R/vis_pcp.R

In jakobbossek/ecr3vis: Evolutionary Computation in R - Version 3

#' @title
#' Parallel coordinate plot (PCP)
#'
#' @description
#' Visualizes a Pareto-front approximation set with parallel coordinate
#' plots. The \eqn{x}-axis shows the objectives as an ordered factor where the order
#' is determined by the \code{obj.cols} argument. On the \eqn{y}-axis objective values
#' are shown colored by solution as lines. Suitable for any number of objectives.
#'
#' @references
#' [1] T. Tušar and B. Filipič, Visualization of Pareto Front Approximations in
#' Evolutionary Multiobjective Optimization: A Critical Review and the Prosection
#' Method, in IEEE Transactions on Evolutionary Computation, vol. 19, no. 2,
#' pp. 225-245, April 2015, doi: 10.1109/TEVC.2014.2313407.
#'
#' @template arg_df
#' @template arg_obj_cols
#' @template return_ggplot
#' @template family_multi_objective_visualizations
#' @export
plot_pcp = function(
  df,
  obj.cols = c("y1", "y2")) {

  checkmate::assert_data_frame(df, min.rows = 2L, min.cols = 2L)
  checkmate::assert_character(obj.cols, min.len = 2L)

  df = prepare_pf_for_visualization(df, obj.cols)
  df = to_long_with_objective_column(df, obj.cols)

  g = ggplot2::ggplot(df, mapping = ggplot2::aes_string(x = "objective", y = "value", group = "nr"))
  g = g + ggplot2::geom_path()
  g = g + ggplot2::geom_point()
  g = g + ggplot2::labs(x = "Objective", y = "Value")
  g = g + ggplot2::theme_minimal()
  return(g)
}