Set Up

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

library(tidyverse)
library(gapminder)
library(gganimate)
theme_set(theme_bw())

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.

Transition through distinct states in time

Begin with a 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

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}")

11.12.4 Create facets by continent

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

11.12.5 Let the view follow the data in each frame

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

11.12.6 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)

11.12.7 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)

Reveal data along a given dimension

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

11.12.8 Let data gradually appear

• Reveal by day (x-axis)

p + transition_reveal(Day)

• Show points:

p + 
  geom_point() +
  transition_reveal(Day)

• Points can be kept by giving them a unique group:

p + 
  geom_point(aes(group = seq_along(Day))) +
  transition_reveal(Day)

Transition between several distinct stages of the data

Data preparation:

mean.temp <- airquality %>%
  group_by(Month) %>%
  summarise(Temp = mean(Temp), .groups = "drop_last")
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()

• enter_grow() + enter_fade()

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

Save your animation

The code to create animations can take a long time to run. So after you have created an animation, you’ll want to save it somewhere so you can display it without having to run the code.

The key function to use is anim_save(), which is very similar to saving static plots using ggsave(). It will save your animation as a gif. The first argument is the filename you want to give the animation and the second is the animation object, so if you have an animation object called p that you wanted to save in a file called “p.gif,” you would save it like so:

anim_save("p.gif", p)

If you don’t supply the second argument, anim_save() will default to saving the most recent animation you rendered. So anim_save("animation.gif") will save the most recent animation as “animation.gif.”

If you don’t want to save your gif in the current directory, you can specify a directory using the path argument. Let’s say there is a subdirectory of your working directory called “gifs.” You can thus save “animation.gif” in “gifs” with anim_save("animation.gif", path = "gifs").

After you have created a gif, you can post it online. You can post it to Facebook by selecting “Photo/Video” on your Facebook status or to Twitter by clicking the photo icon.