From static to motion: Animated charts

Lecture 19

Dr. Benjamin Soltoff

Cornell University
INFO 3312/5312 - Spring 2025

April 8, 2025

Announcements

Announcements

• Project 02 proposals
• Homework 05

Visualization critique

Population through the ages

• What are the stories?
• What is the added value of animating the chart?

Animation

Philosophy

• The purpose of interactivity is to display more than can be achieved with persistent plot elements, and to invite the reader to engage with the plot.

• Animation allows more information to be displayed, but developer keeps control

• Beware that it is easy to forget what was just displayed, so keeping some elements persistent, maybe faint, can be useful for the reader

{gganimate}

• {gganimate} extends the grammar of graphics as implemented by {ggplot2} to include the description of animation

• It provides a range of new grammar classes that can be added to the plot object in order to customize how it should change with time

Animation example

Animation example

Animation example

Animation example

How does {gganimate} work?

• Start with a {ggplot2} specification

• Add layers with graphical primitives (geoms)

• Add formatting specification

• Add animation specification

A simple example

ggplot(
  data = babynames |> filter(name == "Benjamin", sex == "M")
  )

A simple example

ggplot(
  data = babynames |> filter(name == "Benjamin", sex == "M"),
  mapping = aes(x = year, y = n)
  )

A simple example

ggplot(
  data = babynames |> filter(name == "Benjamin", sex == "M"),
  mapping = aes(x = year, y = n)
  ) +
  geom_line()

A simple example

ggplot(
  data = babynames |> filter(name == "Benjamin", sex == "M"),
  mapping = aes(x = year, y = n)
) +
  geom_line() +
  labs(
    x = "Year", y = "Number of births",
    title = "Popularity of 'Benjamin' over time"
  )

A simple example

ggplot(
  data = babynames |> filter(name == "Benjamin", sex == "M"),
  mapping = aes(x = year, y = n)
) +
  geom_line() +
  labs(
    x = "Year", y = "Number of births",
    title = "Popularity of 'Benjamin' over time"
  ) +
  transition_reveal(year)

Grammar of animation

Grammar of animation

• Transitions: transition_*() defines how the data should be spread out and how it relates to itself across time
• Views: view_*() defines how the positional scales should change along the animation
• Shadows: shadow_*() defines how data from other points in time should be presented in the given point in time
• Entrances/Exits: enter_*()/exit_*() defines how new data should appear and how old data should disappear during the course of the animation
• Easing: ease_aes() defines how different aesthetics should be eased during transitions

Transitions

How the data changes through the animation.

Function	Description
transition_manual()	Build an animation frame by frame (no tweening applied).
transition_states()	Transition between frames of a plot (like moving between facets).
transition_time()	Like transition_states, except animation pacing respects time.
transition_components()	Independent animation of plot elements (by group).
transition_reveal()	Gradually extends the data used to reveal more information.
transition_layers()	Animate the addition of layers to the plot. Can also remove layers.
transition_filter()	Transition between a collection of subsets from the data.
transition_events()	Define entrance and exit times of each visual element (row of data).

Transitions

Which transition was used in the following animations?

transition_layers()

New layers are being added (and removed) over the dots.

Transitions

Which transition was used in the following animations?

transition_filter()

The data is being filtered across each frame.

Views

How the plot window changes through the animation.

Function	Description
view_follow()	Change the view to follow the range of current data.
view_step()	Similar to view_follow(), except the view is static between transitions.
view_step_manual()	Same as view_step(), except view ranges are manually defined.
view_zoom()	Similar to view_step(), but appears smoother by zooming out then in.
view_zoom_manual()	Same as view_zoom(), except view ranges are manually defined.

Views

Which view was used in the following animations?

view_follow()

Plot axis follows the range of the data.

Shadows

How the history of the animation is shown. Useful to indicate speed of changes.

Function	Description
shadow_mark()	Previous (and/or future) frames leave permananent background marks.
shadow_trail()	Similar to shadow_mark(), except marks are from tweened data.
shadow_wake()	Shows a shadow which diminishes in size and/or opacity over time.

Shadows

Which shadow was used in the following animations?

shadow_wake()

The older tails of the points shrink in size, leaving a “wake” behind it.

Shadows

Which shadow was used in the following animations?

shadow_mark()

Permanent marks are left by previous points in the animation.

Entrances and exits

How elements of the plot appear and disappear.

Function	Description
enter_appear/exit_disappear()	Poof! Instantly appears or disappears.
enter_fade/exit_fade()	Opacity is used to fade in or out the elements.
enter_grow/exit_shrink()	Element size will grow from or shrink to zero.
enter_recolor/exit_recolor()	Change element colors to blend into the background.
enter_fly/exit_fly()	Elements will move from/to a specific x,y position.
enter_drift/exit_drift()	Elements will shift relative from/to their x,y position.
enter_reset/exit_reset()	Clear all previously added entrace/exits.

Animation controls

How data moves from one position to another.

p + ease_aes({aesthetic} = {ease})
p + ease_aes(x = "cubic")

Deeper dive

A not-so-simple example, the datasaurus dozen

Pass in the dataset to ggplot

ggplot(datasaurus_dozen)

A not-so-simple example, the datasaurus dozen

For each dataset we have x and y values, in addition we can map dataset to color

ggplot(datasaurus_dozen,
       aes(x, y, color = dataset)) 

A not-so-simple example, the datasaurus dozen

Trying a simple scatter plot first, but there is too much information

ggplot(datasaurus_dozen,
       aes(x, y, color = dataset)) +
  geom_point()

A not-so-simple example, the datasaurus dozen

We can use facets to split up by dataset, revealing the different distributions

ggplot(datasaurus_dozen,
       aes(x, y, color = dataset)) +
  geom_point() +
  facet_wrap(facets = vars(dataset)) +
  coord_fixed() +
  guides(color = "none")

A not-so-simple example, the datasaurus dozen

We can just as easily turn it into an animation, transitioning between dataset states!

ggplot(datasaurus_dozen,
       aes(x, y)) +
  geom_point() +
  coord_fixed() +
  transition_states(dataset, 3, 1) +
  labs(title = "Dataset: {closest_state}")

Tips

Animation options

Sometimes you need more frames, sometimes fewer

• Save plot object, and use animate() with arguments like
  • nframes: number of frames to render (default 100)
  • fps: framerate of the animation in frames/sec (default 10)
  • duration: length of the animation in seconds (unset by default)
  • etc.

• In Quarto, specify the arguments to animate() in the chunk options using gganimate

```{r}
#| gganimate: !expr list(nframes = 50, fps = 20)
# add code here
```

• Learn more at https://gganimate.com/reference/animate.html

Considerations in making effective animations

• Pace: speed of animation

  Quick animations may be hard to follow. Slow animations are boring and tedious.

• Perplexity: amount of information

  It is easy for animations to be overwhelming and confusing. Multiple simple animations can be easier to digest.

• Purpose: Usefulness of using animation

  Is animation needed? Does it provide additional value?

Application exercise

ae-18

Instructions

• Go to the course GitHub org and find your ae-18 (repo name will be suffixed with your GitHub name).
• Clone the repo in RStudio, run renv::restore() to install the required packages, open the Quarto document in the repo, and follow along and complete the exercises.
• Render, commit, and push your edits by the AE deadline – end of the day

Wrap-up

Recap

• Animation is our first step towards dynamic communications using data
• Provides a controlled, dynamic user experience to engage with the data
• Avoid animation for the sake of animation
• Use {gganimate} to animate {ggplot2} charts

Acknowledgements

• Slides derived in part from Advanced Data Visualization