The same plot in 14 other libraries — Python: Altair, Bokeh, lets-plot, Matplotlib, Plotly, plotnine, Pygal, Seaborn; Julia: Makie.jl; JavaScript: Chart.js, D3.js, Apache ECharts, Highcharts, MUI X Charts. Compare all 15 side by side: Basic ECDF Plot in Python, R, Julia and JavaScript.
An ECDF (Empirical Cumulative Distribution Function) plot displays a step function that shows the proportion of observations less than or equal to each value. Unlike histograms, ECDF plots require no binning or smoothing, providing a non-parametric estimate of the cumulative distribution. The y-axis ranges from 0 to 1, allowing direct reading of percentiles and quantiles from the visualization.
#' anyplot.ai
#' ecdf-basic: Basic ECDF Plot
#' Library: ggplot2 3.5.1 | R 4.4.1
#' Quality: 89/100 | Created: 2026-06-25
library(ggplot2)
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"
GRID_COLOR <- if (THEME == "light") "#1A1A1726" else "#F0EFE826"
IMPRINT_PALETTE <- c(
"#009E73", # 1 — brand green (first categorical series)
"#C475FD", # 2 — lavender
"#4467A3", # 3 — blue
"#BD8233", # 4 — ochre
"#AE3030", # 5 — matte red
"#2ABCCD", # 6 — cyan
"#954477", # 7 — rose
"#99B314" # 8 — lime
)
# Data: exam scores comparing two student cohorts
n <- 200
scores_a <- rnorm(n, mean = 68, sd = 14) # standard curriculum
scores_b <- rnorm(n, mean = 76, sd = 10) # enriched curriculum
df <- data.frame(
score = c(scores_a, scores_b),
cohort = rep(c("Class A", "Class B"), each = n)
)
# Medians for reference annotations
median_a <- median(scores_a)
median_b <- median(scores_b)
gap <- round(median_b - median_a)
# Plot
p <- ggplot(df, aes(x = score, color = cohort, linetype = cohort)) +
# Horizontal dotted reference at 50th percentile for reading medians
geom_hline(
yintercept = 0.5,
color = INK_SOFT,
linewidth = 0.4,
linetype = "dotted"
) +
# ECDF step lines
stat_ecdf(linewidth = 1.2, pad = FALSE) +
# Vertical droplines from x-axis to 50th percentile for each cohort
annotate("segment",
x = median_a, xend = median_a, y = 0, yend = 0.5,
color = IMPRINT_PALETTE[1], linewidth = 0.5, linetype = "dotted", alpha = 0.8
) +
annotate("segment",
x = median_b, xend = median_b, y = 0, yend = 0.5,
color = IMPRINT_PALETTE[2], linewidth = 0.5, linetype = "dotted", alpha = 0.8
) +
# Markers at median intersections on each ECDF curve
annotate("point",
x = median_a, y = 0.5,
color = IMPRINT_PALETTE[1], size = 2.0
) +
annotate("point",
x = median_b, y = 0.5,
color = IMPRINT_PALETTE[2], size = 2.0
) +
scale_color_manual(
name = "Student Cohort",
values = c("Class A" = IMPRINT_PALETTE[1], "Class B" = IMPRINT_PALETTE[2])
) +
scale_linetype_manual(
name = "Student Cohort",
values = c("Class A" = "solid", "Class B" = "dashed")
) +
scale_x_continuous(breaks = seq(20, 120, by = 10)) +
scale_y_continuous(
labels = percent_format(accuracy = 1),
breaks = seq(0, 1, by = 0.25)
) +
labs(
title = "Exam Score Distribution by Curriculum Type · ecdf-basic · r · ggplot2 · anyplot.ai",
subtitle = sprintf(
"Median: Class A (standard) = %.0f pts vs. Class B (enriched) = %.0f pts — %+.0f-pt gap at the 50th percentile",
median_a, median_b, gap
),
x = "Exam Score",
y = "Cumulative Proportion"
) +
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 = 0.5),
panel.grid.minor = element_blank(),
panel.border = 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, linewidth = 0.5),
plot.title = element_text(color = INK, size = 10),
plot.subtitle = element_text(color = INK_SOFT, size = 8),
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 = 10),
legend.position = "bottom",
plot.margin = margin(20, 20, 20, 20)
)
# 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/ecdf-basic/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": "ecdf-basic",
"language": "r",
"library": "ggplot2",
"page": "https://anyplot.ai/ecdf-basic/r/ggplot2",
"hub": "https://anyplot.ai/ecdf-basic",
"code_json": "https://api.anyplot.ai/specs/ecdf-basic/ggplot2/code",
"spec_json": "https://api.anyplot.ai/specs/ecdf-basic",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/ecdf-basic/r/ggplot2/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/ecdf-basic/r/ggplot2/plot-dark.png",
"quality_score": 89.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Basic ECDF Plot on anyplot.ai.