R/set_scientific_name.R

In corella: Prepare, Manipulate and Check Data to Comply with Darwin Core Standard

#' Set, create or modify columns with scientific name & authorship information
#'
#' Format the field `scientificName`, the lowest identified taxonomic name of an
#' occurrence, along with the rank and authorship of the provided name to a
#' `tibble` using Darwin Core Standard.
#'
#' In practice this function is used no differently from `mutate()`, but gives
#' users some informative errors, and serves as a useful lookup for accepted
#' column names in the Darwin Core Standard.
#' @param .df A `data.frame` or `tibble` that the column should be appended to.
#' @param scientificName The full scientific name in the lower level taxonomic
#' rank that can be determined.
#' @param taxonRank The taxonomic rank of `scientificName`.
#' @param scientificNameAuthorship The authorship information for `scientificName`.
#' @param .keep Control which columns from .data are retained in the output.
#' Note that unlike [dplyr::mutate()], which defaults to `"all"` this defaults to
#' `"unused"`; i.e. only keeps Darwin Core columns, and not those columns used to
#' generate them.
#' @returns A tibble with the requested columns added/reformatted.
#' @details
#' Examples of `scientificName` values (we specify the rank in parentheses, but
#' users should not include this information):
#' * `Coleoptera` (order)
#' * `Vespertilionidae` (family)
#' * `Manis` (genus)
#' * `Ctenomys sociabilis` (genus + specificEpithet)
#' * `Ambystoma tigrinum diaboli` (genus + specificEpithet + infraspecificEpithet)
#'
#' Examples of `scientificNameAuthorship`:
#' * `(Györfi, 1952)`
#' * `R. A. Graham`
#' * `(Martinovský) Tzvelev`
#'
#' Examples of `taxonRank`:
#' * `order`
#' * `genus`
#' * `subspecies`
#' * `infraspecies`
#'
#' @seealso [set_taxonomy()] for taxonomic name information.
#' @examples
#' df <- tibble::tibble(
#'   name = c("Crinia Signifera", "Crinia Signifera", "Litoria peronii"),
#'   latitude = c(-35.27, -35.24, -35.83),
#'   longitude = c(149.33, 149.34, 149.34),
#'   eventDate = c("2010-10-14", "2010-10-14", "2010-10-14")
#'   )
#'
#' # Reformat columns to Darwin Core Standard terms
#' df |>
#'   set_scientific_name(
#'     scientificName = name
#'     )
#'
#' @importFrom dplyr mutate
#' @importFrom rlang abort
#' @export
set_scientific_name <- function(
    .df,
    scientificName = NULL,
    scientificNameAuthorship = NULL,
    taxonRank = NULL,
    .keep = "unused"
){
  if(missing(.df)){
    abort(".df is missing, with no default")
  }

  fn_args <- ls()

  # capture arguments as a list of quosures
  # NOTE: enquos() must be listed alphabetically
  fn_quos <- enquos(scientificName, scientificNameAuthorship, taxonRank)
  names(fn_quos) <- fn_args

  # find arguments that are NULL but exist already in `df`
  # then remove their names before `mutate()`
  # otherwise, these DwC columns are deleted by `mutate(.keep = "unused")`
  fn_quo_is_null <- fn_quos |>
    purrr::map(\(user_arg)
               rlang::quo_is_null(user_arg)) |>
    unlist()

  null_col_exists_in_df <- fn_quo_is_null & (names(fn_quos) %in% colnames(.df))

  if(any(null_col_exists_in_df)){
    fn_quos <- fn_quos |>
      purrr::keep(!names(fn_quos) %in% names(which(null_col_exists_in_df)))
  }

  # Update df
  result <- .df |>
    mutate(!!!fn_quos,
           .keep = .keep)

  check_missing_all_args(fn_call = match.call(),
                         fn_args = fn_args,
                         user_cols = colnames(result))

  # inform user which columns will be checked
  matched_cols <- names(result)[names(result) %in% fn_args]
  if(length(matched_cols > 0)) {
    col_progress_bar(cols = matched_cols)
  }

  # run column checks
  check_scientificName(result, level = "abort")
  check_taxonRank(result, level = "abort")
  check_scientificNameAuthorship(result, level = "abort")

  result
}


#' check scientificName
#' @param level what action should the function take for non-conformance?
#' Defaults to `"inform"`.
#' @noRd
#' @keywords Internal
check_scientificName <- function(.df,
                                 level = c("inform", "warn", "abort")
){
  level <- match.arg(level)
  if(any(colnames(.df) == "scientificName")){
    .df |>
      select("scientificName") |>
      check_is_string(level = level)
  }
  .df
}
# TODO: Currently only checks whether input is a string


#' check taxonRank
#' @param level what action should the function take for non-conformance?
#' Defaults to `"inform"`.
#' @noRd
#' @keywords Internal
check_taxonRank <- function(.df,
                                     level = c("inform", "warn", "abort")
){
  level <- match.arg(level)
  if(any(colnames(.df) == "taxonRank")){
    .df |>
      select("taxonRank") |>
      check_is_string(level = level)
    # Should this check a list of valid values?
  }
  .df
}
# TODO: Currently only checks whether input is a string


#' check scientificNameAuthorship
#' @param level what action should the function take for non-conformance?
#' Defaults to `"inform"`.
#' @noRd
#' @keywords Internal
check_scientificNameAuthorship <- function(.df,
                                           level = c("inform", "warn", "abort")
){
  level <- match.arg(level)
  if(any(colnames(.df) == "scientificNameAuthorship")){
    .df |>
      select("scientificNameAuthorship") |>
      check_is_string(level = level)
  }
  .df
}