Graphical Queries - Brush Events

An introduction to brush events in Shiny.

Code, Recording

  1. Click events are useful for referring to individual samples. However, they are not ideal for referring to groups of samples. In this case, a useful type of plot input is a brush. This is a selection that can be defined by clicking and dragging over a region.

  2. In shiny, brush events are treated similarly to click events. For example, to define a new brush input, we can set the brush argument to plotOutput.

    ui <- fluidPage(
   plotOutput("plot", brush = "plot_brush")
 )

    Just like the click argument, the value "plot_brush" is an ID that can be used in the server. Also like in click events, we can setup an observer to change a reactive value every time a brush is drawn1. The general pattern is similar to what we had before,

    server <- function(input, output) {
  selected <- reactiveVal(initial value)
  observeEvent(
    input$plot_brush,
    ... computation using get new_value ...
    selected(new_value)
  )

  output$plot <- renderPlot(... use scatter() reactive val...)
}

  3. The example below is similar to the plot_click example from the previous notes. Instead of sorting points by proximity to the click, though, prints the subset of rows that have been currently brushed.

    library(tidyverse)
 library(shiny)
 mtcars <- add_rownames(mtcars)
 
 reset_selection <- function(x, brush) {
   brushedPoints(x, brush, allRows = TRUE)$selected_
 }
 
 scatter <- function(x, selected_) {
   x %>%
     mutate(selected_ = selected_) %>%
     ggplot() +
     geom_point(aes(mpg, hp, alpha = as.numeric(selected_))) +
     scale_alpha(range = c(0.1, 1))
 }
 
 ui <- fluidPage(
   plotOutput("plot", brush = "plot_brush"),
   dataTableOutput("table")
 )
 
 server <- function(input, output) {
   selected <- reactiveVal(rep(TRUE, nrow(mtcars)))
   observeEvent(
     input$plot_brush,
     selected(reset_selection(mtcars, input$plot_brush))
   )
   
   output$plot <- renderPlot(scatter(mtcars, selected()))
   output$table <- renderDataTable(filter(mtcars, selected()))
 }
 
 shinyApp(ui, server)

  4. It is often useful to combine multi-view composition (i.e., faceting or compound figures) with dynamic queries. The basic idea is to (a) show different aspects of a dataset using different views, and then (b) link the views using dynamic queries. This strategy is sometimes called dynamic linking.

  5. The example below implements dynamic linking with the penguins dataset. Brushing over either scatterplot highlights the corresponding points in the adjacent plot (it also updates the data table). This is a way of understanding structure beyond two dimensions. The implementation is similar to the brushing above, except that the reactive value selected() is called in two renderPlot contexts, leading to changes in both plots every time the brush is moved.

    library(tidyverse)
 library(shiny)
 penguins <- read_csv("https://uwmadison.box.com/shared/static/ijh7iipc9ect1jf0z8qa2n3j7dgem1gh.csv")
 
 reset_selection <- function(x, brush) {
   brushedPoints(x, brush, allRows = TRUE)$selected_
 }
 
 scatter <- function(x, selected_, var1, var2) {
   x %>%
     mutate(selected_ = selected_) %>%
     ggplot(aes_string(var1, var2)) +
     geom_point(aes(alpha = as.numeric(selected_), col = species)) +
     scale_alpha(range = c(0.1, 1))
 }
 
 ui <- fluidPage(
   fluidRow(
     column(6, plotOutput("scatter1", brush = "plot_brush")),
     column(6, plotOutput("scatter2", brush = "plot_brush"))
   ),
   dataTableOutput("table")
 )
 
 server <- function(input, output) {
   selected <- reactiveVal(rep(TRUE, nrow(penguins)))
   observeEvent(
     input$plot_brush,
     selected(reset_selection(penguins, input$plot_brush))
   )
   
   output$scatter1 <- renderPlot({
     scatter(penguins, selected(), "bill_length_mm", "bill_depth_mm")
   })
   output$scatter2 <- renderPlot({
     scatter(penguins, selected(), "flipper_length_mm", "body_mass_g")
   })
   
   output$table <- renderDataTable(filter(penguins, selected()))
 }
 
 shinyApp(ui, server)