{"app.R": "", "data.R": "library(ggplot2)\nlibrary(dplyr)\n\n", "ui.R": "library(shiny)\nlibrary(ggplot2)\nlibrary(dplyr)\n\n# Generate sample dataset\nset.seed(123)\n\ngenerate_sample_data <- function() {\n data.frame(\n date = seq(as.Date('2023-01-01'), as.Date('2023-12-31'), by='day'),\n sales = rnorm(365, mean = 1000, sd = 200),\n customers = rpois(365, lambda = 500),\n satisfaction = runif(365, min = 3.5, max = 5.0)\n )\n}\n\nsample_data <- generate_sample_data()\n\nui <- fluidPage(\n theme = bslib::bs_theme(bootswatch = 'flatly'),\n \n # Header\n titlePanel('Interactive Data Explorer'),\n \n # Sidebar with controls\n sidebarLayout(\n sidebarPanel(\n selectInput('metric',\n 'Choose Metric:',\n choices = c('Sales' = 'sales',\n 'Customers' = 'customers',\n 'Satisfaction' = 'satisfaction')),\n \n dateRangeInput('date_range',\n 'Select Date Range:',\n start = '2023-01-01',\n end = '2023-12-31'),\n \n checkboxInput('show_trend',\n 'Show Trend Line',\n value = TRUE),\n \n radioButtons('plot_type',\n 'Plot Type:',\n choices = c('Line' = 'line',\n 'Point' = 'point',\n 'Both' = 'both')),\n \n width = 3\n ),\n \n # Main panel with plots and summary\n mainPanel(\n tabsetPanel(\n tabPanel('Plot',\n br(),\n plotOutput('main_plot', height = '400px'),\n br(),\n div(class = 'well',\n h4('Summary Statistics'),\n tableOutput('summary_stats'))\n ),\n tabPanel('Data Table',\n br(),\n DT::dataTableOutput('data_table'))\n ),\n width = 9\n )\n )\n)\n\n\nserver <- function(input, output, session) {\n \n # Filtered data based on date range\n filtered_data <- reactive({\n sample_data %>%\n filter(date >= input$date_range[1],\n date <= input$date_range[2])\n })\n \n # Main plot\n output$main_plot <- renderPlot({\n p <- ggplot(filtered_data(), aes(x = date, y = .data[[input$metric]])) +\n labs(title = paste('Time Series of', tools::toTitleCase(input$metric)),\n x = 'Date',\n y = tools::toTitleCase(input$metric)) +\n theme_minimal() +\n theme(plot.title = element_text(size = 16, face = 'bold'),\n axis.title = element_text(size = 12))\n \n # Add geometry based on plot type\n if (input$plot_type %in% c('line', 'both')) {\n p <- p + geom_line(color = '#2c3e50', alpha = 0.7)\n }\n if (input$plot_type %in% c('point', 'both')) {\n p <- p + geom_point(color = '#2c3e50', alpha = 0.5)\n }\n \n # Add trend line if requested\n if (input$show_trend) {\n p <- p + geom_smooth(method = 'loess', se = TRUE, color = '#e74c3c')\n }\n \n p\n })\n \n # Summary statistics\n output$summary_stats <- renderTable({\n data <- filtered_data()[[input$metric]]\n data.frame(\n Statistic = c('Mean', 'Median', 'Standard Deviation', 'Minimum', 'Maximum'),\n Value = c(\n round(mean(data), 2),\n round(median(data), 2),\n round(sd(data), 2),\n round(min(data), 2),\n round(max(data), 2)\n )\n )\n })\n \n # Data table\n output$data_table <- DT::renderDataTable({\n filtered_data() %>%\n mutate(across(where(is.numeric), round, 2))\n }, options = list(pageLength = 10))\n}\nshinyApp(ui = ui, server = server)", "server.R": "library(ggplot2)\nlibrary(dplyr)\n\nserver <- function(input, output, session) {\n \n # Filtered data based on date range\n filtered_data <- reactive({\n sample_data %>%\n filter(date >= input$date_range[1],\n date <= input$date_range[2])\n })\n \n # Main plot\n output$main_plot <- renderPlot({\n p <- ggplot(filtered_data(), aes(x = date, y = .data[[input$metric]])) +\n labs(title = paste('Time Series of', tools::toTitleCase(input$metric)),\n x = 'Date',\n y = tools::toTitleCase(input$metric)) +\n theme_minimal() +\n theme(plot.title = element_text(size = 16, face = 'bold'),\n axis.title = element_text(size = 12))\n \n # Add geometry based on plot type\n if (input$plot_type %in% c('line', 'both')) {\n p <- p + geom_line(color = '#2c3e50', alpha = 0.7)\n }\n if (input$plot_type %in% c('point', 'both')) {\n p <- p + geom_point(color = '#2c3e50', alpha = 0.5)\n }\n \n # Add trend line if requested\n if (input$show_trend) {\n p <- p + geom_smooth(method = 'loess', se = TRUE, color = '#e74c3c')\n }\n \n p\n })\n \n # Summary statistics\n output$summary_stats <- renderTable({\n data <- filtered_data()[[input$metric]]\n data.frame(\n Statistic = c('Mean', 'Median', 'Standard Deviation', 'Minimum', 'Maximum'),\n Value = c(\n round(mean(data), 2),\n round(median(data), 2),\n round(sd(data), 2),\n round(min(data), 2),\n round(max(data), 2)\n )\n )\n })\n \n # Data table\n output$data_table <- DT::renderDataTable({\n filtered_data() %>%\n mutate(across(where(is.numeric), round, 2))\n }, options = list(pageLength = 10))\n}"}