I started an R package a while back, and a few people have shown interest, so I thought it was time to revist the code. govdat is an interface to various APIs for government data: currently the Sunlight Labs APIs, and the New York Times congress API. Returned objects from functions are simple lists. In future versions of govdat, I may change how data is returned. The following are examples (which is also the package vignette) of using the Sunlight Labs API. I will add examples of using the New York Times Congress API once their site is up again; I’m doing this on 2013-08-28, just after the takedown of their site.
I show just a bit of each data object returned for brevity. And yes, I realize this is not related at all to ecology.
You will need an API key to use both Sunlight Labs APIs and the New York Times APIs. Get your API key at Sunlight Labs here and NYT here. You can pass in your key within each function or you can put the key in your .Rprofile file on your machine (which is read from the default R working directory) and the key will be read in automatically inside the function. I recommend the latter option.
Do let me know of bugs or feature requests over at the Github issues page here.
Install govdat
install.packages("devtools")
library(devtools)
install_github("govdat", "sckott")
Load govdat
library(govdat)
Gets details (subcommittees + membership) for a committee by id.
key <- getOption("SunlightLabsKey")
out <- sll_cg_getcommittees(id = "JSPR")
out$response$committee$members[[1]]$legislator[1:5]
$website
[1] "http://www.alexander.senate.gov"
$fax
[1] "202-228-3398"
$govtrack_id
[1] "300002"
$firstname
[1] "Lamar"
$chamber
[1] "senate"
Gets a list of all committees that a member serves on, including subcommittes.
out <- sll_cg_getcommitteesallleg(bioguide_id = "S000148")
out$response$committees[[1]]
$committee
$committee$chamber
[1] "Senate"
$committee$id
[1] "SSRA"
$committee$name
[1] "Senate Committee on Rules and Administration"
Get districts for a latitude/longitude.
out <- sll_cg_getdistrictlatlong(latitude = 35.778788, longitude = -78.787805)
out$response$districts
[[1]]
[[1]]$district
[[1]]$district$state
[1] "NC"
[[1]]$district$number
[1] "2"
Get districts that overlap for a certain zip code.
out <- sll_cg_getdistrictzip(zip = 27511)
out$response$districts
[[1]]
[[1]]$district
[[1]]$district$state
[1] "NC"
[[1]]$district$number
[1] "2"
[[2]]
[[2]]$district
[[2]]$district$state
[1] "NC"
[[2]]$district$number
[1] "4"
[[3]]
[[3]]$district
[[3]]$district$state
[1] "NC"
[[3]]$district$number
[1] "13"
Search congress people and senate members.
out <- sll_cg_getlegislatorsearch(name = "Reed")
out$response$results[[1]]$result$legislator[1:5]
$website
[1] "http://www.reed.senate.gov"
$fax
[1] "202-224-4680"
$govtrack_id
[1] "300081"
$firstname
[1] "John"
$chamber
[1] "senate"
Search congress people and senate members for a zip code.
out <- sll_cg_legislatorsallforzip(zip = 77006)
library(plyr)
ldply(out$response$legislators, function(x) data.frame(x$legislator[c("firstname",
"lastname")]))
firstname lastname
1 Sheila Jackson Lee
2 Ted Cruz
3 John Cornyn
4 Ted Poe
Find the popularity of a phrase over a period of time.
Get a list of how many times the phrase “united states” appears in the Congressional Record in each month between January and June, 2010:
sll_cw_timeseries(phrase = "united states", start_date = "2009-01-01", end_date = "2009-04-30",
granularity = "month")
4 records returned
count month
1 3805 2009-01-01
2 3512 2009-02-01
3 6018 2009-03-01
4 2967 2009-04-01
Plot data
library(ggplot2)
dat <- sll_cw_timeseries(phrase = "climate change")
1354 records returned
ggplot(dat, aes(day, count)) + geom_line() + theme_grey(base_size = 20)
Plot more data
dat_d <- sll_cw_timeseries(phrase = "climate change", party = "D")
908 records returned
dat_d$party <- rep("D", nrow(dat_d))
dat_r <- sll_cw_timeseries(phrase = "climate change", party = "R")
623 records returned
dat_r$party <- rep("R", nrow(dat_r))
dat_both <- rbind(dat_d, dat_r)
ggplot(dat_both, aes(day, count, colour = party)) + geom_line() + theme_grey(base_size = 20) +
scale_colour_manual(values = c("blue", "red"))
Search OpenStates bills.
out <- sll_os_billsearch(terms = "agriculture", state = "tx", chamber = "upper")
lapply(out, "[[", "title")[100:110]
[[1]]
[1] "Relating to the sale by the Brazos River Authority of certain property at Possum Kingdom Lake."
[[2]]
[1] "Proposing a constitutional amendment providing immediate additional revenue for the state budget by creating the Texas Gaming Commission, and authorizing and regulating the operation of casino games and slot machines by a limited number of licensed operators and certain Indian tribes."
[[3]]
[1] "Relating to production requirements for holders of winery permits."
[[4]]
[1] "Relating to the use of human remains in the training of search and rescue animals."
[[5]]
[1] "Relating to end-of-course assessment instruments administered to public high school students and other measures of secondary-level performance."
[[6]]
[1] "Relating to public high school graduation, including curriculum and assessment requirements for graduation and funding in support of certain curriculum authorized for graduation."
[[7]]
[1] "Relating to certain residential and other structures and mitigation of loss to those structures resulting from natural catastrophes; providing a criminal penalty."
[[8]]
[1] "Recognizing March 28, 2013, as Texas Water Conservation Day at the State Capitol."
[[9]]
[1] "Recognizing March 26, 2013, as Lubbock Day at the State Capitol."
[[10]]
[1] "In memory of Steve Jones."
[[11]]
[1] "Relating to the regulation of dangerous wild animals."
Search Legislators on OpenStates.
out <- sll_os_legislatorsearch(state = "tx", party = "democratic", active = TRUE)
out[[1]][1:5]
$last_name
[1] "Naishtat"
$updated_at
[1] "2013-08-29 03:03:22"
$nimsp_candidate_id
[1] "112047"
$full_name
[1] "Elliott Naishtat"
$`+district_address`
[1] " P.O. Box 2910\nAustin, TX 78768\n(512) 463-0668"
Search for entities - that is, politicians, individuals, or organizations with the given name
out <- sll_ts_aggregatesearch("Nancy Pelosi")
out <- lapply(out, function(x) {
x[sapply(x, is.null)] <- "none"
x
})
ldply(out, data.frame)
name count_given firm_income count_lobbied seat
1 Nancy Pelosi (D) 0 0 0 federal:house
2 Nancy Pelosi for Congress 7 0 0 none
total_received state lobbying_firm count_received party total_given type
1 14173534 CA none 10054 D 0 politician
2 0 none <NA> 0 none 7250 organization
id non_firm_spending is_superpac
1 85ab2e74589a414495d18cc7a9233981 0 none
2 afb432ec90454c8a83a3113061e7be27 0 <NA>
Return the top contributoring organizations, ranked by total dollars given. An organization’s giving is broken down into money given directly (by the organization’s PAC) versus money given by individuals employed by or associated with the organization.
out <- sll_ts_aggregatetopcontribs(id = "85ab2e74589a414495d18cc7a9233981")
ldply(out, data.frame)
employee_amount total_amount total_count name
1 64000.00 101300.00 79 Akin, Gump et al
2 3500.00 90000.00 29 American Fedn of St/Cnty/Munic Employees
3 0 86600.00 48 National Assn of Realtors
4 0 85000.00 32 United Auto Workers
5 0 82500.00 37 National Education Assn
6 0 77000.00 19 Teamsters Union
7 0 76000.00 36 National Assn of Letter Carriers
8 0 72500.00 18 Intl Brotherhood of Electrical Workers
9 0 72500.00 21 Sheet Metal Workers Union
10 8000.00 72000.00 31 Wells Fargo
direct_count employee_count id direct_amount
1 16 63 105dcfc8c9384875a099af230dad9917 37300.00
2 25 4 fb702029157e4c7c887172eba71c66c5 86500.00
3 48 0 bb98402bd4d3471cad392a671ecd733a 86600.00
4 32 0 4d3167b97c9f48deb433aad57bb0ee44 85000.00
5 37 0 1b8fea7e453d4e75841eac48ff9df550 82500.00
6 19 0 f89c8e3ab2b44f72971f91b764868231 77000.00
7 36 0 390767dc6b4b491ca775b1bdf8a36eea 76000.00
8 18 0 b53b4ad137d743a996f4d7467700fc88 72500.00
9 21 0 425be85642b24cc2bc3d8a0bb3c7bc92 72500.00
10 20 11 793070ae7f5e42c2a76a58663a588f3d 64000.00