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gghinton

Hinton diagrams for ggplot2.

A Hinton diagram visualises a numerical matrix. Each element is displayed as a square and 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.

pak::pkg_install("robin-foster-rf/gghinton")
# install.packages("gghinton")  # once on CRAN

library(gghinton)

m <- matrix(runif(10*10, -1, 1), nrow = 10)

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

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

#development version:
pak::pkg_install("robin-foster-rf/gghinton")

# install.packages("gghinton")  # from CRAN (once available)

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)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.