Quantile lines and coloring by quantiles or probabilities
By setting the option quantile_lines = TRUE, we can make stat_density_ridges calculate the position of lines indicating quantiles. By default, three lines are drawn, corresponding to the first, second, and third quartile:
We can change the number of quantiles by specifying it via the quantiles option. Note that quantiles = 2 implies one line (the median) at the boundary between the two quantiles.
We can also specify quantiles by cut points rather than number. E.g., we can indicate the 2.5% and 97.5% tails.
Using the geom geom_density_ridges_gradient we can also color by quantile, via the calculated stat(quantile) aesthetic. Note that this aesthetic is only calculated if calc_ecdf = TRUE.
We can use the same approach to 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]")
)
Finally, 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.
Jittering points
The stat stat_density_ridges also provides the option to visualize the original data points from which the distributions are generated. This can be done by setting jittered_points = TRUE, either in stat_density_ridges or in geom_density_ridges:
Where the points are shown can be controlled with position options, e.g. “raincloud” for the raincloud effect:
We can also simulate a rug:
Note that we are using position_points_jitter() here, not position_jitter(). We do this because position_points_jitter() knows to jitter only the points in a ridgeline plot, without touching the density lines.
Styling the jittered points is a bit tricky but is possible with special scales provided by ggridges. First, there is scale_discrete_manual() which can be used to make arbitrary discrete scales for arbitrary aesthetics. We use it in the next example to style the point shapes. Second, there are various point aesthetic scales, such as scale_point_color_hue(). See the reference documentation for these scales for more details.
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))
All common aesthetics for points can be applied to the jittered points. However, the aesthetic names start with point_. In the next example, we have mapped an additional variable onto the size of the points.
ggplot(iris, aes(x = Sepal.Length, y = Species, fill = Species)) +
geom_density_ridges(
aes(point_shape = Species, point_fill = Species, point_size = Petal.Length),
alpha = .2, point_alpha = 1, jittered_points = TRUE
) +
scale_point_color_hue(l = 40) + scale_point_size_continuous(range = c(0.5, 4)) +
scale_discrete_manual(aesthetics = "point_shape", values = c(21, 22, 23))
Similarly, we have aesthetics for the vertical lines, named vline_. And the vertical lines can also be shifted so they are aligned with the jittered points. This allows us to generate figures such as the following:
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)
)
Using alternative stats
The stat stat_density_ridges may not always do exactly what you want it to do. If this is the case, you can use other stats that may be better for your respective application. First, stat_density_ridges estimates the data range and bandwidth for the density estimation from the entire data at once, rather than from each individual group of data. This choice makes ridgeline plots look more uniform, but the density estimates can in some cases look quite different from what you would get from geom_density or stat_density. This problem can be remedied by using stat_density with geom_density_ridges. This works just fine, we just need to make sure that we map the calculated density onto the height aesthetic.
Second, there may be scenarios in which you don’t want geom_density_ridges to do any density estimation, for example because you have done so already yourself. In this case, you can use stat_identity. The benefit of using geom_density_ridges with stat_identiy over using geom_ridgeline directly is that geom_density_ridges provides automatic scaling.
As an example, assume we have calculated density curves for the Sepal.Length column in the iris dataset:
## # A tibble: 1,536 x 7
## # Groups: Species [3]
## Species Sepal.Length density scaled ndensity count n
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 setosa 3.93 0.000869 0.000701 0.000701 0.0435 50
## 2 setosa 3.94 0.000973 0.000785 0.000785 0.0487 50
## 3 setosa 3.94 0.00109 0.000876 0.000876 0.0543 50
## 4 setosa 3.94 0.00121 0.000975 0.000975 0.0604 50
## 5 setosa 3.95 0.00135 0.00109 0.00109 0.0673 50
## 6 setosa 3.95 0.00150 0.00121 0.00121 0.0749 50
## 7 setosa 3.96 0.00166 0.00134 0.00134 0.0831 50
## 8 setosa 3.96 0.00184 0.00149 0.00149 0.0922 50
## 9 setosa 3.97 0.00205 0.00165 0.00165 0.102 50
## 10 setosa 3.97 0.00226 0.00183 0.00183 0.113 50
## # … with 1,526 more rows
We can plot these as follows:
Notice how this plot looks different from the one generated using stat = "density", even though the density computation was exactly the same: (i) The density curves extend all the way to zero. (ii) There is no horizontal line extending all the way to the limits of the x axis.
Finally, if you prefer histograms to density plots, you can also use stat_binline. Note that overlapping histograms can look strange, so this option is probably best used with a scale parameter < 1. The option draw_baseline = FALSE removes trailing lines to either side of the histogram. (For histograms, the rel_min_height parameter doesn’t work very well.)
