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Data Analytics – GGANIMATE: HOW TO CREATE PLOTS WITH BEAUTIFUL ANIMATION IN R

By on Tuesday, June 15, 2021

GGANIMATE: HOW TO CREATE PLOTS WITH BEAUTIFUL ANIMATION IN R

 

This article describes how to create animation in R using the gganimate R package.

gganimate is an extension of the ggplot2 package for creating animated ggplots. It provides a range of new functionality that can be added to the plot object in order to customize how it should change with time.

Key features of gganimate:

  • transitions: you want your data to change
  • views: you want your viewpoint to change
  • shadows: you want the animation to have memory

 

Contents:

  • Prerequisites
  • Demo dataset
  • Static plot
  • Transition through distinct states in time
    • Basics
    • Let the view follow the data in each frame
    • Show preceding frames with gradual falloff
    • Show the original data as background marks
  • Reveal data along a given dimension
    • Static plot
    • Let data gradually appear
  • Transition between several distinct stages of the data
  • Save animation

 

Prerequisites

gganimate stable version is available on CRAN and can be installed with install.packages('gganimate'). The latest development version can be installed as follow: devtools::install_github('thomasp85/gganimate').

Note that, in this tutorial, we used the latest developmental version.

Load required packages and set the default ggplot2 theme to theme_bw():

library(ggplot2)
library(gganimate)
theme_set(theme_bw())

Demo dataset

library(gapminder)
head(gapminder)
## # A tibble: 6 x 6
##   country     continent  year lifeExp      pop gdpPercap
##   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
## 1 Afghanistan Asia       1952    28.8  8425333      779.
## 2 Afghanistan Asia       1957    30.3  9240934      821.
## 3 Afghanistan Asia       1962    32.0 10267083      853.
## 4 Afghanistan Asia       1967    34.0 11537966      836.
## 5 Afghanistan Asia       1972    36.1 13079460      740.
## 6 Afghanistan Asia       1977    38.4 14880372      786.

Static plot

p <- ggplot(
  gapminder, 
  aes(x = gdpPercap, y=lifeExp, size = pop, colour = country)
  ) +
  geom_point(show.legend = FALSE, alpha = 0.7) +
  scale_color_viridis_d() +
  scale_size(range = c(2, 12)) +
  scale_x_log10() +
  labs(x = "GDP per capita", y = "Life expectancy")
p

Transition through distinct states in time

Basics

Key R function: transition_time(). The transition length between the states will be set to correspond to the actual time difference between them.

Label variables: frame_time. Gives the time that the current frame corresponds to.

p + transition_time(year) +
  labs(title = "Year: {frame_time}")

Transition time

Create facets by continent:

p + facet_wrap(~continent) +
  transition_time(year) +
  labs(title = "Year: {frame_time}")

Transition time, facet by continents

Let the view follow the data in each frame

p + transition_time(year) +
  labs(title = "Year: {frame_time}") +
  view_follow(fixed_y = TRUE)

View follow

Show preceding frames with gradual falloff

This shadow is meant to draw a small wake after data by showing the latest frames up to the current. You can choose to gradually diminish the size and/or opacity of the shadow. The length of the wake is not given in absolute frames as that would make the animation susceptible to changes in the framerate. Instead it is given as a proportion of the total length of the animation.

p + transition_time(year) +
  labs(title = "Year: {frame_time}") +
  shadow_wake(wake_length = 0.1, alpha = FALSE)

Shadow Wake

Show the original data as background marks

This shadow lets you show the raw data behind the current frame. Both past and/or future raw data can be shown and styled as you want.

p + transition_time(year) +
  labs(title = "Year: {frame_time}") +
  shadow_mark(alpha = 0.3, size = 0.5)

Shadow Mark

Reveal data along a given dimension

This transition allows you to let data gradually appear, based on a given time dimension.

Static plot

p <- ggplot(
  airquality,
  aes(Day, Temp, group = Month, color = factor(Month))
  ) +
  geom_line() +
  scale_color_viridis_d() +
  labs(x = "Day of Month", y = "Temperature") +
  theme(legend.position = "top")
p

Let data gradually appear

  • Reveal by day (x-axis)
p + transition_reveal(Day)

Let data gradually appear

  • Show points:
p + 
  geom_point() +
  transition_reveal(Day)

Let data gradually appear

  • Points can be kept by giving them a unique group:
p + 
  geom_point(aes(group = seq_along(Day))) +
  transition_reveal(Day)

Let data gradually appear, keep points

Transition between several distinct stages of the data

Data preparation:

library(dplyr)
mean.temp <- airquality %>%
  group_by(Month) %>%
  summarise(Temp = mean(Temp))
mean.temp
## # A tibble: 5 x 2
##   Month  Temp
##   <int> <dbl>
## 1     5  65.5
## 2     6  79.1
## 3     7  83.9
## 4     8  84.0
## 5     9  76.9

Create a bar plot of mean temperature:

p <- ggplot(mean.temp, aes(Month, Temp, fill = Temp)) +
  geom_col() +
  scale_fill_distiller(palette = "Reds", direction = 1) +
  theme_minimal() +
  theme(
    panel.grid = element_blank(),
    panel.grid.major.y = element_line(color = "white"),
    panel.ontop = TRUE
  )
p

  • transition_states():
p + transition_states(Month, wrap = FALSE) +
  shadow_mark()

transition states

  • enter_grow() + enter_fade()
p + transition_states(Month, wrap = FALSE) +
  shadow_mark() +
  enter_grow() +
  enter_fade()

Save animation

If you need to save the animation for later use you can use the anim_save() function.

It works much like ggsave() from ggplot2 and automatically grabs the last rendered animation if you do not specify one directly.

Example of usage:

 

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