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:
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")