#' @importFrom insight print_color
#' @export
print.rope <- function(x, digits = 2, ...) {
  insight::print_color(sprintf(
    "# Proportion%s of samples inside the ROPE [%.*f, %.*f]:\n\n",
    ifelse(all(x$CI[1] == x$CI), "", "s"),
    digits,
    x$ROPE_low[1],
    digits,
    x$ROPE_high[1]
  ), "blue")


  # I think this is something nobody will understand and we'll probably forget
  # why we did this, so I'll comment a bit...

  # These are the base columns we want to print
  cols <- c("Parameter", "ROPE_Percentage", "Component", "Group")

  # In case we have ropes for different CIs, we also want this information
  # So we first check if values in the CI column differ, and if so, we also
  # keep this column for printing
  if (!all(x$CI[1] == x$CI)) {
    cols <- c("CI", cols)
  }

  # Either way, we need to know the different CI-values, so we can
  # split the data frame for printing later...
  ci <- unique(x$CI)

  # now we check which of the requested columns are actually in our data frame "x"
  # "x" may differ, depending on if "rope()" was called with a model-object,
  # or with a simple vector. So we can't hard-code this
  x <- subset(x, select = intersect(cols, colnames(x)))

  # This is just cosmetics, to have nicer column names and values
  x$ROPE_Percentage <- sprintf("%.*f %%", digits, x$ROPE_Percentage * 100)
  colnames(x)[which(colnames(x) == "ROPE_Percentage")] <- "inside ROPE"

  # In case we have multiple CI values, we create a subset for each CI value.
  # Else, parameter-rows would be mixed up with both CIs, which is a bit
  # more difficult to read...

  if (length(ci) == 1) {
    # print complete data frame, because we have no different CI values here
    print_data_frame(x, digits = digits)
  } else {
    for (i in ci) {
      xsub <- x[x$CI == i, -which(colnames(x) == "CI"), drop = FALSE]
      insight::print_color(sprintf("ROPE for the %s%% HDI:\n\n", i), "cyan")
      print_data_frame(xsub, digits = digits)
      cat("\n")
    }
  }
}