线图

注记

Hiplot 网站

本页面为 Hiplot Line 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/line?lang=zh_cn

线图是指使用线性刻度或对数刻度在二维或三维视图中绘制数据，从而显示数据集或跟踪数据随时间变化的变化特征的统计图表。

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     2026-01-28
 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
 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

载入数据为横轴数值及其对应的纵轴数值和分组。

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

# 整理数据格式
data[,3] <- factor(data[,3], levels = unique(data[,3]))

# 查看数据
head(data)

  Value1 Value2  Group
1      1      1 treat1
2      2      4 treat1
3      3      9 treat1
4      4     16 treat1
5      5     25 treat1
6      6     36 treat1

可视化

# 线图
p <- ggplot(data, aes(x = Value1, y = Value2)) +
  geom_line(alpha = 1, aes(color = Group, linetype = Group)) +
  geom_point(aes(color = Group, shape = Group)) +
  ggtitle("Line Regression Plot") +
  scale_fill_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: 线图

图示表明治疗方案一中 value1 值与 value2 值呈正相关，而治疗方案二 value1 值与 value2 值呈负相关。