Vignette for the voteogram package
Derek Sollberger
May 9, 2017
This document was last updated at 2017-05-09 02:46:55.
A blogger by the name of hrbrmstr posted a nice article about his voteogram package, and that article was posted on R Bloggers (https://www.r-bloggers.com/plot-the-vote-making-u-s-senate-house-cartograms-in-r/). Here I hope to explore some of the package’s features and abilites.
Gathering the Data
# INSTALLATION (if needed)
#install.packages("devtools")
library("devtools")
#devtools::install_github("hrbrmstr/voteogram")
# recommended libraries
library("voteogram")
library("jsonlite")
library("hrbrthemes")
library("tidyverse")The author of the voteogram package provided the following line2 of code to get users started
sen <- roll_call("senate", 115, 1, 110)
rep <- roll_call("house", 115, 1, 256)which retrieves data from the United States Senate or House of Representatives representively, followed by the group number (here, the 115th Congress), session number, and roll call number. This data comes from websites such as https://www.propublica.org/ and https://www.govtrack.us/. Calling the list gives a summary of the results
rep## 115th Congress / Session: 1 / House Roll Call: 256 / May 4, 2017
##
## American Health Care Act
##
## Result: Passedwhile we will probably later focus on the voting records extracted by rep$votes
rep$votes## # A tibble: 435 × 11
## bioguide_id role_id member_name sort_name party state_abbrev
## * <chr> <int> <chr> <chr> <chr> <chr>
## 1 A000374 274 Ralph Abraham Abraham R LA
## 2 A000370 294 Alma Adams Adams D NC
## 3 A000055 224 Robert B. Aderholt Aderholt R AL
## 4 A000371 427 Pete Aguilar Aguilar D CA
## 5 A000372 268 Rick Allen Allen R GA
## 6 A000367 131 Justin Amash Amash R MI
## 7 A000369 388 Mark Amodei Amodei R NV
## 8 A000375 320 Jodey Arrington 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>For thorough exploration, we can view the structure of the list.
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"For now, I am assuming that there have been at least \(N = 256\) roll call votes for this current House of Representatives, so I hope to gather that much into one structure. I will first retrieve the results of the first roll call of the 115th House of Representatives 1st session.
HOR115 <- tbl_df(data.frame(roll_id = 1, roll_call("house", 115, 1, 1)$votes))Now I hope that a simple for loop will be able to retrieve the subsequent roll calls’ votes. Note that every roll call describes the set number of 435 representatives.
N <- 256
for(roll_call_counter in 200:N){
thisRollCall_description <- roll_call("house", 115, 1, roll_call_counter)$description
thisRollCall_votes <- roll_call("house", 115, 1, roll_call_counter)$votes
thisRollCall_id <- rep(roll_call_counter, 435)
HOR115 <- bind_rows(HOR115,
tbl_df(data.frame(roll_id = thisRollCall_id,
thisRollCall_votes,
description = thisRollCall_description)))
}## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to characterAt this time, my computer can load about one roll call per second, but the data retrieval seems to time out if I try to grab every roll call. Thank goodness for code chunk caches.
Focusing on a Representative
I live in Merced, which is a relatively small city in the middle of California. Our representative is Jim Costa. Fortunately, with the dplyr tools, it is easy to filter his votes. For starters, here is Jim Costa’s voting distribution for this session.
JimCostaRecord <- HOR115 %>%
filter(member_name == "Jim Costa")
table(JimCostaRecord$position)##
## No Present Yes
## 27 1 30From there, we can filter which roll calls were approved by Representative Costa.
JimCostaRecord %>% filter(position == "Yes") %>%
select(roll_id, member_name, party, state_abbrev, district, position, description) %>%
print(n = nrow(.)) #print all rows for dramatic effect## # A tibble: 30 × 7
## roll_id member_name party state_abbrev district position
## <dbl> <chr> <chr> <chr> <int> <chr>
## 1 205 Jim Costa D CA 16 Yes
## 2 206 Jim Costa D CA 16 Yes
## 3 207 Jim Costa D CA 16 Yes
## 4 209 Jim Costa D CA 16 Yes
## 5 210 Jim Costa D CA 16 Yes
## 6 214 Jim Costa D CA 16 Yes
## 7 215 Jim Costa D CA 16 Yes
## 8 216 Jim Costa D CA 16 Yes
## 9 218 Jim Costa D CA 16 Yes
## 10 220 Jim Costa D CA 16 Yes
## 11 221 Jim Costa D CA 16 Yes
## 12 222 Jim Costa D CA 16 Yes
## 13 223 Jim Costa D CA 16 Yes
## 14 226 Jim Costa D CA 16 Yes
## 15 227 Jim Costa D CA 16 Yes
## 16 231 Jim Costa D CA 16 Yes
## 17 232 Jim Costa D CA 16 Yes
## 18 233 Jim Costa D CA 16 Yes
## 19 235 Jim Costa D CA 16 Yes
## 20 236 Jim Costa D CA 16 Yes
## 21 237 Jim Costa D CA 16 Yes
## 22 238 Jim Costa D CA 16 Yes
## 23 239 Jim Costa D CA 16 Yes
## 24 242 Jim Costa D CA 16 Yes
## 25 243 Jim Costa D CA 16 Yes
## 26 247 Jim Costa D CA 16 Yes
## 27 248 Jim Costa D CA 16 Yes
## 28 249 Jim Costa D CA 16 Yes
## 29 250 Jim Costa D CA 16 Yes
## 30 255 Jim Costa D CA 16 Yes
## # ... with 1 more variables: description <chr>Plotting our Representatives’ Votes
Above being able to extract Congress’ voting records with the roll_call command, the voteogram package allows us to easily graph the results of a single roll call as a United States map visualization.
Here I will focus on roll call 255 of the 115th House of Representatives: the most recent bill that my representative Jim Costa approved. First, the package’s author recommended a ProPublica view.
rep255 <- roll_call("house", 115, 1, 255)
house_carto(rep255) +
labs(x = NULL, y = NULL,
title = rep255$description) +
theme_ipsum_rc(plot_title_size = 24) +
theme_voteogram()
print(rep255$description)## [1] "To amend the Public Health Service Act to eliminate the non-application of certain State waiver provisions to Members of Congress and congressional staff"Next, you can also use the hexagonal layout with this voteogram package as well (note the "gt")
house_carto(rep255, "gt") +
labs(x = NULL, y = NULL,
title = rep255$description) +
theme_ipsum_rc(plot_title_size = 24) +
theme_voteogram()
That’s it for now. In the future we will need to see how to access the Amazon AWS database for longer periods of time (to gather all of the votes).