The goal of maocpf is to pull candidate fundraising data from the Massachusetts Office of Campaign and Political Finance.
Here’s a typical workflow.
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.
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.
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")
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)