R/data.R

In azizka/IUCNN: Neural Networks to Approximate IUCN Red List Conservation Assessments

#' IUCN threat categories for 884 orchid species
#'
#' A dataset containing the International Union for the Conservation of Nature's
#' Global Red List conservation assessments for 884 species of orchids.
#'
#' @format A data frame with 889 rows and 2 variables:
#' \describe{
#'   \item{species}{The canonical species name}
#'   \item{labels}{The IUCN conservation assessment,
#'   converted to numerical for use with IUCNN.
#'   0 =  Critically Endangered (CR),
#'   1 = Endangered (EN),
#'   2 = Vulnerable (VU),
#'   3 = Near Threatened (NT),
#'   4 = Least Concern (LC)}
#' }
#' @source \url{https://www.iucnredlist.org/}
"training_labels"


#' Geographic Occurrence Records for Not Evaluated Orchids
#'
#' A dataset containing geo-referenced occurrences of 100 Orchid species without
#' existing IUCN Red List assessment ("Not Evaluated"). This is example data
#' to predict the IUCN status.
#'
#' @format A data frame with 14,900 rows and 3 variables:
#' \describe{
#'   \item{species}{The canonical species name}
#'   \item{decimallongitude}{longitudinal coordinates}
#'   \item{decimallatitude}{latitudinal coordinates}
#' }
#' @source \url{https://www.gbif.org/}
"prediction_occ"


#' Geographic Occurrence Records for Orchids with IUCN assessment
#'
#' A dataset containing geo-referenced occurrences of 884 Orchid species with
#' existing IUCN Red List assessment ("CR", "EN", "VU", "NT", "LC").
#' This is example data to train an IUCNN model.
#'
#' @format A data frame with 125,412 rows and 3 variables:
#' \describe{
#'   \item{species}{The canonical species name}
#'   \item{decimallongitude}{longitudinal coordinates}
#'   \item{decimallatitude}{latitudinal coordinates}
#' }
#' @source \url{https://www.gbif.org/}
"training_occ"