GitHub

U.S. House and Senate Voting Cartogram Generators

Description

‘ProPublica’ https://projects.propublica.org/represent/ makes United States Congress member votes available and has developed their own unique cartogram to visually represent this data as has ‘GovTrack’ <URL_AT_SOME_POINT> . Tools are provided to retrieve voting data, prepare voting data for plotting with ‘ggplot2’, create vote cartograms and theme them.

Ref: (these are replicated below)

You can grab the results of a roll call vote (House or Senate) with roll_call(). It returns a list with a ton of information that you can use outside this package. One element of that list is the data.frame of vote results. You can pass in the entire object to either _carto() function and it’ll “fortify” it before shunting it off to ggplot2. Try to cache this data (I do, below, in R markdown chunk) as you’re ticking credits off of ProPublica’s monthly free S3 allotment each call. Consider donating to them if you’re too lazy to cache the data).

TODO

  • House cartogram generator
  • Param bulletproofing (param checking, et al)
  • Add in ability to retrieve votes from ProPublica.
  • Make a voteogram theme
  • GovTrack Senate cartogram polygons (this is pretty much covered in ggparliament since GT only has the seat view for the Senate)
  • “Independent” colors for “not voting” & “present”
  • Vignette
  • htmlwidget version

What’s In The Tin

The following functions are implemented:

  • house_carto: Produce a ProPublica- or GovTrack-style House roll call vote cartogram
  • senate_carto: Produce a Senate cartogram
  • roll_call: Get Voting Record for House or Senate By Number, Session & Roll Call Number

Helpers:

  • theme_voteogram: voteogram ggplot2 theme
  • print.pprc: Better default ‘print’ function for roll_call() (pprc) objects
  • fortify.pprc : In case you want to use the voting data frame from a roll_call() (pprc) object in your own plots and forget to just $votes it out. #helping

Working with voteogram

Installation

remotes::install_github("hrbrmstr/voteogram")

Basic Usage

library(voteogram)
library(hrbrthemes)
library(ggplot2)
# current verison
packageVersion("voteogram")
## [1] '0.3.2'

sen <- roll_call("senate", 115, 1, 110)
rep <- roll_call("house", 115, 1, 256)
str(sen)
## List of 29
##  $ vote_id              : chr "S_115_1_110"
##  $ chamber              : chr "Senate"
##  $ year                 : int 2017
##  $ congress             : chr "115"
##  $ session              : chr "1"
##  $ roll_call            : int 110
##  $ needed_to_pass       : int 51
##  $ date_of_vote         : chr "April  6, 2017"
##  $ time_of_vote         : chr "12:35 PM"
##  $ result               : chr "Cloture Motion Agreed to"
##  $ vote_type            : chr "1/2"
##  $ question             : chr "On the Cloture Motion"
##  $ description          : chr "Neil M. Gorsuch, of Colorado, to be an Associate Justice of the Supreme Court of the United States"
##  $ nyt_title            : chr "On the Cloture Motion"
##  $ total_yes            : int 55
##  $ total_no             : int 45
##  $ total_not_voting     : int 0
##  $ gop_yes              : int 52
##  $ gop_no               : int 0
##  $ gop_not_voting       : int 0
##  $ dem_yes              : int 3
##  $ dem_no               : int 43
##  $ dem_not_voting       : int 0
##  $ ind_yes              : int 0
##  $ ind_no               : int 2
##  $ ind_not_voting       : int 0
##  $ dem_majority_position: chr "No"
##  $ gop_majority_position: chr "Yes"
##  $ votes                : tibble [100 × 11] (S3: tbl_df/tbl/data.frame)
##   ..$ bioguide_id         : chr [1:100] "A000360" "B001230" "B001261" "B001267" ...
##   ..$ role_id             : int [1:100] 526 481 498 561 535 547 507 551 480 555 ...
##   ..$ member_name         : chr [1:100] "Lamar  Alexander" "Tammy Baldwin" "John Barrasso" "Michael Bennet" ...
##   ..$ sort_name           : chr [1:100] "Alexander" "Baldwin" "Barrasso" "Bennet" ...
##   ..$ party               : chr [1:100] "R" "D" "R" "D" ...
##   ..$ state_abbrev        : chr [1:100] "TN" "WI" "WY" "CO" ...
##   ..$ display_state_abbrev: chr [1:100] "Tenn." "Wis." "Wyo." "Colo." ...
##   ..$ district            : chr [1:100] "2" "1" "1" "1" ...
##   ..$ position            : chr [1:100] "Yes" "No" "Yes" "No" ...
##   ..$ dw_nominate         : logi [1:100] NA NA NA NA NA NA ...
##   ..$ pp_id               : chr [1:100] "TN" "WI" "WY" "CO" ...
##  - attr(*, "class")= chr [1:2] "pprc" "list"

sen$votes
## # A tibble: 100 × 11
##    bioguide_id role_id member_name        sort_name  party state_abbrev display_state_ab…¹ distr…² posit…³ dw_no…⁴ pp_id
##    <chr>         <int> <chr>              <chr>      <chr> <chr>        <chr>              <chr>   <chr>   <lgl>   <chr>
##  1 A000360         526 Lamar  Alexander   Alexander  R     TN           Tenn.              2       Yes     NA      TN
##  2 B001230         481 Tammy Baldwin      Baldwin    D     WI           Wis.               1       No      NA      WI
##  3 B001261         498 John Barrasso      Barrasso   R     WY           Wyo.               1       Yes     NA      WY
##  4 B001267         561 Michael Bennet     Bennet     D     CO           Colo.              1       No      NA      CO
##  5 B001277         535 Richard Blumenthal Blumenthal D     CT           Conn.              2       No      NA      CT
##  6 B000575         547 Roy  Blunt         Blunt      R     MO           Mo.                2       Yes     NA      MO
##  7 B001288         507 Cory  Booker       Booker     D     NJ           N.J.               2       No      NA      NJ
##  8 B001236         551 John  Boozman      Boozman    R     AR           Ark.               1       Yes     NA      AR
##  9 B000944         480 Sherrod  Brown     Brown      D     OH           Ohio               1       No      NA      OH
## 10 B001135         555 Richard M. Burr    Burr       R     NC           N.C.               1       Yes     NA      NC
## # … with 90 more rows, and abbreviated variable names ¹​display_state_abbrev, ²​district, ³​position, ⁴​dw_nominate

str(rep)
## List of 29
##  $ vote_id              : chr "H_115_1_256"
##  $ chamber              : chr "House"
##  $ year                 : int 2017
##  $ congress             : chr "115"
##  $ session              : chr "1"
##  $ roll_call            : int 256
##  $ needed_to_pass       : int 216
##  $ date_of_vote         : chr "May  4, 2017"
##  $ time_of_vote         : chr "02:18 PM"
##  $ result               : chr "Passed"
##  $ vote_type            : chr "RECORDED VOTE"
##  $ question             : chr "On Passage"
##  $ description          : chr "American Health Care Act"
##  $ nyt_title            : chr "On Passage"
##  $ total_yes            : int 217
##  $ total_no             : int 213
##  $ total_not_voting     : int 1
##  $ gop_yes              : int 217
##  $ gop_no               : int 20
##  $ gop_not_voting       : int 1
##  $ dem_yes              : int 0
##  $ dem_no               : int 193
##  $ dem_not_voting       : int 0
##  $ ind_yes              : int 0
##  $ ind_no               : int 0
##  $ ind_not_voting       : int 0
##  $ dem_majority_position: chr "No"
##  $ gop_majority_position: chr "Yes"
##  $ votes                : tibble [435 × 11] (S3: tbl_df/tbl/data.frame)
##   ..$ bioguide_id         : chr [1:435] "A000374" "A000370" "A000055" "A000371" ...
##   ..$ role_id             : int [1:435] 274 294 224 427 268 131 388 320 590 206 ...
##   ..$ member_name         : chr [1:435] "Ralph Abraham" "Alma  Adams" "Robert B. Aderholt" "Pete Aguilar" ...
##   ..$ sort_name           : chr [1:435] "Abraham" "Adams" "Aderholt" "Aguilar" ...
##   ..$ party               : chr [1:435] "R" "D" "R" "D" ...
##   ..$ state_abbrev        : chr [1:435] "LA" "NC" "AL" "CA" ...
##   ..$ display_state_abbrev: chr [1:435] "La." "N.C." "Ala." "Calif." ...
##   ..$ district            : int [1:435] 5 12 4 31 12 3 2 19 36 2 ...
##   ..$ position            : chr [1:435] "Yes" "No" "Yes" "No" ...
##   ..$ dw_nominate         : logi [1:435] NA NA NA NA NA NA ...
##   ..$ pp_id               : chr [1:435] "LA_5" "NC_12" "AL_4" "CA_31" ...
##  - attr(*, "class")= chr [1:2] "pprc" "list"

fortify(rep)
## # A tibble: 435 × 11
##    bioguide_id role_id member_name        sort_name party state_abbrev display_state_abb…¹ distr…² posit…³ dw_no…⁴ pp_id
##    <chr>         <int> <chr>              <chr>     <chr> <chr>        <chr>                 <int> <chr>   <lgl>   <chr>
##  1 A000374         274 Ralph Abraham      Abraham   R     LA           La.                       5 Yes     NA      LA_5
##  2 A000370         294 Alma  Adams        Adams     D     NC           N.C.                     12 No      NA      NC_12
##  3 A000055         224 Robert B. Aderholt Aderholt  R     AL           Ala.                      4 Yes     NA      AL_4
##  4 A000371         427 Pete Aguilar       Aguilar   D     CA           Calif.                   31 No      NA      CA_31
##  5 A000372         268 Rick Allen         Allen     R     GA           Ga.                      12 Yes     NA      GA_12
##  6 A000367         131 Justin Amash       Amash     R     MI           Mich.                     3 Yes     NA      MI_3
##  7 A000369         388 Mark Amodei        Amodei    R     NV           Nev.                      2 Yes     NA      NV_2
##  8 A000375         320 Jodey Arrington    Arrington R     TX           Texas                    19 Yes     NA      TX_19
##  9 B001291         590 Brian Babin        Babin     R     TX           Texas                    36 Yes     NA      TX_36
## 10 B001298         206 Don Bacon          Bacon     R     NE           Neb.                      2 Yes     NA      NE_2
## # … with 425 more rows, and abbreviated variable names ¹​display_state_abbrev, ²​district, ³​position, ⁴​dw_nominate

ProPublica

senate_carto(sen) +
  labs(title="Senate Vote 110 - Invokes Cloture on Neil Gorsuch Nomination") +
  theme_ipsum_rc(plot_title_size = 24) +
  theme_voteogram()

house_carto(rep, pp_square=TRUE) +
  labs(x=NULL, y=NULL,
       title="House Vote 256 - Passes American Health Care Act,\nRepealing Obamacare") +
  theme_ipsum_rc(plot_title_size = 24) +
  theme_voteogram()

house_carto(rep, pp_square=FALSE) +
  labs(x=NULL, y=NULL,
       title="House Vote 256 - Passes American Health Care Act,\nRepealing Obamacare") +
  theme_ipsum_rc(plot_title_size = 24) +
  theme_voteogram()

GovTrack

house_carto(rep, "gt") +
  labs(x=NULL, y=NULL,
       title="House Vote 256 - Passes American Health Care Act,\nRepealing Obamacare") +
  theme_ipsum_rc(plot_title_size = 24) +
  theme_voteogram()

Tiny Cartograms

They can be shrunk down well (though that means annotating them in some other way):

senate_carto(sen) + theme_voteogram(legend=FALSE)

house_carto(rep) + theme_voteogram(legend=FALSE)

house_carto(rep, pp_square=TRUE) + theme_voteogram(legend=FALSE)

Test Results

library(voteogram)
library(testthat)
date()
## [1] "Wed Mar  8 06:36:49 2023"

test_dir("tests/")
## ✔ | F W S  OK | Context
## ⠏ |         0 | all                                                                                                     [ FAIL 0 | WARN 0 | SKIP 0 | PASS 7 ]
##
## ══ Results ═════════════════════════════════════════════════════════════════════════════════════════════════════════════
## Duration: 0.8 s
##
## [ FAIL 0 | WARN 0 | SKIP 0 | PASS 0 ]

Code of Conduct

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

Read the original on github.com ↗