Custom Heatmap

Authors

[Editor] Hu Zheng;

[Contributors]

Modified

2026-01-17

Note

Hiplot website

This page is the tutorial for source code version of the Hiplot Custom Heatmap 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/custom-heat-map?lang=en

Custom Heatmap, directly plot a heatmap based on the given data.

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     2026-01-17
 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
 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 case data is a grayscale image of the official logo of hiplot.org.

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

# convert data structure
draw_data <- as.matrix(data[, 2:ncol(data)])
row_num <- nrow(draw_data)
col_num <- ncol(draw_data)
col_labels <- colnames(data)
col_labels <- col_labels[2:ncol(data)]
row_labels <- data$name
rm(data)
df <- expand.grid(row = 1:row_num, col = 1:col_num)
df$value <- c(draw_data)

# View data
head(df)
  row col value
1   1   1   236
2   2   1   236
3   3   1   236
4   4   1   236
5   5   1   236
6   6   1   236

Visualization

# Custom Heatmap
p <- ggplot(df, aes(x = col, y = row, fill = value)) +
  geom_point(shape = 21, size = 8, aes(fill = value), color = "white") +
  scale_fill_gradient(low = "#DDDDDD", high = "#0000F5") +
  guides(fill = guide_colorbar(title = "Value")) +
  theme(
    panel.background = element_rect(fill = "white"),
    panel.grid = element_blank(),
    axis.text = element_text(size = 10),
    axis.ticks = element_blank(),
    axis.title = element_blank()
    ) +
  scale_x_continuous(breaks = 1:col_num, labels = col_labels, position = "top") +
  scale_y_reverse(breaks = 1:row_num, labels = row_labels, position = "left")

p
FigureΒ 1: Custom Heatmap