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#' @title plot_commonness
#'
#' @description Plot commonness between observed and optimized data
#'
#' @param x Results object of run_optimization_min_conf()
#' @param target Pairwise matrix of species in common.
#'
#' @details
#' Plot a heatmap of commonness between observed data and optimized data. This visual style allows for easier spatial understanding of commonness differences to be ascertained.
#'
#' @return ggplot
#' @export
plot_commonness <- function(x, target) {
species_grid <- x$optimized_grid
commonness_species_grid <- calculate_solution_commonness_rcpp(species_grid)
n_row <- nrow(commonness_species_grid)
n_col <- ncol(commonness_species_grid)
commonness_difference_grid <- t(commonness_species_grid - target)
commonness_difference <- expand.grid(x = seq_len(n_row),
y = seq_len(n_col))
commonness_difference$value <- commonness_difference_grid[seq_len(n_row * n_col)]
plot <- ggplot2::ggplot(data = commonness_difference) +
ggplot2::geom_raster(ggplot2::aes(x = x, y = y, fill = value)) +
ggplot2::scale_fill_gradient2(name = "Differences in \ncommonness",
low = "#440154FF",
mid = "white",
high = "#FDE725FF") +
ggplot2::coord_equal() +
ggplot2::theme_void()
return(plot)
}
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