R/srsEx.R

In dcarver1/gapAnalysisR: An R package to calculate conservation indicators using species distribution models

#' @title Sample representativeness score estimation (Ex-situ conservation indicators).
#' @name srs_exsitu
#' @description Performs an estimation of sample representativeness score for ex-situ gap analysis (SRSex) using Ramirez-Villegas et al., (2010) methodology
#' This function uses counts from herbarium an germplasm accessions and calculate the SRS ex-situ score as:
#'  \deqn{SRSex = Number of germplasm accessions / Number of herbarium accessions}
#'
#' @param species A name species compiled using '_'  to call occurrences files from Workspace/parameter/occurrences folder
#' @param Workspace A forder where the pipeline will be executed
#' @param  run_version The version of the analysis used (e.g 'v1')
#'
#' @return It returns a data frame file saved at gap_analysis folder with eight columns:
#'
#' \tabular{lcc}{
#'  ID \tab Species name \cr
#'  NTOTAL \tab Number of records available for a given species \cr
#'  NTOTAL_COORDS \tab Number of records with geographical coordinates available for a given species \cr
#'  NG \tab Number of germplasm accessions available for a given species \cr
#'  NG_COORDS \tab Number of germplasm accessions with geographical coordinates Savailable for a given species \cr
#'  NH \tab Number of herbarium accessions available for a given species \cr
#'  NH_COORDS \tab Number of herbarium accessions with geographical coordinates Savailable for a given species \cr
#'  SRS \tab SRSex score calculated from a CSV file summarizing the number of records for a given species \cr
#' }
#'
#' @examples srs_exsitu('Cucurbita_digitata',Workspace,'v1')
#'
#' Workspace  <-  'E:/CIAT/workspace/Workspace_test/workspace'
#' run_version  <- 'v1'
#' species_list <- c('Cucurbita_cordata',
#'  'Cucurbita_digitata',
#'  'Cucurbita_foetidissima',
#'  'Cucurbita_palmata')
#'
#'  run_version <-'v1'
#
#' lapply(1:length(species_list),function(i){
#'    species <- species_list[[i]]
#'    x <- srs_exsitu(species,Workspace,run_version)
#'    print(paste0(species,' DONE!'))
#' })
#'
#'@references
#'
#'Ramírez-Villegas, J., Khoury, C., Jarvis, A., Debouck, D. G., & Guarino, L. (2010).
#'A Gap Analysis Methodology for Collecting Crop Genepools: A Case Study with Phaseolus Beans.
#'PLOS ONE, 5(10), e13497. Retrieved from https://doi.org/10.1371/journal.pone.0013497
#'
#' Khoury, C. K., Amariles, D., Soto, J. S., Diaz, M. V., Sotelo, S., Sosa, C. C., … Jarvis, A. (2019).
#' Comprehensiveness of conservation of useful wild plants: An operational indicator for biodiversity
#' and sustainable development targets. Ecological Indicators. https://doi.org/10.1016/j.ecolind.2018.11.016
#'
#' @export

##########################################   Start Functions    ###############################################
# This function calculates the ex-situ SRS. It loads counts.csv and computes SRS
# @param (string) species: species ID
# @return (data.frame): This function returns a data frame with SRS, and numbers
#                       of G, H, and total samples, with and without coordinates.


srs_exsitu <- function(countsData,species_list) {

  #importFrom("methods", "as")
  #importFrom("stats", "complete.cases", "filter", "median")
  #importFrom("utils", "data", "memory.limit", "read.csv", "write.csv")
  dt1 <- data.frame(matrix(nrow = length(species_list), ncol = 2))
  colnames(dt1) <- c("species", "SRSex")

  for(i in 1:length(species_list)){
    sp_counts <- countsData %>%
    dplyr::filter(species == species_list[i])

    if(sp_counts$totalGRecords >= 1 & sp_counts$totalHRecords == 0){
      srs <-100
    }

    #### this works for full distributions
    if (sp_counts$totalGRecords == 0 & sp_counts$totalHRecords ==0) {
      srs <- 0
    } else {
      srs <- min(c(100,(sp_counts$totalGRecords/sp_counts$totalHRecords)*100))
    }
    # add values to empty df
    dt1$species[i] <- as.character(species_list[i])
    dt1$SRSex[i] <- srs

  }
  return(dt1)
}