R/plot_profile.R

In SaskiaSchirmer/CONSURE: Estimating Spatially Continuous Survival, Migratory Connectivity and Recovery Probability

# CONSURE - Continuous Survival, Use of Space and Recovery Probability
# Estimates.
# Copyright (C) 2021  Saskia Schirmer
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <https://www.gnu.org/licenses/>.

#' plot parameter values (and bootstrape confidence interval, if
#' available) along a profile line
#'
#' This function plots the parameter values along a profile line given in the
#' sfc-object profile_of_parameter.
#' @param profile_of_parameter profile of a raster object along a segmented
#' profile line.
#' @param ylab name of the y-axis, which is normally the name of the parameter
#' @importFrom rlang .data
#'
#' @return vector of length res with survival probabilities dependent on space
#' @export
#' @examples{
#'  pop <- wrap_profile_of_param(c(0, 0), c(1, 1), mro2D, param = "s")
#'  plot_profile(pop, ylab = "survival")
#' }
#'
#'
plot_profile <- function(profile_of_parameter, ylab) {
  profile <- ggplot2::ggplot()

  if (rlang::is_installed("units")) {
    if ("bootstrap_lower_quantile" %in% names(profile_of_parameter) &&
      "bootstrap_upper_quantile" %in% names(profile_of_parameter)) {
      profile <- profile +
        ggplot2::geom_ribbon(
          data = profile_of_parameter,
          ggplot2::aes(
            x = units::drop_units(.data$dist),
            ymin = .data$bootstrap_lower_quantile,
            ymax = .data$bootstrap_upper_quantile,
            color = "variability"
          ), alpha = 0.7,
          fill = "grey"
        )
    }

    range <- range(units::drop_units(profile_of_parameter$dist))

    profile <- profile +
      ggplot2::geom_point(
        data = profile_of_parameter,
        ggplot2::aes(units::drop_units(.data$dist),
          .data$parameter_value,
          color = "estimate"
        )
      ) +
      ggplot2::labs(x = "distance [km]", y = ylab) +
      ggplot2::labs(color = "type") +
      ggplot2::scale_x_continuous(
        breaks = seq(range[1], range[2],
          by = 1000000
        ),
        labels = seq(range[1], range[2],
          by = 1000000
        ) / 1000,
        limits =
        ) +
      ggplot2::scale_colour_manual("",
        breaks = c("variability", "estimate"),
        values = c("grey", "black"),
        labels = c("bootstrap\nconfidence\ninterval", "estimate")
      ) +
      ggplot2::ylim(c(0, 1)) +
      ggplot2::theme_classic() +
      ggplot2::theme(
        text = ggplot2::element_text(size = 20),
        axis.text.x = ggplot2::element_text(margin = ggplot2::margin(
          t = 10,
          unit = "pt"
        )),
        axis.text.y = ggplot2::element_text(margin = ggplot2::margin(r = 10)),
        axis.line = ggplot2::element_blank(),
        panel.background = ggplot2::element_rect(fill = "transparent"),
        panel.spacing.x = ggplot2::unit(30, "mm"),
        panel.spacing.y = ggplot2::unit(10, "mm"),
        plot.background = ggplot2::element_rect(fill = "white", color = NA)
      )
  } else {
    rlang::check_installed("units")
  }

  profile
}