A Kaplan-Meier survival plot visualizes the probability of survival (or event-free time) over a time period using a step function. It is the standard method for estimating survival functions from time-to-event data, handling censored observations where the event has not yet occurred. The plot shows how survival probability decreases over time, with optional confidence intervals and comparison between groups.
#' anyplot.ai
#' survival-kaplan-meier: Kaplan-Meier Survival Plot
#' Library: ggplot2 3.5.1 | R 4.4.1
#' Quality: 92/100 | Created: 2026-09-09
library(ggplot2)
library(dplyr)
library(survival)
library(ragg)
set.seed(42)
# --- Theme tokens -----------------------------------------------------------
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"
IMPRINT_PALETTE <- c("#009E73", "#C475FD", "#4467A3", "#BD8233",
"#AE3030", "#2ABCCD", "#954477", "#99B314")
# --- Data: clinical trial follow-up (2 treatment arms, right-censored) -----
n_per_arm <- 110
follow_up <- 36 # months of administrative follow-up
arm <- factor(rep(c("Standard care", "New therapy"), each = n_per_arm),
levels = c("Standard care", "New therapy"))
event_rate <- ifelse(arm == "Standard care", 1 / 17, 1 / 25)
event_time <- rexp(2 * n_per_arm, rate = event_rate)
dropout_time <- runif(2 * n_per_arm, 18, follow_up)
patients <- tibble::tibble(
time = pmin(event_time, dropout_time, follow_up),
event = as.integer(event_time <= pmin(dropout_time, follow_up)),
arm = arm
)
fit <- survfit(Surv(time, event) ~ arm, data = patients)
fit_summary <- summary(fit, censored = TRUE)
km_curve <- tibble::tibble(
time = fit_summary$time,
surv = fit_summary$surv,
lower = fit_summary$lower,
upper = fit_summary$upper,
n_censor = fit_summary$n.censor,
arm = factor(sub("^arm=", "", as.character(fit_summary$strata)), levels = levels(arm))
)
# Prepend a t=0, survival=1 anchor per arm so the step curve starts at the top
km_start <- tibble::tibble(
time = 0, surv = 1, lower = 1, upper = 1, n_censor = 0,
arm = factor(levels(arm), levels = levels(arm))
)
km_curve <- bind_rows(km_start, km_curve) %>% arrange(arm, time)
# Stairstep confidence band: one rectangle per interval, held constant until
# the next event/censoring time (geom_ribbon interpolates linearly, which
# misrepresents a step function; geom_rect renders the true stairs instead)
km_band <- km_curve %>%
group_by(arm) %>%
mutate(time_end = lead(time, default = follow_up)) %>%
ungroup()
censor_marks <- km_curve %>% filter(n_censor > 0)
log_rank <- survdiff(Surv(time, event) ~ arm, data = patients)
p_value <- 1 - pchisq(log_rank$chisq, length(log_rank$n) - 1)
p_label <- if (p_value < 0.001) "p < 0.001" else sprintf("p = %.3f", p_value)
title_text <- "survival-kaplan-meier · r · ggplot2 · anyplot.ai"
# --- Plot --------------------------------------------------------------------
p <- ggplot(km_curve, aes(x = time, y = surv, color = arm, fill = arm)) +
geom_rect(
data = km_band,
aes(xmin = time, xmax = time_end, ymin = lower, ymax = upper, fill = arm),
inherit.aes = FALSE, alpha = 0.15, color = NA
) +
geom_step(linewidth = 1.0) +
geom_point(
data = censor_marks, aes(x = time, y = surv, color = arm),
shape = 3, size = 2.5, stroke = 1, show.legend = FALSE
) +
scale_color_manual(values = IMPRINT_PALETTE) +
scale_fill_manual(values = IMPRINT_PALETTE) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1), limits = c(0, 1)) +
labs(
title = title_text,
subtitle = sprintf("Log-rank test: %s", p_label),
x = "Time Since Enrollment (months)",
y = "Survival Probability",
color = "Treatment Arm",
fill = "Treatment Arm"
) +
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.y = element_line(color = INK, linewidth = 0.3),
panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank(),
axis.title = element_text(color = INK, size = 10),
axis.text = element_text(color = INK_SOFT, size = 8),
axis.line = element_line(color = INK_SOFT),
plot.title = element_text(color = INK, size = 12),
plot.subtitle = element_text(color = INK_SOFT, size = 9),
legend.position = "inside",
legend.position.inside = c(0.82, 0.82),
legend.background = element_rect(fill = ELEVATED_BG, color = INK_SOFT, linewidth = 0.2),
legend.text = element_text(color = INK_SOFT, size = 8),
legend.title = element_text(color = INK, size = 9),
plot.margin = margin(10, 14, 10, 10)
)
# --- 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/survival-kaplan-meier/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": "survival-kaplan-meier",
"language": "r",
"library": "ggplot2",
"page": "https://anyplot.ai/survival-kaplan-meier/r/ggplot2",
"hub": "https://anyplot.ai/survival-kaplan-meier",
"code_json": "https://api.anyplot.ai/specs/survival-kaplan-meier/ggplot2/code",
"spec_json": "https://api.anyplot.ai/specs/survival-kaplan-meier",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/survival-kaplan-meier/r/ggplot2/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/survival-kaplan-meier/r/ggplot2/plot-dark.png",
"quality_score": 92.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Kaplan-Meier Survival Plot on anyplot.ai.