Stacked Area Chart with Confidence Bands in ggplot2 (R)

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: Stacked Area Chart with Confidence Bands in Python, R, Julia and JavaScript.

A stacked area chart that displays multiple data series as cumulative areas, with each series surrounded by uncertainty or confidence bands. This visualization combines the composition insight of stacked areas with the statistical rigor of confidence intervals, making it ideal for showing how parts contribute to a whole while simultaneously communicating uncertainty in each component. The bands reveal where estimates are precise versus uncertain across the stacked series.

Stacked Area Chart with Confidence Bands rendered with ggplot2

Renders

R source (ggplot2)

#' anyplot.ai
#' area-stacked-confidence: Stacked Area Chart with Confidence Bands
#' Library: ggplot2 3.5.1 | R 4.4.1
#' Quality: 87/100 | Created: 2026-05-18

library(ggplot2)
library(dplyr)
library(tidyr)
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   <- c("#009E73", "#C475FD", "#4467A3", "#BD8233",
                 "#AE3030", "#2ABCCD", "#954477")

# --- Data -------------------------------------------------------------------
# Quarterly energy consumption forecast by source with confidence bands
quarters <- 1:20
sources_order <- c("Solar", "Wind", "Hydro", "Battery")

df_base <- expand.grid(
  quarter = quarters,
  source = factor(sources_order, levels = sources_order)
) %>%
  arrange(quarter, source)

# Generate central values and confidence bands
df_data <- df_base %>%
  mutate(
    base = case_when(
      source == "Solar" ~ 800 + quarter * 50 + rnorm(n(), 0, 30),
      source == "Wind" ~ 1200 + quarter * 30 + rnorm(n(), 0, 40),
      source == "Hydro" ~ 600 - quarter * 10 + rnorm(n(), 0, 25),
      source == "Battery" ~ 200 + quarter * 15 + rnorm(n(), 0, 15)
    ),
    # Confidence bands (uncertainty increases over forecast horizon)
    uncertainty = case_when(
      source == "Solar" ~ 50 + quarter * 3,
      source == "Wind" ~ 60 + quarter * 4,
      source == "Hydro" ~ 40 + quarter * 2,
      source == "Battery" ~ 30 + quarter * 2
    ),
    value = pmax(base, 100),  # Ensure positive values
    value_lower = pmax(value - uncertainty, 50),
    value_upper = value + uncertainty
  ) %>%
  select(quarter, source, value, value_lower, value_upper)

# Calculate cumulative stacked values
df_plot <- df_data %>%
  arrange(quarter, source) %>%
  group_by(quarter) %>%
  mutate(
    # Cumulative sum for stacking
    prev_cumsum = lag(cumsum(value), default = 0),
    y_base = prev_cumsum,
    y_center = y_base + value,
    y_lower = y_base + value_lower,
    y_upper = y_base + value_upper
  ) %>%
  ungroup() %>%
  arrange(quarter, source)

# --- Plot -------------------------------------------------------------------
anyplot_theme <- theme_minimal(base_size = 14) +
  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 = INK_SOFT, linewidth = 0.2),
    panel.grid.minor  = element_blank(),
    axis.title        = element_text(color = INK, size = 20),
    axis.text         = element_text(color = INK_SOFT, size = 16),
    plot.title        = element_text(color = INK, size = 24, face = "bold"),
    legend.background = element_rect(fill = ELEVATED_BG, color = INK_SOFT),
    legend.text       = element_text(color = INK_SOFT, size = 16),
    legend.title      = element_text(color = INK, size = 18)
  )

p <- ggplot(df_plot, aes(x = quarter, fill = source, color = source)) +
  # Confidence bands (lighter, more transparent)
  geom_ribbon(aes(ymin = y_lower, ymax = y_upper), alpha = 0.2, color = NA) +
  # Central stacked areas
  geom_area(aes(y = y_center), alpha = 0.7, color = NA) +
  scale_fill_manual(values = IMPRINT[1:4]) +
  scale_color_manual(values = IMPRINT[1:4]) +
  labs(
    title = "area-stacked-confidence · R · ggplot2 · anyplot.ai",
    x = "Quarter",
    y = "Energy (MWh)",
    fill = "Source"
  ) +
  anyplot_theme +
  theme(
    legend.position = "top",
    legend.direction = "horizontal"
  ) +
  guides(color = "none")

# --- Save -------------------------------------------------------------------
ggsave(
  filename = sprintf("plot-%s.png", THEME),
  plot     = p,
  device   = ragg::agg_png,
  width    = 16,
  height   = 9,
  units    = "in",
  dpi      = 300
)

Retrieve this implementation

Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/area-stacked-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": "area-stacked-confidence",
  "language": "r",
  "library": "ggplot2",
  "page": "https://anyplot.ai/area-stacked-confidence/r/ggplot2",
  "hub": "https://anyplot.ai/area-stacked-confidence",
  "code_json": "https://api.anyplot.ai/specs/area-stacked-confidence/ggplot2/code",
  "spec_json": "https://api.anyplot.ai/specs/area-stacked-confidence",
  "render_light_png": "https://storage.googleapis.com/anyplot-images/plots/area-stacked-confidence/r/ggplot2/plot-light.png",
  "render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/area-stacked-confidence/r/ggplot2/plot-dark.png",
  "quality_score": 87.0,
  "license": "MIT",
  "guide": "https://anyplot.ai/llms.txt"
}

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