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.
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 axisy
: position on the Y axisfill
: 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$Z <- runif(400, 0, 5)
# Heatmap
ggplot(data, aes(X, Y, fill= Z)) +
geom_tile()
Color palette can be changed like in any ggplot2 chart. Above are 3 examples using different methods:
scale_fill_gradient()
to provide extreme colors of the
palette
scale_fill_distiller)
to provide a
ColorBrewer palette
scale_fill_viridis()
to use Viridis. Do not forget
discrete=FALSE
for a continuous variable.
# Library
library(ggplot2)
library(hrbrthemes)
# Dummy data
x <- LETTERS[1:20]
y <- paste0("var", seq(1,20))
data <- expand.grid(X=x, Y=y)
data$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()
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")
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"))