A cumulative gains chart visualizes the effectiveness of a classification model by showing what percentage of positive cases is captured when targeting increasing percentages of the population, ranked by predicted probability. It answers the question: "If I target the top X% of my predictions, what percentage of all actual positives will I capture?" This plot is essential for evaluating targeting strategies in marketing, risk assessment, and resource allocation scenarios.
#' anyplot.ai
#' gain-curve: Cumulative Gains Chart
#' Library: ggplot2 3.5.1 | R 4.4.1
#' Quality: 86/100 | Created: 2026-09-05
library(ggplot2)
library(dplyr)
library(ragg)
set.seed(42)
# --- Theme tokens -----------------------------------------------------------
THEME <- Sys.getenv("ANYPLOT_THEME", "light")
PAGE_BG <- if (THEME == "light") "#FAF8F1" else "#1A1A17"
INK <- if (THEME == "light") "#1A1A17" else "#F0EFE8"
INK_SOFT <- if (THEME == "light") "#4A4A44" else "#B8B7B0"
INK_MUTED <- if (THEME == "light") "#6B6A63" else "#A8A79F"
ELEVATED_BG <- if (THEME == "light") "#FFFDF6" else "#242420"
GRID_COLOR <- scales::alpha(INK, 0.05)
ANYPLOT_NEUTRAL <- INK
IMPRINT_PALETTE <- c("#009E73", "#C475FD", "#4467A3", "#BD8233",
"#AE3030", "#2ABCCD", "#954477", "#99B314")
# --- Data ---------------------------------------------------------------
# Churn-prevention scenario: a risk model scores customers by likelihood of
# churning; the cumulative gains curve shows how much of the actual churn
# base is captured as we contact increasing shares of the customer list,
# ranked by predicted score.
n_customers <- 2000
churn_rate <- 0.18
true_risk <- rnorm(n_customers)
noise <- rnorm(n_customers, sd = 1.35)
model_score <- true_risk + noise
churn_threshold <- quantile(true_risk, probs = 1 - churn_rate)
churned <- as.integer(true_risk >= churn_threshold)
order_idx <- order(model_score, decreasing = TRUE)
churned_sorted <- churned[order_idx]
cumulative_churners <- cumsum(churned_sorted)
total_churners <- sum(churned_sorted)
gains <- tibble::tibble(
pct_targeted = seq_len(n_customers) / n_customers * 100,
pct_captured = cumulative_churners / total_churners * 100,
series = "Churn model"
)
gains <- bind_rows(
tibble::tibble(pct_targeted = 0, pct_captured = 0, series = "Churn model"),
gains
)
baseline <- tibble::tibble(
pct_targeted = c(0, 100), pct_captured = c(0, 100),
series = "Random targeting"
)
df <- bind_rows(gains, baseline)
# --- Plot -----------------------------------------------------------------
title_text <- "Churn Model · gain-curve · r · ggplot2 · anyplot.ai"
p <- ggplot(
df,
aes(x = pct_targeted, y = pct_captured,
color = series, linetype = series, linewidth = series)
) +
geom_line() +
scale_color_manual(values = c("Churn model" = IMPRINT_PALETTE[1], "Random targeting" = ANYPLOT_NEUTRAL)) +
scale_linetype_manual(values = c("Churn model" = "solid", "Random targeting" = "dashed")) +
scale_linewidth_manual(values = c("Churn model" = 1.8, "Random targeting" = 1.0)) +
scale_x_continuous(
limits = c(0, 100), expand = expansion(mult = c(0, 0.02), add = c(0, 6)),
labels = scales::label_number(suffix = "%")
) +
scale_y_continuous(
limits = c(0, 100), expand = expansion(mult = c(0, 0.02)),
labels = scales::label_number(suffix = "%")
) +
labs(
title = title_text,
x = "Percentage of Customers Targeted",
y = "Percentage of Churners Captured"
) +
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.major = element_line(color = GRID_COLOR, linewidth = 0.25),
panel.grid.minor = element_blank(),
axis.title = element_text(color = INK, size = 10),
axis.text = element_text(color = INK_SOFT, size = 8),
axis.ticks = element_blank(),
plot.title = element_text(color = INK, size = 11),
panel.border = element_blank(),
legend.title = element_blank(),
legend.text = element_text(color = INK_SOFT, size = 8),
legend.background = element_rect(fill = ELEVATED_BG, color = NA),
legend.position = "inside",
legend.position.inside = c(0.80, 0.18),
plot.margin = margin(t = 8, r = 22, b = 8, l = 8)
)
# --- Save -------------------------------------------------------------------
ggsave(
filename = sprintf("plot-%s.png", THEME),
plot = p,
device = ragg::agg_png,
width = 8,
height = 4.5,
units = "in",
dpi = 400
)
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/gain-curve/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": "gain-curve",
"language": "r",
"library": "ggplot2",
"page": "https://anyplot.ai/gain-curve/r/ggplot2",
"hub": "https://anyplot.ai/gain-curve",
"code_json": "https://api.anyplot.ai/specs/gain-curve/ggplot2/code",
"spec_json": "https://api.anyplot.ai/specs/gain-curve",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/gain-curve/r/ggplot2/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/gain-curve/r/ggplot2/plot-dark.png",
"quality_score": 86.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Cumulative Gains Chart on anyplot.ai.