A line plot with a confidence interval displays a central trend line (typically mean or median) surrounded by a shaded band representing uncertainty or variability. The combination of a clear central line and semi-transparent confidence region effectively communicates both the estimated value and its associated uncertainty, making it essential for visualizing statistical estimates, model predictions, and forecast ranges.
#' anyplot.ai
#' line-confidence: Line Plot with Confidence Interval
#' Library: ggplot2 3.5.1 | R 4.4.1
#' Quality: 91/100 | Created: 2026-09-05
library(ggplot2)
library(tibble)
library(scales)
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")
BRAND <- IMPRINT_PALETTE[1]
# --- Data ---------------------------------------------------------------------
# 90-day daily-active-user forecast: growth trend plus weekly seasonality,
# with a 95% prediction interval that widens with the forecast horizon
# (sqrt-of-horizon growth, the standard random-walk-forecast uncertainty shape).
horizon_days <- 90
horizon <- seq_len(horizon_days)
forecast_dates <- as.Date("2026-09-05") + horizon
trend <- 48000 + 180 * horizon
seasonality <- 1400 * sin(2 * pi * horizon / 7)
noise <- rnorm(horizon_days, mean = 0, sd = 250)
dau_forecast <- trend + seasonality + noise
standard_error <- 300 + 55 * sqrt(horizon)
dau_lower <- dau_forecast - 1.96 * standard_error
dau_upper <- dau_forecast + 1.96 * standard_error
df <- tibble(
date = forecast_dates,
dau = dau_forecast,
lower = dau_lower,
upper = dau_upper
)
# --- Narrative anchors ------------------------------------------------------
# Weekly seasonality peak (first cycle) - gives viewers a concrete landmark
# for the sawtooth pattern instead of leaving it purely implicit. The label
# sits just above the peak's own ribbon (not the chart's global max) so it
# stays visually anchored to the point it describes.
peak_row <- df[which.max(df$dau[1:14]), ]
y_span <- diff(range(c(df$lower, df$upper)))
peak_label_y <- peak_row$upper + y_span * 0.035
# Final-horizon interval width - turns "the band widens" into a concrete
# number, anchoring the growing-uncertainty story at the point it matters most.
last_row <- df[horizon_days, ]
half_width <- (last_row$upper - last_row$lower) / 2
# --- Plot -----------------------------------------------------------------------
p <- ggplot(df, aes(x = date)) +
geom_ribbon(aes(ymin = lower, ymax = upper, fill = "95% prediction interval"),
alpha = 0.25) +
geom_line(aes(y = dau, color = "Forecast mean"), linewidth = 1.1) +
geom_vline(xintercept = peak_row$date, linetype = "dashed",
color = INK_SOFT, linewidth = 0.4) +
annotate("text", x = peak_row$date, y = peak_label_y, label = "Weekly peak",
hjust = -0.1, vjust = 0, size = 2.6, color = INK_SOFT) +
geom_segment(data = last_row,
aes(x = date, xend = date, y = lower, yend = upper),
inherit.aes = FALSE, color = INK, linewidth = 0.5,
arrow = grid::arrow(ends = "both", length = grid::unit(0.05, "in"))) +
annotate("text", x = last_row$date, y = (last_row$lower + last_row$upper) / 2,
label = sprintf("95%% CI: ±%s", comma(round(half_width))),
hjust = 1.1, vjust = 0.5, size = 2.6, color = INK, fontface = "italic") +
scale_fill_manual(name = NULL, values = c("95% prediction interval" = BRAND)) +
scale_color_manual(name = NULL, values = c("Forecast mean" = BRAND)) +
scale_x_date(expand = expansion(mult = c(0.01, 0.05))) +
scale_y_continuous(labels = label_comma()) +
coord_cartesian(clip = "off") +
labs(
title = "line-confidence · r · ggplot2 · anyplot.ai",
x = "Forecast Date",
y = "Daily Active Users"
) +
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.x = element_blank(),
panel.grid.major.y = element_line(color = INK, linewidth = 0.3),
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.ticks = element_blank(),
axis.line = element_line(color = INK_SOFT),
plot.title = element_text(color = INK, size = 12),
legend.position = "top",
legend.background = element_rect(fill = ELEVATED_BG, color = NA),
legend.key = element_rect(fill = ELEVATED_BG, color = NA),
legend.text = element_text(color = INK_SOFT, size = 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/line-confidence/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": "line-confidence",
"language": "r",
"library": "ggplot2",
"page": "https://anyplot.ai/line-confidence/r/ggplot2",
"hub": "https://anyplot.ai/line-confidence",
"code_json": "https://api.anyplot.ai/specs/line-confidence/ggplot2/code",
"spec_json": "https://api.anyplot.ai/specs/line-confidence",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/line-confidence/r/ggplot2/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/line-confidence/r/ggplot2/plot-dark.png",
"quality_score": 91.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Line Plot with Confidence Interval on anyplot.ai.