R/calc_veg_core_area_species.R

In inbo/forrescalc: Calculation of Aggregated Values on Dendrometry, Regeneration and Vegetation of Forests, Starting from Individual Tree Measures from Fieldmap

#' aggregate vegetation parameters by core area, species and year
#'
#' This function calculates for each plot, species and year the percentage of
#' subplots in which the species is present and the percentage of subplots
#' where the species is browsed (relative to the plots where it is present).
#' A difference is made between browsed (which contains all damage) and
#' seriously browsed, which is reported if the damage is more than 1/20.
#' This calculation is designed for core areas, that consist of different
#' subplots.
#' Year refers to year of recording of that specific species
#' (source is table `data_herblayer`), and is possibly different for
#' spring flora than for other species in the same subplot.
#'
#' @inheritParams calculate_vegetation
#'
#' @return dataframe with columns `plot`, `species`, `year` (year of recording
#' of specific species, possibly different for spring flora),
#' `number_of_subplots` (= number of subplots where the species occurs),
#' `perc_of_subplots` (= percentage of subplots with species),
#' `number_of_subplots_browsed`, `perc_of_subplots_browsed`,
#' `number_of_subplots_seriously_browsed`, `perc_of_subplots_seriously_browsed`
#' and `mean_coverage_class_average_perc`
#'
#' @examples
#' library(forrescalc)
#' # (add path to your own fieldmap database here)
#' path_to_fieldmapdb <-
#'   system.file("example/database/mdb_bosres.sqlite", package = "forrescalc")
#' data_herblayer_CA <- load_data_herblayer(path_to_fieldmapdb, plottype = "CA")
#' calc_veg_core_area_species(data_herblayer_CA)
#'
#' @export
#'
#' @importFrom dplyr %>% group_by n_distinct summarise ungroup
#' @importFrom rlang .data
#'
calc_veg_core_area_species <- function(data_herblayer) {
  check_forrescalc_version_attr(data_herblayer)
  by_core_area_species <- data_herblayer %>%
    group_by(.data$plot_id, .data$period) %>%
    mutate(
      n_subplots = n_distinct(.data$subplot_id)
    ) %>%
    ungroup() %>%
    group_by(
      .data$plottype, .data$plot_id, .data$period, .data$year, .data$species
    ) %>%
    summarise(
      number_of_subplots_with_vegetation = n_distinct(.data$subplot_id),
      perc_of_subplots =
        .data$number_of_subplots_with_vegetation * 100 /
          unique(.data$n_subplots),
      number_of_subplots_browsed =
        ifelse(
          all(is.na(.data$browse_index_id)),
          NA,
          sum(!is.na(.data$browse_index_id) &
                .data$browse_index_id %in% c(110, 120))
        ),
      number_of_subplots_seriously_browsed =
        ifelse(
          all(is.na(.data$browse_index_id)),
          NA,
          sum(!is.na(.data$browse_index_id) & .data$browse_index_id == 120)
        ),
      perc_of_subplots_browsed =
        .data$number_of_subplots_browsed * 100 /
          .data$number_of_subplots_with_vegetation,
      perc_of_subplots_seriously_browsed =
        .data$number_of_subplots_seriously_browsed * 100 /
          .data$number_of_subplots_with_vegetation,
      mean_coverage_class_average_perc = mean(.data$coverage_class_average_perc)
    ) %>%
    ungroup()

  attr(by_core_area_species, "database") <- attr(data_herblayer, "database")
  attr(by_core_area_species, "forrescalc") <- attr(data_herblayer, "forrescalc")

  return(by_core_area_species)
}