Hands-On Exercise 3: Interactive Data Visualisation in R

Importing Data

# Read CSV file
exam_data <- read_csv("/Users/sharon/OneDrive - Singapore Management University/isss608data/hands-on_exercise2/Exam_data.csv")

Rows: 322 Columns: 7
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (4): ID, CLASS, GENDER, RACE
dbl (3): ENGLISH, MATHS, SCIENCE

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Tooltip with ID (ggiraph)

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)

Tooltip with ID and CLASS

exam_data$tooltip <- paste0("Name = ", exam_data$ID, "\nClass = ", exam_data$CLASS)

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

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

Customising Tooltip Style

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)))

Tooltip with Summary Statistics

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 = "lightblue") +
  stat_summary(aes(y = MATHS),
               fun.data = mean_se,
               geom = "errorbar", width = 0.2, size = 0.2)

Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.

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

Hover Effect (data_id)

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)

Custom Hover Style

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;")
       ))

Tooltip + Hover Combined

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;")
       ))

Click Effect

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, y = 0)) +
  geom_point_interactive(aes(tooltip = ID), position = position_jitter(height = 0.1)) +
  scale_y_continuous(NULL, breaks = NULL)

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

Coordinated Multiple Views (ggiraph + patchwork)

p1 <- ggplot(data=exam_data, aes(x = MATHS)) +
  geom_dotplot_interactive(aes(data_id = ID),
                           stackgroups = TRUE,
                           binwidth = 1,
                           method = "histodot") +
  coord_cartesian(xlim = c(0,100)) +
  scale_y_continuous(NULL, breaks = NULL)

p2 <- ggplot(data=exam_data, aes(x = ENGLISH)) +
  geom_dotplot_interactive(aes(data_id = ID),
                           stackgroups = TRUE,
                           binwidth = 1,
                           method = "histodot") +
  coord_cartesian(xlim = c(0,100)) +
  scale_y_continuous(NULL, breaks = NULL)

girafe(code = print(p1 + p2),
       width_svg = 6,
       height_svg = 3,
       options = list(
         opts_hover(css = "fill: #202020;"),
         opts_hover_inv(css = "opacity:0.2;")
       ))

Interactive Plots with plotly

plot_ly(data = exam_data, 
        x = ~ENGLISH, 
        y = ~MATHS, 
        type = "scatter", 
        mode = "markers")

plot_ly(data = exam_data, 
        x = ~ENGLISH, 
        y = ~MATHS, 
        color = ~RACE,
        type = "scatter", 
        mode = "markers")

gg <- ggplot(data = exam_data, aes(x = ENGLISH, y = MATHS, color = RACE)) +
  geom_point()

ggplotly(gg)

shared_exam <- highlight_key(exam_data)

p1 <- ggplot(shared_exam, aes(x = ENGLISH, y = MATHS, color = RACE)) +
  geom_point()

p2 <- ggplot(shared_exam, aes(x = MATHS, y = SCIENCE, color = RACE)) +
  geom_point()

subplot(ggplotly(p1), ggplotly(p2), nrows = 1) %>%
  highlight("plotly_selected", dynamic = TRUE)

Adding more colors to the selection color palette.

Setting the `off` event (i.e., 'plotly_deselect') to match the `on` event (i.e., 'plotly_selected'). You can change this default via the `highlight()` function.

Interactive Data Table with DT + Crosstalk

datatable(shared_exam, class = "compact", extensions = "Scroller", options = list(scrollY = 200))

Linked Plot + Table (Brushing via Crosstalk)

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)        

Setting the `off` event (i.e., 'plotly_deselect') to match the `on` event (i.e., 'plotly_selected'). You can change this default via the `highlight()` function.