帕累托图

作者

[编辑] 郑虎;

[审核] .

修改于

2026-01-27

注记

Hiplot 网站

本页面为 Hiplot Pareto Chart 插件的源码版本教程，您也可以使用 Hiplot 网站实现无代码绘图，更多信息请查看以下链接:

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

环境配置

系统: Cross-platform (Linux/MacOS/Windows)
编程语言: R
依赖包: data.table; jsonlite; ggplot2

# 安装包
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")
}

# 加载包
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     2026-01-28
 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.

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

数据准备

案例数据为某商品在多个平台的销量数据，插件会为销量数据自动降序排列绘制条形图，同时计算累积销量绘制累积折线图。

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

# 整理数据格式
data <- data[order(-data[["sales"]]), ]
data[["channel"]] <- factor(data[["channel"]], levels = data[["channel"]])
## 计算百分比
data$accumulating <- cumsum(data[["sales"]])
max_y <- max(data[["sales"]])
cal_num <- sum(data[["sales"]]) / max_y
data$accumulating <- data$accumulating / cal_num

# 查看数据
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

可视化

# 帕累托图
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