R Shiny Basics

SHINY APP STRUCTURE:

app.R (or separate ui.R + server.R)
  ├── ui      — What users see (HTML layout)
  │     ├── Input widgets (sliders, dropdowns, date pickers)
  │     └── Output placeholders (plots, tables, text)
  └── server  — What R computes (reactive logic)
        ├── render*() — Creates outputs
        └── reactive() — Intermediate computations

REACTIVITY:
  Input changes → reactive() recomputes → render*() updates output
  All connected automatically — no manual event handling!

library(shiny)
library(ggplot2)
library(dplyr)

# Generate data
set.seed(42)
n <- 500
sales_data <- data.frame(
  date     = seq(as.Date("2024-01-01"), by="day", length.out=n),
  region   = sample(c("North","South","East","West"), n, TRUE),
  product  = sample(c("Laptop","Phone","Tablet","Monitor"), n, TRUE),
  revenue  = round(runif(n, 500, 5000), -2),
  profit   = round(runif(n, 100, 2000), -2)
)

# ─── UI ───────────────────────────────────────────────
ui <- fluidPage(
  titlePanel("📊 Sales Analytics Dashboard"),
  tags$head(tags$style(HTML("
    body { font-family: &#039;Inter', sans-serif; background: #F8F9FA; }
    .well { background: white; border-radius: 8px; }
    h2 { color: #1565C0; }
  "))),

  sidebarLayout(
    sidebarPanel(
      h4("Filters"),
      dateRangeInput("date_range", "Date Range:",
                     start=min(sales_data$date),
                     end=max(sales_data$date)),
      selectInput("region", "Region:",
                  choices=c("All", unique(sales_data$region)),
                  selected="All"),
      checkboxGroupInput("products", "Products:",
                          choices=unique(sales_data$product),
                          selected=unique(sales_data$product)),
      hr(),
      actionButton("reset", "Reset Filters", class="btn-primary btn-sm"),
      br(), br(),
      downloadButton("download_data", "Download CSV")
    ),

    mainPanel(
      # KPI cards
      fluidRow(
        valueBoxOutput("total_revenue", width=4),
        valueBoxOutput("total_profit",  width=4),
        valueBoxOutput("total_orders",  width=4)
      ),
      # Charts
      fluidRow(
        column(8, plotOutput("revenue_trend", height="300px")),
        column(4, plotOutput("region_pie",    height="300px"))
      ),
      fluidRow(
        column(6, plotOutput("product_bar", height="250px")),
        column(6, plotOutput("profit_scatter", height="250px"))
      ),
      # Data table
      h4("Transaction Details"),
      DT::dataTableOutput("data_table")
    )
  )
)

# ─── SERVER ───────────────────────────────────────────
server <- function(input, output, session) {

  # Reactive: filtered data (recomputes whenever input changes)
  filtered <- reactive({
    df <- sales_data %>%
      filter(date >= input$date_range[1],
             date <= input$date_range[2],
             product %in% input$products)
    if (input$region != "All") df <- df %>% filter(region == input$region)
    df
  })

  # Reset filters
  observeEvent(input$reset, {
    updateDateRangeInput(session, "date_range",
                          start=min(sales_data$date), end=max(sales_data$date))
    updateSelectInput(session, "region", selected="All")
    updateCheckboxGroupInput(session, "products",
                              selected=unique(sales_data$product))
  })

  # KPI Outputs
  output$total_revenue <- renderValueBox({
    valueBox(paste0("$", format(round(sum(filtered()$revenue)/1000,1), nsmall=1), "K"),
              "Total Revenue", icon=icon("dollar-sign"), color="blue")
  })
  output$total_profit <- renderValueBox({
    valueBox(paste0("$", format(round(sum(filtered()$profit)/1000,1), nsmall=1), "K"),
              "Total Profit", icon=icon("trending-up"), color="green")
  })
  output$total_orders <- renderValueBox({
    valueBox(nrow(filtered()), "Total Orders", icon=icon("shopping-cart"), color="purple")
  })

  # Revenue trend chart
  output$revenue_trend <- renderPlot({
    filtered() %>%
      group_by(date) %>%
      summarise(revenue=sum(revenue), .groups="drop") %>%
      ggplot(aes(date, revenue)) +
      geom_line(color="#1565C0", size=1) +
      geom_smooth(method="loess", color="red", alpha=0.2) +
      scale_y_continuous(labels=function(x) paste0("$",x/1000,"K")) +
      labs(title="Daily Revenue Trend", x=NULL, y="Revenue") +
      theme_minimal()
  })

  # Region pie
  output$region_pie <- renderPlot({
    filtered() %>%
      group_by(region) %>%
      summarise(revenue=sum(revenue), .groups="drop") %>%
      ggplot(aes(x="", y=revenue, fill=region)) +
      geom_col(width=1) + coord_polar("y") +
      scale_fill_manual(values=c("#1565C0","#2E7D32","#E65100","#6A1B9A")) +
      labs(title="Revenue by Region") +
      theme_void() + theme(legend.position="bottom")
  })

  # Product bar
  output$product_bar <- renderPlot({
    filtered() %>%
      group_by(product) %>%
      summarise(revenue=sum(revenue), .groups="drop") %>%
      ggplot(aes(reorder(product,revenue), revenue, fill=product)) +
      geom_col(show.legend=FALSE) + coord_flip() +
      scale_y_continuous(labels=function(x) paste0("$",x/1000,"K")) +
      labs(title="Revenue by Product", x=NULL) + theme_minimal()
  })

  # Scatter
  output$profit_scatter <- renderPlot({
    ggplot(filtered(), aes(revenue, profit, color=region)) +
      geom_point(alpha=0.4, size=2) +
      scale_color_manual(values=c("#1565C0","#2E7D32","#E65100","#6A1B9A")) +
      labs(title="Revenue vs Profit", x="Revenue", y="Profit") +
      theme_minimal() + theme(legend.position="bottom")
  })

  # Data table
  output$data_table <- DT::renderDataTable(
    filtered() %>% arrange(desc(date)) %>%
      mutate(revenue=paste0("$", format(revenue, big.mark=",")),
             profit=paste0("$", format(profit, big.mark=","))),
    options=list(pageLength=10, scrollX=TRUE)
  )

  # Download
  output$download_data <- downloadHandler(
    filename = function() paste0("sales_", Sys.Date(), ".csv"),
    content  = function(file) write.csv(filtered(), file, row.names=FALSE)
  )
}

shinyApp(ui, server)
# Run: app.R → Click "Run App" in RStudio