1 Lecture Note

• Static plot - ggplot
  • Interactive plot - plotly
  • Animation - gganimate
• Static plot - ggplot
  • Small Multiple - facet_*
  • Small Interactive Many - trelliscopeJS

2 Static plot - ggplot

library(tidyverse)
library(gapminder)

gapminder %>% 
  DT::datatable()

gapminder %>% 
  filter(str_detect(country, "Korea, Rep")) %>% 
  ggplot(aes(x=year, y=lifeExp)) +
    geom_line() +
    geom_point()

gapminder %>% 
  filter(str_detect(country, "Korea, Rep")) %>% 
  select(year, lifeExp, gdpPercap) %>% 
  gather(variable, value, -year) %>% 
  ggplot(aes(x=year, y=value)) +
    geom_line() +
    geom_point() +
    facet_wrap(~variable, scale="free")

library(extrafont)
loadfonts()

gapminder %>% 
  filter(str_detect(country, "Korea, Rep")) %>% 
  select(year, lifeExp, gdpPercap) %>% 
  gather(variable, value, -year) %>% 
  ggplot(aes(x=year, y=value)) +
    geom_line() +
    geom_point() +
    facet_wrap(~variable, scale="free") +
    labs(x="", y="", 
         title="GDP per capita and Life Expectancy over time",
         subtitle="Data from gapminder, Korea") +
    theme_bw(base_family = "NanumGothic")

3 Interactive plot - plotly

life_p <- gapminder %>% 
    ggplot(aes(x=year, y=lifeExp, group=country)) +
    geom_line()  

library(plotly)  
ggplotly(life_p)

tooltip_p <- gapminder %>% 
     ggplot(aes(x=year, y=lifeExp, 
                group=country, 
                text=paste0("Continent: ", continent, "\n", 
                            "Country: ", country, "\n", 
                            "Life Expectancy: ", lifeExp))) + 
     geom_line()
 
ggplotly(tooltip_p, tooltip = "text")

lifeExp_highlight <- highlight_key(gapminder, ~country)

highlight_p <- lifeExp_highlight %>% 
     ggplot(aes(x=year, y=lifeExp, 
                group=country, 
                text=paste0("Continent: ", continent, "\n", 
                            "Country: ", country, "\n", 
                            "Life Expectancy: ", lifeExp))) + 
     geom_line()
 
highlight_gg <- ggplotly(highlight_p, tooltip = "text")

highlight(highlight_gg, on = "plotly_click",
          selectize = TRUE,
          dynamic = TRUE,
          persistent = TRUE)

life_cjk <- gapminder %>% 
    filter(country %in% c("China", "Japan", "Korea, Rep.")) %>% 
    ggplot(aes(x=year, y=lifeExp, group=country, 
             text=paste0("Continent: ", continent, "\n", 
                         "Country: ", country, "\n", 
                         "Life Expectancy: ", lifeExp))) + 
    geom_line() +
    geom_point() +
    facet_wrap(~country)

ggplotly(life_cjk, tooltip = "text")

gapminder_cjk <- gapminder %>% 
  filter(country %in% c("China", "Japan", "Korea, Rep."))

gapminder_sd <- crosstalk::SharedData$new(gapminder_cjk)

## life expectancy by year
life_cjk <- gapminder_sd %>% 
  plot_ly(x = ~year, y= ~lifeExp, color=~country) %>% 
    add_markers() %>% 
    add_lines() %>% 
    highlight("plotly_selected", dynamic = TRUE)

gdp_cjk <- gapminder_sd %>% 
  plot_ly(x = ~gdpPercap, y= ~lifeExp) %>% 
    add_histogram2d() %>% 
    highlight("plotly_selected", dynamic = TRUE)

## bind two hetergeneous plots
crosstalk::bscols(life_cjk, gdp_cjk)

4 Animation

life_g <- gapminder %>% 
  ggplot(aes(x=gdpPercap, y=lifeExp, group=country, color=country, 
             text=paste0("Continent: ", continent, "\n", 
                         "Country: ", country, "\n", 
                         "Life Expectancy: ", lifeExp))) + 
    geom_point(aes(frame=year), color="red") +
    geom_point(alpha=0.2) +
    facet_wrap(~continent, ncol=2) +
    scale_x_sqrt() +
    theme(legend.position = "none")

ggplotly(life_g, tooltip = "text")

library(gganimate)
gapminder %>% 
  ggplot(aes(x=gdpPercap, y=lifeExp, group=country, color=country, 
             text=paste0("Continent: ", continent, "\n", 
                         "Country: ", country, "\n", 
                         "Life Expectancy: ", lifeExp))) + 
    geom_point(aes(size=gdpPercap)) +
    facet_wrap(~continent, ncol=2) +
    scale_x_sqrt() +
    theme(legend.position = "none") +
    transition_time(year) +
    labs(title = "Year: {frame_time}")

5 Small Multiples

library(gganimate)
gapminder %>% 
  count(continent, year) %>% 
  filter(year == max(year))

# A tibble: 5 x 3
  continent  year     n
  <fct>     <int> <int>
1 Africa     2007    52
2 Americas   2007    25
3 Asia       2007    33
4 Europe     2007    30
5 Oceania    2007     2

gapminder %>% 
  filter(continent %in% "Americas") %>% 
  ggplot(aes(x=year, y=lifeExp, group=country, color=country)) +
    geom_point(aes(size=gdpPercap)) +
    geom_line() +
    facet_wrap(~country, ncol=2) +
    scale_x_sqrt() +
    theme(legend.position = "none") 

6 Small Many - trelliscopeJS

library(trelliscopejs)

## list-column dataset
gapminder_nest <- gapminder %>% 
  filter(continent == "Americas") %>% 
  group_by(continent, country) %>% 
  nest()

## plotly graph function
lifeExp_plot <- function(df) {
    plot_ly(df, x = ~year, y = ~lifeExp) %>% 
    add_markers() %>% 
    add_lines()
}

## Generate trelliscopeJS interactive graph 
gapminder_plot_df <- gapminder_nest %>% 
  mutate(panel = map_plot(data, lifeExp_plot))

trelliscope(gapminder_plot_df,
            name = "gapminder", nrow = 2, ncol = 3,
            state = list(labels = c("continent", "country", "lifeExp")),
            path="gapminder_lifeExp")