Getting Started
gganimate is an extension of the grammar of
graphics, as implemented by the ggplot2 package, that
adds support for declaring animations using an API familiar to users of
ggplot2.
The following introduction assumes familiarity with ggplot2
to the extent that constructing static plots and reading standard
ggplot2 code feels natural. If you are new to both
ggplot2 and gganimate you’ll benefit from exploring
the trove of ggplot2 documentation, tutorials, and courses
available online first (see the ggplot2
webpage for some pointers).
Your First Animation
We’ll jump right into our first animation. Don’t worry too much about
understanding the code, as we’ll dissect it later.
library(gganimate)
#> Loading required package: ggplot2
# We'll start with a static plot
p <- ggplot(iris, aes(x = Petal.Width, y = Petal.Length)) +
geom_point()
plot(p)
You go from a static plot made with ggplot2 to an animated
one, simply by adding on functions from gganimate.
anim <- p +
transition_states(Species,
transition_length = 2,
state_length = 1)
anim
❗ transition_states() splits up plot data by a discrete
variable and animates between the different states.
As can be seen, very few additions to the plot results in a quite
complex animation. So what did we do to get this animation? We added a
type of transition. Transitions are functions that interpret
the plot data in order to somehow distribute it over a number of frames.
transition_states() specifically splits the data into
subsets based on a variable in the data (here Species), and
calculates intermediary data states that ensures a smooth transition
between the states (something referred to as tweening).
gganimate provides a range of different transitions, but for
the rest of the examples we’ll be sticking to
transition_states() and see how we can modify the
output.
Easing
When transition_states() calculates intermediary data
for the tweening, it needs to decide how the change from one value to
another should progress. This is a concept called easing. The
default easing is linear, but others can be used, potentially
only targeting specific aesthetics. Setting easing is done with the
ease_aes() function. The first argument sets the default
easing and subsequent named arguments sets it for specific
aesthetics.
anim +
ease_aes('cubic-in-out') # Slow start and end for a smoother look
❗ ease_aes() defines the velocity with which aesthetics
change during an animation.
anim +
ease_aes(y = 'bounce-out') # Sets special ease for y aesthetic
Labeling
It can be quite hard to understand an animation without any
indication as to what each time point relates to. gganimate
solves this by providing a set of variables for each frame, which can be
inserted into plot labels using glue syntax.
anim +
ggtitle('Now showing {closest_state}',
subtitle = 'Frame {frame} of {nframes}')
❗ Use glue syntax to insert frame variables in plot labels
and titles.
Different transitions provide different frame variables.
closest_state only makes sense for
transition_states() and is thus only available when that
transition is used.
Object Permanence
In the animation above, it appears as if data in a single measurement
changes gradually as the flower being measured on somehow morphs between
three different iris species. This is probably not how Fisher conducted
the experiment and got those numbers. In general, when you make an
animation, graphic elements should only transition between instances
of the same underlying phenomenon. This sounds complicated but it
is more or less the same principle that governs makes sense to draw a
line between two observations. You wouldn’t connect observations from
different iris species, but repeated observations on the same plant
would be fine to connect. Same thing with animations.
Just to make this very clear (it is an important concept). The line
plot equivalent of our animation above is:
ggplot(iris, aes(x = Petal.Width, y = Petal.Length)) +
geom_line(aes(group = rep(1:50, 3)), colour = 'grey') +