Dotchart

Authors

[Editor] Hu Zheng;

[Contributors]

Modified

2026-01-17

Note

Hiplot website

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

Sliding bead chart is a graph of beads sliding on a column. It is the superposition of bar chart and scatter chart.

Setup

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

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

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

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
 ggpubr     * 0.6.2   2025-10-17 [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 gene names and their corresponding gene expression values and groups.

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

# View data
head(data)

      Name Value  Group
1     BMP2  18.7 Group1
2     XIST  14.3 Group1
3 C19orf38  16.4 Group1
4    PDZD3  17.3 Group1
5   MAPRE2  15.2 Group1
6     IRF4  10.4 Group1

Visualization

# Dotchart
p <- ggdotchart(data, x = "Name", y = "Value", group = "Group", color = "Group",
                rotate = T, sorting = "descending",
                y.text.col = F, add = "segments", dot.size = 2) +
  xlab("Name") +
  ylab("Value") +
  ggtitle("DotChart Plot") +
  scale_color_manual(values = c("#e04d39","#5bbad6","#1e9f86")) +
  theme_classic() +
  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

Each color represents a different grouping, so that the differences in gene expression values can be intuitively understood.