R/utils_data_cleaning.R

In filibustr: Data Utilities for Congressional Research

# convert state and expectation to factors (using `haven::as_factor()` if applicable)
# used in `fix_hvw_coltypes()` and `fix_les_coltypes()`
create_factor_columns <- function(df, local_path) {
  if (isTRUE(tools::file_ext(local_path) == "dta")) {
    df <- df |>
      # no need to specify levels if data is already coming from saved DTA file
      dplyr::mutate(dplyr::across(.cols = c(dplyr::any_of(c("state", "st_name")),
                                            dplyr::matches("expectation[12]?$")),
                                  .fns = haven::as_factor))
  } else {
    df <- df |>
      dplyr::mutate(dplyr::across(.cols = dplyr::any_of(c("state", "st_name")),
                                  .fns = ~ factor(.x, levels = datasets::state.abb))) |>
      # LES vs. expectation (`expectation`/`expectation1`/`expectation2`)
      dplyr::mutate(dplyr::across(.cols = dplyr::matches("expectation[12]?$"),
                                  .fns = as.factor))
  }

  df
}

# filter (Voteview) data by chamber
filter_chamber <- function(df, chamber) {
  # filter chamber
  chamber_code <- match_chamber(chamber = chamber)
  if (chamber_code == "H") {
    df <- df |>
      dplyr::filter(chamber != "Senate")
  } else if (chamber_code == "S") {
    df <- df |>
      dplyr::filter(chamber != "House")
  }

  # remove filtered-out chambers
  # so filtered local files are identical to single-chamber online downloads
  df |> dplyr::mutate(chamber = droplevels(chamber))
}

# filter (Voteview) data by Congress number
filter_congress <- function(df, congress, call = rlang::caller_env()) {
  if (!is.null(congress)) {
    # check for invalid Congress numbers
    match_congress(congress = congress, call = call)

    # check that Congress numbers are present in data
    # NOTE: only error if no data is present. Ok if only some Congress numbers are present
    if (!any(congress %in% unique(df$congress))) {
      len <- length(congress)
      cli::cli_abort(
        # `qty()`: pluralize based on the length of `congress`, not its value
        paste("Congress {cli::qty(length(congress))} number{?s} ({.val {congress}})",
              "{cli::qty(length(congress))} {?was/were} not found in data."),
        call = call
      )
    }

    df <- df |>
      dplyr::filter(congress %in% {{ congress }})
  }

  df
}