Scatter

Authors

[Editor] Hu Zheng;

[Contributors]

Modified

2026-01-27

Note

Hiplot website

This page is the tutorial for source code version of the Hiplot Scatter 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/scatter?lang=en

Two groups of data are used to form multiple coordinate points. By observing the distribution of coordinate points, it can judge whether there is correlation between variables or summarize the data processing mode of coordinate point distribution.

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     2026-01-27
 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 loaded data are the horizontal axis values and their corresponding vertical axis values and groups.

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

# View data
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

Visualization

# Scatter
p <- ggplot(data, aes(x = Value1, y = Value2)) +
  geom_point(size = 1, alpha = 1, aes(color = Group, shape = Group)) +
  ggtitle("Scatter Plot") +
  scale_color_manual(values = c("#00468BFF", "#ED0000FF")) +
  theme_bw() +
  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

Value1 represents the horizontal axis and Value2 represents the vertical axis. The diagram shows that Value1 and Value2 in treatment plan 1 are positively correlated: that is, when Value1 becomes larger, Value2 will become larger; In treatment plan 2, two variables are negatively correlated: that is, when Value1 becomes larger, Value2 becomes smaller.