analyzing results from Tuesday's US elections

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analyzing results from Tuesday's US elections

Spencer Graves-4
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

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Re: analyzing results from Tuesday's US elections

aBBy Spurdle, ⍺XY
> 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.

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Re: analyzing results from Tuesday's US elections

Spencer Graves-4


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

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Re: analyzing results from Tuesday's US elections

Bert Gunter-2
Unless I misunderstand, clearly such a repository already exists -- the NY
Times, AP, CNN, etc. etc. already have interactive web pages that did
this!. It doesn't seem to make any difference to Trump conspiracy theorists
and partisans, though.

Also, as usual, a web search (on "central repository of US election
results") brought up what seemed like many relevant hits of historical
data. You may wish to contact one of these sources for further ino.


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 Sun, Nov 8, 2020 at 12:25 AM Spencer Graves <
[hidden email]> wrote:

>
>
> 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
>
> ______________________________________________
> [hidden email] 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]]

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Re: analyzing results from Tuesday's US elections

aBBy Spurdle, ⍺XY
> such a repository already exists -- the NY Times, AP, CNN, etc. etc. already have interactive web pages that did this

I've been looking for presidential election results, by ***county***.
I've found historic results, including results for 2016.

However, I can't find such a dataset, for 2020.
(Even though this seems like an obvious thing to publish).

I suspect that the NY Times has the data, but I haven't been able to
work where the data is on their website, or how to access it.

More ***specific*** suggestions would be appreciated...?

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Re: analyzing results from Tuesday's US elections

Bert Gunter-2
NYT  had interactive maps that reported  votes by county. So try contacting
them.


Bert

On Sun, Nov 8, 2020, 8:10 PM Abby Spurdle <[hidden email]> wrote:

> > such a repository already exists -- the NY Times, AP, CNN, etc. etc.
> already have interactive web pages that did this
>
> I've been looking for presidential election results, by ***county***.
> I've found historic results, including results for 2016.
>
> However, I can't find such a dataset, for 2020.
> (Even though this seems like an obvious thing to publish).
>
> I suspect that the NY Times has the data, but I haven't been able to
> work where the data is on their website, or how to access it.
>
> More ***specific*** suggestions would be appreciated...?
>

        [[alternative HTML version deleted]]

______________________________________________
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Re: analyzing results from Tuesday's US elections

Matthew McCormack
You can try here: https://decisiondeskhq.com/

I think they have what you are looking for. From their website:

"Create a FREE account to access up to the minute election results and
insights on all U.S. Federal elections. Decision Desk HQ & Øptimus
provide live election night coverage, race-specific results including
county-level returns, and exclusive race probabilities for key
battleground races."

    Also, this article provides a little, emphasis on little, of
statistical analysis of election results, but it may be a place to start.

https://www.theepochtimes.com/statistical-anomalies-in-biden-votes-analyses-indicate_3570518.html?utm_source=newsnoe&utm_medium=email&utm_campaign=breaking-2020-11-08-5

Matthew

On 11/8/20 11:25 PM, Bert Gunter wrote:

>          External Email - Use Caution
>
> NYT  had interactive maps that reported  votes by county. So try contacting
> them.
>
>
> Bert
>
> On Sun, Nov 8, 2020, 8:10 PM Abby Spurdle <[hidden email]> wrote:
>
>>> such a repository already exists -- the NY Times, AP, CNN, etc. etc.
>> already have interactive web pages that did this
>>
>> I've been looking for presidential election results, by ***county***.
>> I've found historic results, including results for 2016.
>>
>> However, I can't find such a dataset, for 2020.
>> (Even though this seems like an obvious thing to publish).
>>
>> I suspect that the NY Times has the data, but I haven't been able to
>> work where the data is on their website, or how to access it.
>>
>> More ***specific*** suggestions would be appreciated...?
>>
> [[alternative HTML version deleted]]
>
> ______________________________________________
> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
> https://secure-web.cisco.com/1C8m4dUQtDXEQdbAFTH153ehiJcvHuL_FkvDGeJBHhMRYZauAp6gdevfmLIh2MLpRjBx7LXAG9QpagRV63oMY5AyQF6uOkNa7JGw-0zGZKIFHoSuZtjpcIokATDMxqoJlVfCiktqIYXEiJcrovbnxo-DAgLEiREocQrn0yMbLc2A-gwR3CN9XurWkU21TUD1CLJ-3gpiCLKKe9BdHWdaeEA/https%3A%2F%2Fstat.ethz.ch%2Fmailman%2Flistinfo%2Fr-help
> PLEASE do read the posting guide http://secure-web.cisco.com/1ppZyk8SO6U25PKNDKtGQ-VIADLxXgKvnHc8QlV3cUMNPzLQvS8E0i9cg05EyzUyHnFjj2QWDjvAjyuduvE1P8Nr0TogQweiuBysM9a1rXjQn1EOaypHdqwa2_inODK1icu0Ff33AZDB00N4x-nYxZ2e16nArVuaMEddaLXBhtBYMn2LAcPYJ8s2wGN10heiFWywn-r8--Hw77GJx1hkTgg/http%3A%2F%2Fwww.R-project.org%2Fposting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>

        [[alternative HTML version deleted]]

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Re: analyzing results from Tuesday's US elections

Alexandra Thorn-3
This thread strikes me as pretty far off-topic for a forum dedicated to
software support on R.

https://www.r-project.org/mail.html#instructions
"The ‘main’ R mailing list, for discussion about problems and solutions
using R, announcements (not covered by ‘R-announce’ or ‘R-packages’,
see above), about the availability of new functionality for R and
documentation of R, comparison and compatibility with S-plus, and for
the posting of nice examples and benchmarks. Do read the posting guide
before sending anything!"

https://www.r-project.org/posting-guide.html
"The R mailing lists are primarily intended for questions and
discussion about the R software. However, questions about statistical
methodology are sometimes posted. If the question is well-asked and of
interest to someone on the list, it may elicit an informative
up-to-date answer. See also the Usenet groups sci.stat.consult (applied
statistics and consulting) and sci.stat.math (mathematical stat and
probability)."

On Mon, 9 Nov 2020 00:53:46 -0500
Matthew McCormack <[hidden email]> wrote:

> You can try here: https://decisiondeskhq.com/
>
> I think they have what you are looking for. From their website:
>
> "Create a FREE account to access up to the minute election results
> and insights on all U.S. Federal elections. Decision Desk HQ &
> Øptimus provide live election night coverage, race-specific results
> including county-level returns, and exclusive race probabilities for
> key battleground races."
>
>     Also, this article provides a little, emphasis on little, of
> statistical analysis of election results, but it may be a place to
> start.
>
> https://www.theepochtimes.com/statistical-anomalies-in-biden-votes-analyses-indicate_3570518.html?utm_source=newsnoe&utm_medium=email&utm_campaign=breaking-2020-11-08-5
>
> Matthew
>
> On 11/8/20 11:25 PM, Bert Gunter wrote:
> >          External Email - Use Caution
> >
> > NYT  had interactive maps that reported  votes by county. So try
> > contacting them.
> >
> >
> > Bert
> >
> > On Sun, Nov 8, 2020, 8:10 PM Abby Spurdle <[hidden email]>
> > wrote:
> >>> such a repository already exists -- the NY Times, AP, CNN, etc.
> >>> etc.  
> >> already have interactive web pages that did this
> >>
> >> I've been looking for presidential election results, by
> >> ***county***. I've found historic results, including results for
> >> 2016.
> >>
> >> However, I can't find such a dataset, for 2020.
> >> (Even though this seems like an obvious thing to publish).
> >>
> >> I suspect that the NY Times has the data, but I haven't been able
> >> to work where the data is on their website, or how to access it.
> >>
> >> More ***specific*** suggestions would be appreciated...?
> >>  
> > [[alternative HTML version deleted]]
> >
> > ______________________________________________
> > [hidden email] mailing list -- To UNSUBSCRIBE and more, see
> > https://secure-web.cisco.com/1C8m4dUQtDXEQdbAFTH153ehiJcvHuL_FkvDGeJBHhMRYZauAp6gdevfmLIh2MLpRjBx7LXAG9QpagRV63oMY5AyQF6uOkNa7JGw-0zGZKIFHoSuZtjpcIokATDMxqoJlVfCiktqIYXEiJcrovbnxo-DAgLEiREocQrn0yMbLc2A-gwR3CN9XurWkU21TUD1CLJ-3gpiCLKKe9BdHWdaeEA/https%3A%2F%2Fstat.ethz.ch%2Fmailman%2Flistinfo%2Fr-help
> > PLEASE do read the posting guide
> > http://secure-web.cisco.com/1ppZyk8SO6U25PKNDKtGQ-VIADLxXgKvnHc8QlV3cUMNPzLQvS8E0i9cg05EyzUyHnFjj2QWDjvAjyuduvE1P8Nr0TogQweiuBysM9a1rXjQn1EOaypHdqwa2_inODK1icu0Ff33AZDB00N4x-nYxZ2e16nArVuaMEddaLXBhtBYMn2LAcPYJ8s2wGN10heiFWywn-r8--Hw77GJx1hkTgg/http%3A%2F%2Fwww.R-project.org%2Fposting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
> [hidden email] 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.

______________________________________________
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Re: analyzing results from Tuesday's US elections

Matthew McCormack

    Benford Analysis for Data Validation and Forensic Analytics

Provides tools that make it easier to validate data using Benford's Law.

https://www.rdocumentation.org/packages/benford.analysis/versions/0.1.5


Matthew

On 11/9/20 9:23 AM, Alexandra Thorn wrote:

>          External Email - Use Caution
>
> This thread strikes me as pretty far off-topic for a forum dedicated to
> software support on R.
>
> https://secure-web.cisco.com/15MzwKoUQfDzeGBDx9gweXKgiYtAPv1UlnW2dg9CuDtSNWgxy3ffTf_uuPizbjoJnovoOD6lrPDluOgGvIUTEF1d_rOTfaF3nUKLvNiZa3fHZ_IHD-SjKotr4lurHjmNPlSrljLipPsrDk2aoo63-GLwvaw64By_MnLST7lt4FgA2pYXgE3x15Xn-kRZ85m29f0BxhHJMVfilvVUoUEBPrw/https%3A%2F%2Fwww.r-project.org%2Fmail.html%23instructions
> "The ‘main’ R mailing list, for discussion about problems and solutions
> using R, announcements (not covered by ‘R-announce’ or ‘R-packages’,
> see above), about the availability of new functionality for R and
> documentation of R, comparison and compatibility with S-plus, and for
> the posting of nice examples and benchmarks. Do read the posting guide
> before sending anything!"
>
> https://secure-web.cisco.com/1V05G8mWSPHU-YvLbL-UQMy49XX7n7-EivE-gTOlh2nZ3P0oxp6DGUUZQ_Q5VIkE3J0qmhrrSXxJaqZjv-Tllghba8lQrbkazuAHTcltsfo3I-C-SMqhb-CDdFbeEgIsr7py_gKW9BqumTZacywhHVnzhGGR2s1A-2akqQLYSYpYeX5EcVJAYvX1KPCs9kJbOEveOr5yYjetokaZpLTzdMA/https%3A%2F%2Fwww.r-project.org%2Fposting-guide.html
> "The R mailing lists are primarily intended for questions and
> discussion about the R software. However, questions about statistical
> methodology are sometimes posted. If the question is well-asked and of
> interest to someone on the list, it may elicit an informative
> up-to-date answer. See also the Usenet groups sci.stat.consult (applied
> statistics and consulting) and sci.stat.math (mathematical stat and
> probability)."
>
> On Mon, 9 Nov 2020 00:53:46 -0500
> Matthew McCormack <[hidden email]> wrote:
>
>> You can try here: https://secure-web.cisco.com/17WRivozTB0Frts23cTlTBd3SYWzVXQsLa_jDRN8SldAl35F0SYXRMZczzIXrQFTzbfRV4YfPOVhMSwopcdTU9Sva396s3bX3-KM7-51KjSnY0aXxlADYaHdvs4y4YXrUfk1GT2801rVL26MCEEn2E1azdQ8ECllu1roS_Z8MIj8d6kyCtUYVdOYN1i9DuWBSXPlEi-iOtrQsBp6ELRXNFw/https%3A%2F%2Fdecisiondeskhq.com%2F
>>
>> I think they have what you are looking for. From their website:
>>
>> "Create a FREE account to access up to the minute election results
>> and insights on all U.S. Federal elections. Decision Desk HQ &
>> Øptimus provide live election night coverage, race-specific results
>> including county-level returns, and exclusive race probabilities for
>> key battleground races."
>>
>>      Also, this article provides a little, emphasis on little, of
>> statistical analysis of election results, but it may be a place to
>> start.
>>
>> https://secure-web.cisco.com/1JA34S9tw27K78g7scwo2aGe4lPpV7HThBE81hhJjb4Ban7fxqbnOZqx7HxfcyqKrcB5BX7oJFHhMPumrxjm6aQJ0trW1Jgk0h9s2mNhZg4T_gTUls8y4l0KZ-AstUtw0eC0TtR9mHblU7KWid-7OO4mg0TfsxWyNpcqkA8MBuGftOEgUF7WtakShYgmCNYJkEfQJHK5_vjwK0taJeUheVw/https%3A%2F%2Fwww.theepochtimes.com%2Fstatistical-anomalies-in-biden-votes-analyses-indicate_3570518.html%3Futm_source%3Dnewsnoe%26utm_medium%3Demail%26utm_campaign%3Dbreaking-2020-11-08-5
>>
>> Matthew
>>
>> On 11/8/20 11:25 PM, Bert Gunter wrote:
>>>           External Email - Use Caution
>>>
>>> NYT  had interactive maps that reported  votes by county. So try
>>> contacting them.
>>>
>>>
>>> Bert
>>>
>>> On Sun, Nov 8, 2020, 8:10 PM Abby Spurdle <[hidden email]>
>>> wrote:
>>>>> such a repository already exists -- the NY Times, AP, CNN, etc.
>>>>> etc.
>>>> already have interactive web pages that did this
>>>>
>>>> I've been looking for presidential election results, by
>>>> ***county***. I've found historic results, including results for
>>>> 2016.
>>>>
>>>> However, I can't find such a dataset, for 2020.
>>>> (Even though this seems like an obvious thing to publish).
>>>>
>>>> I suspect that the NY Times has the data, but I haven't been able
>>>> to work where the data is on their website, or how to access it.
>>>>
>>>> More ***specific*** suggestions would be appreciated...?
>>>>  
>>> [[alternative HTML version deleted]]
>>>
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>>> and provide commented, minimal, self-contained, reproducible code.
>> [[alternative HTML version deleted]]
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>> minimal, self-contained, reproducible code.
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Re: analyzing results from Tuesday's US elections

Marc Roos
 
Publish the results/graphs please, like to see what your are doing.



-----Original Message-----
From: Matthew McCormack [mailto:[hidden email]]
Sent: Monday, November 09, 2020 6:14 PM
To: [hidden email]
Subject: Re: [R] analyzing results from Tuesday's US elections


    Benford Analysis for Data Validation and Forensic Analytics

Provides tools that make it easier to validate data using Benford's Law.

https://www.rdocumentation.org/packages/benford.analysis/versions/0.1.5


Matthew

On 11/9/20 9:23 AM, Alexandra Thorn wrote:

>          External Email - Use Caution
>
> This thread strikes me as pretty far off-topic for a forum dedicated
> to software support on R.
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> (mathematical stat and probability)."
>
> On Mon, 9 Nov 2020 00:53:46 -0500
> Matthew McCormack <[hidden email]> wrote:
>
>> You can try here:
>> https://secure-web.cisco.com/17WRivozTB0Frts23cTlTBd3SYWzVXQsLa_jDRN8
>> SldAl35F0SYXRMZczzIXrQFTzbfRV4YfPOVhMSwopcdTU9Sva396s3bX3-KM7-51KjSnY
>> 0aXxlADYaHdvs4y4YXrUfk1GT2801rVL26MCEEn2E1azdQ8ECllu1roS_Z8MIj8d6kyCt
>> UYVdOYN1i9DuWBSXPlEi-iOtrQsBp6ELRXNFw/https%3A%2F%2Fdecisiondeskhq.co
>> m%2F
>>
>> I think they have what you are looking for. From their website:
>>
>> "Create a FREE account to access up to the minute election results
>> and insights on all U.S. Federal elections. Decision Desk HQ &
>> Øptimus provide live election night coverage, race-specific results
>> including county-level returns, and exclusive race probabilities for
>> key battleground races."
>>
>>      Also, this article provides a little, emphasis on little, of
>> statistical analysis of election results, but it may be a place to
>> start.
>>
>> https://secure-web.cisco.com/1JA34S9tw27K78g7scwo2aGe4lPpV7HThBE81hhJ
>> jb4Ban7fxqbnOZqx7HxfcyqKrcB5BX7oJFHhMPumrxjm6aQJ0trW1Jgk0h9s2mNhZg4T_
>> gTUls8y4l0KZ-AstUtw0eC0TtR9mHblU7KWid-7OO4mg0TfsxWyNpcqkA8MBuGftOEgUF
>> 7WtakShYgmCNYJkEfQJHK5_vjwK0taJeUheVw/https%3A%2F%2Fwww.theepochtimes
>> .com%2Fstatistical-anomalies-in-biden-votes-analyses-indicate_3570518
>> .html%3Futm_source%3Dnewsnoe%26utm_medium%3Demail%26utm_campaign%3Dbr
>> eaking-2020-11-08-5
>>
>> Matthew
>>
>> On 11/8/20 11:25 PM, Bert Gunter wrote:
>>>           External Email - Use Caution
>>>
>>> NYT  had interactive maps that reported  votes by county. So try
>>> contacting them.
>>>
>>>
>>> Bert
>>>
>>> On Sun, Nov 8, 2020, 8:10 PM Abby Spurdle <[hidden email]>
>>> wrote:
>>>>> such a repository already exists -- the NY Times, AP, CNN, etc.
>>>>> etc.
>>>> already have interactive web pages that did this
>>>>
>>>> I've been looking for presidential election results, by
>>>> ***county***. I've found historic results, including results for
>>>> 2016.
>>>>
>>>> However, I can't find such a dataset, for 2020.
>>>> (Even though this seems like an obvious thing to publish).
>>>>
>>>> I suspect that the NY Times has the data, but I haven't been able
>>>> to work where the data is on their website, or how to access it.
>>>>
>>>> More ***specific*** suggestions would be appreciated...?
>>>>  
>>> [[alternative HTML version deleted]]
>>>
>>> ______________________________________________
>>> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
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>>> 2Fmailman%2Flistinfo%2Fr-help
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>>> n1EOaypHdqwa2_inODK1icu0Ff33AZDB00N4x-nYxZ2e16nArVuaMEddaLXBhtBYMn2L
>>> AcPYJ8s2wGN10heiFWywn-r8--Hw77GJx1hkTgg/http%3A%2F%2Fwww.R-project.o
>>> rg%2Fposting-guide.html and provide commented, minimal,
>>> self-contained, reproducible code.
>> [[alternative HTML version deleted]]
>>
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>> ilman%2Flistinfo%2Fr-help
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oyYeDl5wcBOTRwYoSBBVqYBzzgdU6ya5v-iShroKo9PEfRnJ6_mwzPKinWeTh-OLAWbiz9A8
qqZrVFd1SrWIMiCrlpLXzKEYXYDspvy7N50KWJLR7ZEuZDysXng2zp2ZrCMdq2cJ_ilGKkUK
5XeaShoIBifwm39A7Zy4wmUNNWeLaA/http%3A%2F%2Fwww.R-project.org%2Fposting-
guide.html and provide commented, minimal, self-contained, reproducible
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Re: analyzing results from Tuesday's US elections

aBBy Spurdle, ⍺XY
In reply to this post by Spencer Graves-4
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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Re: analyzing results from Tuesday's US elections

Bert Gunter-2
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 <[hidden email]> 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]]

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Re: analyzing results from Tuesday's US elections

Martin Møller Skarbiniks Pedersen
In reply to this post by Spencer Graves-4
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 <
[hidden email]> 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
>
> ______________________________________________
> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
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> 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]]

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Re: analyzing results from Tuesday's US elections

Rolf Turner

On Thu, 12 Nov 2020 01:23:06 +0100
Martin Møller Skarbiniks Pedersen <[hidden email]> wrote:

> Please watch this video if you wrongly believe that Benford's law
> easily can be applied to elections results.
>
> https://youtu.be/etx0k1nLn78

Just 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

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Re: analyzing results from Tuesday's US elections

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

On November 13, 2020 6:59:49 PM PST, Rolf Turner <[hidden email]> wrote:

>
>On Thu, 12 Nov 2020 01:23:06 +0100
>Martin Møller Skarbiniks Pedersen <[hidden email]> wrote:
>
>> Please watch this video if you wrongly believe that Benford's law
>> easily can be applied to elections results.
>>
>> https://youtu.be/etx0k1nLn78
>
>Just 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

--
Sent from my phone. Please excuse my brevity.

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Re: analyzing results from Tuesday's US elections

Rolf Turner

On Fri, 13 Nov 2020 19:02:19 -0800
Jeff Newmiller <[hidden email]> 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

>
> On November 13, 2020 6:59:49 PM PST, Rolf Turner
> <[hidden email]> wrote:
> >
> >On Thu, 12 Nov 2020 01:23:06 +0100
> >Martin Møller Skarbiniks Pedersen <[hidden email]> wrote:
> >
> >> Please watch this video if you wrongly believe that Benford's law
> >> easily can be applied to elections results.
> >>
> >> https://youtu.be/etx0k1nLn78
> >
> >Just 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?

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Re: analyzing results from Tuesday's US elections

aBBy Spurdle, ⍺XY
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 Sun, Nov 15, 2020 at 8:51 AM Rolf Turner <[hidden email]> wrote:

>
>
> On Fri, 13 Nov 2020 19:02:19 -0800
> Jeff Newmiller <[hidden email]> 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

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plot1.png (33K) Download Attachment
plot2.png (30K) Download Attachment
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Re: analyzing results from Tuesday's US elections

Matthew McCormack
In reply to this post by Rolf Turner
   I really like this guy's video as well. (He also has another nice
video critiquing a statistical analysis of vote results from Kent
county, Michigan that was presented by a Massachusetts Senate candidate,
who has some impressive academic credentials. )

   And continuing in this same vein of the complexities of statistical
analysis by intelligent people here is a video by Mark Nigrini using
Benfords analysis on Maricopa County vote results.

https://www.youtube.com/watch?v=FrJui5d7BrI&ab_channel=MarkNigrini

     If you search for Mark Nigrini on Amazon you will see that he has
written a major text on Forensic Analysis, specifically forensic
accounting investigations, that is now in its second edition as well as
an additional two books on analysis with Benford's Law for accounting,
auditing, and fraud detection (He plugs the text in the last part of the
video). All four books have 4-5 star reviews with 2-48 reviewers. From
the tiny amount of reading I have done on Benford's Law, it seems that
Nigirini is a leading figure in the use of Benford's Law. In the video
he shows that voting results for both Trump and Biden from Maricopa
county AZ both agree with Benfords Law. However, he uses the last digit
and not the first. A word of caution before you click on that link: he
uses Excel !

Matthew

On 11/13/20 9:59 PM, Rolf Turner wrote:

>          External Email - Use Caution
>
> On Thu, 12 Nov 2020 01:23:06 +0100
> Martin Møller Skarbiniks Pedersen <[hidden email]> wrote:
>
>> Please watch this video if you wrongly believe that Benford's law
>> easily can be applied to elections results.
>>
>> https://secure-web.cisco.com/1nXQfJ050onRLM1UOwgj-z0o0L3Hj6hd0rCZ7zMpqnBfCDuZcCkxAJZnj7o7Z8ZAUVxYBTf5FBjL2Y-Ca8T_ecO-N54S0KhgRtLoVDgxiEKX9N7eqzuxO0k0HloVcc2lXrXFNAiansI8zHgyUS4gTdKtRsJCHttTn5bwmV8J7d0_6iqrjee_toWiGnTsDSFaKVkev7tKKV3ERLFwzTPtNf2Rm99EBbdA75FvsXfBk3WXuVop4GZbN3ZGkd2SssFJaw9AgTHmM1k3C2bnB_STO_w/https%3A%2F%2Fyoutu.be%2Fetx0k1nLn78
> Just 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
>

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Re: analyzing results from Tuesday's US elections

aBBy Spurdle, ⍺XY
In reply to this post by aBBy Spurdle, ⍺XY
I've come to the conclusion this whole thing was a waste of time.
This is after evaluating much of the relevant information.

The main problem is a large number of red herrings (some in the data,
some in the context), leading pointless data analysis and pointless
data collection.
It's unlikely that sophisticated software, or sophisticated
statistical modelling tools will make any difference.
Although pretty plots, and pretty web-graphics are achievable.

Sorry list, for encouraging this discussion...

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Re: analyzing results from Tuesday's US elections

Matthew McCormack
      No reason to apologize. It's a timely and very interesting topic
that provides a glimpse into the application of statistics in forensics.
I had never heard of Benford's Law before and I think it is really
fascinating. One of those very counter intuitive rules that show up in
statistics and probability; like the Monty Hall problem. Why in the
world does Benford's Law work ?  I have been wondering if it could in
any way be applied to biological data analysis. (Also, I discovered
Stand-up-maths !).

    Often things are not as easy to figure out as we may first estimate.
I think you would have to start with how you would envision a fraud to
be committed and then figure out if there is a statistical analysis that
could detect it, or develop an anlalysis. For example, if a voting
machine were weighting votes and giving 8/10ths of a vote to 'yes' and
10/10ths vote to a 'no'. Is there some statistical analysis that could
detect this ?  I, Or if someone dumped a couple of thousand fraudulent
ballots in a vote counting center, is there some statistical analysis
that could detect this ?  Who knows, maybe a whole new field waiting to
be explored. A oncee-in-a-while dive into a practical application of
statistics that has current interest can be fun and enlightening for
those interested.

Matthew

On 11/16/20 9:01 PM, Abby Spurdle wrote:

>          External Email - Use Caution
>
> I've come to the conclusion this whole thing was a waste of time.
> This is after evaluating much of the relevant information.
>
> The main problem is a large number of red herrings (some in the data,
> some in the context), leading pointless data analysis and pointless
> data collection.
> It's unlikely that sophisticated software, or sophisticated
> statistical modelling tools will make any difference.
> Although pretty plots, and pretty web-graphics are achievable.
>
> Sorry list, for encouraging this discussion...

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12