Code Editor for app.R

library(shiny)
library(bslib)
library(dplyr)
library(ggplot2)
library(DT)

source("data_prep.R")

ui <- page_fillable(
  title = "Titanic Dashboard",
  theme = bs_theme(bootswatch = "simplex"),
  
  # Key metrics in value boxes
  layout_columns(
    fill = FALSE,
    value_box(
      title = "Total Passengers",
      value = textOutput("total_passengers"),
      showcase = bsicons::bs_icon("people-fill")
    ),
    value_box(
      title = "Survival Rate",
      value = textOutput("survival_rate"),
      showcase = bsicons::bs_icon("heart-pulse-fill"),
      theme_color = "success"
    ),
    value_box(
      title = "Average Fare",
      value = textOutput("avg_fare"),
      showcase = bsicons::bs_icon("cash-coin"),
      theme_color = "info"
    )
  ),
  
  # Main content
  layout_columns(
    col_widths = c(8, 4),
    card(
      full_screen = TRUE,
      card_header("Survival Analysis by Class and Gender"),
      card_body(
        plotOutput("survival_plot", height = "400px")
      )
    ),
    card(
      full_screen = TRUE,
      card_header("Age Distribution"),
      card_body(
        plotOutput("age_plot", height = "400px")
      )
    )
  ),
  
  # Data table
  card(
    full_screen = TRUE,
    card_header("Passenger Details"),
    card_body(
      DTOutput("passenger_table")
    )
  )
)

server <- function(input, output) {
  bs_themer()
  # Load and prepare data
  titanic_data <- load_titanic_data()
  
  # Key metrics
  output$total_passengers <- renderText({
    nrow(titanic_data)
  })
  
  output$survival_rate <- renderText({
    paste0(round(mean(titanic_data$Survived) * 100, 1), "%")
  })
  
  output$avg_fare <- renderText({
    paste0("$", round(mean(titanic_data$Fare, na.rm = TRUE), 2))
  })
  
  # Survival plot
  output$survival_plot <- renderPlot({
    ggplot(titanic_data, aes(x = factor(Pclass), fill = factor(Survived))) +
      geom_bar(position = "fill") +
      facet_wrap(~Sex) +
      scale_fill_manual(values = c("#FF4136", "#2ECC40"),
                       labels = c("No", "Yes"),
                       name = "Survived") +
      scale_x_discrete(name = "Passenger Class") +
      scale_y_continuous(labels = scales::percent) +
      theme_minimal() +
      labs(y = "Survival Rate",
           title = "Survival Rate by Passenger Class and Gender")
  })
  
  # Age distribution plot
  output$age_plot <- renderPlot({
    ggplot(titanic_data, aes(x = Age, fill = factor(Survived))) +
      geom_density(alpha = 0.5) +
      scale_fill_manual(values = c("#FF4136", "#2ECC40"),
                       labels = c("No", "Yes"),
                       name = "Survived") +
      theme_minimal() +
      labs(x = "Age",
           y = "Density",
           title = "Age Distribution by Survival Status")
  })
  
  # Passenger table
  output$passenger_table <- renderDT({
    titanic_data %>%
      select(Name, Sex, Age, Pclass, Survived, Fare) %>%
      mutate(
        Survived = ifelse(Survived == 1, "Yes", "No"),
        Pclass = paste0("Class ", Pclass)
      ) %>%
      datatable(
        options = list(
          pageLength = 10,
          scrollX = TRUE,
          dom = 'Bfrtip'
        ),
        rownames = FALSE
      )
  })
}

shinyApp(ui = ui, server = server)