R/remove_unknown_lengths.R

In Delta-Stewardship-Council/deltafish: Accesses an Integrated Fish Count and Length Dataset from the San Francisco Delta

#' Remove unknown fish lengths
#'
#' Removes unknown fish lengths. Returns a DBI table.
#'
#' @param data A DBI table that can be treated like a data.frame, with fish data. See `open_fish()`
#' @param univariate (logical) Will these data be used for univariate analyses
#' (\code{univariate=TRUE})? Or multi-species analyses (\code{univariate=FALSE})?
#' If univariate, when a \code{Length_NA_flag=="Unknown length"} record is found,
#' all records of that taxa from that sample are removed. In effect, this is
#' transforming those records into missing data. If \code{univariate=FALSE}, when
#' a \code{Length_NA_flag=="Unknown length"} record is found, the entire sample
#' is removed and no 0s are filled in, since accurate community data cannot be
#' confirmed for that sample.
#'
#'
#' @importFrom magrittr %>%
#' @importFrom rlang .data
#'
#' @return data_known A DBI table with only known lengths
#' @export
#'

remove_unknown_lengths <- function(data, univariate) {
  if (!any(c("Length", "Count", "SampleID", "Taxa") %in% colnames(data))) {
    stop("Input data must have 'Length', 'Count', 'SampleID', and 'Taxa' column names")
  }

  if (!univariate) {
    remove <- data %>%
      dplyr::filter(is.na(.data$Length) & .data$Count != 0) %>%
      dplyr::pull("SampleID") %>%
      unique()

    data_f <- data %>%
      dplyr::filter(!.data$SampleID %in% remove)
  }

  if (univariate) {
    remove <- data %>%
      dplyr::filter(is.na(.data$Length) & .data$Count != 0) %>%
      dplyr::select("Taxa", "SampleID") %>%
      dplyr::distinct()

    data_f <- data %>%
      dplyr::anti_join(remove, by = c("Taxa", "SampleID"))
  }


  return(data_f)
}