A triangular matrix visualization showing cumulative insurance claim payments developing over time. Rows represent accident/origin years, columns represent development periods (e.g., 1-10 years), with the upper-left triangle displaying actual observed cumulative claims and the lower-right triangle showing projected/estimated values (IBNR). This plot is essential for actuarial reserving, enabling analysts to visualize the chain-ladder method and identify development patterns in loss data.
#' anyplot.ai
#' heatmap-loss-triangle: Actuarial Loss Development Triangle
#' Library: ggplot2 3.5.1 | R 4.4.1
#' Quality: 85/100 | Created: 2026-06-03
library(ggplot2)
library(dplyr)
library(scales)
library(ragg)
set.seed(42)
# Theme tokens (Imprint palette, theme-adaptive chrome)
THEME <- Sys.getenv("ANYPLOT_THEME", "light")
PAGE_BG <- if (THEME == "light") "#FAF8F1" else "#1A1A17"
ELEVATED_BG <- if (THEME == "light") "#FFFDF6" else "#242420"
INK <- if (THEME == "light") "#1A1A17" else "#F0EFE8"
INK_SOFT <- if (THEME == "light") "#4A4A44" else "#B8B7B0"
INK_MUTED <- if (THEME == "light") "#6B6A63" else "#A8A79F"
# Data: actuarial loss development triangle (chain-ladder method)
accident_years <- 2015:2024
dev_periods <- 1:10
# Cumulative percent of ultimate paid by development period
pct_paid <- c(0.40, 0.65, 0.80, 0.88, 0.93, 0.96, 0.98, 0.990, 0.995, 1.000)
# Ultimate claims per accident year (in $M)
ultimates <- c(42.5, 38.2, 51.3, 47.8, 55.1, 49.6, 61.2, 57.4, 63.8, 68.5)
# Age-to-age development factors (period p -> period p+1)
ata_factors <- pct_paid[2:10] / pct_paid[1:9]
# y-axis levels: accident years + ATA row at bottom
y_levels <- c(as.character(accident_years), "ATA Factor")
y_limits <- c("ATA Factor", rev(as.character(accident_years)))
# Build full 10 x 10 triangle grid
df <- expand.grid(
accident_year = accident_years,
dev_period = dev_periods,
stringsAsFactors = FALSE
) %>%
mutate(
ay_idx = accident_year - 2014L,
is_actual = dev_period <= (11L - ay_idx),
cumulative = ultimates[ay_idx] * pct_paid[dev_period],
cumulative = ifelse(
is_actual,
round(cumulative * runif(n(), 0.97, 1.03), 1),
round(cumulative, 1)
),
label = sprintf("$%.1fM", cumulative),
dev_fac = factor(dev_period, levels = 1:10),
acc_fac = factor(as.character(accident_year), levels = y_levels)
)
df_proj <- filter(df, !is_actual)
# ATA factor row: background tiles and text annotations
df_ata_bg <- data.frame(
acc_fac = factor(rep("ATA Factor", 10), levels = y_levels),
dev_fac = factor(1:10, levels = 1:10)
)
# ATA factors at periods 2-10 (multiplicative factor from prior period)
df_ata <- data.frame(
acc_fac = factor(rep("ATA Factor", 9), levels = y_levels),
dev_fac = factor(2:10, levels = 1:10),
label = sprintf("×%.3f", ata_factors)
)
# Semi-transparent red tint for projected cells (~15% opacity) + opaque border
proj_fill <- "#AE303026"
# Plot
p <- ggplot(df, aes(x = dev_fac, y = acc_fac)) +
# ATA factor row: muted elevated background
geom_tile(data = df_ata_bg,
fill = ELEVATED_BG, color = INK_SOFT, linewidth = 0.1) +
# Main triangle: colored by cumulative claims (Imprint sequential: green -> blue)
geom_tile(aes(fill = cumulative), color = INK_SOFT, linewidth = 0.15) +
# Projected cells: semi-transparent red tint + opaque border for stronger contrast
geom_tile(data = df_proj,
fill = proj_fill, color = "#AE3030", linewidth = 0.5) +
# Cell value labels
geom_text(aes(label = label),
color = "white", size = 2.2, fontface = "bold") +
# ATA factor annotations in bottom row
geom_text(data = df_ata, aes(label = label),
color = INK_MUTED, size = 1.8) +
# Imprint sequential colormap: brand green -> blue
scale_fill_gradient(
low = "#009E73",
high = "#4467A3",
name = "Claims ($M)",
labels = function(x) sprintf("%.0f", x)
) +
# Oldest accident year at top (actuarial convention); ATA row at bottom
scale_y_discrete(limits = y_limits) +
labs(
title = "heatmap-loss-triangle · r · ggplot2 · anyplot.ai",
x = "Development Period (Years)",
y = "Accident Year",
caption = "▪ Actual (observed) ▫ Projected / IBNR estimate (red tint + border)"
) +
theme_minimal(base_size = 8) +
theme(
plot.background = element_rect(fill = PAGE_BG, color = PAGE_BG),
panel.background = element_rect(fill = PAGE_BG, color = NA),
panel.grid = element_blank(),
axis.title = element_text(color = INK, size = 10),
axis.text = element_text(color = INK_SOFT, size = 8),
plot.title = element_text(color = INK, size = 12, face = "bold"),
plot.caption = element_text(color = INK_MUTED, size = 7, hjust = 0),
legend.background = element_rect(fill = ELEVATED_BG, color = INK_SOFT, linewidth = 0.3),
legend.text = element_text(color = INK_SOFT, size = 8),
legend.title = element_text(color = INK, size = 9),
legend.position = "right",
plot.margin = margin(t = 10, r = 10, b = 10, l = 10)
)
# Save: square canvas (2400 x 2400 px at 400 dpi)
ggsave(
filename = sprintf("plot-%s.png", THEME),
plot = p,
device = ragg::agg_png,
width = 6,
height = 6,
units = "in",
dpi = 400
)
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/heatmap-loss-triangle/ggplot2/code. Any spec id and library id listed in llms-full.txt fit the same URL shape; every URL below is complete and callable.
{
"spec_id": "heatmap-loss-triangle",
"language": "r",
"library": "ggplot2",
"page": "https://anyplot.ai/heatmap-loss-triangle/r/ggplot2",
"hub": "https://anyplot.ai/heatmap-loss-triangle",
"code_json": "https://api.anyplot.ai/specs/heatmap-loss-triangle/ggplot2/code",
"spec_json": "https://api.anyplot.ai/specs/heatmap-loss-triangle",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/heatmap-loss-triangle/r/ggplot2/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/heatmap-loss-triangle/r/ggplot2/plot-dark.png",
"quality_score": 85.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Actuarial Loss Development Triangle on anyplot.ai.