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The Washington Explorer (WashEx) package creates an interface between R and the Washington State Legislative Web Services. It serves as a companion to Legislative Explorer: Washington, a project developed by John Wilkerson and Rohnin Randles at the University of Washington. The package contains functions to retrieve data regarding the following:
You can install the released version of washex from CRAN with:
install.packages("washex")And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("rwrandles/washex-r")Some possible implementations of the washex package
include
library(washex)
library(tidyverse)
sponsors_2007 <- getSponsors("2007-08")
sponsors_2007 %>%
group_by(District) %>%
summarize(prop_repub = round(mean(Party == "R"),3))
#> # A tibble: 49 x 2
#> District prop_repub
#> <chr> <dbl>
#> 1 1 0
#> 2 10 0.75
#> 3 11 0
#> 4 12 1
#> 5 13 1
#> 6 14 1
#> 7 15 1
#> 8 16 0.667
#> 9 17 0.667
#> 10 18 1
#> # ... with 39 more rows
head(getCommittees("2007-08")[,"Name"])
#> [1] "Agriculture & Natural Resources"
#> [2] "Appropriations"
#> [3] "Appropriations Subcommittee on Education"
#> [4] "Appropriations Subcommittee on General Government & Audit Review"
#> [5] "Appropriations Subcommittee on Health & Human Services"
#> [6] "Capital Budget"
head(getStatusChanges("2007-08", "1001") %>%
select("HistoryLine", "ActionDate"))
#> HistoryLine
#> 1 First reading, referred to Public Safety & Emerg Prep.
#> 2 PSEP - Executive action taken by committee.
#> 3 PSEP - Majority; 1st substitute bill be substituted, do pass.
#> 4 Referred to Human Services.
#> 5 HS - Executive action taken by committee.
#> 6 HS - Majority; 2nd substitute bill be substituted, do pass.
#> ActionDate
#> 1 2007-01-08T00:00:00
#> 2 2007-02-01T00:00:00
#> 3 2007-02-01T00:00:00
#> 4 2007-02-06T00:00:00
#> 5 2007-02-22T00:00:00
#> 6 2007-02-22T00:00:00
ag_bills <- getRCWBills("2007-08", c("15","16"))
getRollCalls.votes("2007-08", ag_bills$BillNumber) %>%
filter(str_detect("Final Passage", Motion)) %>%
mutate(Party = sponsors_2007$Party[match(MemberId, sponsors_2007$Id)]) %>%
group_by(BillId) %>%
summarize(repub_support = sum(Vote == "Yea" & Party == "R") / sum(Party == "R"),
dem_support = sum(Vote == "Yea" & Party == "D") / sum(Party == "D"))
#> # A tibble: 16 x 3
#> BillId repub_support dem_support
#> <chr> <dbl> <dbl>
#> 1 E2SHB 1303 0.457 1
#> 2 E2SHB 2798 0.943 0.984
#> 3 EHB 3381 0.0571 0.841
#> 4 ESB 5204 1 0.984
#> 5 ESHB 1151 1 1
#> 6 HB 1311 0.971 0.873
#> 7 HB 1418 0.114 0.937
#> 8 HB 1775 0.957 0.968
#> 9 HB 1888 0.971 1
#> 10 HB 2467 0.943 0.984
#> 11 HB 3106 0.886 0.952
#> 12 SB 6283 0.943 0.984
#> 13 SB 6284 0.943 0.921
#> 14 SHB 1128 0.0286 0.968
#> 15 SHB 1312 1 0.984
#> 16 SHB 1338 0.943 0.889If you encounter any bugs or issues while using the package, please file an issue with a minimum reproducible example on Github
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They may not be fully stable and should be used with caution. We make no claims about them.
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