#' @name robins_subset
#' @title robins_subset
#' @docType data
#' @description This is the `robins_subset` dataset, which is a subset of the
#' full robins data set used to demonstrate a spatially varying trend coefficient
#' model in Meehan et al. 2019. The dataset includes American Robin counts,
#' along with time, location, and effort information, from Audubon Christimas Bird
#' Counts (CBC) conducted in six US states between 1987 and 2016.
#'
#' @format The data are a data.frame with variables
#' \describe{
#' \item{`circle`:}{
#' Four-letter code of the CBC circle.
#' }
#' \item{`bcr`:}{
#' Numeric code for the bird conservation region encompassing the
#' count circle.
#' }
#' \item{`state`:}{
#' US state encompassing the count circle.
#' }
#' \item{`year`:}{
#' calendar year the count was conducted.
#' }
#' \item{`std_yr`:}{
#' transformed year, with 2016 = 0.
#' }
#' \item{`count`:}{
#' number of robins recorded.
#' }
#' \item{`log_hrs`:}{
#' the natural log of party hours.
#' }
#' \item{`lon`:}{
#' longitude of the count circle centroid.
#' }
#' \item{`lat`:}{
#' latitude of the count circle centroid.
#' }
#' \item{`obs`:}{
#' unique record identifier.
#' }
#' }
#' @source
#' https://github.com/tmeeha/inlaSVCBC
#'
#'
#' @references
#' Meehan, T.D., Michel, N.L., and Rue, H. 2019. Spatial modeling of Audubon
#' Christmas Bird Counts reveals fine-scale patterns and drivers of relative
#' abundance trends. Ecosphere, 10(4), p.e02707.
#'
#' @examples
#' if (require(ggplot2, quietly = TRUE)) {
#' data(robins_subset, package = "inlabru") # get the data
#'
#' # plot the counts for one year of data
#' ggplot(robins_subset[robins_subset$std_yr == 0, ]) +
#' geom_point(aes(lon, lat, colour = count + 1)) +
#' scale_colour_gradient(low = "blue", high = "red", trans = "log")
#' }
"robins_subset"
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