comparison analyze.r @ 0:1fcd81d65467 draft default tip

planemo upload for repository https://github.com/galaxyecology/tools-ecology/tree/master/tools/obisindicators commit b377ff767e3051f301c2f02cfe3e1a17b285ede4

0:1fcd81d65467

#' Calculate Biodiversity Indicators, including ES50 (Hurlbert index)
#'
#' Calculate the expected number of marine species in a random sample of 50
#' individuals (records)
#'
#' @param df data frame with unique species observations containing columns:
#'   `cell`, `species`, `records`
#' @param esn expected number of marine species
#'
#' @return Data frame with the following extra columns: - `n`: number of records
#'   - `sp`: species richness - `shannon`: Shannon index - `simpson`: Simpson
#'   index - `es`: Hurlbert index (n = 50), i.e. expected species from 50
#'   samples ES(50) - `hill_1`: Hill number `exp(shannon)` - `hill_2`: Hill
#'   number `1/simpson` - `hill_inf`: Hill number `1/maxp`
#'
#' @details The expected number of marine species in a random sample of 50
#'  individuals (records) is an indicator on marine biodiversity richness. The
#'  ES50 is defined in OBIS as the `sum(esi)` over all species of the following
#'  per species calculation:
#'
#'  - when `n - ni >= 50 (with n as the total number of records in the cell and
#'  ni the total number of records for the ith-species)
#'    - `esi = 1 - exp(lngamma(n-ni+1) + lngamma(n-50+1) - lngamma(n-ni-50+1) - lngamma(n+1))`
#'
#'  - when `n >= 50` - `esi = 1`
#'
#'  - else - `esi = NULL`
#'
#'  Warning: ES50 assumes that individuals are randomly distributed, the sample
#'  size is sufficiently large, the samples are taxonomically similar, and that
#'  all of the samples have been taken in the same manner.
#'
#' @export
#' @concept analyze
#' @examples
#' @importFrom gsl lngamma
calc_indicators <- function(df, esn = 50) {

  stopifnot(is.data.frame(df))
  stopifnot(is.numeric(esn))
  stopifnot(all(c("cell", "species", "records") %in% names(df)))

  df %>%
    dplyr::group_by(cell, species) %>%
    dplyr::summarize(
      ni = sum(records),
      .groups = "drop_last") %>%
    dplyr::mutate(n = sum(ni)) %>%
    dplyr::group_by(cell, species) %>%
    dplyr::mutate(
      hi = -(ni / n * log(ni / n)),
      si = (ni / n)^2,
      qi = ni / n,
      esi = dplyr::case_when(
        n - ni >= esn ~ 1 - exp(gsl::lngamma(n - ni + 1) + gsl::lngamma(n - esn + 1) - gsl::lngamma(n - ni - esn + 1) - gsl::lngamma(n + 1)),
        n >= esn ~ 1
      )
    ) %>%
    dplyr::group_by(cell) %>%
    dplyr::summarize(
      n = sum(ni),
      sp = dplyr::n(),
      shannon = sum(hi),
      simpson = sum(si),
      maxp = max(qi),
      es = sum(esi),
      .groups = "drop") %>%
    dplyr::mutate(
      hill_1   = exp(shannon),
      hill_2   = 1 / simpson,
      hill_inf = 1 / maxp)
}