Pareto Chart

Authors

[Editor] Hu Zheng;

[Contributors]

Modified

2026-01-17

Note

Hiplot website

This page is the tutorial for source code version of the Hiplot Pareto Chart plugin. You can also use the Hiplot website to achieve no code ploting. For more information please see the following link:

https://hiplot.cn/basic/pareto-chart?lang=en

Setup

System Requirements: Cross-platform (Linux/MacOS/Windows)
Programming language: R
Dependent packages: data.table; jsonlite; ggplot2

# Install packages
if (!requireNamespace("data.table", quietly = TRUE)) {
  install.packages("data.table")
}
if (!requireNamespace("jsonlite", quietly = TRUE)) {
  install.packages("jsonlite")
}
if (!requireNamespace("ggplot2", quietly = TRUE)) {
  install.packages("ggplot2")
}

# Load packages
library(data.table)
library(jsonlite)
library(ggplot2)

sessioninfo::session_info("attached")

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.5.2 (2025-10-31)
 os       Ubuntu 24.04.3 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  C.UTF-8
 ctype    C.UTF-8
 tz       UTC
 date     2026-01-17
 pandoc   3.1.3 @ /usr/bin/ (via rmarkdown)
 quarto   1.8.27 @ /usr/local/bin/quarto

─ Packages ───────────────────────────────────────────────────────────────────
 package    * version date (UTC) lib source
 data.table * 1.18.0  2025-12-24 [1] RSPM
 ggplot2    * 4.0.1   2025-11-14 [1] RSPM
 jsonlite   * 2.0.0   2025-03-27 [1] RSPM

 [1] /home/runner/work/_temp/Library
 [2] /opt/R/4.5.2/lib/R/site-library
 [3] /opt/R/4.5.2/lib/R/library
 * ── Packages attached to the search path.

──────────────────────────────────────────────────────────────────────────────

Data Preparation

The case data represents the sales data of a product on multiple platforms. The plugin will automatically plot a bar chart with sales data in descending order and simultaneously calculate the cumulative sales to draw the cumulative line chart.

# Load data
data <- data.table::fread(jsonlite::read_json("https://hiplot.cn/ui/basic/pareto-chart/data.json")$exampleData$textarea[[1]])
data <- as.data.frame(data)

# Convert data structure
data <- data[order(-data[["sales"]]), ]
data[["channel"]] <- factor(data[["channel"]], levels = data[["channel"]])
## Calculate percentage number
data$accumulating <- cumsum(data[["sales"]])
max_y <- max(data[["sales"]])
cal_num <- sum(data[["sales"]]) / max_y
data$accumulating <- data$accumulating / cal_num

# View data
head(data)

    channel sales accumulating
2        JD   500     132.9787
5     Tmall   400     239.3617
4 Pinduoduo   300     319.1489
3    Amazon   230     380.3191
6    Shopee   200     433.5106
1    TaoBao   100     460.1064

Visualization

# Pareto Chart
p <- ggplot(data, aes(x = channel, y = sales, fill = channel)) +
  geom_bar(stat = "identity") +
  geom_line(aes(y = accumulating), group = 1) +
  geom_point(aes(y = accumulating), show.legend = FALSE) +
  scale_y_continuous(sec.axis = sec_axis(trans = ~ . / max_y * 100, name = "Percentage")) +
  scale_fill_manual(values = c("#E64B35FF","#4DBBD5FF","#00A087FF","#3C5488FF",
                               "#F39B7FFF","#8491B4FF","#91D1C2FF","#DC0000FF")) +
  theme_bw()

p