Skip to contents

ggcorrheatmap

ggcorrheatmap is a convenient package for generating correlation heatmaps made with ggplot2, with support for triangular layouts, clustering and annotation. As the output is a ggplot2 object you can further customise the appearance using familiar ggplot2 functions. Besides correlation heatmaps, there is also support for making general heatmaps.

Installation

You can install ggcorrheatmap from CRAN using:

install.packages("ggcorrheatmap")

Or you can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("leod123/ggcorrheatmap")

Example

Below is an example of how to generate a correlation heatmap with clustered rows and columns and row annotation, using a triangular layout that excludes redundant cells.

library(ggcorrheatmap)

set.seed(123)
# Make a correlation heatmap with a triangular layout, annotations and clustering
row_annot <- data.frame(.names = colnames(mtcars),
                        annot1 = sample(letters[1:3], ncol(mtcars), TRUE),
                        annot2 = rnorm(ncol(mtcars)))
ggcorrhm(mtcars, layout = "bottomright",
         cluster_rows = TRUE, cluster_cols = TRUE,
         show_dend_rows = FALSE, annot_rows_df = row_annot)

A correlation heatmap showing the correlations between the columns of the mtcars dataset. Only the bottom triangle of the symmetric matrix is displayed, oriented as a bottom right triangle. The colour scale goes from blue at -1 to white at 0 and red at 1. The names of the columns are displayed on the diagonal. Below the heatmap is a dendrogram showing that the data has been clustered. Along the right side two columns are placed, showing one discrete and one continuous annotation (called annot1 and annot2, respectively). Three legends are found to the right of the plot, one for the main heatmap scale and one for each annotation.

Or a mixed layout that displays different things in the different triangles.

# With correlation values and p-values
ggcorrhm(mtcars, layout = c("topright", "bottomleft"),
         cell_labels = c(FALSE, TRUE), p_values = c(FALSE, TRUE))

A correlation heatmap of mtcars columns. Half of the plot, above the diagonal, is a normal heatmap with coloured cells and the other half has white cells with coloured text inside. The diagonal has coloured cells with names column names written inside. The coloured text in the bottom triangle shows correlation values with asterisks added indicating p-value thresholds.

It is also possible to make a normal heatmap, for a more flexible output.

gghm(mtcars, scale_data = "col", cluster_rows = TRUE, cluster_cols = TRUE)

A heatmap of the mtcars data (scaled). Cells are coloured with a scale going from dark blue to light blue. Both rows and columns have been clustered, with dendrograms to the right and below the heatmap.

More examples

Below is a showcase of more things you could do with the package (the code is a bit long and can be found at the bottom).

Six heatmaps. 1: mtcars correlation heatmap showing just the bottom right triangle. Rows and columns are clustered and there are gaps splitting the heatmap into two clusters in each dimension. A legend is placed in the empty space of the top left triangle. 2: The volcano dataset using the Viridis magma colour scale. 3: mtcars correlation heatmap with circles in the bottom left triangle and correlation values in text in the top right. 4: a 7 by 10 heatmap of categorical values using the Brewer Pastel2 palette. 5: mtcars correlation heatmap split into two along the diagonal. The top right triangle uses the Brewer RdBu palette and the bottom left triangle uses the Viridis mako scale. 6: 10 by 30 heatmap using the Viridis scale with mostly low values (dark blue) and some higher values (green-yellow) sprinkled in randomly. Two annotation rows are at the top, one numeric and one categorical. The heatmap has gaps at rows 10 and columns 10 and 20.

More examples can be found in the articles of the package:

library(ggplot2)   # moving legend for plot 1
library(patchwork) # for combining into one big figure

# Clustered triangular heatmap with gaps
plt1 <- ggcorrhm(mtcars, layout = "bottomright",
                 split_diag = TRUE,
                 cluster_rows = TRUE, cluster_cols = TRUE,
                 split_rows = 2, split_cols = 2,
                 names_diag_params = list(angle = -45, hjust = 1.3)) +
  theme(legend.position = "inside", legend.position.inside = c(0.25, 0.75))

# Heatmap with other colour scale
plt2 <- gghm(volcano, col_scale = "A", border_col = NA, legend_order = NA,
             show_names_rows = FALSE, show_names_cols = FALSE)

# Mixed correlation heatmap with circles and correlation values
plt3 <- ggcorrhm(mtcars, layout = c("bl", "tr"), mode = c("21", "none"),
                 legend_order = NA, cell_labels = c(FALSE, TRUE))

# Heatmap with categorical values
set.seed(123)
plt4 <- gghm(matrix(letters[sample(1:3, 70, TRUE)], nrow = 7),
             col_scale = "Pastel2", border_col = "#FFE1FF", legend_order = NA,
             border_lwd = 1, show_names_rows = FALSE, show_names_cols = FALSE)

# Split square heatmap with two colour scales
plt5 <- ggcorrhm(mtcars, layout = c("tr", "bl"), mode = c("hm", "hm"),
                 col_scale = c("RdBu_rev", "G"), split_diag = TRUE,
                 border_lwd = 0.3, border_col = 1,
                 legend_order = NA, show_names_diag = FALSE)

# Annotations and gaps
set.seed(123)
dat6 <- sapply(sample(1:5, 30, TRUE), \(x) {
    c(runif(x, runif(1, 0.5, 0.8), runif(1, 0.81, 1)), runif(20 - x, 0, 0.2))[sample(1:20, 20, FALSE)]
})
plt6 <- gghm(dat6, border_col = 0, col_scale = "D",
             show_names_rows = FALSE, show_names_cols = FALSE,
             legend_order = NA, split_rows = 10, split_cols = c(10, 20),
             annot_cols_df = data.frame(a = sample(letters[1:3], 30, TRUE), b = 1:30),
             annot_cols_side = "top", annot_size = 1, dend_height = 1)

wrap_plots(plt1, plt2, plt3, plt4, plt5, plt6, nrow = 2)