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scale_fill_grey

scale_fill_grey(
    start: Number | None = None,
    end: Number | None = None,
    name: str | None = None,
    breaks: Breaks | None = None,
    labels: Labels | None = None,
    lablim: int | None = None,
    limits: Limits | None = None,
    oob: Oob | None = None,
    na_value: Color | None = None,
    guide: Guide | None = None,
    trans: Transform | None = None,
    format: str | None = None,
) -> FeatureSpec

Map fill to greys, from lightness start to end.

Works for categories and continuous data (a grey gradient).

Parameters:

  • start (float, default: 0.2 ) –

    Lightness of the low end, 0 (black) to 1 (white).

  • end (float, default: 0.8 ) –

    Lightness of the high end, 0 to 1.

  • name (str, default: None ) –

    Legend title.

  • breaks (list, default: None ) –

    Values that get a legend key or a colorbar tick.

  • labels (list of str or dict, default: None ) –

    One label per break (a dict maps breaks to labels).

  • lablim (int, default: None ) –

    Maximum label length in characters; longer labels are cut.

  • limits (list, default: None ) –

    [low, high] data range of a continuous scale, or the levels (and their order) of a discrete one. Data outside the limits gets na_value.

  • oob ((censor, squish), default: "censor" ) –

    What continuous data outside limits maps to: "censor" (the default) draws na_value, "squish" clamps it to the nearest limit so it takes the end color. Missing data stays na_value. Not in lets-plot (ggplot2 oob).

  • na_value (str, default: None ) –

    Color drawn for missing data and data outside limits.

  • guide (str or FeatureSpec, default: None ) –

    "legend", "colorbar", "none", or a guide spec.

  • trans ((identity, log10, log2, sqrt, reverse), default: "identity" ) –

    Transformation of the data.

  • format (str, default: None ) –

    Label format, e.g. ".1f". labels take precedence.

Returns:

  • FeatureSpec –

    The scale.

Raises:

  • ValueError –

    start or end outside 0..1 (after lets_plot's rescaling of 0..100 values).

Examples:

>>> from ggrust import *
>>> data = {"age": [1, 3, 5, 7, 2, 4, 6, 8, 3, 9],
...         "weight": [4, 6, 6, 7, 8, 12, 14, 15, 2, 3],
...         "pet": ["cat"] * 4 + ["dog"] * 4 + ["rabbit"] * 2}
>>> plot = ggplot(data, aes("age", "weight", fill="pet")) + geom_point(shape=21, size=6)
>>> plot = plot + scale_fill_grey(start=0.1, end=0.7)