ggplot2 heatmap



This document provides several examples of heatmaps built with R and ggplot2. It describes the main customization you can apply, with explanation and reproducible code.
Note: The native heatmap() function provides more options for data normalization and clustering. Consider it as a valuable option.

Heatmap section Data to Viz

Most basic heatmap with ggplot2


This is the most basic heatmap you can build with R and ggplot2, using the geom_tile() function.

Input data must be a long format where each row provides an observation. At least 3 variables are needed per observation:

  • x: position on the X axis
  • y: position on the Y axis
  • fill: the numeric value that will be translated in a color

# Library
library(ggplot2)

# Dummy data
x <- LETTERS[1:20]
y <- paste0("var", seq(1,20))
data <- expand.grid(X=x, Y=y)
data$Z <- runif(400, 0, 5)
 
# Heatmap 
ggplot(data, aes(X, Y, fill= Z)) + 
  geom_tile()

Control color palette


Color palette can be changed like in any ggplot2 chart. Above are 3 examples using different methods:

# Library
library(ggplot2)
library(hrbrthemes)

# Dummy data
x <- LETTERS[1:20]
y <- paste0("var", seq(1,20))
data <- expand.grid(X=x, Y=y)
data$Z <- runif(400, 0, 5)
 
# Give extreme colors:
ggplot(data, aes(X, Y, fill= Z)) + 
  geom_tile() +
  scale_fill_gradient(low="white", high="blue") +
  theme_ipsum()

# Color Brewer palette
ggplot(data, aes(X, Y, fill= Z)) + 
  geom_tile() +
  scale_fill_distiller(palette = "RdPu") +
  theme_ipsum()

# Color Brewer palette
library(viridis)
ggplot(data, aes(X, Y, fill= Z)) + 
  geom_tile() +
  scale_fill_viridis(discrete=FALSE) +
  theme_ipsum()

From wide input format


It is a common issue to have a wide matrix as input, as for the volcano dataset. In this case, you need to tidy it with the gather() function of the tidyr package to visualize it with ggplot.

# Library
library(ggplot2)
library(tidyr)
library(tibble)
library(hrbrthemes)
library(dplyr)

# Volcano dataset
#volcano

# Heatmap 
volcano %>%
  
  # Data wrangling
  as_tibble() %>%
  rowid_to_column(var="X") %>%
  gather(key="Y", value="Z", -1) %>%
  
  # Change Y to numeric
  mutate(Y=as.numeric(gsub("V","",Y))) %>%

  # Viz
  ggplot(aes(X, Y, fill= Z)) + 
    geom_tile() +
    theme_ipsum() +
    theme(legend.position="none")

Turn it interactive with plotly


One of the nice feature of ggplot2 is that charts can be turned interactive in seconds thanks to plotly. You just need to wrap your chart in an object and call it in the ggplotly() function.

Often, it is a good practice to custom the text available in the tooltip.

Note: try to hover cells to see the tooltip, select an area to zoom in.

# Library
library(ggplot2)
library(hrbrthemes)
library(plotly)

# Dummy data
x <- LETTERS[1:20]
y <- paste0("var", seq(1,20))
data <- expand.grid(X=x, Y=y)
data$Z <- runif(400, 0, 5)
 
# new column: text for tooltip:
data <- data %>%
  mutate(text = paste0("x: ", x, "\n", "y: ", y, "\n", "Value: ",round(Z,2), "\n", "What else?"))

# classic ggplot, with text in aes
p <- ggplot(data, aes(X, Y, fill= Z, text=text)) + 
  geom_tile() +
  theme_ipsum()

ggplotly(p, tooltip="text")

# save the widget
# library(htmlwidgets)
# saveWidget(pp, file=paste0( getwd(), "/HtmlWidget/ggplotlyHeatmap.html"))

Related chart types


Scatter
Heatmap
Correlogram
Bubble
Connected scatter
Density 2d



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