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