Interactive Dashboards

Static charts tell a story once. Shiny and Plotly let your audience explore the data themselves — filtering, zooming, and interacting with live dashboards built entirely in R.

1. What is Shiny?

Shiny is an R package for building interactive web applications directly from R code — no HTML, CSS, or JavaScript required (though you can add them for polish).

install.packages("shiny")
library(shiny)

2. Anatomy of a Shiny App

Every Shiny app has two core parts: a UI (what the user sees) and a server function (the logic that responds to input).

library(shiny)

ui <- fluidPage(
  titlePanel("Simple Shiny Dashboard"),
  sidebarLayout(
    sidebarPanel(
      sliderInput("bins", "Number of bins:", min = 5, max = 50, value = 20)
    ),
    mainPanel(
      plotOutput("histPlot")
    )
  )
)

server <- function(input, output) {
  output$histPlot <- renderPlot({
    hist(rnorm(500), breaks = input$bins,
         col = "steelblue", main = "Live Histogram")
  })
}

shinyApp(ui = ui, server = server)

3. Key UI Input Widgets

4. Reactive Expressions

Use reactive() to cache and reuse computed values across multiple outputs, avoiding redundant calculations:

server <- function(input, output) {
  filtered_data <- reactive({
    subset(df, department == input$dept)
  })

  output$table <- renderTable({ filtered_data() })
  output$plot <- renderPlot({ hist(filtered_data()$salary) })
}

5. Plotly — Interactive Charts in R

plotly converts static ggplot2 charts into fully interactive ones (hover tooltips, zoom, pan) with a single function call:

install.packages("plotly")
library(plotly)
library(ggplot2)

p <- ggplot(df, aes(x = age, y = salary, color = department)) +
  geom_point()

ggplotly(p)   # instantly interactive!

6. Combining Shiny + Plotly

library(shiny)
library(plotly)

ui <- fluidPage(
  plotlyOutput("interactivePlot")
)

server <- function(input, output) {
  output$interactivePlot <- renderPlotly({
    ggplotly(ggplot(df, aes(age, salary)) + geom_point())
  })
}

shinyApp(ui, server)

7. Production-Ready Checklist

• ✅ Understand ui + server structure — the two building blocks of every Shiny app.
• ✅ Use reactive() to avoid redundant computation — for cleaner, faster apps.
• ✅ Use ggplotly() for instant interactivity — on top of existing ggplot2 charts.
• ✅ Test with runApp() — before considering deployment (covered in the final lesson).