R/read-expd.R

In arcenis-r/cepumd: Calculate Consumer Expenditure Survey (CE) Annual Estimates

#' Read in and modify EXPD files
#'
#' @details This is a hidden file called only by exported package functions.
#'
#' @param fp File to extract from zip file
#' @param zp Zip file path
#' @param year Year
#' @param uccs Vector of UCC's to filter for
#' @param integrate_data Whether to prepare data for integrated estimates
#' @param hg Hierarchical grouping data

#' @importFrom dplyr left_join

read.expd <- function(fp, zp, year, uccs, integrate_data, hg) {

  df <- suppressWarnings(
    readr::read_csv(
      unz(zp, fp),
      na = c("NA", "", " ", "."),
      progress = FALSE,
      show_col_types = FALSE
    )
  )

  names(df) <- tolower(names(df))

  if (year >= 1996 && year <= 2011) {
    df <- df |>
      dplyr::mutate(
        expnyr = as.integer(stringr::str_sub(.data$qredate, 7, 10)),
        expnmo = as.integer(stringr::str_sub(.data$qredate, 3, 4))
      )
  }

  df <- df |>
    dplyr::select(
      tidyselect::all_of(
        c("newid", "expnyr", "expnmo", "ucc", "cost", "pub_flag")
      )
    ) |>
    dplyr::rename(
      ref_yr = "expnyr",
      ref_mo = "expnmo"
    ) |>
    dplyr::mutate(
      newid = stringr::str_pad(
        .data$newid,
        width = 8,
        side = "left",
        pad = "0"
      ),
      ucc = stringr::str_pad(.data$ucc, width = 6, side = "left", pad = "0"),
      dplyr::across(
        tidyselect::all_of(c("ref_yr", "ref_mo", "cost")),
        as.numeric
      )
    )

  if (integrate_data) df <- df |> dplyr::filter(.data$pub_flag %in% "2")

  df <- df |>
    dplyr::filter(.data$ref_yr %in% year, .data$ucc %in% uccs) |>
    dplyr::left_join(
      hg |> dplyr::select(tidyselect::all_of(c("ucc", "factor"))),
      by = "ucc"
    ) |>
    dplyr::mutate(
      cost = .data$cost * 13 * as.numeric(as.character(.data$factor))
    ) |>
    dplyr::group_by(.data$newid, .data$ucc, .data$ref_yr, .data$ref_mo) |>
    dplyr::summarise(cost = sum(.data$cost), .groups = "drop")

  return(df)
}