R/sched_parse_teams.R

In jesse-smith/covidassign: COVID-19 Case Assignment for SCHD

#' Parse the Team Schedule Workbook to Standardized Format
#'
#' `sched_parse_teams()` parses the data returned from `sched_load_teams()` into
#' a standard format, with one column per team and one row per individual.
#'
#' @param .data The teams data read by `sched_load_teams()`
#'
#' @return A `tibble` containing one column for each team (named with that
#' team's letter designation) and one row per team member. Team member names
#' are standardized with
#' \code{\link[covidassign:sched_std_names]{sched_std_names()}}.
#'
#' @export
sched_parse_teams <- function(.data) {
  .data %>%
    # Find rows with investigator names
    dplyr::mutate(
      row = vec_seq_along(.),
      inv_role = tidyr::replace_na(.data[["role"]] == "Investigators", FALSE),
      nurse_role = tidyr::replace_na(.data[["role"]] == "PH Nurse", FALSE),
      inv_start_row = .data[["row"]][.data[["inv_role"]]],
      # nurse_row = if (any(.data)) .data[["row"]][.data[["nurse_role"]]],
      .before = 1L
    ) %>%
    # Filter to rows with investigator names
    dplyr::filter(
      .data[["row"]] >= .data[["inv_start_row"]]
        # .data[["row"]] == .data[["nurse_row"]]
    ) %>%
    # Remove helper variables
    dplyr::select(-dplyr::ends_with(c("role", "row"))) %>%
    # Parse investigator names
    dplyr::mutate(
      dplyr::across(.fns = ~ .x %>% sched_std_names() %>% sched_remove_phn())
    ) %>%
    # Remove empty rows
    janitor::remove_empty(which = "rows")
}

#' Remove "PHN" Placeholder From Nurse Role
#'
#' @param string A character vector
#'
#' @return A character vector with "PHN" removed when surrounded by only
#'  symbols and/or numbers. Not case sensitive.
sched_remove_phn <- function(string) {
  stringr::str_replace(
    string,
    "(?i)^[^A-Za-z]*Phn[^A-Za-z]*$",
    replacement = NA_character_
  )
}