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)

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))

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)

More examples

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

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

• Making a heatmap
• Correlation heatmaps
• Annotation
• Clustering
• Facets
• Mixed layouts
• Scales and legends
• Using tidy data

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)