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votesmart deck

This package is a wrapper around the VoteSmart API written by your friendly neighborhood progressive tech organization, 🌟 Deck Technologies 🌟. Feel free to use this package in any way you like.

VoteSmart provides information on US political candidates’ positions on issues, votes on bills, and ratings by third party organizations, among other data.

Installation

install.packages("votesmart")

Or the development version:

devtools::install_github("decktools/votesmart", build_vignettes = TRUE)

API Keys

You’ll need a VoteSmart API key in order to use this package. You can register for one here.

Store your key in an environment variable named VOTESMART_API_KEY with

Sys.setenv(VOTESMART_API_KEY = "<your_key>")

You can check that it’s there with

Sys.getenv("VOTESMART_API_KEY")

This package never stores your key in your R session’s global environment.

An Example

VoteSmart collects ratings on various issues that Special Interest Groups (SIGs) give to political candidates.

Let’s say we want to know how Elizabeth Warren tends to be rated on a few issues.

library(votesmart)
suppressPackageStartupMessages(library(dplyr))
conflicted::conflict_prefer("filter", "dplyr")
#> [conflicted] Will prefer dplyr::filter over any other package.

We’ll first want to know what her VoteSmart candidate_id is. We can search for her using candidates_get_by_lastname:

warrens <-
  candidates_get_by_lastname(
    "warren",
    election_years = 2012
  )
#> Requesting data for {last_name: warren, election_year: 2012, stage_id: }.

knitr::kable(warrens)
candidate_id first_name nick_name middle_name last_name suffix title ballot_name stage_id election_year preferred_name election_parties election_status election_stage election_district_id election_district_name election_office election_office_id election_state_id election_office_type_id election_special election_date office_parties office_status office_district_id office_district_name office_state_id office_id office_name office_type_id running_mate_id running_mate_name
139104 Adam NA Lee Warren NA NA Adam Lee Warren 2012 Adam Republican Lost Primary NA NA Attorney General 12 MO S FALSE 08/07/2012 NA NA NA NA NA NA NA NA NA NA
103860 Dennis NA NA Warren NA NA Dennis C. Warren 2012 Dennis Republican Withdrawn General 28446 16 State Senate 9 ID L FALSE 11/06/2012 NA NA NA NA NA NA NA NA NA NA
141272 Elizabeth NA Ann Warren NA Senator Elizabeth A. Warren 2012 Elizabeth Democratic Won General NA NA U.S. Senate 6 MA C FALSE 11/06/2012 Democratic active 20512 Sr MA 6 U.S. Senate C NA NA
117839 Harry NA Joseph Warren NA Representative Harry Warren 2012 Harry Republican Won General 25520 77 State House 8 NC L FALSE 11/06/2012 Republican active 25519 76 NC 8 State House L NA NA
138202 Pete NA NA Warren NA NA Pete Warren 2012 Pete Republican Removed Primary 21842 30 State House 8 FL L FALSE 08/14/2012 NA NA NA NA NA NA NA NA NA NA
137066 Stephen NA NA Warren NA NA Stephen Warren 2012 Stephen Republican Lost Primary 27865 22B State House 8 ID L FALSE 05/15/2012 NA NA NA NA NA NA NA NA NA NA
135832 Tom NA NA Warren NA NA Tom Warren 2012 Tom Democratic Lost General 25782 76 State House 8 OH L FALSE 11/06/2012 NA NA NA NA NA NA NA NA NA NA
139311 Wesley NA G. Warren NA NA Wesley G. Warren 2012 Wesley Republican Lost General 21874 62 State House 8 FL L FALSE 11/06/2012 NA NA NA NA NA NA NA NA NA NA

Filtering to her first name and taking her candidate_id, we can now grab Warren’s ratings by all SIGs with rating_get_candidate_ratings.

(id <-
  warrens %>%
  filter(first_name == "Elizabeth") %>%
  pull(candidate_id)
)
#> [1] "141272"

ratings <-
  rating_get_candidate_ratings(
    candidate_ids = id,
  )
#> Requesting data for {candidate_id: 141272, sig_id: }.

knitr::kable(ratings %>% sample_n(3))
rating_id candidate_id sig_id rating rating_name timespan rating_text category_id_1 category_name_1 category_id_2 category_name_2 category_id_3 category_name_3 category_id_4 category_name_4 category_id_5 category_name_5 category_id_6 category_name_6 category_id_7 category_name_7 category_id_8 category_name_8 category_id_9 category_name_9
8615 141272 1161 100 Positions 2014 Senator Elizabeth Warren supported the interests of the American Federation of Labor and Congress of Industrial Organizations (AFL-CIO) 100 percent in 2014. 43 Labor Unions NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
10219 141272 2412 18 Lifetime Positions 2016 Senator Elizabeth Warren supported the interests of the Conservative Review 18 percent in 2016. 17 Conservative NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
9705 141272 1985 1 Positions 2017-2018 Senator Elizabeth Warren supported the interests of the NumbersUSA 1 percent in 2017-2018. 40 Immigration NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

And compute on them:

ratings %>%
  filter(
    category_name_1 %in%
      c(
        "Environment",
        "Fiscally Conservative",
        "Education",
        "Civil Liberties and Civil Rights",
        "Campaign Finance"
      )
  ) %>%
  group_by(category_name_1) %>%
  summarise(
    avg_rating = mean(as.numeric(rating), na.rm = TRUE)
  ) %>%
  arrange(category_name_1)
#> # A tibble: 5 × 2
#>   category_name_1                  avg_rating
#>   <chr>                                 <dbl>
#> 1 Campaign Finance                     100   
#> 2 Civil Liberties and Civil Rights      86.6 
#> 3 Education                             89.3 
#> 4 Environment                           87.2 
#> 5 Fiscally Conservative                  8.78

For more in-depth examples of how these all fit together, check out the vignette with:

vignette("votesmart")

Available Functions

These functions are named after the snake_cased version of the API endpoints.

If you see an endpoint you want to be made available in this package that isn’t yet, feel free to submit an issue or a pull request!

Summary of Functions

candidates_get_by_lastname

Get a dataframe of candidates given a vector of last_names, election_years (optional, defaulting to current year), and stage_ids (optional)

candidates_get_by_levenshtein

Get a dataframe of fuzzy-matched candidates given a vector of last_names, election_years (optional), and stage_ids (optional)

candidates_get_by_office_state

Get a dataframe of candidates by the state in which they ran for office given a vector of state_ids (optional), office_ids, and election_years (optional)

election_get_election_by_year_state

Get a dataframe of election ids and their attributes given a vector of years and state_ids (optional)

measure_get_measures

Get a dataframe of ballot measure attributes given a measure_id

measure_get_measures_by_year_state

Get a dataframe of ballot measure ids and their attributes given a vector of years and state_ids (optional)

office_get_levels

Get the VoteSmart office_level_ids and their associated names (federal, state, local)

office_get_offices_by_level

Get office_ids and their associated names (e.g. "President") for a given office_level_id

rating_get_candidate_ratings

Get SIG (Special Interest Group) ratings for candidates given a candidate_id and a sig_id (optional)

rating_get_categories

Get rating category_ids and their associated names (e.g. "Abortion", "Environment") given a vector of state_ids (optional)

rating_get_sig

Get information about a vector of SIGs (Special Interest Groups) given a sig_id

rating_get_sig_list

Get a dataframe of SIG (Special Interest Group) given a rating category_id and a state_id (optional)

votes_get_by_official

Get a dataframe of the way officials have voted on bills given a candidate_id, an office_id (optional), a category_id (optional) and a year the vote occurred (optional)

Package Data

Only a subset of all of the VoteSmart endpoints have yet been made available through this package.

You can see a full dataframe of the VoteSmart endpoints and their associated arguments with

data("endpoint_input_mapping")

or

data("endpoint_input_mapping_nested")

Other Details


Feel free to reach out in the Issues with any bugs or feature requests! 💫

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.