An epidemic curve (epi curve) is a histogram showing the number of new disease cases over time, plotted by date of symptom onset. The shape of the curve reveals the outbreak pattern: a sharp peak indicates a point source, successive waves suggest propagated transmission, and a plateau indicates continuous exposure. It is fundamental to epidemiological investigation and public health surveillance.
#' anyplot.ai
#' histogram-epidemic: Epidemic Curve (Epi Curve)
#' Library: ggplot2 3.5.1 | R 4.4.1
#' Quality: 89/100 | Created: 2026-06-02
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"
INK_MUTED <- if (THEME == "light") "#6B6A63" else "#A8A79F"
IMPRINT_PALETTE <- c(
"#009E73", # 1 - confirmed (brand green, always first)
"#C475FD", # 2 - probable (lavender)
"#4467A3" # 3 - suspect (blue)
)
# Data: simulated two-wave influenza outbreak (90 days, Jan-Apr 2024)
n_days <- 90
start_date <- as.Date("2024-01-15")
dates <- seq(start_date, by = "day", length.out = n_days)
days <- seq_len(n_days)
# Two-wave epidemic curve (Gaussian mixture)
primary <- 200 * exp(-((days - 28)^2) / (2 * 10^2))
secondary <- 80 * exp(-((days - 58)^2) / (2 * 8^2))
lambda <- pmax(primary + secondary, 0.5)
total <- rpois(n_days, lambda = lambda)
# Split into case classifications
confirmed <- rbinom(n_days, size = total, prob = 0.62)
remaining <- total - confirmed
probable <- rbinom(n_days, size = remaining, prob = 0.70)
suspect <- remaining - probable
df <- data.frame(
date = rep(dates, 3),
cases = c(confirmed, probable, suspect),
case_type = factor(
rep(c("Confirmed", "Probable", "Suspect"), each = n_days),
levels = c("Confirmed", "Probable", "Suspect")
)
)
# Public health intervention events
events <- data.frame(
date = as.Date(c("2024-02-05", "2024-02-20")),
label = c("School\nclosures", "Vaccination\ncampaign")
)
y_max <- max(tapply(df$cases, df$date, sum), na.rm = TRUE)
# Cumulative case burden for secondary y-axis
total_by_date <- tapply(df$cases, df$date, sum)
cum_df <- data.frame(
date = as.Date(names(total_by_date)),
daily = as.numeric(total_by_date)
)
cum_df <- cum_df[order(cum_df$date), ]
cum_df$cumulative <- cumsum(cum_df$daily)
cum_max <- max(cum_df$cumulative)
scale_factor <- (y_max * 1.15) / cum_max # scale cumulative to primary axis range
# Plot
p <- ggplot(df, aes(x = date, y = cases, fill = case_type)) +
geom_col(width = 1, position = "stack") +
geom_line(
data = cum_df,
aes(x = date, y = cumulative * scale_factor),
color = INK_MUTED,
linewidth = 0.9,
linetype = "solid",
inherit.aes = FALSE
) +
geom_vline(
data = events,
aes(xintercept = date),
color = INK_SOFT,
linewidth = 0.7,
linetype = "dashed"
) +
geom_text(
data = events,
aes(x = date, y = y_max * 0.97, label = label),
color = INK_MUTED,
size = 2.8,
hjust = -0.12,
lineheight = 0.9,
inherit.aes = FALSE
) +
scale_fill_manual(
values = IMPRINT_PALETTE,
name = "Case classification"
) +
scale_x_date(
date_breaks = "2 weeks",
date_labels = "%b %d",
expand = expansion(mult = c(0.01, 0.02))
) +
scale_y_continuous(
expand = expansion(mult = c(0, 0.15)),
labels = label_comma(),
sec.axis = sec_axis(
~ . / scale_factor,
name = "Cumulative cases",
labels = label_comma()
)
) +
labs(
x = "Date of symptom onset",
y = "New cases",
title = "histogram-epidemic · r · ggplot2 · anyplot.ai"
) +
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_MUTED, linewidth = 0.25),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
axis.title = element_text(color = INK, size = 10),
axis.title.y.right = element_text(color = INK_MUTED, size = 9),
axis.text = element_text(color = INK_SOFT, size = 8),
axis.text.x = element_text(angle = 30, hjust = 1),
axis.text.y.right = element_text(color = INK_MUTED, size = 7),
axis.line.x = element_line(color = INK_SOFT, linewidth = 0.4),
plot.title = element_text(color = INK, size = 12, face = "bold"),
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 = "top",
legend.key.size = unit(0.45, "cm"),
plot.margin = margin(t = 10, r = 15, b = 5, l = 5, unit = "pt")
)
# 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/histogram-epidemic/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": "histogram-epidemic",
"language": "r",
"library": "ggplot2",
"page": "https://anyplot.ai/histogram-epidemic/r/ggplot2",
"hub": "https://anyplot.ai/histogram-epidemic",
"code_json": "https://api.anyplot.ai/specs/histogram-epidemic/ggplot2/code",
"spec_json": "https://api.anyplot.ai/specs/histogram-epidemic",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/histogram-epidemic/r/ggplot2/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/histogram-epidemic/r/ggplot2/plot-dark.png",
"quality_score": 89.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Epidemic Curve (Epi Curve) on anyplot.ai.