smach / maocpf

Download campaign contribution data from the Massachusetts Office of Campaign and Political Finance by candidate ID

maocpf

The goal of maocpf is to pull candidate fundraising data from the Massachusetts Office of Campaign and Political Finance.

Here’s a typical workflow.

Setup: Once or infrequently

1. Get a list of all local candidates with get_local_candidates(). This function downloads a file with all candidates from the MA OCPF Web site to a temp subdirectory (which will be created if it doesn’t exist). You can filter by city and/or office, but it’s probably better to stick to the default and download the full list once. Then you can filter that unless you are 100% sure you only want the filtered version. The file downloads each time you run this function.

2. Get the IDs of the candidates you want with get_candidate_id() using the candidate name as the first argument and the data frame you created in step 1 as the second argument. Store this data somewhere for re-use!

Here is a sample workflow to begin using this package:

library(maocpf)
all_candidate_info <- get_local_candidates()
save(all_candidate_info, "data/all_candidate_info.Rdata")

all_framingham_candidates <- dplyr::filter(all_candidate_info, Candidate_City == "Framingham")
save(all_framingham_candidates, "data/all_framingham_candidates.Rdata")

Note that for state legislative districts that encompass areas outside of one community, you’ll want to filter by District column instead.

Data for all_candidate_info and all_framingham_candidates from February 14, 2021 are included with this package.

Run regularly

To get up-to-date data on a candidate, use the get_candidate_contribution_data() function with the candidate’s ID. For example, I can find Framingham Mayor Spicer’s ID with the get_candidate_id() function and all or part of her name:

get_candidate_id("Spicer")
#> # A tibble: 1 x 2
#>   ID    Candidate       
#>   <chr> <chr>           
#> 1 16676 Yvonne M. Spicer

Now I can pull all the contributions since the start of 2021 with

spicer_contributions <- get_latest_candidate_contributions("16676", "2021-01-01")
head(spicer_contributions)
#>         Date         Candidate FirstName LastName              Address
#> 1 2021-01-08 Spicer, Yvonne M.      Eric     Masi      68 Lowell Drive
#> 2 2021-01-14 Spicer, Yvonne M.      Mary    Breen     52 Arthur Street
#> 3 2021-01-14 Spicer, Yvonne M.     Burns    Burns      16 Acorn Street
#> 4 2021-01-14 Spicer, Yvonne M.     Stacy    Cowan    3 Stonegate Drive
#> 5 2021-01-14 Spicer, Yvonne M.    George     Deak 76 Florissant Avenue
#> 6 2021-01-14 Spicer, Yvonne M.     Janet    Drake         66 Linda Ave
#>         City State ZipCode Amount                   Occupation
#> 1       Stow    MA   01775    200 Social Service Administrator
#> 2 Framingham    MA   01702     25                 Not Employed
#> 3     Boston    MA   02108    500                 Not Employed
#> 4   Westwood    MA   02090   1000                       Lawyer
#> 5 Framingham    MA   01701     25                 Not Employed
#> 6 Framingham    MA   01701     15            Circulation Asst.
#>                                Employer                 Report
#> 1 Wayside Youth &family Support Network  1/8/21 Deposit Report
#> 2                          Not Employed 1/14/21 Deposit Report
#> 3                          Not Employed 1/14/21 Deposit Report
#> 4                 Slc Advisory Services 1/14/21 Deposit Report
#> 5                          Not Employed 1/14/21 Deposit Report
#> 6     Town of Framingham (Library Dept) 1/14/21 Deposit Report

To get a data frame with multiple candidates, simply use purrr::map_df() to get a list of candidate IDs and then download their contribution data, such as:

my_ids_df <- purrr::map_df(c("Spicer", "King", "Steiner", "Stefanini"), get_candidate_id, df = all_framingham_candidates)
my_ids_df
#> # A tibble: 4 x 2
#>   ID    Candidate          
#>   <chr> <chr>              
#> 1 16676 Yvonne M. Spicer   
#> 2 16997 George P. King, Jr.
#> 3 16720 Adam C. Steiner    
#> 4 12372 John A. Stefanini

contributions2021 <- purrr::map_df(my_ids_df$ID, get_latest_candidate_contributions, start_date = "2021-01-01")

Installation

This package is not on CRAN, so you can only install it from GitHub. Install with your favorite install-from-GitHub package such as remotes::install_github("smach/maocpf", build_vignettes = TRUE)