Skip to content
Prev 386243 / 398513 Next

analyzing results from Tuesday's US elections

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.
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
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
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
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
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}}

Thread (22 messages)

Spencer Graves analyzing results from Tuesday's US elections Nov 1 Abby Spurdle analyzing results from Tuesday's US elections Nov 7 Spencer Graves analyzing results from Tuesday's US elections Nov 8 Bert Gunter analyzing results from Tuesday's US elections Nov 8 Abby Spurdle analyzing results from Tuesday's US elections Nov 8 Bert Gunter analyzing results from Tuesday's US elections Nov 8 Matthew analyzing results from Tuesday's US elections Nov 8 Alexandra Thorn analyzing results from Tuesday's US elections Nov 9 Matthew analyzing results from Tuesday's US elections Nov 9 Marc Roos analyzing results from Tuesday's US elections Nov 9 Abby Spurdle analyzing results from Tuesday's US elections Nov 9 Bert Gunter analyzing results from Tuesday's US elections Nov 9 Martin Møller Skarbiniks Pedersen analyzing results from Tuesday's US elections Nov 11 Rolf Turner analyzing results from Tuesday's US elections Nov 13 Jeff Newmiller analyzing results from Tuesday's US elections Nov 13 Rolf Turner analyzing results from Tuesday's US elections Nov 14 Abby Spurdle analyzing results from Tuesday's US elections Nov 15 Matthew analyzing results from Tuesday's US elections Nov 15 Abby Spurdle analyzing results from Tuesday's US elections Nov 16 Matthew analyzing results from Tuesday's US elections Nov 16 Matthew analyzing results from Tuesday's US elections Nov 16 Marc Roos analyzing results from Tuesday's US elections Nov 18