Portfolio histogram with tail bins — outlier

Visualize the distribution of a numeric portfolio variable while keeping extreme tails readable.

Insurance portfolios often contain skewed variables such as claim amounts, premium, exposure, insured sums, deductibles, or fitted premiums. A few very large policies or claim events can stretch a regular histogram so much that the body of the portfolio becomes hard to inspect. outlier_histogram() keeps the main range visible and groups values below lower or above upper into dedicated tail bins.

The plot is useful for actuarial portfolio checks, data quality review, and model preparation: it helps show where most risks are concentrated while still making the presence of extreme observations explicit.

Usage

outlier_histogram(
  data,
  x,
  lower = NULL,
  upper = NULL,
  density = FALSE,
  bins = 30,
  bar_fill = "#E6E6E6",
  bar_color = "white",
  tail_fill = "#F28E2B",
  tail_color = "white",
  density_color = "#2C7FB8",
  left = NULL,
  right = NULL,
  line = NULL,
  fill = NULL,
  color = NULL,
  fill_outliers = NULL
)

Arguments

data: A data.frame containing the portfolio variable to inspect.
x: Character; numeric column in data to plot.
lower: Optional numeric lower threshold. Values below this threshold are grouped into one left-tail bin.
upper: Optional numeric upper threshold. Values above this threshold are grouped into one right-tail bin.
density: Logical. If TRUE, add a density line. Default = FALSE.
bins: Integer. Number of bins used for the displayed range. Default = 30.
bar_fill: Fill color for regular histogram bars.
bar_color: Border color for regular histogram bars.
tail_fill: Fill color for tail bins.
tail_color: Border color for tail bins.
density_color: Color for the optional density line.
left, right: Deprecated aliases for lower and upper.
line: Deprecated alias for density.
fill, color, fill_outliers: Deprecated aliases for bar_fill, bar_color, and tail_fill.

Value

A ggplot2::ggplot object.

Details

This function is intended as an exploratory portfolio diagnostic. It does not remove or winsorize observations in data; it only groups tail values in the visual display. The labels on the tail bins show the original range captured by each tail bin.

The method for handling outlier bins is based on https://edwinth.github.io/blog/outlier-bin/.

Author

Martin Haringa

Examples

# Inspect the full premium distribution
outlier_histogram(MTPL2, "premium")


# Keep the portfolio body readable while showing both tails
outlier_histogram(MTPL2, "premium", lower = 30, upper = 120, bins = 30)