuyahoga -35.2 -34
605268 0 5599.987
2745 North Carolina Mecklenburg -29.4 -35
565980 0 5390.506
2894 Ohio Franklin -25.8 -31
606022 0 5112.231
#biggest move toward democrat
> head (cbind (data [J2,], diffs = diffs [J2]), 30)
STATE EQCOUNTY RMARGIN_2016 RMARGIN_2020
NVOTERS_2020 SUB_STATEVAL_2016 diffs
1751 Massachusetts Boston -26.8 -67.00
273133 1 -2987.8625
113 Arizona Maricopa 2.8 -2.00
2046295 0 -2672.8209
3531 Texas Tarrant 8.6 -0.16
830104 0 -1978.7776
2162 Minnesota Hennepin -34.9 -43.00
753716 0 -1661.3194
3564 Texas Collin 16.7 5.00
486917 0 -1550.2480
3495 Texas Dallas -26.1 -32.00
920772 0 -1478.3065
238 California San Diego -19.7 -23.00
1546144 0 -1388.4309
563 Georgia Gwinnett -5.8 -18.00
413166 0 -1371.6547
3565 Texas Denton 20.0 8.00
416610 0 -1360.4147
4073 Washington King -48.8 -53.00
1188152 0 -1357.9434
564 Georgia Cobb -2.2 -14.00
393340 0 -1263.0208
2075 Michigan Oakland -8.1 -14.00
778418 0 -1249.7561
291 Colorado Jefferson -6.9 -19.00
376430 0 -1239.4528
292 Colorado El Paso 22.3 11.00
375058 0 -1153.2866
2321 Missouri St. Louis County -16.2 -24.00
528107 0 -1120.9259
3563 Texas Travis -38.6 -45.00
605034 0 -1053.7077
277 Colorado Arapahoe -14.1 -25.00
346740 0 -1028.4681
2744 North Carolina Wake -20.2 -26.00
624049 0 -984.9339
3942 Virginia Fairfax -35.8 -42.00
578931 0 -976.7398
1116 Kansas Johnson 2.6 -8.00
338343 0 -975.9407
3562 Texas Bexar -13.4 -18.00
757667 0 -948.4110
2077 Michigan Kent 3.1 -6.00
359915 0 -891.2545
257 Colorado Denver -54.8 -64.00
350606 0 -877.7434
110 Arizona Pima -13.6 -20.00
501058 0 -872.6264
2625 New Jersey Monmouth 9.3 -1.60
292654 0 -868.0432
2745 North Carolina Mecklenburg -29.4 -35.00
565980 0 -862.4809
3567 Texas Williamson 9.7 -1.30
287696 0 -861.1660
2894 Ohio Franklin -25.8 -31.00
606022 0 -857.5355
203 California Riverside -5.4 -11.00
558759 0 -851.4770
3966 Virginia Virginia Beach 3.5 -8.00
253477 0 -793.2257
DISCLAIMER:\ I can not guarantee the accuracy of this da...{{dropped:15}}
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