R/densityPlot.R

In collinleiber/benchmarkVis: Benchmark result visualisation

#' @title Create a density plot
#'
#' @description
#' Create a plotly density plot out of a benchmarkVis compatible data table.
#' The created density chart shows the distribution of the specified measure for color.by input
#'
#' @param dt campatible data table
#' @param measure the column name of the measure
#' @param stacked defines if the density curves should be stacked. Alternative is transparent (default: FALSE)
#' @param color.by the column to color the density area with. Possibilities: "algorithm", "problem", "replication" (default: "algorithm")
#' @return a plotly density plot
#' @export
#' @examples
#' createDensityPlot(mlr.benchmark.example, "measure.mmce.test.mean", TRUE)
createDensityPlot = function(dt, measure, stacked = FALSE, color.by = "algorithm") {
  checkmate::assert_data_table(dt)
  checkmate::assert_string(measure)
  checkmate::assert_logical(stacked)
  checkmate::assert_true(measure %in% getMeasures(dt))
  checkmate::assert_string(color.by)
  checkmate::assert_true(color.by %in% getMainColumns(dt))
  # create new dataframe
  new.df = data.frame(
    color = dt[[color.by]],
    meas = dt[[measure]]
  )
  # Create plot
  p = ggplot2::ggplot(data = new.df, ggplot2::aes(meas, fill = color))  + ggplot2::theme_bw()
  if (stacked) {
    p = p + ggplot2::geom_density(position = "stack")
  } else {
    p = p + ggplot2::geom_density(alpha = 0.4)
  }
  p = p + ggplot2::labs(fill = color.by)
  # Convert plot to plotly
  p = plotly::ggplotly(p)
  p = plotly::layout(p, xaxis = list(title = getPrettyMeasureName(measure)))
  return(p)
}