丝带图

作者

[编辑] 郑虎;

[审核] .

修改于

2026-01-17

注记

Hiplot 网站

本页面为 Hiplot Ribbon 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/ribbon?lang=zh_cn

丝带图是一种类似丝带的图形。

环境配置

  • 系统: Cross-platform (Linux/MacOS/Windows)

  • 编程语言: R

  • 依赖包: data.table; jsonlite; ggplot2; ggthemes

# 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")
}
if (!requireNamespace("ggthemes", quietly = TRUE)) {
  install.packages("ggthemes")
}

# 加载包
library(data.table)
library(jsonlite)
library(ggplot2)
library(ggthemes)
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     2026-01-18
 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
 ggthemes   * 5.2.0   2025-11-30 [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

载入数据为 x 轴数值及其对应的两个 y 轴数值和分组。

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

# 整理数据格式
colnames(data) <- c("group", "xvalue", "yvalue1", "yvalue2")
data$yvalue <- (data$yvalue1 + data$yvalue2) / 2

# 查看数据
head(data)
   group xvalue yvalue1 yvalue2 yvalue
1 Group1   1900  -0.279  -0.063 -0.171
2 Group1   1901  -0.271  -0.053 -0.162
3 Group1   1902  -0.285  -0.069 -0.177
4 Group1   1903  -0.303  -0.095 -0.199
5 Group1   1904  -0.328  -0.118 -0.223
6 Group1   1905  -0.348  -0.134 -0.241

可视化

# 丝带图
p <- ggplot(data, aes(xvalue, yvalue, fill = group)) +
  geom_ribbon(alpha = 0.2, aes(ymin = yvalue1, ymax = yvalue2)) +
  geom_line(aes(y = yvalue, color = group), lwd = 1) +
  geom_line(aes(y = yvalue1, color = group), linetype = "dotted") +
  geom_line(aes(y = yvalue2, color = group), linetype = "dotted") +
  ylab("y axis value") +
  xlab("x axis value") +
  ggtitle("Ribbon Plot") +
  scale_fill_manual(values = c("#e04d39","#5bbad6")) +
  scale_color_manual(values = c("#e04d39","#5bbad6")) +
  theme_stata() +
  theme(text = element_text(family = "Arial"),
        plot.title = element_text(size = 12,hjust = 0.5),
        axis.title = element_text(size = 12),
        axis.text = element_text(size = 10),
        axis.text.x = element_text(angle = 0, hjust = 0.5,vjust = 1),
        legend.position = "right",
        legend.direction = "vertical",
        legend.title = element_text(size = 10),
        legend.text = element_text(size = 10))
 
p
图 1: 丝带图

每种颜色表示不同的分组，可以透过其中折线，观测每组数据随时间的变化。