Scatterplot
Two continuous numeric variables plotted as points — correlation explorations, A/B comparisons, sample distributions. Categorical colour and varying size for richer reads.
The demo plots Fisher's Iris (1936): petal length × petal width across the three species setosa, versicolor, and virginica. R.A. Fisher published this dataset in The Use of Multiple Measurements in Taxonomic Problems to demonstrate linear discriminant analysis; it has since anchored statistical classification, machine-learning tutorials, and 90 years of shape-from-petal arguments.
Source: Iris flower data set — Wikipedia
Public surface
let plot = Plot::new(samples)
.mark(Mark::Point { shape: PointShape::Circle })
.encode(plot::x("height", ScaleKind::Linear))
.encode(plot::y("weight", ScaleKind::Linear))
.encode(plot::color("species"))
.encode(plot::size("age"));Point shapes
| Shape | Primitive used |
|---|---|
Circle | Graphics::draw_ellipse |
Square | Graphics::draw_rect |
Diamond | Graphics::draw_polygon (4 verts) |
Triangle | Graphics::draw_polygon (3 verts) |
Plus | Two crossed draw_rect calls |
Both X and Y must use ScaleKind::Linear — scatter requires
continuous numeric axes. Categorical X is the domain of bar
charts.
Size encoding
Adding plot::size(field) maps a numeric column to marker
radius via LinearScale mapped into (3.0, 18.0) pixel range.
Use sparingly — too many sizes overlap and obscure the
distribution shape.
Theme integration
| Theme field | Drives |
|---|---|
theme.plot.line_marker_radius_px | Default marker radius (no Size) |
theme.palette | Per-category fill colour |