analyzing results from Tuesday's US elections

I've updated the dataset.
(Which now includes turnout and population estimates).

Also, I've found some anomalous features in the data.
(Namely, more "straight lines" than what I would intuitively expect).

The dataset/description are on my website.
(Links at bottom).

    ####################################
    #set PATH as required
    ####################################
    data <- read.csv (PATH, header=TRUE)
    head (data, 3)

I took a subset, where the Dem/Rep margins have reversed between the
2016 and 2020 elections.

    rev.results <- (sign (data$RMARGIN_2016) + sign (data$RMARGIN_2020) == 0)
    data2 <- data [data$SUBSV1 != 1 & rev.results,]
    sc <- paste (data2$STATE, data2$EQCOUNTY, sep=": ")
    head (data2, 3)

Then created two plots, attached.
(1) Republican margin vs voter turnout.
(2) Republican margin vs log (number of votes).

In both cases, there are near-straight lines.
Re-iterating, more than what I would intuitively expect.

    library (probhat)

    plot1 <- function ()
    {   x <- with (data2, cbind (x1=RMARGIN_2020, x2=TURNOUT_2020) )
        plot (pdfmv.cks (x, smoothness = c (1, 1) ), contours=FALSE,
hcv=TRUE, n=80,
            xlim = c (-2.5, 10), ylim = c (40, 52.5),
            main="US Counties\n(with reversed results, over 2016/2020
elections)",
            xlab="Republican Margin, 2020", ylab="Voter Turnout, 2020")
        points (x, pch=16, col="#000000")
        abline (v=0, h=50, lty=2)

        I1 <- (sc == "Colorado: Alamosa" | sc == "Georgia: Burke" | sc
== "Ohio: Lorain")
        I2 <- (sc == "South Carolina: Clarendon" | sc == "Ohio: Mahoning")
        sc [! (I1 | I2)] <- ""

        k <- lm (TURNOUT_2020 ~ RMARGIN_2020, data = data2 [I1,])$coef
        abline (a = k [1], b = k [2])

        points (x [I1 | I2,], col="white")
        text (x [,1] + 0.2, x [,2], sc, adj = c (0, 0.5) )
    }

    plot2 <- function ()
    {   x <- with (data2, cbind (x1=RMARGIN_2020, x2 = log (NVOTES_2020) ) )
        plot (pdfmv.cks (x, smoothness = c (1, 1) ), contours=FALSE,
hcv=TRUE, n=80,
            xlim = c (-2.5, 35),
            main="US Counties\n(with reversed results, over 2016/2020
elections)",
            xlab="Republican Margin, 2020", ylab="log (Number of Votes), 2020")
        points (x, pch=16, col="#000000")
        abline (v=0, lty=2)

        sc <- paste (data2$STATE, data2$EQCOUNTY, sep=": ")
        I1 <- (sc == "Texas: Kenedy")
        I2 <- (sc == "Texas: Reeves" | sc == "New York: Rockland")

        k <- lm (log (NVOTES_2020) ~ RMARGIN_2020, data = data2 [I1 | I2,])$coef
        abline (a = k [1], b = k [2])

        points (x [I1 | I2,], col="white")
        text (x [I1, 1] - 0.5, x [I1, 2], sc [I1], adj = c (1, 0.5) )
        text (x [I2, 1] + 0.5, x [I2, 2], sc [I2], adj = c (0, 0.5) )
    }

    plot1 ()
    plot2 ()

https://sites.google.com/site/spurdlea/us_election_2020
https://sites.google.com/site/spurdlea/exts/election_results_2.txt

On Fri, 13 Nov 2020 19:02:19 -0800
Jeff Newmiller <jdnewmil at dcn.davis.ca.us> wrote:

It was explained in the video... his counts were so small that they
spanned the 1-9 and 10-99 ranges.

Sorry, missed that.  I'll have to watch the video again.

Thanks.

cheers,

Rolf

-------------- next part --------------
A non-text attachment was scrubbed...
Name: plot1.png
Type: image/png
Size: 24878 bytes
Desc: not available
URL: <https://stat.ethz.ch/pipermail/r-help/attachments/20201115/55684c86/attachment.png>

-------------- next part --------------
A non-text attachment was scrubbed...
Name: plot2.png
Type: image/png
Size: 22769 bytes
Desc: not available
URL: <https://stat.ethz.ch/pipermail/r-help/attachments/20201115/55684c86/attachment-0001.png>