Dry run \(\alpha\)

The presidential data set in ggplot2 package contains the names of each president, the start and end date of their term, and their party of 11 US presidents from Eisenhower to Obama. Draw the following plot.

library(ggplot2)
library(dplyr)

knitr::kable(presidential)

name	start	end	party
Eisenhower	1953-01-20	1961-01-20	Republican
Kennedy	1961-01-20	1963-11-22	Democratic
Johnson	1963-11-22	1969-01-20	Democratic
Nixon	1969-01-20	1974-08-09	Republican
Ford	1974-08-09	1977-01-20	Republican
Carter	1977-01-20	1981-01-20	Democratic
Reagan	1981-01-20	1989-01-20	Republican
Bush	1989-01-20	1993-01-20	Republican
Clinton	1993-01-20	2001-01-20	Democratic
Bush	2001-01-20	2009-01-20	Republican
Obama	2009-01-20	2017-01-20	Democratic

presidential %>%
  mutate(id = 33 + 1:nrow(presidential)) %>%
  ggplot(aes(start, id, colour = party)) +
  geom_point() +
  geom_segment(aes(xend = end, yend = id)) +
  scale_colour_manual(values = c(Republican = "red", Democratic = "blue")) +
  theme_bw()

Dry run \(\beta\)

Using the gapminder dataset, gg(animate) the temporal evolution of GDP per capita versus life expectancy for all countries in continent Asia in reverse chronological order. Observe the animation and draw your conclusions or issue some questions.

library(ggplot2)
library(gganimate)
library(gapminder)
library(dplyr)

gapminder %>% 
  filter(continent == "Asia") %>%
  ggplot(aes(gdpPercap, lifeExp, size = pop, colour = country)) +
  geom_point(alpha = 0.5, show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 20)) +
  scale_x_log10() +
  theme_minimal() +
  # Here comes the gganimate specific bits
  labs(title = 'Year: {-frame_time}', x = 'GDP per capita (log)', y = 'life expectancy') +
  transition_time(-year) +
  ease_aes('linear') +
  shadow_wake(0.20, wrap = FALSE)

anim_save("gapminderAsia.gif")

Dry run \(\gamma\)

1. Design the following shiny app using gapminder dataset
2. Create an account on Shinyapps.io and deploy your app there

library(gapminder)
library(dplyr)
library(ggplot2)
library(shiny)


ui = fluidPage(
  
  titlePanel("Gapminder Data"),
  
  sidebarLayout(
    sidebarPanel(
      # Year input
      sliderInput("year",
                  "Year",
                  min = 1952,
                  max = 2007,
                  step = 5,
                  value = 1952),
      
      # Continent input
      selectInput("continent", "Continent", 
                  choices = list("Africa", "Americas", "Asia", "Europe", "Oceania", "All"), 
                  selected = "All"),
      # x input
      radioButtons("xaxis", "X",
                   choices = list("Life expectancy" = "lifeExp", 
                                  "Population" = "pop", 
                                  "GDP per capita" = "gdpPercap"), 
                   selected = "gdpPercap"),
      
      
      # y input
      radioButtons("yaxis", "Y",
                   choices = list("Life expectancy" = "lifeExp", 
                                  "Population" = "pop", 
                                  "GDP per capita" = "gdpPercap"), 
                   selected = "lifeExp")
      
    
    ),
    
    
    # scatter plot output
    mainPanel(
      plotOutput("scatterPlot")
    )
  )
)

server <- function(input, output) {
  
  output$scatterPlot <- renderPlot({
    # generate dataset
    if (input$continent == "All") {
      dataset = filter(gapminder, year == input$year)
    } else {
      dataset = filter(gapminder, year == input$year, continent == input$continent)
    }
    
    # draw the scatter plot
    ggplot(dataset, aes_string(input$xaxis, input$yaxis, size = "pop", colour = "country")) +
      geom_point(alpha = 0.5, show.legend = FALSE) +
      scale_colour_manual(values = country_colors) +
      scale_size(range = c(2, 20)) +
      scale_x_log10() +
      scale_y_log10() +
      theme_minimal()
    })
}


shinyApp(ui = ui, server = server)

Dry run \(\delta\)

Use HTML widgets plotly and DataTables to plot and table the gapminder dataset for continent Africa and year 2007.

library(ggplot2)
library(plotly)
library(gapminder)
library(dplyr)

dataset = filter(gapminder, continent == "Africa", year == 2007)

p = dataset %>%
  ggplot(aes(gdpPercap, lifeExp, size = pop, colour = country)) +
  geom_point(alpha = 0.5, show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 10)) +
  scale_x_log10() +
  theme_minimal()


ggplotly(p)

library(DT)
datatable(dataset, options = list(pageLength = 10))

Dry run \(\epsilon\)

Make a storyboard (html, Rmd) depicting the significant moments of the temporal evolution of GDP per capita and life expectancy for countries in continent Africa.