Elegant Visualization of Density Distribution in R Using Ridgeline

Prerequisites

Load required R packages:

library(ggplot2)
library(ggridges)
theme_set(theme_minimal())

Key R functions:

• geom_density_ridges(): first estimates data densities and then draws those using ridgelines. It arranges multiple density plots in a staggered fashion.

Basic density ridgeline plots

Key R functions:

• geom_density_ridges(): Estimates data densities and then draws those using ridgelines. It arranges multiple density plots in a staggered fashion.

# Opened polygons
ggplot(iris, aes(x = Sepal.Length, y = Species, group = Species)) + 
  geom_density_ridges(fill = "#00AFBB")

# Closed polygons
ggplot(iris, aes(x = Sepal.Length, y = Species, group = Species)) + 
  geom_density_ridges2(fill = "#00AFBB")

# Cut off the trailin tails. 
# Specify `rel_min_height`: a percent cutoff
ggplot(iris, aes(x = Sepal.Length, y = Species)) + 
  geom_density_ridges(fill = "#00AFBB", rel_min_height = 0.01)

Control the extent to which the different densities overlap. You can control the overlap between the different density plots using the scale option. Default value is 1. Smaller values create a separation between the curves, and larger values create more overlap.

# scale = 0.6, not touching
ggplot(iris, aes(x = Sepal.Length, y = Species)) + 
  geom_density_ridges(scale = 0.6)

# scale = 1, exactly touching
ggplot(iris, aes(x = Sepal.Length, y = Species)) + 
  geom_density_ridges(scale = 1)

# scale = 5, substantial overlap
ggplot(iris, aes(x = Sepal.Length, y = Species)) + 
  geom_density_ridges(scale = 5, alpha = 0.7)

# Change the density area fill colors by groups
ggplot(iris, aes(x = Sepal.Length, y = Species)) +
  geom_density_ridges(aes(fill = Species)) +
  scale_fill_manual(values = c("#00AFBB", "#E7B800", "#FC4E07")) +
  theme(legend.position = "none")

Density curves with gradient fill colors along the x axis

This effect can be achieved with the function geom_density_ridges_gradient(), which works like geom_density_ridges, except that it allows for varying fill colors.

Visualize the Lincoln weather data:

• Data set: lincoln_weather [in ggridges]. Weather in Lincoln, Nebraska in 2016.
• Create the density ridge plots of the Mean Temperature by Month and change the fill color according to the temperature value (on x axis).

ggplot(
  lincoln_weather, 
  aes(x = `Mean Temperature [F]`, y = `Month`, fill = stat(x))
  ) +
  geom_density_ridges_gradient(scale = 3, size = 0.3, rel_min_height = 0.01) +
  scale_fill_viridis_c(name = "Temp. [F]", option = "C") +
  labs(title = 'Temperatures in Lincoln NE') 

Add summary statistic lines

# Add quantiles Q1, Q2 (median) and Q3
ggplot(iris, aes(x = Sepal.Length, y = Species)) +
  stat_density_ridges(quantile_lines = TRUE)

# Show only the median line (50%)
# Use quantiles = 2 (for Q2) or quantiles = 50/100
ggplot(iris, aes(x = Sepal.Length, y = Species)) +
  stat_density_ridges(quantile_lines = TRUE, quantiles = 0.5)

# Indicate the 2.5% and 97.5% tails
ggplot(iris, aes(x = Sepal.Length, y = Species)) +
  stat_density_ridges(quantile_lines = TRUE, quantiles = c(0.025, 0.975), alpha = 0.7)

# Color by quantiles
ggplot(iris, aes(x = Sepal.Length, y = Species, fill = factor(stat(quantile)))) +
  stat_density_ridges(
    geom = "density_ridges_gradient", calc_ecdf = TRUE,
    quantiles = 4, quantile_lines = TRUE
  ) +
  scale_fill_viridis_d(name = "Quartiles")

# Highlight the tails of the distributions
ggplot(iris, aes(x = Sepal.Length, y = Species, fill = factor(stat(quantile)))) +
  stat_density_ridges(
    geom = "density_ridges_gradient",
    calc_ecdf = TRUE,
    quantiles = c(0.025, 0.975)
  ) +
  scale_fill_manual(
    name = "Probability", values = c("#FF0000A0", "#A0A0A0A0", "#0000FFA0"),
    labels = c("(0, 0.025]", "(0.025, 0.975]", "(0.975, 1]")
  )

ggplot(iris, aes(x = Sepal.Length, y = Species, fill = 0.5 - abs(0.5 - stat(ecdf)))) +
  stat_density_ridges(geom = "density_ridges_gradient", calc_ecdf = TRUE) +
  scale_fill_viridis_c(name = "Tail probability", direction = -1)

Add the original data points onto the density curves

This can be done by setting jittered_points = TRUE, either in stat_density_ridges or in geom_density_ridges.

The position of data points can be controlled using the following options:

• position = "sina": Randomly distributes points in a ridgeline plot between baseline and ridgeline. This is the default option.
• position = "jitter": Randomly jitter the points in a ridgeline plot. Points are randomly shifted up and down and/or left and right.
• position = "raincloud": Creates a cloud of randomly jittered points below a ridgeline plot.

# Add jittered points
ggplot(iris, aes(x = Sepal.Length, y = Species)) +
  geom_density_ridges(jittered_points = TRUE)

# Control the position of points
# position = "raincloud"
ggplot(iris, aes(x = Sepal.Length, y = Species)) +
  geom_density_ridges(
    jittered_points = TRUE, position = "raincloud",
    alpha = 0.7, scale = 0.9
  )

# position = "points_jitter"
ggplot(iris, aes(x = Sepal.Length, y = Species)) +
  geom_density_ridges(
    jittered_points = TRUE, position = "points_jitter",
    alpha = 0.7, scale = 0.9
  )

# Add marginal rug
ggplot(iris, aes(x = Sepal.Length, y = Species)) +
  geom_density_ridges(
    jittered_points = TRUE,
    position = position_points_jitter(width = 0.05, height = 0),
    point_shape = '|', point_size = 3, point_alpha = 1, alpha = 0.7,
  )

Styling the jittered points and add quantile lines.

# Styling jittered points
ggplot(iris, aes(x = Sepal.Length, y = Species, fill = Species)) +
  geom_density_ridges(
    aes(point_color = Species, point_fill = Species, point_shape = Species),
    alpha = .2, point_alpha = 1, jittered_points = TRUE
  ) +
  scale_point_color_hue(l = 40) +
  scale_discrete_manual(aesthetics = "point_shape", values = c(21, 22, 23))

# Styling vertical quantile lines
ggplot(iris, aes(x = Sepal.Length, y = Species)) +
  geom_density_ridges(
    jittered_points = TRUE, quantile_lines = TRUE, scale = 0.9, alpha = 0.7,
    vline_size = 1, vline_color = "red",
    point_size = 0.4, point_alpha = 1,
    position = position_raincloud(adjust_vlines = TRUE)
  )

Create histogram distributions using ridgeline

Use the following options:

• stat = "binline": Creates histogram plots
• draw_baseline = FALSE: Removes trailing lines to either side of the histogram. For histograms, the rel_min_height parameter doesn’t work very well.

ggplot(iris, aes(x = Sepal.Length, y = Species, height = stat(density))) + 
  geom_density_ridges(
    stat = "binline", bins = 20, scale = 0.95,
    draw_baseline = FALSE
    )

Change themes

# Use theme_ridges()
ggplot(iris, aes(x = Sepal.Length, y = Species)) + 
  geom_density_ridges() + 
  theme_ridges()
# Remove grids
ggplot(iris, aes(x = Sepal.Length, y = Species)) + 
  geom_density_ridges() + 
  theme_ridges(grid = FALSE, center_axis_labels = TRUE)

Conclusion

This article how to visualize distribution in R using density ridgeline.