同一图中的几个分布 - 使用ggplot2中的geom_density函数

Question

I think I'm very close to getting this code done, but I'm missing something here. I want to "combine" two plots into just one like this:

The first plot has this code:

  ggplot(test, aes(y=key,x=value)) + 
  geom_path()+
  coord_flip()

And the second one has this one below:

  ggplot(test, aes(x=value, fill=key)) +
  geom_density() +
  coord_flip()

This kind of multiple distributions plot are often seen in stats book when we read about normal distributions. The most useful link I've got so far was this one here .

Please use this code to reproduce my question:

library(tidyverse)
test <- data.frame(key = c("communication","gross_motor","fine_motor"),
                   value = rnorm(n=30,mean=0, sd=1))
ggplot(test, aes(x=value, fill=key)) +
  geom_density() +
  coord_flip()

ggplot(test, aes(y=key,x=value)) + 
  geom_path(size=2)+
  coord_flip()

Thanks much

Answer 1

You might be interested in ridgeline plots from the ggridges package.

Ridgeline plots are partially overlapping line plots that create the impression of a mountain range. They can be quite useful for visualizing changes in distributions over time or space.

library(tidyverse)
library(ggridges)

set.seed(123)
test <- data.frame(
  key = c("communication", "gross_motor", "fine_motor"),
  value = rnorm(n = 30, mean = 0, sd = 1)
)

ggplot(test, aes(x = value, y = key)) + 
  geom_density_ridges(scale = 0.9) + 
  theme_ridges() +
  NULL
#> Picking joint bandwidth of 0.525

Add median line:

ggplot(test, aes(x = value, y = key)) +
  stat_density_ridges(quantile_lines = TRUE, quantiles = 2, scale = 0.9) +
  coord_flip() +
  theme_ridges() +
  NULL
#> Picking joint bandwidth of 0.525

Simulate a rug:

ggplot(test, aes(x = value, y = key)) + 
  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,
  ) +
  theme_ridges() +
  NULL
#> Picking joint bandwidth of 0.525

^{Created on 2018-10-16 by the reprex package (v0.2.1.9000)}

Answer 2

I think the easiest way to do this is with facet_wrap() . If you don't like the default appearance of the facets you can tweak them with theme() , eg:

ggplot(test, aes(x=value, fill=key)) +
    geom_density() +
    facet_wrap(~ key) +
    coord_flip() +
    theme(panel.spacing.x = unit(0, "mm"))

Result: