shiny gadget that removes outliers based on 'brushed' area of a plot

outlier_rm.R

# install.packages("shiny")
# install.packages("miniUI")
# install.packages("ggplot2")
library(shiny)
library(miniUI)
library(ggplot2)

outlier_rm <- function(data, xvar, yvar) {
    
    ui <- miniPage(
        gadgetTitleBar("Drag to select points"),
        miniContentPanel(
            # The brush="brush" argument means we can listen for
            # brush events on the plot using input$brush.
            plotOutput("plot", height = "100%", brush = "brush")
            )
        )
    
    server <- function(input, output, session) {
        
        # Render the plot
        output$plot <- renderPlot({
            # Plot the data with x/y vars indicated by the caller.
            ggplot(data, aes_string(xvar, yvar)) + geom_point()
        })
        
        # Handle the Done button being pressed.
        observeEvent(input$done, {
            
            # create id for data
            data$id <- 1:nrow(data)
            
            # Return the brushed points. See ?shiny::brushedPoints.
            p <- brushedPoints(data, input$brush)
            
            # create vector of ids that match brushed points and data
            g <- which(p$id %in% data$id)
            
            # return a subset of the original data without brushed points
            stopApp(data[-g,])
        })
    }
    
    runGadget(ui, server)
}

# run to open plot viewer
# click and drag to brush
# press done return a subset of the original data without brushed points
# install.packages("gapminder")
library(gapminder)
outlier_rm(gapminder, "lifeExp", "gdpPercap")

# you can also use the same method above but pass the output into a dplyr pipe syntax
# without the selection what is the mean life expectancy by country?
# install.packages("dplyr")
library(dplyr)
outlier_rm(gapminder, "lifeExp", "gdpPercap") %>%
    group_by(country) %>%
    summarise(mean(lifeExp))