A Pareto chart combining descending-sorted bars (by frequency or count) with a cumulative percentage line overlay on a secondary y-axis. This visualization helps identify the most significant factors in a dataset by applying the Pareto principle (80/20 rule), making it one of the "7 Basic Tools of Quality" in Six Sigma and quality management. It reveals which categories contribute the most to an overall effect, enabling data-driven prioritization.
#' anyplot.ai
#' bar-pareto: Pareto Chart with Cumulative Line
#' Library: ggplot2 3.5.1 | R 4.4.1
#' Quality: 91/100 | Created: 2026-06-20
library(ggplot2)
library(dplyr)
library(scales)
library(ragg)
# --- 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"
)
ANYPLOT_AMBER <- "#DDCC77"
# --- Data ---
# Manufacturing defect data: two root causes dominate (Pareto principle)
defect_data <- data.frame(
category = c(
"Surface Scratches", "Misalignment", "Paint Defects",
"Weld Flaws", "Assembly Gaps", "Contamination",
"Thread Damage", "Other"
),
count = c(500, 300, 80, 55, 30, 18, 10, 7)
)
# Sort descending and compute cumulative sums
defect_data <- defect_data |>
arrange(desc(count)) |>
mutate(
category = factor(category, levels = category),
cum_count = cumsum(count)
)
total_count <- sum(defect_data$count)
threshold_count <- 0.8 * total_count
# --- Plot ---
plot_title <- "Manufacturing Defects · bar-pareto · r · ggplot2 · anyplot.ai"
p <- ggplot(defect_data, aes(x = category)) +
# Bars (primary axis: raw counts, fill-mapped for legend)
geom_col(
aes(y = count, fill = "Defect count"),
width = 0.7,
alpha = 0.92
) +
# 80% Pareto threshold reference line (color-mapped for legend)
geom_hline(
aes(yintercept = threshold_count, color = "80% threshold"),
linewidth = 0.85,
linetype = "dashed"
) +
# Cumulative percentage line (color-mapped for legend)
geom_line(
aes(y = cum_count, group = 1, color = "Cumulative %"),
linewidth = 1.3
) +
# Markers at center-top of each bar
geom_point(
aes(y = cum_count, color = "Cumulative %"),
size = 3.5,
shape = 19
) +
# Annotation: highlight where cumulative % crosses 80%
annotate(
"text",
x = 2.5,
y = threshold_count + total_count * 0.04,
label = "Top 2 → 80% of defects",
color = ANYPLOT_AMBER,
size = 2.6,
hjust = 0
) +
# Dual y-axis: primary = count, secondary = cumulative %
scale_y_continuous(
name = "Defect count",
limits = c(0, total_count),
expand = expansion(mult = c(0, 0)),
labels = scales::comma,
sec.axis = sec_axis(
~ . / total_count * 100,
name = "Cumulative percentage (%)",
breaks = seq(0, 100, 20),
labels = function(x) paste0(x, "%")
)
) +
scale_x_discrete(expand = expansion(add = c(0.6, 0.6))) +
# Legend: fill scale for bars
scale_fill_manual(
name = NULL,
values = c("Defect count" = IMPRINT_PALETTE[1])
) +
# Legend: color scale for line and reference
scale_color_manual(
name = NULL,
values = c(
"Cumulative %" = IMPRINT_PALETTE[3],
"80% threshold" = ANYPLOT_AMBER
),
guide = guide_legend(
override.aes = list(
linetype = c("solid", "dashed"),
shape = c(16, NA),
linewidth = c(1.3, 0.85)
)
)
) +
labs(
x = "Defect category",
title = plot_title
) +
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),
# L-frame: bottom and left axis lines only (remove full box border)
panel.border = element_blank(),
axis.line.x.bottom = element_line(color = INK_SOFT, linewidth = 0.4),
axis.line.y.left = element_line(color = INK_SOFT, linewidth = 0.4),
panel.grid.major.y = element_line(
color = adjustcolor(INK, alpha.f = 0.12),
linewidth = 0.35
),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
axis.title.x = element_text(color = INK, size = 10),
axis.title.y.left = element_text(color = INK, size = 10),
axis.title.y.right = element_text(color = IMPRINT_PALETTE[3], size = 10),
axis.text.x = element_text(color = INK_SOFT, size = 7.5, angle = 32, hjust = 1),
axis.text.y.left = element_text(color = INK_SOFT, size = 8),
# Use INK_SOFT for right-axis ticks in dark mode (better contrast vs. #1A1A17)
axis.text.y.right = element_text(
color = if (THEME == "dark") INK_SOFT else IMPRINT_PALETTE[3],
size = 8
),
axis.ticks = element_line(color = INK_SOFT, linewidth = 0.3),
plot.title = element_text(color = INK, size = 12, face = "bold",
margin = margin(b = 8)),
plot.margin = margin(t = 15, r = 15, b = 10, l = 10, unit = "pt"),
legend.position = "bottom",
legend.background = element_rect(fill = ELEVATED_BG, color = NA),
legend.text = element_text(color = INK_SOFT, size = 8),
legend.key = element_rect(fill = NA, color = NA),
legend.key.size = unit(1, "lines"),
legend.box = "horizontal",
legend.margin = margin(t = 2, b = 4, l = 4, r = 4)
)
# --- 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 Pareto Chart with Cumulative Line on anyplot.ai.