{"app.R": "library(shiny)\nlibrary(bslib)\nlibrary(dplyr)\n\nsource(\"data.R\")\nsource(\"ui.R\")\nsource(\"server.R\")\n\nshinyApp(ui = ui, server = server)", "data.R": "# Generate sample real estate data\nset.seed(123)\nn_properties <- 100\n\nproperties <- data.frame(\n id = 1:n_properties,\n price = round(runif(n_properties, 100000, 1000000)),\n bedrooms = sample(1:6, n_properties, replace = TRUE),\n bathrooms = sample(c(1, 1.5, 2, 2.5, 3, 3.5, 4), n_properties, replace = TRUE),\n sqft = round(runif(n_properties, 500, 5000)),\n year_built = sample(1950:2023, n_properties, replace = TRUE),\n property_type = sample(c('Single Family', 'Condo', 'Townhouse', 'Multi-Family'), n_properties, replace = TRUE),\n location = sample(c('Downtown', 'Suburbs', 'Waterfront', 'Urban', 'Rural'), n_properties, replace = TRUE),\n condition = sample(c('Excellent', 'Good', 'Fair', 'Needs Work'), n_properties, replace = TRUE)\n)\n\n# Calculate price per sqft\nproperties$price_per_sqft <- round(properties$price / properties$sqft, 2)\n\n# Add a deal score (lower is better)\nproperties$deal_score <- with(properties, {\n # Normalize factors\n price_factor <- (price - min(price)) / (max(price) - min(price))\n sqft_factor <- 1 - (sqft - min(sqft)) / (max(sqft) - min(sqft))\n age_factor <- (2023 - year_built) / (2023 - min(year_built))\n condition_factor <- match(condition, c('Excellent', 'Good', 'Fair', 'Needs Work')) / 4\n \n # Combine factors (lower score = better deal)\n round((price_factor + sqft_factor + age_factor + condition_factor) / 4, 2)\n})", "server.R": "library(shiny)\nsource('data.R')\nlibrary(ggplot2)\n\nserver <- function(input, output, session) {\n \n # Filtered properties based on user inputs\n filtered_properties <- reactive({\n props <- properties\n \n # Apply filters\n props <- props[props$price >= input$price_range[1] & \n props$price <= input$price_range[2], ]\n \n props <- props[props$bedrooms >= input$bedrooms[1] & \n props$bedrooms <= input$bedrooms[2], ]\n \n if (input$property_type != 'All') {\n props <- props[props$property_type == input$property_type, ]\n }\n \n if (input$location != 'All') {\n props <- props[props$location == input$location, ]\n }\n \n if (input$condition != 'All') {\n props <- props[props$condition == input$condition, ]\n }\n \n props <- props[props$deal_score >= input$deal_score[1] & \n props$deal_score <= input$deal_score[2], ]\n \n props\n })\n \n # Statistics UI\n output$stats_ui <- renderUI({\n props <- filtered_properties()\n \n tagList(\n h4(sprintf('Found %d properties', nrow(props))),\n tags$ul(\n tags$li(sprintf('Average Price: $%s', format(mean(props$price), big.mark = ','))),\n tags$li(sprintf('Average Price/Sqft: $%s', round(mean(props$price_per_sqft), 2))),\n tags$li(sprintf('Average Deal Score: %s', round(mean(props$deal_score), 2)))\n )\n )\n })\n \n # Properties table\n output$properties_table <- renderTable({\n props <- filtered_properties()\n props <- props[order(props$deal_score), ] # Sort by deal score\n \n # Format the table\n data.frame(\n 'Price' = sprintf('$%s', format(props$price, big.mark = ',')),\n 'Beds' = props$bedrooms,\n 'Baths' = props$bathrooms,\n 'Sqft' = format(props$sqft, big.mark = ','),\n 'Type' = props$property_type,\n 'Location' = props$location,\n 'Deal Score' = props$deal_score\n )\n })\n \n # Price distribution plot\n output$price_dist <- renderPlot({\n ggplot(filtered_properties(), aes(x = price)) +\n geom_histogram(fill = '#007bff', bins = 30) +\n theme_minimal() +\n labs(x = 'Price', y = 'Count') +\n scale_x_continuous(labels = scales::dollar_format())\n })\n \n # Price by location plot\n output$price_by_location <- renderPlot({\n ggplot(filtered_properties(), aes(x = location, y = price_per_sqft)) +\n geom_boxplot(fill = '#007bff') +\n theme_minimal() +\n labs(x = 'Location', y = 'Price per Sqft') +\n scale_y_continuous(labels = scales::dollar_format()) +\n theme(axis.text.x = element_text(angle = 45, hjust = 1))\n })\n}", "ui.R": "library(shiny)\nlibrary(bslib)\nsource('data.R')\nui <- page_navbar(\n title = 'Real Estate Deal Finder',\n theme = bs_theme(version = 5, bootswatch = 'lux'),\n \n nav_panel(\n title = 'Property Search',\n layout_sidebar(\n sidebar = sidebar(\n title = 'Search Filters',\n \n sliderInput('price_range',\n 'Price Range',\n min = min(properties$price),\n max = max(properties$price),\n value = c(min(properties$price), max(properties$price)),\n step = 50000,\n pre = '$'),\n \n sliderInput('bedrooms',\n 'Bedrooms',\n min = min(properties$bedrooms),\n max = max(properties$bedrooms),\n value = c(min(properties$bedrooms), max(properties$bedrooms)),\n step = 1),\n \n selectInput('property_type',\n 'Property Type',\n choices = c('All', unique(properties$property_type)),\n selected = 'All'),\n \n selectInput('location',\n 'Location',\n choices = c('All', unique(properties$location)),\n selected = 'All'),\n \n selectInput('condition',\n 'Condition',\n choices = c('All', unique(properties$condition)),\n selected = 'All'),\n \n sliderInput('deal_score',\n 'Deal Score (lower is better)',\n min = 0,\n max = 1,\n value = c(0, 1),\n step = 0.1)\n ),\n \n layout_column_wrap(\n width = 1,\n heights_equal = 'row',\n \n card(\n card_header('Property Statistics'),\n card_body(\n uiOutput('stats_ui')\n )\n ),\n \n card(\n card_header('Available Properties'),\n card_body(\n tableOutput('properties_table')\n )\n )\n )\n )\n ),\n \n nav_panel(\n title = 'Deal Analysis',\n layout_column_wrap(\n width = 1/2,\n card(\n card_header('Price Distribution'),\n card_body(\n plotOutput('price_dist')\n )\n ),\n card(\n card_header('Price per Sqft by Location'),\n card_body(\n plotOutput('price_by_location')\n )\n )\n )\n )\n)", "install.R": "install.packages('bslib')\ninstall.packages('dplyr')\ninstall.packages('ggplot2')\ninstall.packages('shiny')", "startApp.R": "\nlibrary(shiny)\noptions(shiny.host = '0.0.0.0')\noptions(shiny.port = 80)\nrunApp('app.R')\n"}