ggcorrheatmap (version 0.3.0)

ggcorrhm_tidy: ggcorrhm() for long format data.

Description

ggcorrhm() for long format data.

Usage

ggcorrhm_tidy(
  x,
  rows,
  cols,
  values,
  annot_rows = NULL,
  annot_cols = NULL,
  labels = NULL,
  facet_rows = NULL,
  facet_cols = NULL,
  cor_in = TRUE,
  ...
)

Value

A ggplot2 object with the heatmap. If return_data is TRUE, plotting data is returned as well.

Arguments

x

Data containing data to plot or to correlate.

rows, cols, values

Columns to use as rows, columns, and values in the plotted matrix (if cor_in is TRUE) or the matrix to compute correlations from (cor_in is FALSE).

annot_rows, annot_cols

Columns containing values for row and column annotations.

labels

Column to use for cell labels, NULL for no labels, or TRUE to use the cell values. If cor_in is FALSE, only NULL, TRUE or FALSE is supported.

facet_rows, facet_cols

Columns to use for row/column facets.

cor_in

Logical indicating if the values are correlation values (TRUE, default) or values to be correlated. See details for more information.

...

Additional arguments for ggcorrhm().

Details

If cor_in is TRUE (the default), ggcorrhm_tidy() behaves similarly to gghm_tidy() but with the colour scales and arguments of ggcorrhm() instead of gghm().

If cor_in FALSE, the data is converted to wide format and the column-column correlations are computed. This means that if non-square correlation matrices are to be plotted the correlations have to be computed in advance and plotted with cor_in as TRUE. Additionally, annot_rows and annot_cols will both use the cols column for names, and labels can only take TRUE or FALSE.

On the other hand, if cor_in is TRUE any computation of correlations is skipped, meaning that p-values cannot be computed and would have to be generated in advance and passed as cell labels.

Examples

library(dplyr)
# Basic example with long format correlation data
# Make some correlation data in long format
cor_dat <- cor(mtcars)
hm_in <- data.frame(row = rep(colnames(cor_dat), ncol(cor_dat)),
                    col = rep(colnames(cor_dat), each = ncol(cor_dat)),
                    val = as.vector(cor_dat))

ggcorrhm_tidy(hm_in, row, col, val,
              # Indicate that the data consists of correlation coefficients
              cor_in = TRUE)

# Or let the function compute the correlations
# (this limits some other functionality, see details)
raw_dat <- data.frame(row = rep(rownames(mtcars), ncol(mtcars)),
                      col = rep(colnames(mtcars), each = nrow(mtcars)),
                      val = unlist(mtcars))
ggcorrhm_tidy(raw_dat, row, col, val, cor_in = FALSE)