Animation

Lecture 23

Dr. Mine Çetinkaya-Rundel

Duke University
STA 313 - Spring 2023

Warm up

Announcements

Remainder of the semester:

  • Labs:

    • This week: Work on projects + fill out peer evals
    • Next week: Project peer code review
    • Following week (LDOC): Work on HW 6

  • HW: HW 6 is optional, you’ll need to explicitly indicate if you’re opting out

  • Project: Presentations Thu, May 4, 2-5pm (all team members must be there!)

Setup

# load packages
library(countdown)
library(tidyverse)
library(gt)
library(readxl)
library(gganimate)
library(gifski)
library(knitr)
library(kableExtra)
library(palmerpenguins)
library(transformr)
library(datasauRus)

# set theme for ggplot2
ggplot2::theme_set(ggplot2::theme_minimal(base_size = 14))

# set width of code output
options(width = 65)

# set figure parameters for knitr
knitr::opts_chunk$set(
  fig.width = 7, # 7" width
  fig.asp = 0.618, # the golden ratio
  fig.retina = 3, # dpi multiplier for displaying HTML output on retina
  fig.align = "center", # center align figures
  dpi = 300 # higher dpi, sharper image
)

From last time…

Your turn: Add color to the previous table.


Popular Bachelor's degrees over the years
Field Trend 1971 1976 1981 1986 1991 1996 2001 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Business 14% 15% 21% 24% 23% 19% 21% 22% 21% 21% 21% 22% 22% 21% 20% 20% 19% 19%
Health professions 3% 6% 7% 7% 5% 7% 6% 6% 6% 7% 7% 8% 8% 8% 9% 10% 11% 11%
Social sciences and history 18% 14% 11% 9% 11% 11% 10% 11% 11% 11% 11% 11% 10% 10% 10% 10% 9% 9%
Other 65% 65% 61% 60% 60% 62% 62% 62% 62% 61% 61% 60% 60% 60% 60% 61% 61% 61%
10:00

10 guidelines for better tables

  1. Offset the heads from the body
  2. Use subtle dividers rather than heavy gridlines
  3. Right-align numbers and heads
  4. Left-align text and heads
  5. Select the appropriate level of precision
  6. Guide your reader with space between rows and columns
  7. Remove unit repetition
  8. Highlight outliers
  9. Group similar data and increase white space
  10. Add visualizations when appropriate

Other packages

  • knitr::kable(): “Cheapest” pretty tables in R Markdown
  • Other (than HTML) outputs:
  • gtsummary: For summarizing statistical output with gt
  • Interactivity: We will work with these when we learn Shiny! - DT - reactable

Table inspiration

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

How does gganimate work?

  • Start with a ggplot2 specification

  • Add layers with graphical primitives (geoms)

  • Add formatting specification

  • Add animation specification

A simple example

freedom_ranked |>
  filter(country == "Turkey") |>
  ggplot()

A simple example

freedom_ranked |>
  filter(country == "Turkey") |>
  ggplot(
    aes(
      x = year, 
      y = civil_liberty
    )
  )

A simple example

freedom_ranked |>
  filter(country == "Turkey") |>
  ggplot(
    aes(
      x = year, 
      y = civil_liberty
    )
  ) +
  geom_line(linewidth = 2)

A simple example

freedom_ranked |>
  filter(country == "Turkey") |>
  ggplot(
    aes(
      x = year, 
      y = civil_liberty
    )
  ) +
  geom_line(linewidth = 2) +
  labs(
    x = "Year", 
    y = "Civil liberty score",
    title = "Turkey's civil liberty score"
  )

A simple example

turkey_cl <- freedom_ranked |>
  filter(country == "Turkey") |>
  ggplot(
    aes(
      x = year, 
      y = civil_liberty
    )
  ) +
  geom_line(linewidth = 2) +
  labs(
    x = "Year", 
    y = "Civil liberty score",
    title = "Turkey's civil liberty score"
  ) +
  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.

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")

ease examples

ease examples

Deeper dive

A not-so-simple example

Pass in the dataset to ggplot

ggplot(datasaurus_dozen)

A not-so-simple example

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

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

ggplot(
  datasaurus_dozen,
  aes(x, y, color = dataset)
) +
  geom_point(show.legend = FALSE)

A not-so-simple example

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

ggplot(
  datasaurus_dozen,
  aes(x, y, color = dataset)
) +
  geom_point(show.legend = FALSE) +
  facet_wrap(~dataset)

A not-so-simple example

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

datasaurus_dozen <- ggplot(
  datasaurus_dozen,
  aes(x, y, color = dataset)
) +
  geom_point(size = 2, show.legend = FALSE) +
  transition_states(
    dataset, 
    transition_length = 3, 
    state_length = 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, save the plot and animate it with animate().

Considerations in making effective animations

  • Pace: speed of animation Quick animations may be hard to follow. Slow animations are boring and tedious.
  • Perplex: 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?

Racing bar chart, the making of

Go to ae-20. We’ll live-code tasks 1 and 2. You’ll work on Task 3.

Acknowledgements

🔗 vizdata.org