mkearney / presidential_election_county_results_2016

🏁 presidential_election_county_results_2016

presidential_election_county_results_2016

presidential_election_county_results_2016

tidy election results updated 11/13/2016

the two files are identical. different names provided for continuity.

UPDATED JAN 3, 2018

Data

Find the most up-to-date version of the data as a CSV or RDS (R data file) in the data directory.

Long format

Long format as .csv: ./data/pres.elect16.results.2018.csv

## long format CSV
readr::read_csv("data/pres.elect16.results.2018.csv")
Parsed with column specification:
cols(
  county = col_character(),
  fips = col_character(),
  cand = col_character(),
  st = col_character(),
  pct_report = col_integer(),
  votes = col_integer(),
  total_votes = col_integer(),
  lead = col_character(),
  pct = col_double(),
  state.name = col_character()
)
# A tibble: 18,351 x 10
   county  fips                     cand    st pct_report    votes
    <chr> <chr>                    <chr> <chr>      <int>    <int>
 1   <NA>    US             Donald Trump    US          1 62984825
 2   <NA>    US          Hillary Clinton    US          1 65853516
 3   <NA>    US             Gary Johnson    US          1  4489221
 4   <NA>    US               Jill Stein    US          1  1429596
 5   <NA>    US            Evan McMullin    US          1   510002
 6   <NA>    US           Darrell Castle    US          1   186545
 7   <NA>    US           Gloria La Riva    US          1    74117
 8   <NA>    US       Rocky De La Fuente    US          1    33010
 9   <NA>    US None of these candidates    US          1    28863
10   <NA>    US           Richard Duncan    US          1    24235
# ... with 18,341 more rows, and 4 more variables: total_votes <int>,
#   lead <chr>, pct <dbl>, state.name <chr>

Long format as .rds: ./data/pres.elect16.results.2018.rds

## wide vote share data RDS
readr::read_rds("data/pres.elect16.results.2018.rds")
                           county  fips                      cand st
1                            <NA>    US              Donald Trump US
2                            <NA>    US           Hillary Clinton US
3                            <NA>    US              Gary Johnson US
4                            <NA>    US                Jill Stein US
5                            <NA>    US             Evan McMullin US
6                            <NA>    US            Darrell Castle US
7                            <NA>    US            Gloria La Riva US
8                            <NA>    US        Rocky De La Fuente US
9                            <NA>    US  None of these candidates US
10                           <NA>    US            Richard Duncan US
      pct_report    votes total_votes            lead         pct
1              1 62984825   135691978    Donald Trump 0.464175008
2              1 65853516   135691978    Donald Trump 0.485316206
3              1  4489221   135691978    Donald Trump 0.033083909
4              1  1429596   135691978    Donald Trump 0.010535597
5              1   510002   135691978    Donald Trump 0.003758527
6              1   186545   135691978    Donald Trump 0.001374768
7              1    74117   135691978    Donald Trump 0.000546215
8              1    33010   135691978    Donald Trump 0.000243272
9              1    28863   135691978    Donald Trump 0.000212710
10             1    24235   135691978    Donald Trump 0.000178603
          state.name
1               <NA>
2               <NA>
3               <NA>
4               <NA>
5               <NA>
6               <NA>
7               <NA>
8               <NA>
9               <NA>
10              <NA>
 [ reached getOption("max.print") -- omitted 18341 rows ]

Wide format - vote share

Wide format vote share in .csv: ./data/pres.elect16.results.wide.pct.2018.csv

## wide vote share data CSV
readr::read_csv("data/pres.elect16.results.wide.pct.2018.csv")
Parsed with column specification:
cols(
  .default = col_double(),
  county = col_character(),
  fips = col_character(),
  st = col_character(),
  pct_report = col_integer(),
  total_votes = col_integer(),
  lead = col_character(),
  state.name = col_character()
)
See spec(...) for full column specifications.
# A tibble: 3,165 x 39
   county  fips    st pct_report total_votes            lead
    <chr> <chr> <chr>      <int>       <int>           <chr>
 1   <NA>    US    US          1   135691978    Donald Trump
 2   <NA>    CA    CA          1    14060856 Hillary Clinton
 3   <NA>    FL    FL          1     9419886    Donald Trump
 4   <NA>    TX    TX          1     8917965    Donald Trump
 5   <NA>    NY    NY          1     7660190 Hillary Clinton
 6   <NA>    PA    PA          1     6115402    Donald Trump
 7   <NA>    IL    IL          1     5523142 Hillary Clinton
 8   <NA>    OH    OH          1     5480173    Donald Trump
 9   <NA>    MI    MI          1     4790329    Donald Trump
10   <NA>    NC    NC          1     4682073    Donald Trump
# ... with 3,155 more rows, and 33 more variables: state.name <chr>, `None
#   of these candidates` <dbl>, `Alyson Kennedy` <dbl>, `Bradford
#   Lyttle` <dbl>, `Chris Keniston` <dbl>, `Dan Vacek` <dbl>, `Darrell
#   Castle` <dbl>, `Donald Trump` <dbl>, `Emidio Soltysik` <dbl>, `Evan
#   McMullin` <dbl>, `Frank Atwood` <dbl>, `Gary Johnson` <dbl>, `Gloria
#   La Riva` <dbl>, `Hillary Clinton` <dbl>, `Jerry White` <dbl>, `Jill
#   Stein` <dbl>, `Jim Hedges` <dbl>, `Joseph Maldonado` <dbl>, `Kyle
#   Kopitke` <dbl>, `Laurence Kotlikoff` <dbl>, `Lynn Kahn` <dbl>,
#   `Michael Maturen` <dbl>, `Mike Smith` <dbl>, `Monica Moorehead` <dbl>,
#   `Peter Skewes` <dbl>, `Princess Jacob` <dbl>, `Richard Duncan` <dbl>,
#   `Rocky De La Fuente` <dbl>, `Rocky Giordani` <dbl>, `Rod Silva` <dbl>,
#   `Ryan Scott` <dbl>, `Scott Copeland` <dbl>, `Tom Hoefling` <dbl>

Wide format vote share in .rds: ./data/pres.elect16.results.wide.pct.2018.rds

## wide vote share data RDS
readr::read_rds("data/pres.elect16.results.wide.pct.2018.rds")
# A tibble: 3,165 x 39
   county  fips    st pct_report total_votes            lead
 *  <chr> <chr> <chr>      <dbl>       <int>           <chr>
 1   <NA>    US    US          1   135691978    Donald Trump
 2   <NA>    CA    CA          1    14060856 Hillary Clinton
 3   <NA>    FL    FL          1     9419886    Donald Trump
 4   <NA>    TX    TX          1     8917965    Donald Trump
 5   <NA>    NY    NY          1     7660190 Hillary Clinton
 6   <NA>    PA    PA          1     6115402    Donald Trump
 7   <NA>    IL    IL          1     5523142 Hillary Clinton
 8   <NA>    OH    OH          1     5480173    Donald Trump
 9   <NA>    MI    MI          1     4790329    Donald Trump
10   <NA>    NC    NC          1     4682073    Donald Trump
# ... with 3,155 more rows, and 33 more variables: state.name <chr>, `
#   None of these candidates` <dbl>, `Alyson Kennedy` <dbl>, `Bradford
#   Lyttle` <dbl>, `Chris Keniston` <dbl>, `Dan Vacek` <dbl>, `Darrell
#   Castle` <dbl>, `Donald Trump` <dbl>, `Emidio Soltysik` <dbl>, `Evan
#   McMullin` <dbl>, `Frank Atwood` <dbl>, `Gary Johnson` <dbl>, `Gloria
#   La Riva` <dbl>, `Hillary Clinton` <dbl>, `Jerry White` <dbl>, `Jill
#   Stein` <dbl>, `Jim Hedges` <dbl>, `Joseph Maldonado` <dbl>, `Kyle
#   Kopitke` <dbl>, `Laurence Kotlikoff` <dbl>, `Lynn Kahn` <dbl>,
#   `Michael Maturen` <dbl>, `Mike Smith` <dbl>, `Monica Moorehead` <dbl>,
#   `Peter Skewes` <dbl>, `Princess Jacob` <dbl>, `Richard Duncan` <dbl>,
#   `Rocky De La Fuente` <dbl>, `Rocky Giordani` <dbl>, `Rod Silva` <dbl>,
#   `Ryan Scott` <dbl>, `Scott Copeland` <dbl>, `Tom Hoefling` <dbl>

Wide Format - total votes

Wide format total votes in .csv: ./data/pres.elect16.results.wide.pct.2018.csv

## wide vote totals data CSV
readr::read_csv("data/pres.elect16.results.wide.votes.2018.csv")
Parsed with column specification:
cols(
  .default = col_integer(),
  county = col_character(),
  fips = col_character(),
  st = col_character(),
  lead = col_character(),
  state.name = col_character()
)
See spec(...) for full column specifications.
# A tibble: 3,165 x 39
   county  fips    st pct_report total_votes            lead
    <chr> <chr> <chr>      <int>       <int>           <chr>
 1   <NA>    US    US          1   135691978    Donald Trump
 2   <NA>    CA    CA          1    14060856 Hillary Clinton
 3   <NA>    FL    FL          1     9419886    Donald Trump
 4   <NA>    TX    TX          1     8917965    Donald Trump
 5   <NA>    NY    NY          1     7660190 Hillary Clinton
 6   <NA>    PA    PA          1     6115402    Donald Trump
 7   <NA>    IL    IL          1     5523142 Hillary Clinton
 8   <NA>    OH    OH          1     5480173    Donald Trump
 9   <NA>    MI    MI          1     4790329    Donald Trump
10   <NA>    NC    NC          1     4682073    Donald Trump
# ... with 3,155 more rows, and 33 more variables: state.name <chr>, `None
#   of these candidates` <int>, `Alyson Kennedy` <int>, `Bradford
#   Lyttle` <int>, `Chris Keniston` <int>, `Dan Vacek` <int>, `Darrell
#   Castle` <int>, `Donald Trump` <int>, `Emidio Soltysik` <int>, `Evan
#   McMullin` <int>, `Frank Atwood` <int>, `Gary Johnson` <int>, `Gloria
#   La Riva` <int>, `Hillary Clinton` <int>, `Jerry White` <int>, `Jill
#   Stein` <int>, `Jim Hedges` <int>, `Joseph Maldonado` <int>, `Kyle
#   Kopitke` <int>, `Laurence Kotlikoff` <int>, `Lynn Kahn` <int>,
#   `Michael Maturen` <int>, `Mike Smith` <int>, `Monica Moorehead` <int>,
#   `Peter Skewes` <int>, `Princess Jacob` <int>, `Richard Duncan` <int>,
#   `Rocky De La Fuente` <int>, `Rocky Giordani` <int>, `Rod Silva` <int>,
#   `Ryan Scott` <int>, `Scott Copeland` <int>, `Tom Hoefling` <int>

Wide format total votes in .rds: ./data/pres.elect16.results.wide.pct.2018.rds

## wide vote share data RDS
readr::read_rds("data/pres.elect16.results.wide.votes.2018.rds")
# A tibble: 3,165 x 39
   county  fips    st pct_report total_votes            lead
 *  <chr> <chr> <chr>      <dbl>       <int>           <chr>
 1   <NA>    US    US          1   135691978    Donald Trump
 2   <NA>    CA    CA          1    14060856 Hillary Clinton
 3   <NA>    FL    FL          1     9419886    Donald Trump
 4   <NA>    TX    TX          1     8917965    Donald Trump
 5   <NA>    NY    NY          1     7660190 Hillary Clinton
 6   <NA>    PA    PA          1     6115402    Donald Trump
 7   <NA>    IL    IL          1     5523142 Hillary Clinton
 8   <NA>    OH    OH          1     5480173    Donald Trump
 9   <NA>    MI    MI          1     4790329    Donald Trump
10   <NA>    NC    NC          1     4682073    Donald Trump
# ... with 3,155 more rows, and 33 more variables: state.name <chr>, `
#   None of these candidates` <int>, `Alyson Kennedy` <int>, `Bradford
#   Lyttle` <int>, `Chris Keniston` <int>, `Dan Vacek` <int>, `Darrell
#   Castle` <int>, `Donald Trump` <int>, `Emidio Soltysik` <int>, `Evan
#   McMullin` <int>, `Frank Atwood` <int>, `Gary Johnson` <int>, `Gloria
#   La Riva` <int>, `Hillary Clinton` <int>, `Jerry White` <int>, `Jill
#   Stein` <int>, `Jim Hedges` <int>, `Joseph Maldonado` <int>, `Kyle
#   Kopitke` <int>, `Laurence Kotlikoff` <int>, `Lynn Kahn` <int>,
#   `Michael Maturen` <int>, `Mike Smith` <int>, `Monica Moorehead` <int>,
#   `Peter Skewes` <int>, `Princess Jacob` <int>, `Richard Duncan` <int>,
#   `Rocky De La Fuente` <int>, `Rocky Giordani` <int>, `Rod Silva` <int>,
#   `Ryan Scott` <int>, `Scott Copeland` <int>, `Tom Hoefling` <int>

presidential_election_county_results_2016's Issues

County name?

I see neither the county names nor the county FIPS in any of the data files.

Data Source

What is the source of this dataset?

Missing Alaska data?

It looks like there's only statewide data for Alaska?

Citable source?

Source generation

Hey Michael,

I'm a huge fan of this project, and I've been using the data for some interesting analysis.

As my project concludes, I'd love to know the source of this data, and the process by which it's been generated.

Thank you so much for aggregating the election results, and sharing them on Github.

-Evan