In-Class_Ex03

Installing and loading R packages

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

pacman::p_load(ggiraph,tidyverse, plotly)

Import data

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

ggplot2

ggplot(data=exam_data, aes(x = MATHS)) +
  geom_histogram(bins = 10,
                 boundary = 100,
                 color="black",
                 fill="grey") +
  ggtitle("Distribution of maths scores")

Dot Plot

ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot(dotsize = 0.5)

Visual Interactivity with girafe

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

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

To display multiple information on tooltip

exam_data$tooltip <- c(paste0(     #<<
  "Name = ", exam_data$ID,         #<<
  "\n Class = ", exam_data$CLASS)) #<<

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(
    aes(tooltip = exam_data$tooltip), #<<
    stackgroups = TRUE,
    binwidth = 1,
    method = "histodot") +
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(
  ggobj = p,
  width_svg = 8,
  height_svg = 8*0.618
)

Customize tooltip: change background and font.

tooltip_css <- "background-color:white; #<<
font-style:bold; color:black;" #<<

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(              
    aes(tooltip = ID),                   
    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_tooltip(    #<<
      css = tooltip_css)) #<<
)                                        

To add in statistics on tooltip

We write in function and pass mean, standard error, and paste them onto the plot.

tooltip <- function(y, ymax, accuracy = .01) {   #<<
  mean <- scales::number(y, accuracy = accuracy) #<<
  sem <- scales::number(ymax - y, accuracy = accuracy) #<<
  paste("Mean maths scores:", mean, "+/-", sem) #<<
} #<<

gg_point <- ggplot(data=exam_data, 
                   aes(x = RACE),
) +
  stat_summary(aes(y = MATHS, 
                   tooltip = after_stat(  #<<
                     tooltip(y, ymax))),  #<<
    fun.data = "mean_se", 
    geom = GeomInteractiveCol,  #<<
    fill = "light blue"
  ) +
  stat_summary(aes(y = MATHS),
    fun.data = mean_se,
    geom = "errorbar", width = 0.2, size = 0.2
  )

girafe(ggobj = gg_point,
       width_svg = 8,
       height_svg = 8*0.618)

Using data_id() to show clusters

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(           
    aes(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                      
)                                        

We use hover effect

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(              
    aes(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;") #<<
  )                                      #<<  
)                                        

Combine the tooltip and hover effect

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;") 
  )                                        
)                                        

Using onclick()

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)                                        

Try Plotly()

plot_ly(data = exam_data, 
             x = ~MATHS, 
             y = ~ENGLISH)

Use plotly() color mapping

plot_ly(data = exam_data, 
        x = ~ENGLISH, 
        y = ~MATHS, 
        color = ~RACE) #<<

Change color pallete

plot_ly(data = exam_data, 
        x = ~ENGLISH, 
        y = ~MATHS, 
        color = ~RACE, 
        colors = "Set1") #<<