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library(shiny)
library(datasets)
# Data pre-processing ----
# Tweak the "am" variable to have nicer factor labels -- since this
# doesn't rely on any user inputs, we can do this once at startup
# and then use the value throughout the lifetime of the app
mpgData <- mtcars
mpgData$am <- factor(mpgData$am, labels = c("Automatic", "Manual"))
# Define UI for miles per gallon app ----
ui <- fluidPage(
# App title ----
titlePanel("Miles Per Gallon"),
# Sidebar layout with input and output definitions ----
sidebarLayout(
# Sidebar panel for inputs ----
sidebarPanel(
# Input: Selector for variable to plot against mpg ----
selectInput("variable", "Variable:",
c("Cylinders" = "cyl",
"Transmission" = "am",
"Gears" = "gear")),
# Input: Checkbox for whether outliers should be included ----
checkboxInput("outliers", "Show outliers", TRUE)
),
# Main panel for displaying outputs ----
mainPanel(
# Output: Formatted text for caption ----
h3(textOutput("caption")),
# Output: Plot of the requested variable against mpg ----
plotOutput("mpgPlot")
)
)
)
# Define server logic to plot various variables against mpg ----
server <- function(input, output) {
# Compute the formula text ----
# This is in a reactive expression since it is shared by the
# output$caption and output$mpgPlot functions
formulaText <- reactive({
paste("mpg ~", input$variable)
})
# Return the formula text for printing as a caption ----
output$caption <- renderText({
formulaText()
})
# Generate a plot of the requested variable against mpg ----
# and only exclude outliers if requested
output$mpgPlot <- renderPlot({
boxplot(as.formula(formulaText()),
data = mpgData,
outline = input$outliers,
col = "#75AADB", pch = 19)
})
}
# Create Shiny app ----
shinyApp(ui, server)
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