Handling Color Overflow in Brewer Palettes

ColorBrewer palettes have a limited number of colors (typically 8–12).
When your data has more categories than the palette provides, a larger palette is created either by interpolating the original colors, or by cycling, or by generating additional colors.

By default, qualitative palettes use "generate" (creating new colors with varied luminance),
while sequential and diverging palettes use "interpolate".

The overflow parameter controls the way the extended palette is generated: "interpolate", "cycle", or "generate".

%useLatestDescriptors
%use lets-plot

val letters = ('A'..'Z').map { it.toString() }
val data = mapOf(
    "letter" to letters,
    "x" to (0 until letters.size).toList()
)

val p = letsPlot(data) { x = "x"; color = "letter" } +
    geomPoint(size = 7) +
    guides(color = guideLegend(nrow = 9))

Default: "generate"

Additional colors are created based on the original palette colors.

p + scaleColorBrewer(palette = "Set1")

Cycle Palette Colors

p + scaleColorBrewer(palette = "Set1", overflow = "cycle")

Interpolate Palette Colors

p + scaleColorBrewer(palette = "Set1", overflow = "interpolate")

Choosing the Right Palette for Many Categories

When displaying a large number of categories (like countries on a world map), not all Brewer palettes work equally well with the generate overflow.
Palettes with high contrast colors like Set1 can produce jarring results when extended.
Softer palettes like Pastel1 work better, while Paired and Set3 tend to produce the most visually balanced results for large categorical datasets.

@file:Repository("https://repo.osgeo.org/repository/release/")
@file:DependsOn("org.geotools:gt-shapefile:33.2")
@file:DependsOn("org.geotools:gt-epsg-hsql:33.2")

%use lets-plot-gt
%use dataframe

import org.geotools.data.shapefile.ShapefileDataStoreFactory
import java.net.URL

val factory = ShapefileDataStoreFactory()

fun loadShp(name: String) =
    factory.createDataStore(
        URL("https://raw.githubusercontent.com/JetBrains/lets-plot-kotlin/master/docs/examples/shp/$name/$name.shp")
    ).featureSource.features.toSpatialDataset()

val worldSds = loadShp("naturalearth_lowres")

val df = worldSds.toDataFrame()
df.take(5)

DataFrame: rowsCount = 5, columnsCount = 6

continent	pop_est	gdp_md_est	name	iso_a3	geometry
Oceania	920938,000000	8374,000000	Fiji	FJI	{"type":"MultiPolygon","coordinates":...
Africa	53950935,000000	150600,000000	Tanzania	TZA	{"type":"MultiPolygon","coordinates":...
Africa	603253,000000	906,500000	W. Sahara	ESH	{"type":"MultiPolygon","coordinates":...
North America	35623680,000000	1674000,000000	Canada	CAN	{"type":"MultiPolygon","coordinates":...
North America	326625791,000000	18560000,000000	United States of America	USA	{"type":"MultiPolygon","coordinates":...

val world = letsPlot() +
    geomMap(map = worldSds, color = "white", size = 0.5) { fill = "name" } +
    coordMap(ylim = -60 to null) +
    ggsize(900, 500) +
    theme().legendPositionNone() +
    ggtb()

The Default Qualitative Palette (Set1)

Works, but not ideal for many categories.

world + scaleFillBrewer()

Pastel1

A softer palette that works better with many categories.

world + scaleFillBrewer(palette = "Pastel1")

Paired and Set3

These palettes produce the most balanced results for large categorical datasets.

// sort countries to ensure a consistent color palette order
val sortedByPop = df.sortBy("pop_est")["name"].toList().filterNotNull()

world + scaleFillBrewer(palette = "Paired", limits = sortedByPop)

world + scaleFillBrewer(palette = "Set3", limits = sortedByPop)