gghinton

Hinton diagrams for ggplot2.

A Hinton diagram visualises a numerical matrix. Each element is displayed as a square where the square's area is proportional to the absolute value of the element. Positive values are white, negative values are black, on a grey background. This simple clear perceptual mapping makes interpretation of the structure of the matrix easy.

install.packages("hinton")

library(ggplot2)
library(gghinton)

m <- matrix(rnorm(10*10), nrow = 10)

matrix_to_hinton(m) |>
  ggplot(aes(x = col, y = row, weight = weight)) +
  geom_hinton() +
  scale_fill_hinton() +
  coord_fixed() +
  theme_hinton() + 
  theme(axis.text = element_blank())

Why not a heatmap?

	Heatmap	Hinton diagram
Encodes magnitude	via colour intensity	via square area
Encodes sign	requires diverging palette	white vs black
Works for colourblind readers	depends on palette	yes
Near-zero values	invisible colour	invisible square
Works at a glance	partially	yes

Colour saturation is a notoriously unreliable channel for magnitude judgements. Square area is not: humans compare areas accurately, pre-attentively. When you care about how big and which sign, a Hinton diagram beats a heatmap.

Heatmaps are better when: you have large matrices (> ~50x50, where at typical plotting sizes you would only have a handful of pixels per element), continuous gradients matter more than individual entries, or values are all positive and the magnitude range is narrow.

Installation

install.packages("gghinton")  # from CRAN

# pak::pkg_install("robin-foster-rf/gghinton")
install.packages("gghinton", repos = c('https://robin-foster-rf.r-universe.dev', 'https://cloud.r-project.org')) # development version from github

Core functions

Function	Purpose
`geom_hinton()`	Draw a Hinton diagram as a ggplot2 layer
`stat_hinton()`	The underlying `stat_*` (for advanced use)
`scale_fill_hinton()`	White/black colour scale for signed data
`theme_hinton()`	Clean theme: removes grid lines
`matrix_to_hinton()`	Convert a matrix to a tidy data frame
`as_hinton_df()`	Generic converter (matrix, data.frame, table)

Key aesthetics

aes(x = col,    # column position
    y = row,    # row position (row 1 of the matrix is at the top)
    weight = w) # the value: determines size and colour

The `scale_by` parameter

# Default: each facet panel normalised independently
geom_hinton(scale_by = "panel")

# Global: all panels share the same scale, allows cross-panel comparison
geom_hinton(scale_by = "global")

Use cases

Correlation matrices: immediately spot large positive and negative correlations
Factor loadings: which variables load strongly on which factors
Transition matrices: Markov chain structure at a glance
PCA weight matrices: understand what a principal component captures

Design

gghinton follows standard ggplot2 extension conventions:

GeomHinton extends GeomRect
StatHinton computes rectangle bounds in compute_panel so normalization is consistent within each panel
Sign detection is automatic (no need to tell the package whether your data is signed)