The gganimate package in R is an extension of the
ggplot2
package, designed to
create animated visualizations.
This post
showcases the key features of
gganimate and provides a set of
animated graph examples using the package.
{gganimate}
The gganimate package in R is an extension of the
ggplot2
package, designed to
create animated and interactive visualizations.
It offers a set of grammar extensions that make it easy to specify animations in a declarative manner.
✍️ author → Thomas Lin Pedersen
📘 documentation → gganimate.com
⭐️ more than 2000 stars on github
To get started with gganimate, you can install it
directly from CRAN using the install.packages function:
The gganimate package extends the grammar of graphics to
include the description of animation. You start with a regular ggplot2
plot and add animation-specific layers to it.
Here’s a basic example where the year variable is used to
animate the plot:
library(ggplot2)
library(gganimate)
library(gapminder)
p <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, colour = country)) +
geom_point(alpha = 0.7, show.legend = FALSE) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
scale_x_log10() +
labs(title = "Year: {frame_time}", x = "GDP per capita", y = "life expectancy")
anim <- p + transition_time(year) +
ease_aes("linear")
anim_save("../img/graph/gganimate-1.gif", anim)
Transitions allow you to animate changes in your data over time or between different states.
This animation transitions between different gears (3, 4, and 5).
The transition_states() function creates smooth
transitions between these discrete states. Points
fade in and out as they enter or exit each state.
library(ggplot2)
library(gganimate)
# Create a basic scatter plot
p <- ggplot(mtcars, aes(x = wt, y = mpg, color = factor(cyl))) +
geom_point(size = 3) +
labs(
title = "Car Weight vs MPG",
subtitle = "Gear: {closest_state}",
x = "Weight (1000 lbs)", y = "Miles per Gallon"
)
# Add animation
anim <- p +
transition_states(gear,
transition_length = 2,
state_length = 1
) +
enter_fade() +
exit_fade()
anim_save("../img/graph/gganimate-2.gif", anim)
Tweening interpolates between different aesthetic values to create smooth animations over continuous variables.
This animation shows how life expectancy, GDP per capita, and
population have changed over time for different countries. The
transition_time() function creates a smooth animation
over the years, with the
points moving to their new positions each year.
library(ggplot2)
library(gganimate)
library(gapminder)
# Create a basic scatter plot
p <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, color = continent)) +
geom_point(alpha = 0.7) +
scale_color_viridis_d() +
scale_size(range = c(2, 12)) +
scale_x_log10() +
labs(
title = "Year: {frame_time}",
x = "GDP per capita",
y = "Life expectancy"
)
# Add animation
anim <- p +
transition_time(year) +
ease_aes("linear")
anim_save("../img/graph/gganimate-3.gif", anim)
You can control how data points appear and disappear during transitions.
This animation shows the average MPG for cars with different numbers
of cylinders. The enter_grow() function makes
new bars grow from the bottom, while
exit_shrink() makes
disappearing bars shrink down.
library(ggplot2)
library(gganimate)
library(dplyr)
# Prepare data
data <- mtcars %>%
mutate(cyl = factor(cyl)) %>%
group_by(cyl) %>%
summarize(mpg = mean(mpg))
# Create a basic bar plot
p <- ggplot(data, aes(x = cyl, y = mpg, fill = cyl)) +
geom_col() +
labs(
title = "Average MPG by Number of Cylinders",
subtitle = "Showing: {closest_state}",
x = "Number of Cylinders",
y = "Average Miles per Gallon"
)
# Add animation
anim <- p +
transition_states(cyl) +
enter_grow() +
exit_shrink()
anim_save("../img/graph/gganimate-4.gif", anim)
The gallery is filled with gganimate examples that
showcase the power and flexibility of the package.
Each example includes a brief explanation and a link to the full code!
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