Hinton Diagrams for 'ggplot2'.
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:
GeomHintonextendsGeomRectStatHintoncomputes rectangle bounds incompute_panelso normalization is consistent within each panel- Sign detection is automatic (no need to tell the package whether your data is signed)