Interactive and Dynamic Animation

In this R Notebook we will try out some interactive visualizations and some dynamic visualizations that are possible using R.

library(pacman)
p_load(tidyverse, purrr, gapminder, plotly, trelliscopejs, echarts4r, gganimate)

Using the gapminder dataset.

gapminder

# A tibble: 1,704 × 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.
 7 Afghanistan Asia       1982    39.9 12881816      978.
 8 Afghanistan Asia       1987    40.8 13867957      852.
 9 Afghanistan Asia       1992    41.7 16317921      649.
10 Afghanistan Asia       1997    41.8 22227415      635.
# ℹ 1,694 more rows

Plot by continent.

gapm <- gapminder %>% group_by(continent, country) %>%
  ggplot(aes(x = gdpPercap, y = lifeExp, 
             size = pop, colour = country )) +
  geom_point() +
  scale_x_log10() + 
  facet_wrap(~continent) +
  theme(legend.position = "none")
gapm

ggplotly(gapm)

Using facet_trelliscope can be used to create an interactive collection of plots. Can you image making a million plots?

gapminder %>% ggplot(aes(x = log10(gdpPercap), y = lifeExp, 
                         color = country, size = pop)) +  
  geom_point() + 
  theme_bw() +
  theme(legend.position = "none") +
  facet_trelliscope(~ continent, 
                    nrow = 2, ncol = 3)

gapm_trel <- gapminder %>% 
  ggplot(aes(x = log10(gdpPercap), y = lifeExp,
             color = country, size = pop)) +
  geom_point() +
  theme_bw() +
  theme(legend.position = "none") +
  facet_trelliscope(~ country + continent, 
                    nrow = 2, ncol = 3) 

gapm_trel

Try echarts.

titles <- map(unique(gapminder$year), function(x){
  list(
    text = "Gapminder",
    subtext = x,
    left = "center"
  )
})

gapminder %>% 
  group_by(year) %>% 
  e_charts(gdpPercap, timeline = TRUE) %>% 
  e_scatter(lifeExp, pop, country) %>% 
  e_y_axis(min = 20, max = 85) %>% 
  e_x_axis(type = "log", min = 100, max = 100000) %>% 
  e_timeline_serie(title = titles) %>% 
  e_tooltip() %>% 
  e_timeline_opts(
    show = TRUE,
    orient = "vertical",
    symbol = "none",
    right = 0,
    top = 20,
    bottom = 20,
    height = NULL,
    width = 45,
    inverse = TRUE,
    playInterval = 1000,
    autoPlay  = TRUE,
    controlStyle = list(
      showNextBtn = FALSE,
      showPrevBtn = FALSE
    ),
    label = list(
      fontSize = 8
    )
  ) %>% 
  e_theme("dark")

gapminder %>% ggplot(aes(x = gdpPercap, y = 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() +
  facet_wrap(~ continent)

Try gganimate. Takes a long to process before the animation is available.

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() +
  facet_wrap(~continent) +
  # Here comes the gganimate specific bits
  labs(title = 'Year: {frame_time}', x = 'GDP per capita', y = 'life expectancy') +
  transition_time(year) +
  ease_aes('linear')

goo <- gapminder %>% ggplot(aes(x = gdpPercap, y = 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() +
  facet_wrap(~continent) +
  # Here comes the gganimate specific bits
  labs(title = 'Year: {frame_time}', 
       x = 'GDP per capita', y = 'life expectancy') +
  transition_time(year) +
  ease_aes('cubic-in-out')

anim_save("goo.gif", goo)

Try echarts. Globe

Run the following code once to install the *echarts4r.assets$ package.

# install.packages("remotes")
# remotes::install_github("JohnCoene/echarts4r.assets")

library(echarts4r.assets)

airports <- read.csv(
  paste0("https://raw.githubusercontent.com/plotly/datasets/",
         "master/2011_february_us_airport_traffic.csv")
)

airports |> 
  e_charts(long) |> 
  e_globe(
    environment = ea_asset("starfield"),
    base_texture = ea_asset("world"), 
    globeOuterRadius = 100
  ) |> 
  e_scatter_3d(lat, cnt, coord_system = "globe", blendMode = 'lighter') |> 
  e_visual_map(inRange = list(symbolSize = c(1, 10)))

flights <- read.csv(
  paste0("https://raw.githubusercontent.com/plotly/datasets/",
         "master/2011_february_aa_flight_paths.csv")
)

flights |> 
  e_charts() |> 
  e_globe(
    environment = ea_asset("starfield"),
    base_texture = ea_asset("world topo"), 
    height_texture = ea_asset("world topo"),
    displacementScale = 0.05
  ) |> 
  e_lines_3d(
    start_lon, 
    start_lat, 
    end_lon, 
    end_lat,
    name = "flights",
    effect = list(show = TRUE)
  ) |> 
  e_legend(FALSE)