biomonitoR: biomonitoring indices and metrics

Documented in whpt

#' Whalley Hawkes Paisley Trigg index
#'
#' @description
#' \Sexpr[results=rd, stage=render]{ lifecycle::badge("maturing") }
#'
#' This function calculates the Whalley Hawkes Paisley Trigg index (WHPT).
#'
#' @param x Result of `aggregate_taxa()`.
#' @param method The only choice is `uk`. Users can provide their own data.frame (see examples) with a column called *Taxon*, a column called *ABUCLASS* and a column called *Scores*.
#' @param type Presence only `po` or abundance `ab`.
#' @param metric Possible choices are `aspt`, `ntaxa`, `bmwp`.
#' @param agg This option allows the composite family approach. It can be `FALSE`, `TRUE` or a `data.frame`.
#' If `FALSE` no aggregation will be performed, while if `TRUE` aggregation will be performed according to the rules described in Details.
#' A `data.frame` containing the aggregation rules can be provided by the user.
#' This `data.frame` needs a column called *Taxon* containing the taxon to aggregate and a column called *Correct_Taxon* with the aggregation specifications.
#' `agg` cannot be `TRUE` when a `data.frame` is provided as method.
#' @param abucl Log abundance categories. Treshold are set to 1, 9, 99 and 999.
#' @param exceptions Taxa that need to be exluded from the calculation.
#' This option can be useful, for instance, to exclude an alien species belonging to an autochthonous family.
#' @param traceB If set to `TRUE` a list as specified below will be returned.
#' @keywords whpt
#' @details WHPT is a revision of BMWP and it takes into account the abundances of organisms. The following aggregation is used if `agg` is set equal to `TRUE`:
#'
#' \enumerate{
#'   \item Psychomyiidae (inc. Ecnomidae)
#'   \item Rhyacophilidae (inc. Glossomatidae)
#'   \item Ancylidae (inc. Acroloxidae)
#'   \item Gammaridae (inc. Crangonyctidae)
#'   \item Planariidae (inc. Dugesidae)
#'   \item Hydrobiidae (inc. Bithyniidae)
#' }
#'
#' `whpt()` automatically check for parent-child pairs in the scoring system, see the return section for a definition.
#' All the information used for WHPT calculation can be retrieved with the function \code{\link{show_scores}}.
#'
#' @return If `traceB` is set to `TRUE` a list with the following elements will be returned:
#' \itemize{
#'  \item `results` Results of `whpt()`.
#'  \item `taxa_df` The `data.frame` used for the calculation containing the abundance of taxa receiving a score.
#'  \item `abu_df` The `data.frame` containing abundance classes for each site.
#'  \item `whpt_df` The `data.frame` used for the calculation containing scores for each site.
#'  \item `composite_taxa` Taxa aggregated following the aggregation of the default method or set in `agg`.
#'  \item `exceptions` A `data.frame` containing the changes made by excluding the taxa included in `exceptions`.
#'  \item `parent_child_pairs` For instance in Spanish BMWP both *Ferrissia* and Planorbidae receive a score.
#'  Abundances of the higher taxonomic level need therefore to be adjusted by subtracting the abundances of the lower taxonomic level.
#' }
#' @section Acknowledgements: We thank Carol Fitzpatrick, Richard Chadd, Judy England and Rachel Stubbington for providing us with the most updated WHPT scores and algorithms.
#' @importFrom dplyr '%>%' select inner_join group_by summarise filter
#' @importFrom tidyr pivot_longer
#' @importFrom tibble deframe
#' @importFrom stats aggregate
#' @export
#' @seealso [as_biomonitor], [aspt], [bmwp]
#' @examples
#' data(macro_ex)
#' data_bio <- as_biomonitor(macro_ex)
#' data_agr <- aggregate_taxa(data_bio)
#' whpt(data_agr)
#' whpt(data_agr, metric = "bmwp")
#' whpt(data_agr, type = "po", metric = "bmwp")
#'
#' # take a look to the metrics used for whpt calculation
#' # only the first 6 rows of each database are shown
#'
#' lapply(show_scores("whpt", "uk"), head)
whpt <- function(x, method = "uk", type = "ab", metric = "aspt", agg = FALSE, abucl = c(1, 9, 99, 999), exceptions = NULL, traceB = FALSE) {

  # check if the object x is of class "biomonitoR"
  classCheck(x)

  # useful for transforming data to 0-1 later
  if (inherits(x, "bin")) {
    BIN <- TRUE
  } else {
    BIN <- FALSE
  }


  if (!identical(method, "uk") & !is.data.frame(method)) {
    stop("Method need to be set to uk or a custom data.frame")
  }

  if (!identical(type, "po") & !identical(type, "ab")) {
    stop("Please provide a valide type: po or ab")
  }

  if (!identical(metric, "aspt") & !identical(metric, "ntaxa") & !identical(metric, "bmwp")) {
    stop("Please provide a valide metric: aspt, ntaxa or bmwp")
  }


  # Store tree for searching for inconsistencies
  Tree <- x[["Tree"]][, 1:10]

  numb <- c(which(names(x) == "Tree"), which(names(x) == "Taxa")) # position of the Tree and Taxa data.frame in the biomonitoR object that need to be removed

  # remove Tree and Taxa data.frame
  x <- x[-numb]
  st.names <- names(x[[1]][-1]) # names of the sampled sites

  # create dummy variables to avoid R CMD check NOTES


  z <- Abundance <- Taxon <- Sample <- Scores <- ABUCLASS <- NULL


  # the following if statement is to allow the users to provide their own psi scores and aggregation rules.
  # y represents the method to be used

  # the following if statement is to allow the users to provide their own bmwp scores and aggregation rules.
  # y represents the method to be used


  if (is.data.frame(method) == TRUE) {
    if (!(isFALSE(agg) | is.data.frame(agg))) {
      stop("When method is a data.frame agg needs to be FALSE or a data.frame containing the aggregation rules")
    }

    if (isFALSE(agg)) {
      y <- method
    } else {
      y <- method
      z <- agg
    }
  } else {
    if (!(isTRUE(agg) | isFALSE(agg))) stop("When using the deafult method agg can only be TRUE or FALSE")

    # assign the default scores and aggregation rules as needed by the user

    if (identical(method, "uk")) {
      y <- whpt_scores_fam_uk

      if (isTRUE(agg)) {
        z <- whpt_acc_fam_uk
      }
    }
  }


  # the calculation of the index in biomonitoR consists in rbind all the taxonomic levels
  # in the biomonitoR object that has been previously deprived of Taxa and Tree elements and then merge
  # it with the scores data.frame.
  # The first step is to change the column name of the first column of each data.frame to
  # an unique name

  for (i in 1:length(x)) {
    colnames(x[[i]])[1] <- "Taxon"
  }

  # rbind the data.frames representing a taxonomic level each
  # aggregate is not necessary here
  DF <- do.call("rbind", x)
  rownames(DF) <- NULL
  DF <- aggregate(. ~ Taxon, DF, sum)

  if (!is.null(exceptions)) {
    DF <- manage_exceptions(DF = DF, Tree = Tree, y = y, Taxon = exceptions)
    if (!is.data.frame(DF)) {
      exce <- DF[[2]]
      DF <- DF[[1]]
    }
  }

  # WHPT scores have a long format, unsuitable for merging with DF
  # make a temp data.frame to store unique taxa names.

  y.temp <- data.frame(Taxon = unique(y[, "Taxon"]))

  # merge the new data.frame with the score data.frame and change
  # the names of the taxa according to the aggregation rules if needed
  DF <- merge(DF, y.temp[, "Taxon", drop = FALSE])
  if (!is.null(z)) {
    taxa.to.change <- as.character(DF$Taxon[DF$Taxon %in% z$Taxon])
    DF <- checkBmwpFam(DF = DF, famNames = z, stNames = st.names)
  } else {
    DF <- DF
  }


  DF <- manage_inconsistencies(DF = DF, Tree = Tree)
  if (!is.data.frame(DF)) {
    incon <- DF[[2]]
    DF <- DF[[1]]
  }

  # transform the data.frame from abundance to presence-absence if needed
  if (BIN) {
    DF <- to_bin(DF)
  }

  if (traceB) {
    df2 <- DF
  }


  class.fun <- function(x) cut(x, breaks = c(abucl, 10^18), labels = 1:length(abucl), include.lowest = TRUE, right = TRUE)
  abu.class <- apply(apply(DF[, -1, drop = FALSE], 2, class.fun), 2, as.numeric)
  abu.class[is.na(abu.class)] <- 0
  tot.mer <- data.frame(DF[, 1, drop = FALSE], abu.class, check.names = FALSE)

  # to avoid warning from inner_join
  y$Taxon <- as.character(y$Taxon)
  tot.mer$Taxon <- as.character(tot.mer$Taxon)

  if (identical(type, "ab") & BIN) stop("Type cannot be set to abundance if presence-absence data are used")

  if (type == "po") {
    y <- subset(y, ABUCLASS == -1)
    res.tot <- tot.mer %>%
      pivot_longer(-c(Taxon), names_to = "Sample", values_to = "Abundance") %>%
      filter(Abundance > 0) %>%
      inner_join(y, by = c("Taxon")) %>%
      select(c("Sample", "Scores"))
    whpt_df <- tot.mer %>%
      pivot_longer(-c(Taxon), names_to = "Sample", values_to = "Abundance") %>%
      filter(Abundance > 0) %>%
      inner_join(y, by = c("Taxon")) %>%
      as.data.frame()
  }

  if (type == "ab") {
    y <- subset(y, ABUCLASS != -1)
    res.tot <- tot.mer %>%
      pivot_longer(-c(Taxon), names_to = "Sample", values_to = "ABUCLASS") %>%
      inner_join(y, by = c("Taxon", "ABUCLASS"))
    whpt.df <- res.tot %>% as.data.frame()
  }

  if (metric == "aspt") {
    res <- res.tot %>%
      group_by(Sample) %>%
      summarise(mean(Scores)) %>%
      deframe()
  }
  if (metric == "ntaxa") {
    res <- res.tot %>%
      group_by(Sample) %>%
      summarise(length(Scores)) %>%
      deframe()
  }
  if (metric == "bmwp") {
    res <- res.tot %>%
      group_by(Sample) %>%
      summarise(sum(Scores)) %>%
      deframe()
  }

  # assure to have results if all indicator taxa are missing from a sample
  if (length(res) != length(st.names)) {
    temp <- merge(data.frame(Sites = st.names), data.frame(Sites = names(res), res_tot = res), all = TRUE)
    res <- temp$res_tot
    names(res) <- temp$Sites
  }


  res <- res[match(st.names, names(res))]

  if (traceB == FALSE) {
    res
  } else {
    if (!exists("taxa.to.change", inherits = FALSE)) {
      df3 <- "none"
    } else {
      df3 <- taxa.to.change
    }
    if (exists("exce", inherits = FALSE)) {
      df1 <- exce
    } else {
      df4 <- "none"
    }
    if (exists("incon", inherits = FALSE)) {
      df5 <- incon
    } else {
      df5 <- "none"
    }

    res <- list(results = res, taxa_df = df2, abu_cl = tot.mer, whpt_df = whpt_df, composite_taxa = df3, exceptions = df4, parent_child_pairs = df5)
    res
  }
}