A WHO/CDC-style growth chart displaying smooth percentile curves (3rd, 10th, 25th, 50th, 75th, 90th, 97th) as colored bands, with individual patient data points overlaid and connected by a line. This chart is a standard clinical tool for monitoring child development metrics such as height, weight, or BMI across age. It enables quick visual assessment of whether a child's growth trajectory falls within expected population ranges.
#' anyplot.ai
#' line-growth-percentile: Pediatric Growth Chart with Percentile Curves
#' Library: ggplot2 3.5.1 | R 4.4.1
#' Quality: 90/100 | Created: 2026-06-20
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"
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"
# Approximate 15%-opacity INK blended onto PAGE_BG (ggplot2 lacks alpha on grid lines)
GRID <- if (THEME == "light") "#D8D6D0" else "#3A3936"
# Imprint palette
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
)
BRAND <- IMPRINT_PALETTE[1] # #009E73 — patient data (contrasting color)
ROSE <- IMPRINT_PALETTE[7] # #954477 — girls' reference percentile bands
# Reference data: WHO weight-for-age for girls, 0–60 months (synthetic)
age_months <- 0:60
# Smooth parametric model approximating WHO LMS growth curves (girls)
p50_fn <- function(t) 3.3 + 2.0 * sqrt(t) - 0.016 * t
sd_fn <- function(t) 0.45 + 0.12 * sqrt(t)
ref_df <- data.frame(
age = age_months,
P3 = p50_fn(age_months) - 1.880 * sd_fn(age_months),
P10 = p50_fn(age_months) - 1.280 * sd_fn(age_months),
P25 = p50_fn(age_months) - 0.675 * sd_fn(age_months),
P50 = p50_fn(age_months),
P75 = p50_fn(age_months) + 0.675 * sd_fn(age_months),
P90 = p50_fn(age_months) + 1.280 * sd_fn(age_months),
P97 = p50_fn(age_months) + 1.880 * sd_fn(age_months)
)
# Individual patient: girl tracking near the 30th percentile
patient_ages <- c(0, 2, 4, 6, 9, 12, 15, 18, 24, 30, 36, 42, 48, 54, 60)
base_weight <- p50_fn(patient_ages) - 0.45 * sd_fn(patient_ages)
patient_df <- data.frame(
age = patient_ages,
weight = base_weight + rnorm(length(patient_ages), 0, 0.10)
)
# Right-margin percentile labels at age = 60 (last row of ref_df)
n_last <- nrow(ref_df)
label_df <- data.frame(
x = 61.5,
y = c(ref_df$P3[n_last], ref_df$P10[n_last], ref_df$P25[n_last],
ref_df$P50[n_last], ref_df$P75[n_last], ref_df$P90[n_last],
ref_df$P97[n_last]),
label = c("P3", "P10", "P25", "P50", "P75", "P90", "P97")
)
# Title (49 chars < 67 baseline → default font size is fine)
plot_title <- "line-growth-percentile · r · ggplot2 · anyplot.ai"
# Plot
p <- ggplot(ref_df, aes(x = age)) +
# Percentile bands: graduated intensity (darker at extremes, lighter near median)
geom_ribbon(aes(ymin = P3, ymax = P10), fill = ROSE, alpha = 0.45) +
geom_ribbon(aes(ymin = P90, ymax = P97), fill = ROSE, alpha = 0.45) +
geom_ribbon(aes(ymin = P10, ymax = P25), fill = ROSE, alpha = 0.28) +
geom_ribbon(aes(ymin = P75, ymax = P90), fill = ROSE, alpha = 0.28) +
geom_ribbon(aes(ymin = P25, ymax = P75), fill = ROSE, alpha = 0.12) +
# Percentile boundary lines (subtle)
geom_line(aes(y = P3), color = ROSE, linewidth = 0.4, alpha = 0.6) +
geom_line(aes(y = P10), color = ROSE, linewidth = 0.4, alpha = 0.6) +
geom_line(aes(y = P25), color = ROSE, linewidth = 0.4, alpha = 0.6) +
geom_line(aes(y = P75), color = ROSE, linewidth = 0.4, alpha = 0.6) +
geom_line(aes(y = P90), color = ROSE, linewidth = 0.4, alpha = 0.6) +
geom_line(aes(y = P97), color = ROSE, linewidth = 0.4, alpha = 0.6) +
# Emphasized median (P50) line
geom_line(aes(y = P50), color = ROSE, linewidth = 1.4) +
# Individual patient data (brand green — contrasting with rose reference)
geom_line(
data = patient_df,
aes(x = age, y = weight),
color = BRAND,
linewidth = 1.2
) +
geom_point(
data = patient_df,
aes(x = age, y = weight),
color = BRAND,
size = 2.5,
shape = 16
) +
# Percentile labels in right margin (clip = "off" allows drawing past panel edge)
geom_text(
data = label_df,
aes(x = x, y = y, label = label),
color = INK_MUTED,
size = 2.8,
hjust = 0,
fontface = "plain"
) +
# Axis scales
scale_x_continuous(
breaks = seq(0, 60, by = 12),
labels = c("Birth", paste0(1:5, " yr"))
) +
scale_y_continuous(
breaks = seq(0, 30, by = 5),
minor_breaks = seq(0, 30, by = 1),
expand = expansion(mult = c(0.02, 0.05))
) +
labs(
title = plot_title,
x = "Age",
y = "Weight (kg)"
) +
# clip = "off" lets right-margin labels render past the panel boundary
coord_cartesian(clip = "off") +
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, linewidth = 0.3),
panel.grid.minor = element_line(color = GRID, linewidth = 0.15),
panel.border = element_rect(color = INK_SOFT, fill = NA, linewidth = 0.4),
axis.line = 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.margin = margin(t = 10, r = 55, b = 10, l = 10, unit = "pt"),
legend.position = "none"
)
# Save
ggsave(
filename = sprintf("plot-%s.png", THEME),
plot = p,
device = ragg::agg_png,
width = 8,
height = 4.5,
units = "in",
dpi = 400
)
Part of Pediatric Growth Chart with Percentile Curves on anyplot.ai.