README.md

In JosiahParry/opensecrets: Access OpenSecrets API from R

opensecrets

The goal of opensecrets is to create a reimagining of the OpenSecrets API. The package is intended to follow almost verbatim with the API documentation. Refer to the documentation for more information of the data that is retrieved.

Installation

You can install the released version of opensecrets from GitHub with:

remotes::install_github("josiahparry/opensecrets")

This package is in very experimental phases and is subject to breaking. There is much to do, but a this serves as a minimal viable product.

Please submit issues and feature requests as you come about them.

API Key

Register with OpenSecrets and register for an API key at this link.

Set your key using set_os_key("my_key_here") or store it in .Renviron

Examples

List all available legislators for a given state.

library(opensecrets)

ca_legislators <- get_legislators("CA")
ca_legislators
#> # A tibble: 56 x 21
#>    cid   firstlast lastname party office gender first_elected exit_code
#>    <chr> <chr>     <chr>    <chr> <chr>  <chr>  <chr>         <chr>    
#>  1 N000… Doug LaM… LAMALFA  R     CA01   M      2012          0        
#>  2 N000… Jared Hu… HUFFMAN  D     CA02   M      2012          0        
#>  3 N000… John Gar… GARAMEN… D     CA03   M      2009          0        
#>  4 N000… Tom McCl… MCCLINT… R     CA04   M      2008          0        
#>  5 N000… Mike Tho… THOMPSON D     CA05   M      1998          0        
#>  6 N000… Doris O … MATSUI   D     CA06   F      2005          0        
#>  7 N000… Ami Bera  BERA     D     CA07   M      2012          0        
#>  8 N000… Paul Cook COOK     R     CA08   M      2012          0        
#>  9 N000… Jerry Mc… MCNERNY  D     CA09   M      2006          0        
#> 10 N000… Jeff Den… DENHAM   R     CA10   M      2010          21       
#> # … with 46 more rows, and 13 more variables: comments <chr>, phone <chr>,
#> #   fax <chr>, website <chr>, webform <chr>, congress_office <chr>,
#> #   bioguide_id <chr>, votesmart_id <chr>, feccandid <chr>,
#> #   twitter_id <chr>, youtube_url <chr>, facebook_id <chr>,
#> #   birthdate <chr>

Get personal finance information for a given legislator.

library(tidyverse)

# get Nancy Pelosi
ca_legislators %>% 
  filter(firstlast == "Nancy Pelosi") %>% 
  pull(cid) %>% 
  personal_finance()
#> # A tibble: 1 x 18
#>   name  data_year member_id net_low net_high positions_held_… asset_count
#>   <chr> <chr>     <chr>     <chr>   <chr>    <chr>            <chr>      
#> 1 Pelo… 2016      N00007360 -21225… 1380509… 0                44         
#> # … with 11 more variables: asset_low <chr>, asset_high <chr>,
#> #   transaction_count <chr>, tx_low <chr>, tx_high <chr>, source <chr>,
#> #   origin <chr>, update_timestamp <chr>, positions <list>, assets <list>,
#> #   transactions <list>

Use purrr::map() to iterate over multiple individuals.

ca_legislators %>% 
  # take only 3 legislators
  slice(1:3) %>% 
  mutate(pf = map(cid, personal_finance)) %>% 
  unnest()
#> # A tibble: 3 x 39
#>   cid   firstlast lastname party office gender first_elected exit_code
#>   <chr> <chr>     <chr>    <chr> <chr>  <chr>  <chr>         <chr>    
#> 1 N000… Doug LaM… LAMALFA  R     CA01   M      2012          0        
#> 2 N000… Jared Hu… HUFFMAN  D     CA02   M      2012          0        
#> 3 N000… John Gar… GARAMEN… D     CA03   M      2009          0        
#> # … with 31 more variables: comments <chr>, phone <chr>, fax <chr>,
#> #   website <chr>, webform <chr>, congress_office <chr>,
#> #   bioguide_id <chr>, votesmart_id <chr>, feccandid <chr>,
#> #   twitter_id <chr>, youtube_url <chr>, facebook_id <chr>,
#> #   birthdate <chr>, name <chr>, data_year <chr>, member_id <chr>,
#> #   net_low <chr>, net_high <chr>, positions_held_count <chr>,
#> #   asset_count <chr>, asset_low <chr>, asset_high <chr>,
#> #   transaction_count <chr>, tx_low <chr>, tx_high <chr>, source <chr>,
#> #   origin <chr>, update_timestamp <chr>, positions <list>, assets <list>,
#> #   transactions <list>

Reference Tables

opensecrets::legislators
#> # A tibble: 555 x 22
#>    cid   firstlast lastname state party office gender first_elected
#>    <chr> <chr>     <chr>    <chr> <chr> <chr>  <chr>  <chr>        
#>  1 N000… Bradley … BYRNE    AL    R     AL01   M      2013         
#>  2 N000… Martha R… ROBY     AL    R     AL02   F      2010         
#>  3 N000… Mike D R… ROGERS   AL    R     AL03   M      2002         
#>  4 N000… Robert B… ADERHOLT AL    R     AL04   M      1996         
#>  5 N000… Mo Brooks BROOKS   AL    R     AL05   M      2010         
#>  6 N000… Gary Pal… PALMER   AL    R     AL06   M      2014         
#>  7 N000… Terri A … SEWELL   AL    D     AL07   F      2010         
#>  8 N000… Jeff Ses… SESSIONS AL    R     ALS1   M      1996         
#>  9 N000… Doug Jon… JONES    AL    D     ALS1   M      2017         
#> 10 N000… Luther S… Strange  AL    R     ALS1   M      2017         
#> # … with 545 more rows, and 14 more variables: exit_code <chr>,
#> #   comments <chr>, phone <chr>, fax <chr>, website <chr>, webform <chr>,
#> #   congress_office <chr>, bioguide_id <chr>, votesmart_id <chr>,
#> #   feccandid <chr>, twitter_id <chr>, youtube_url <chr>,
#> #   facebook_id <chr>, birthdate <chr>

opensecrets::categories
#> # A tibble: 459 x 6
#>    catcode catname            catorder industry         sector  sector_long
#>    <chr>   <chr>              <chr>    <chr>            <chr>   <chr>      
#>  1 A0000   Agriculture        A11      Misc Agriculture Agribu… Agribusine…
#>  2 A1000   Crop production &… A01      Crop Production… Agribu… Agribusine…
#>  3 A1100   Cotton             A01      Crop Production… Agribu… Agribusine…
#>  4 A1200   Sugar cane & suga… A01      Crop Production… Agribu… Agribusine…
#>  5 A1300   Tobacco & Tobacco… A02      Tobacco          Agribu… Agribusine…
#>  6 A1400   Vegetables, fruit… A01      Crop Production… Agribu… Agribusine…
#>  7 A1500   Wheat, corn, soyb… A01      Crop Production… Agribu… Agribusine…
#>  8 A1600   Other commodities… A01      Crop Production… Agribu… Agribusine…
#>  9 A2000   Milk & dairy prod… A04      Dairy            Agribu… Agribusine…
#> 10 A2300   Poultry & eggs     A05      Poultry & Eggs   Agribu… Agribusine…
#> # … with 449 more rows

JosiahParry/opensecrets documentation built on Sept. 3, 2020, 10:32 p.m.