Hello:
What can you tell me about plans to analyze data from this year's
general election, especially to detect possible fraud?
I might be able to help with such an effort. I have NOT done
much with election data, but I have developed tools for data analysis,
including web scraping, and included them in R packages available on the
Comprehensive R Archive Network (CRAN) and GitHub.[1]
Penny Abernathy, who holds the Knight Chair in Journalism and
Digital Media Economics at UNC-Chapel Hill, told me that the electoral
fraud that disqualified the official winner from NC-09 to the US House
in 2018 was detected by a college prof, who accessed the data two weeks
after the election.[2]
Spencer Graves
[1]
https://github.com/sbgraves237
[2]
https://en.wikiversity.org/wiki/Local_Journalism_Sustainability_Act
> What can you tell me about plans to analyze data from this year's > general election, especially to detect possible fraud?I was wondering if there's any R packages with out-of-the-box functions for this sort of thing. Can you please let us know, if you find any.> I might be able to help with such an effort. I have NOT done > much with election data, but I have developed tools for data analysis, > including web scraping, and included them in R packages available on the > Comprehensive R Archive Network (CRAN) and GitHub.[1]Do you have a URL for detailed election results? Or even better, a nice R-friendly CSV file... I recognize that the results aren't complete. And that such a file may need to be updated later. But that doesn't necessarily prevent modelling now.
On 2020-11-07 23:39, Abby Spurdle wrote:>> What can you tell me about plans to analyze data from this year's >> general election, especially to detect possible fraud? > > I was wondering if there's any R packages with out-of-the-box > functions for this sort of thing. > Can you please let us know, if you find any. > >> I might be able to help with such an effort. I have NOT done >> much with election data, but I have developed tools for data analysis, >> including web scraping, and included them in R packages available on the >> Comprehensive R Archive Network (CRAN) and GitHub.[1] > > Do you have a URL for detailed election results? > Or even better, a nice R-friendly CSV file... > > I recognize that the results aren't complete. > And that such a file may need to be updated later. > But that doesn't necessarily prevent modelling now.I asked, because I don't know of any such. With the increasingly vicious, widespread and systematic attacks on the integrity of elections in the US, I think it would be good to have a central database of election results with tools regularly scraping websites of local and state election authorities. Whenever new data were posted, the software would update the central repository and send emails to anyone interested. That could simplify data acquisition, because historical data could already be available there. And it would be one standard format for the entire US and maybe the world. This could be extremely valuable in exposing electoral fraud, thereby reducing its magnitude and effectiveness. This is a global problem, but it seems to have gotten dramatically worse in the US in recent years.[2] I'd like to join -- or organize -- a team of people working on this. If we can create the database and data analysis tools in a package like Ecfun on CRAN, I think we can interest college profs, especially those teaching statistics to political science students, who would love to involve their students in something like this. They could access data real time in classes, analyze it using standard tools that we could develop, and involve their students in discussing what it means and what it doesn't. They could discuss Bayesian sequential updating and quality control concepts using data that are real and relevant to the lives of their students. It could help get students excited about both statistics and elections. Such a project may already exist. I know there are projects at some major universities that sound like they might support this. However with the limited time I've invested in this so far, I didn't find any that seemed to provide easy access to such data and an easy way to join such a project. Ballotpedia has such data but don't want help in analyzing it and asked for a few hundred dollars for data for one election cycle in Missouri, which is what I requested. I can get that for free from the web site of the Missouri Secretary of State. I thought I might next ask the Carter Center about this. However, but I'm totally consumed with other priorities right now. I don't plan to do anything on this in the short term -- unless I can find collaborators. If such a central database doesn't exist -- and maybe even if it does -- I thought it might be good to make all the data available in a standard format in Wikidata, which is a project of the Wikimedia Foundation, which is also the parent organization of Wikipedia. Then I could help create software and documentation on how to scrape data from the web sites of different election organizations that have it and automatically update Wikidata while also sending emails to people who express interest in those election results. Then we could create software for analyzing such data and make that available, e.g., on Wikiversity, which is another project of the Wikimedia Foundation -- with the R code in Ecfun or some other CRAN package. If we start now, I think we could have something mediocre in time for various local elections that occur next year with improvements for the 2022 US Congressional elections and something even better for the 2024 US presidential elections. Thanks for asking. Spencer Graves [1] https://github.com/sbgraves237 [2] https://en.wikiversity.org/wiki/Electoral_integrity_in_the_United_States
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}}
For those who are interested: Very nice examples of (static) statistical graphics on election results can be found here: https://www.nytimes.com/interactive/2020/11/09/us/arizona-election-battleground-state-counties.html?action=click&module=Spotlight&pgtype=Homepage Takes multidisciplinary teams and lots of hard work to produce, I would guess. Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Mon, Nov 9, 2020 at 4:46 PM Abby Spurdle <spurdle.a at gmail.com> wrote:> 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 data, or any conclusions. > > NOTE: > Reiterating, several states used state-level values for 2016. > (So, the Boston value above, may be off). > > Monospaced fonts are required for reading the contents of this email. > > LINKS: > > https://sites.google.com/site/spurdlea/temp_election > > https://sites.google.com/site/spurdlea/exts/election_data.txt >[[alternative HTML version deleted]]
Martin Møller Skarbiniks Pedersen
2020-Nov-12 00:23 UTC
[R] analyzing results from Tuesday's US elections
Please watch this video if you wrongly believe that Benford's law easily can be applied to elections results. https://youtu.be/etx0k1nLn78 On Sun, Nov 1, 2020, 21:17 Spencer Graves < spencer.graves at effectivedefense.org> wrote:> Hello: > > > What can you tell me about plans to analyze data from this year's > general election, especially to detect possible fraud? > > > I might be able to help with such an effort. I have NOT done > much with election data, but I have developed tools for data analysis, > including web scraping, and included them in R packages available on the > Comprehensive R Archive Network (CRAN) and GitHub.[1] > > > Penny Abernathy, who holds the Knight Chair in Journalism and > Digital Media Economics at UNC-Chapel Hill, told me that the electoral > fraud that disqualified the official winner from NC-09 to the US House > in 2018 was detected by a college prof, who accessed the data two weeks > after the election.[2] > > > Spencer Graves > > > [1] > https://github.com/sbgraves237 > > > [2] > https://en.wikiversity.org/wiki/Local_Journalism_Sustainability_Act > > ______________________________________________ > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. >[[alternative HTML version deleted]]
On Thu, 12 Nov 2020 01:23:06 +0100 Martin M?ller Skarbiniks Pedersen <traxplayer at gmail.com> wrote:> Please watch this video if you wrongly believe that Benford's law > easily can be applied to elections results. > > https://youtu.be/etx0k1nLn78Just watched this video and found it to be delightfully enlightening and entertaining. (Thank you Martin for posting the link.) However a question springs to mind: why is it the case that Trump's vote counts in Chicago *do* seem to follow Benford's law (at least roughly) when, as is apparently to be expected, Biden's don't? Has anyone any explanation for this? Any ideas? cheers, Rolf Turner -- Honorary Research Fellow Department of Statistics University of Auckland Phone: +64-9-373-7599 ext. 88276