rud.is

Earlier this year, I made a package that riffed off of ProPublica’s really neat voting cartograms (maps) for the U.S. House and Senate. You can see one for disaster relief spending in the House and one for the ACA “Skinny Repeal” in the Senate.

We can replicate both here with the voteogram package (minus the interactivity, for now):

library(voteogram)
library(ggplot2)

hr_566 <- roll_call(critter="house", number=115, session=1, rcall=566)

house_carto(hr_566) +
  coord_equal() +
  theme_voteogram()

sen_179 <- roll_call(critter="senate", number=115, session=1, rcall=179)

senate_carto(sen_179) +
  coord_equal() +
  theme_voteogram()

I think folks might have more fun with the roll_call() objects though:

str(hr_566)
## List of 29
##  $ vote_id              : chr "H_115_1_566"
##  $ chamber              : chr "House"
##  $ year                 : int 2017
##  $ congress             : chr "115"
##  $ session              : chr "1"
##  $ roll_call            : int 566
##  $ needed_to_pass       : int 282
##  $ date_of_vote         : chr "October 12, 2017"
##  $ time_of_vote         : chr "03:23 PM"
##  $ result               : chr "Passed"
##  $ vote_type            : chr "2/3 YEA-AND-NAY"
##  $ question             : chr "On Motion to Suspend the Rules and Agree"
##  $ description          : chr "Providing for the concurrence by the House in the Senate amendment to H.R. ## 2266, with an amendment"
##  $ nyt_title            : chr "On Motion to Suspend the Rules and Agree"
##  $ total_yes            : int 353
##  $ total_no             : int 69
##  $ total_not_voting     : int 11
##  $ gop_yes              : int 164
##  $ gop_no               : int 69
##  $ gop_not_voting       : int 7
##  $ dem_yes              : int 189
##  $ dem_no               : int 0
##  $ dem_not_voting       : int 5
##  $ ind_yes              : int 0
##  $ ind_no               : int 0
##  $ ind_not_voting       : int 0
##  $ dem_majority_position: chr "Yes"
##  $ 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" "Yes" "Yes" "Yes" ...
##   ..$ dw_nominate         : num [1:435] 0.493 -0.462 0.36 -0.273 0.614 0.684 0.388 NA 0.716 NA ...
##   ..$ pp_id               : chr [1:435] "LA_5" "NC_12" "AL_4" "CA_31" ...
##  - attr(*, "class")= chr [1:2] "pprc" "list"

as they hold tons of info on the votes.

I need to explore the following a bit more but there are some definite “patterns” in the way the 115th Senate has voted this year:

library(hrbrthemes)

# I made a mistake in how I exposed these that I'll correct next month
# but we need to munge it a bit anyway for this view
fills <- voteogram:::vote_carto_fill
names(fills) <- tolower(names(fills))

rcalls <- map(1:280, ~voteogram::roll_call(critter="senate", session=1, number=115, rcall=.x))
# save it off so you don't have to waste those calls again
write_rds(rcalls, "2017-115-1-sen-280-roll-calls.rds")

# do a bit of wrangling
map_df(rcalls, ~{
  mutate(.x$votes, vote_id = .x$vote_id) %>% 
    arrange(party, position) %>% 
    mutate(fill = tolower(sprintf("%s-%s", party, position))) %>% 
    mutate(ques = .x$question) %>% 
    mutate(x = 1:n())
}) -> votes_df

# plot it
ggplot(votes_df, aes(x=x, y=vote_id, fill=fill)) +
  geom_tile() +
  scale_x_discrete(expand=c(0,0)) +
  scale_y_discrete(expand=c(0,0)) +
  scale_fill_manual(name=NULL, values=fills) +
  labs(x=NULL, y=NULL, title="Senate Roll Call Votes",
       subtitle="2017 / 115th, Session 1, Votes 1-280",
       caption="Note free-Y scales") +
  facet_wrap(~ques, scales="free_y", ncol=3) +
  theme_ipsum_rc(grid="") +
  theme(axis.text = element_blank()) +
  theme(legend.position="right")

Hopefully I’ll get some time to dig into the differences and report on anything interesting. If you get to it before me definitely link to your blog post in a comment!

FIN

I still want to make an htmlwidgets version of the plots and also add the ability to get the index of roll call votes by Congress number and session to make it easier to iterate.

I’m also seriously considering creating different palettes. I used the ones from the source interactive site but am not 100% happy with them. Suggestions/PRs welcome.

Hopefully this package will make it easier for U.S. folks to track what’s going on in Congress and keep their representatives more accountable to the truth.

Everything’s on GitHub so please file issues, questions or PRs there.