This article how to **visualize distribution in R** using density ridgeline. The density ridgeline plot [ggridges package] is an alternative to the standard `geom_density()`

[ggplot2 R package] function that can be useful for visualizing changes in distributions, of a continuous variable, over time or space. Ridgeline plots are partially overlapping line plots that create the impression of a mountain range.

You will learn how to:

- Create basic density ridgeline plots
- Add gradient fill colors under the curves
- Add summary statistics such as quantile lines on the density plots
- Add original data points onto the density curves

Contents:

## 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)
```

Note that, the grouping aesthetic does not need to be provided if a categorical variable is mapped onto the y axis, but it does need to be provided if the variable is numerical.

**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.

Note that, for technical reasons, the `geom_density_ridges_gradient()`

do not allow for alpha transparency in the fill.

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 quantile lines

Key R function: `stat_density_ridges()`

. By default, three lines are drawn, corresponding to the first, second, and third quartile.

```
# 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 the density area by quantiles

You need to specify the option `calc_ecdf = TRUE`

required for calculating quantiles. The ECDF represents the empirical cumulative density function for the distribution.

```
# 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]")
)
```

### Color the density curves by probabilities

When `calc_ecdf = TRUE`

, we also have access to a calculated aesthetic `stat(ecdf)`

, which represents the empirical cumulative density function for the distribution. This allows us to map the probabilities directly onto color.

```
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.

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