In-class Exercise

Installing and loading R packages

Two packages will be installed and loaded. They are: tidyverse and ggiraph

pacman::p_load(ggiraph, plotly, gganimate, DT, patchwork, gganimate, gifski, gapminder, readxl, tidyverse)

importing data

exam_data <- read_csv("data/Exam_data.csv")

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(
    aes(tooltip = ID),
    stackgroups = TRUE, 
    binwidth = 1, 
    method = "histodot")

girafe(
  ggobj = p,
  width_svg = 6,
  height_svg = 6*0.618
)

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(              
    aes(tooltip = CLASS, 
        data_id = CLASS),              
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") + scale_y_continuous(NULL,               
                     breaks = NULL)            

girafe(                                  
  ggobj = p,                             
  width_svg = 6,                         
  height_svg = 6*0.618, options = list(                        
    opts_hover(css = "fill: #202020;"),  
    opts_hover_inv(css = "opacity:0.2;") 
  ) )                                       

exam_data$onclick <- sprintf("window.open(\"%s%s\")",
"https://www.moe.gov.sg/schoolfinder?journey=Primary%20school",
as.character(exam_data$ID))

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(              
    aes(onclick = onclick),              
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") +               
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(                                  
  ggobj = p,                             
  width_svg = 6,                         
  height_svg = 6*0.618)                                        

plot_ly(data = exam_data, 
        x = ~ENGLISH, 
        y = ~MATHS,
        text = ~paste("Student ID:", ID,     
                      "<br>Class:", CLASS),  
        color = ~RACE, 
        colors = "Set1") %>%
  layout(title = 'English Score versus Maths Score ', 
         xaxis = list(range = c(0, 100)),             
         yaxis = list(range = c(0, 100)))

d <- highlight_key(exam_data)  
p1 <- ggplot(data=d, 
            aes(x = MATHS,
                y = ENGLISH)) +
  geom_point(size=1) +
  coord_cartesian(xlim=c(0,100),
                  ylim=c(0,100))

p2 <- ggplot(data=d, 
            aes(x = MATHS,
                y = SCIENCE)) +
  geom_point(size=1) +
  coord_cartesian(xlim=c(0,100),
                  ylim=c(0,100))
subplot(ggplotly(p1),
        ggplotly(p2))

DT::datatable(exam_data, class= "compact")

d <- highlight_key(exam_data) 
p <- ggplot(d, 
            aes(ENGLISH, 
                MATHS)) + 
  geom_point(size=1) +
  coord_cartesian(xlim=c(0,100),
                  ylim=c(0,100))

gg <- highlight(ggplotly(p),        
                "plotly_selected")  

crosstalk::bscols(gg,               
                  DT::datatable(d), 
                  widths = 5)        

col <- c("Country", "Continent")
globalPop <- read_xls("data/GlobalPopulation.xls",
                      sheet="Data") %>% 
  mutate_each_(funs(factor(.)), col) %>% 
  mutate(Year = as.integer(Year))

ggplot(globalPop, aes(x = Old, y = Young, 
                      size = Population, 
                      colour = Country)) +
  geom_point(alpha = 0.7, 
             show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(title = 'Year: {frame_time}', 
       x = '% Aged', 
       y = '% Young') +
  transition_time(Year) +       #<<
  ease_aes('linear')            #<<