rsunlight is an R package to search and retrieve data from the Sunlight Labs APIs.
Returned objects from functions are simple lists. That is, you likely will want to take output objects and make data.frames, vectors, matrices, etc. In future versions of rsunlight, I will return data.frame's when possible as those are easy to work with in R for beginners, though advanced users may prefer lists or raw responses from the API with lots of info, including header, etc.
devtools::install_github("ropengov/rsunlight")
library('rsunlight')
cg_committees(id = 'JSPR')
#> <Sunlight data>
#> Dimensions: [20 X 5]
#>
#> chamber committee_id
#> 1 senate SSGA19
#> 2 senate SSGA18
#> 3 senate SSGA16
#> 4 senate SSGA15
#> 5 senate SSGA17
#> 6 senate SSGA01
#> 7 senate SSFR14
#> 8 senate SSFR15
#> 9 senate SSFR13
#> 10 senate SSFR12
#> .. ... ...
#> Variables not shown: name (chr), parent_committee_id (chr), subcommittee
#> (lgl)
cg_districts(latitude = 35.778788, longitude = -78.787805)
#> <Sunlight data>
#> Dimensions: [1 X 2]
#>
#> state district
#> 1 NC 2
cg_districts(zip = 27511)
#> <Sunlight data>
#> Dimensions: [3 X 2]
#>
#> state district
#> 1 NC 2
#> 2 NC 4
#> 3 NC 13
cg_legislators(last_name = 'Reed')
#> <Sunlight data>
#> Dimensions: [2 X 36]
#>
#> bioguide_id birthday chamber
#> 1 R000585 1971-11-18 house
#> 2 R000122 1949-11-12 senate
#> Variables not shown: contact_form (chr), crp_id (chr), district (int),
#> facebook_id (chr), fax (chr), fec_ids (list), first_name (chr),
#> gender (chr), govtrack_id (chr), icpsr_id (int), in_office (lgl),
#> last_name (chr), middle_name (chr), name_suffix (chr), nickname
#> (chr), oc_email (chr), ocd_id (chr), office (chr), party (chr), phone
#> (chr), state (chr), state_name (chr), term_end (chr), term_start
#> (chr), thomas_id (chr), title (chr), twitter_id (chr), votesmart_id
#> (int), website (chr), youtube_id (chr), lis_id (chr), senate_class
#> (int), state_rank (chr)
And get their names and Twitter handles
cg_legislators(zip = 77006)
#> <Sunlight data>
#> Dimensions: [4 X 37]
#>
#> bioguide_id birthday chamber
#> 1 C001098 1970-12-22 senate
#> 2 P000592 1948-09-10 house
#> 3 J000032 1950-01-12 house
#> 4 C001056 1952-02-02 senate
#> Variables not shown: contact_form (chr), crp_id (chr), district (int),
#> facebook_id (chr), fax (chr), fec_ids (list), first_name (chr),
#> gender (chr), govtrack_id (chr), icpsr_id (int), in_office (lgl),
#> last_name (chr), lis_id (chr), middle_name (lgl), name_suffix (lgl),
#> nickname (lgl), oc_email (chr), ocd_id (chr), office (chr), party
#> (chr), phone (chr), senate_class (int), state (chr), state_name
#> (chr), state_rank (chr), term_end (chr), term_start (chr), thomas_id
#> (chr), title (chr), twitter_id (chr), votesmart_id (int), website
#> (chr), youtube_id (chr), leadership_role (chr)
Get a list of how many times the phrase “united states” appears in the Congressional Record in each month between January and June, 2010:
cw_timeseries(phrase='united states', start_date='2009-01-01', end_date='2009-04-30', granularity='month')
#> <Sunlight data>
#> Dimensions: [4 X 2]
#>
#> count month
#> 1 3805 2009-01-01
#> 2 3512 2009-02-01
#> 3 6018 2009-03-01
#> 4 2967 2009-04-01
library('ggplot2')
dat <- cw_timeseries(phrase = 'climate change')
ggplot(dat, aes(day, count)) +
geom_line() +
theme_grey(base_size=20)
dat_d <- cw_timeseries(phrase='climate change', party="D")
dat_d$party <- rep("D", nrow(dat_d))
dat_r <- cw_timeseries(phrase='climate change', party="R")
dat_r$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"))
os_billsearch(terms = 'agriculture', state = 'tx', chamber = 'upper')
#> <Sunlight data>
#> Dimensions: [320 X 10]
#>
#> title
#> 1 Relating to certain committees and programs to develop the wine industry in
#> 2 Relating to a waiver of fees by the Department of Agriculture and the Parks
#> 3 Urging the United States Department of Agriculture Food and Nutrition Servi
#> 4 Relating to the designation of an office in the Department of Agriculture t
#> 5 Relating to the office of water and the water advisory committee in the Dep
#> 6 Relating to establishing an agriculture ombudsman office in the Department
#> 7 Relating to authorizing the issuance of revenue bonds to fund capital proje
#> 8 Relating to authorizing the issuance of revenue bonds to fund capital proje
#> 9 Relating to authorizing the issuance of revenue bonds to fund capital proje
#> 10 Relating to authorizing the issuance of revenue bonds to fund capital proje
#> .. ...
#> Variables not shown: created_at (chr), updated_at (chr), id (chr), chamber
#> (chr), state (chr), session (chr), type (list), subjects (list),
#> bill_id (chr)
os_legislatorsearch(state = 'tx', party = 'democratic', active = TRUE)
#> <Sunlight data>
#> Dimensions: [63 X 29]
#>
#> last_name updated_at nimsp_candidate_id full_name
#> 1 Naishtat 2015-12-16 09:56:01 112047 Elliott Naishtat
#> 2 Romero 2015-12-16 09:56:01 NA Ramon Romero, Jr.
#> 3 Minjarez 2015-12-16 09:56:01 NA Ina Minjarez
#> 4 Gutierrez 2015-12-16 09:56:01 111938 Roland Gutierrez
#> 5 Israel 2015-12-16 09:56:01 NA Celia Israel
#> 6 Coleman 2015-12-16 09:56:01 99959 Garnet Coleman
#> 7 Guillen 2015-12-16 09:56:01 111937 Ryan Guillen
#> 8 Johnson 2015-12-16 09:56:01 NA Eric Johnson
#> 9 Moody 2015-12-16 09:56:01 100029 Joseph Moody
#> 10 Turner 2015-12-16 09:56:01 112130 Sylvester Turner
#> .. ... ... ... ...
#> Variables not shown: +district_address (chr), first_name (chr),
#> middle_name (chr), district (chr), id (chr), state (chr),
#> votesmart_id (chr), party (chr), all_ids (list), leg_id (chr), active
#> (lgl), transparencydata_id (chr), photo_url (chr), +capital_address
#> (chr), url (chr), country (chr), created_at (chr), level (chr),
#> nimsp_id (chr), chamber (chr), offices (list), suffixes (chr), email
#> (chr), nickname (chr), +birth_date (chr)