scale_fill_nejm ¶

scale_fill_nejm(
    palette: Literal | None = None,
    alpha: float = 1,
    overflow: Literal | None = None,
    name: str | None = None,
    breaks: Breaks | None = None,
    labels: Labels | None = None,
    lablim: int | None = None,
    limits: Limits | None = None,
    na_value: Color | None = None,
    guide: Guide | None = None,
    format: str | None = None,
) -> FeatureSpec

Map fill with the ggsci nejm palette. See scale_color_nejm.

A discrete scale (8 colors). Not in lets-plot (R package ggsci).

Parameters:

palette (default, default: "default" ) –

ggsci palette name.
alpha (float, default: 1 ) –

Opacity of the palette colors, in (0, 1].
overflow ((generate, cycle, interpolate), default: "generate" ) –

How to get more colors than the palette has ("g", "c", "i"). Default: "generate", as scale_*_brewer() for qualitative palettes. ggsci leaves the extra categories missing; ggrust colors them and warns.
name (str, default: None ) –

Legend title.
breaks (list, default: None ) –

Categories that get a legend key.
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 ) –

The categories (and their order) of the scale. Other data gets na_value.
na_value (str, default: None ) –

Color drawn for missing data and data outside limits.
guide (str or FeatureSpec, default: None ) –

"legend", "none", or a guide_legend() spec.
format (str, default: None ) –

Label format: a number format (".2f", ",", "%") or a template ("{.1f} %"). labels take precedence.

Returns:

FeatureSpec –

The scale.

Raises:

SpecError –

If palette is not a nejm palette name, alpha is not in (0, 1], or overflow is not an overflow name.
UnsupportedValueError –

If palette is not a string or alpha is not a number.

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_nejm(alpha=0.8)