Create a density-colored scatter plot — geom_dense

This geom creates a scatter plot where points are colored by their local density, using a custom color gradient.

geom_dense_scatter(
  mapping = NULL,
  data = NULL,
  stat = "DenseScatter",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  pal = NULL,
  size = 0.8,
  alpha = 1,
  ...
)

Arguments

mapping: Set of aesthetic mappings created by aes
data: The data to be displayed in this layer
position: Position adjustment, either as a string, or the result of a call to a position adjustment function
na.rm: If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed
show.legend: logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped
inherit.aes: If FALSE, overrides the default aesthetics, rather than combining with them
pal: Color palette. A vector of colors to use for the density gradient, from lowest to highest density
size: Point size
alpha: Point alpha/transparency
...: Other arguments passed on to layer

Value

A ggplot2 layer that can be added to a plot

Examples

# Create large dataset with multiple clusters
library(ggplot2)
#> Want to understand how all the pieces fit together? Read R for Data
#> Science: https://r4ds.hadley.nz/
set.seed(60427)
n <- 1e4
df <- data.frame(
    x = c(rnorm(n * 0.5), rnorm(n * 0.3, 3, 0.5), rnorm(n * 0.2, 0, 2)),
    y = c(rnorm(n * 0.5), rnorm(n * 0.3, 3, 0.5), rnorm(n * 0.2, 0, 2))
)

# Basic usage with default settings
ggplot(df, aes(x, y)) +
    geom_dense_scatter()


# Custom color palette to highlight density variations
ggplot(df, aes(x, y)) +
    geom_dense_scatter(
        pal = c("lightblue", "darkblue", "purple", "red"),
        size = 0.5
    )


# Create large dataset with non-linear relationship
x <- runif(n, -3, 3)
df2 <- data.frame(
    x = x,
    y = sin(x) * 2 + rnorm(n, 0, 0.5)
)

# Visualize non-linear relationship with density
ggplot(df2, aes(x, y)) +
    geom_dense_scatter(
        pal = c("gray90", "gray50", "orange", "red"),
        size = 0.4,
        alpha = 0.8
    ) +
    labs(title = "Non-linear Pattern with Density Coloring")


# Create large dataset with varying spread
x <- rnorm(n)
df3 <- data.frame(
    x = x,
    y = x * rnorm(n, mean = 1, sd = abs(x) / 2)
)

# Visualize heteroscedastic pattern
ggplot(df3, aes(x, y)) +
    geom_dense_scatter(
        pal = c("#F5F5F5", "#4169E1", "#FF4500"),
        size = 0.3
    ) +
    theme_minimal() +
    labs(title = "Varying Spread Pattern")