R/plotVarImp.R

In SDMtune: Species Distribution Model Selection

#' Plot Variable Importance
#'
#' Plot the variable importance as a bar plot.
#'
#' @param df data.frame. A data.frame containing the the name of the variables
#' as first column and the value of the variable importance as second column.
#' @param color character. The colour of the bar plot.
#'
#' @return A \link[ggplot2]{ggplot} object.
#' @export
#' @importFrom rlang .data
#' @importFrom ggplot2 ggplot aes
#'
#' @author Sergio Vignali
#'
#' @examples
#' # Acquire environmental variables
#' files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
#'                     pattern = "grd",
#'                     full.names = TRUE)
#'
#' predictors <- terra::rast(files)
#'
#' # Prepare presence and background locations
#' p_coords <- virtualSp$presence
#' bg_coords <- virtualSp$background
#'
#' # Create SWD object
#' data <- prepareSWD(species = "Virtual species",
#'                    p = p_coords,
#'                    a = bg_coords,
#'                    env = predictors,
#'                    categorical = "biome")
#'
#' # Train a model
#' model <- train(method = "Maxnet",
#'                data = data,
#'                fc = "l")
#'
#' # Compute variable importance
#' vi <- varImp(model,
#'              permut = 1)
#'
#' # Plot variable importance
#' plotVarImp(vi)
#'
#' # Plot variable importance with custom color
#' plotVarImp(vi,
#'            color = "red")
plotVarImp <- function(df,
                       color = "grey") {

  if (!requireNamespace("scales", quietly = TRUE)) {
    cli::cli_abort(
      "Please install package {.pkg scales} to use this function",
      call = NULL
    )
  }

  df <- df[order(df[, 2]), ]
  df[, 2] <- df[, 2] / 100
  df[, 1] <- factor(df[, 1], levels = df[, 1])
  y_name <- colnames(df)[2]

  ggplot(df, aes(x = .data$Variable, y = .data[[y_name]])) +
    ggplot2::labs(x = "", y = sub("_", " ", y_name)) +
    ggplot2::scale_y_continuous(labels = scales::percent) +
    ggplot2::geom_bar(position = "dodge",
                      stat = "identity",
                      fill = color) +
    ggplot2::coord_flip() +
    ggplot2::theme_minimal() +
    ggplot2::theme(text = ggplot2::element_text(colour = "#666666"))
}