Basic usage only requires this package and ggplot2:

library(voteogram)
library(ggplot2)

Getting Vote Data

The cartograms need data and the best way to do that is by obtaining roll call vote data from ProPublica via the roll_call() function. Data can be retrieved for any House or Senate vote by specificing the target vote parameters:

sen <- roll_call("senate", 115, 1, 110)
rep <- roll_call("house", 115, 1, 256)

Their structures look the same and there is a print-method to make the console output easier on the eyes:

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                :Classes 'tbl_df', 'tbl' and 'data.frame':   100 obs. of  11 variables:
#>   ..$ 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 x 11
#>    bioguide_id role_id member_name sort_name party state_abbrev
#>  * <chr>         <int> <chr>       <chr>     <chr> <chr>       
#>  1 A000360         526 Lamar  Ale… Alexander R     TN          
#>  2 B001230         481 Tammy Bald… Baldwin   D     WI          
#>  3 B001261         498 John Barra… Barrasso  R     WY          
#>  4 B001267         561 Michael Be… Bennet    D     CO          
#>  5 B001277         535 Richard Bl… Blumenth… D     CT          
#>  6 B000575         547 Roy  Blunt  Blunt     R     MO          
#>  7 B001288         507 Cory  Book… Booker    D     NJ          
#>  8 B001236         551 John  Booz… Boozman   R     AR          
#>  9 B000944         480 Sherrod  B… Brown     D     OH          
#> 10 B001135         555 Richard M.… Burr      R     NC          
#> # … with 90 more rows, and 5 more variables: display_state_abbrev <chr>,
#> #   district <chr>, position <chr>, dw_nominate <lgl>, pp_id <chr>
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                :Classes 'tbl_df', 'tbl' and 'data.frame':   435 obs. of  11 variables:
#>   ..$ 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 x 11
#>    bioguide_id role_id member_name sort_name party state_abbrev
#>  * <chr>         <int> <chr>       <chr>     <chr> <chr>       
#>  1 A000374         274 Ralph Abra… Abraham   R     LA          
#>  2 A000370         294 Alma  Adams Adams     D     NC          
#>  3 A000055         224 Robert B. … Aderholt  R     AL          
#>  4 A000371         427 Pete Aguil… Aguilar   D     CA          
#>  5 A000372         268 Rick Allen  Allen     R     GA          
#>  6 A000367         131 Justin Ama… Amash     R     MI          
#>  7 A000369         388 Mark Amodei Amodei    R     NV          
#>  8 A000375         320 Jodey Arri… Arrington R     TX          
#>  9 B001291         590 Brian Babin Babin     R     TX          
#> 10 B001298         206 Don Bacon   Bacon     R     NE          
#> # … with 425 more rows, and 5 more variables: display_state_abbrev <chr>,
#> #   district <int>, position <chr>, dw_nominate <lgl>, pp_id <chr>

That data may be useful on its own (ouside of plotting).

Note, also, that ggplot2’s fortify() method uses the provided object class method for roll call objects to know how to extract the rectangular data necessary for plotting.

Making Cartograms

These cartograms have a few style options:

ProPublica

senate_carto(sen) +
  labs(title="Senate Vote 110 - Invokes Cloture on Neil Gorsuch Nomination") +
  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_voteogram()

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

GovTrack

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

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

Tiny Cartograms

senate_carto(sen) + theme_voteogram(legend=FALSE)

house_carto(rep) + theme_voteogram(legend=FALSE)

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