Background

Let’s visualize the quantiles of a normal distribution.

Setup

library(tidyverse)

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library(gganimate)
set.seed(123)

Simulate normal data

n <- 1000
data <- data.frame(x = rnorm(n))
data$decile <- cut(data$x, breaks = quantile(data$x, probs = seq(0, 1, 0.1)), include.lowest = TRUE)
data$decile2 <- cut(data$x, breaks = quantile(data$x, probs = seq(0, 1, 0.1)), include.lowest = TRUE, labels = 1:10)

Using decile and decile2 we have marked for each data point to which decile it belongs.

Build up the data frame

data2 <-
  data |> 
  group_by(decile2) |> 
  mutate(x_max = max(x)) 

data3 <-
  data2 |> 
  ungroup() |> 
  select(x)

And plot:

p <- 
  ggplot(data2, aes(x = x)) +
  geom_histogram(data = data3, fill = "grey", alpha = .5, color = NA) +
  geom_histogram(binwidth = 0.2, fill = "blue", color = NA) +
  geom_vline(aes(xintercept = x_max)) +
  geom_label(aes(label = decile2, x = x_max), y= 0) +
  theme_minimal()
p

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

And now: animate

animated_plot <- 
  p +
  transition_states(decile2, transition_length = 2, state_length = 1) +
  ggtitle("Decile: {closest_state}") +
  enter_fade() +
  exit_fade()

And safe to disk:

anim_save("normal_distribution_animation.gif", animated_plot, renderer = gifski_renderer())