{"app.R": "library(shiny)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(bslib)\n\nsource(\"data.R\")\nsource(\"ui.R\") \nsource(\"server.R\")\n\nshinyApp(ui = ui, server = server)", "data.R": "library(magrittr)\nlibrary(dplyr)\n\n# Generate mock stock data\ngenerate_stock_data <- function(symbol, seed = 123) {\n set.seed(seed)\n \n # Generate dates for the past year\n dates <- seq(as.Date(Sys.Date() - 365), Sys.Date(), by = 'day')\n dates <- dates[!weekdays(dates) %in% c('Saturday', 'Sunday')]\n n <- length(dates)\n \n # Generate price series with random walk\n initial_price <- runif(1, 50, 200)\n returns <- rnorm(n, mean = 0.0002, sd = 0.02)\n price <- initial_price * exp(cumsum(returns))\n \n # Generate related prices and volume\n high <- price * (1 + abs(rnorm(n, 0, 0.01)))\n low <- price * (1 - abs(rnorm(n, 0, 0.01)))\n open <- price + rnorm(n, 0, 2)\n close <- price\n volume <- round(rnorm(n, mean = 1000000, sd = 300000))\n \n data.frame(\n Date = dates,\n Symbol = symbol,\n Open = open,\n High = high,\n Low = low,\n Close = close,\n Volume = abs(volume)\n )\n}\n\n# Generate data for multiple stocks\nstocks <- c('AAPL', 'GOOGL', 'MSFT', 'AMZN', 'META')\nstock_data <- do.call(rbind, lapply(seq_along(stocks), function(i) {\n generate_stock_data(stocks[i], seed = i)\n}))\n\n# Calculate technical indicators\ncalculate_ma <- function(data, n) {\n data %>%\n group_by(Symbol) %>%\n arrange(Date) %>%\n mutate(MA = zoo::rollmean(Close, k = n, fill = NA, align = 'right')) %>%\n ungroup()\n}\n\n# Calculate basic stats\ncalculate_stats <- function(data) {\n data %>%\n group_by(Symbol) %>%\n summarise(\n Mean_Price = mean(Close),\n Volatility = sd(Close) / mean(Close) * 100,\n Avg_Volume = mean(Volume),\n Price_Change = (last(Close) - first(Close)) / first(Close) * 100\n ) %>%\n ungroup()\n}", "server.R": "library(magrittr)\nsource('data.R')\nlibrary(shiny)\nlibrary(ggplot2)\nlibrary(dplyr)\n\nserver <- function(input, output, session) {\n \n # Filtered data based on user selection\n filtered_data <- reactive({\n stock_data %>%\n filter(\n Symbol %in% input$stock,\n Date >= input$date_range[1],\n Date <= input$date_range[2]\n )\n })\n \n # Price chart\n output$price_chart <- renderPlot({\n req(nrow(filtered_data()) > 0)\n \n if (input$chart_type == \"line\") {\n ggplot(filtered_data(), aes(x = Date, y = Close, color = Symbol)) +\n geom_line() +\n theme_minimal() +\n labs(y = \"Price\", title = \"Stock Price Trends\") +\n theme(legend.position = \"bottom\")\n } else {\n ggplot(filtered_data(), aes(x = Date)) +\n geom_segment(aes(xend = Date, y = Low, yend = High, color = Symbol), size = 0.5) +\n geom_rect(aes(xmin = Date - 0.25, xmax = Date + 0.25,\n ymin = pmin(Open, Close), ymax = pmax(Open, Close),\n fill = Symbol)) +\n theme_minimal() +\n labs(y = \"Price\", title = \"Stock Price Candlesticks\") +\n theme(legend.position = \"bottom\")\n }\n })\n \n # Moving averages chart\n output$ma_chart <- renderPlot({\n req(nrow(filtered_data()) > 0)\n \n ma_data <- filtered_data() %>%\n group_by(Symbol) %>%\n arrange(Date) %>%\n mutate(\n MA20 = calculate_ma(., 20)$MA,\n MA50 = calculate_ma(., 50)$MA\n )\n \n ggplot(ma_data, aes(x = Date)) +\n geom_line(aes(y = Close, color = Symbol)) +\n geom_line(aes(y = MA20, linetype = \"20-day MA\"), color = \"blue\", alpha = 0.5) +\n geom_line(aes(y = MA50, linetype = \"50-day MA\"), color = \"red\", alpha = 0.5) +\n theme_minimal() +\n labs(y = \"Price\", title = \"Moving Averages\") +\n theme(legend.position = \"bottom\")\n })\n \n # Volume chart\n output$volume_chart <- renderPlot({\n req(nrow(filtered_data()) > 0)\n \n ggplot(filtered_data(), aes(x = Date, y = Volume, fill = Symbol)) +\n geom_col(alpha = 0.7) +\n theme_minimal() +\n labs(y = \"Volume\", title = \"Trading Volume\") +\n theme(legend.position = \"bottom\")\n })\n \n # Statistics table\n output$stats_table <- renderTable({\n req(nrow(filtered_data()) > 0)\n \n calculate_stats(filtered_data()) %>%\n mutate(\n Mean_Price = round(Mean_Price, 2),\n Volatility = round(Volatility, 2),\n Avg_Volume = round(Avg_Volume / 1e6, 2),\n Price_Change = round(Price_Change, 2)\n ) %>%\n rename(\n \"Mean Price\" = Mean_Price,\n \"Volatility (%)\" = Volatility,\n \"Avg Volume (M)\" = Avg_Volume,\n \"Price Change (%)\" = Price_Change\n )\n }, digits = 2)\n}", "ui.R": "source('data.R')\nlibrary(shiny)\nlibrary(bslib)\n\nui <- page_fluid(\n theme = bs_theme(version = 5, bootswatch = \"flatly\"),\n \n # Header\n h1(\"Stock Analysis Dashboard\"),\n \n # Controls\n fluidRow(\n column(3,\n selectInput(\"stock\", \"Select Stock\",\n choices = unique(stock_data$Symbol),\n multiple = TRUE,\n selected = \"AAPL\"\n )\n ),\n column(5,\n dateRangeInput(\"date_range\", \"Date Range\",\n start = min(stock_data$Date),\n end = max(stock_data$Date)\n )\n ),\n column(4,\n selectInput(\"chart_type\", \"Chart Type\",\n choices = c(\"Candlestick\" = \"candlestick\", \"Line\" = \"line\"),\n selected = \"line\"\n )\n )\n ),\n \n # Main content\n fluidRow(\n # Price chart\n column(8,\n card(\n card_header(\"Price Trends\"),\n card_body(\n plotOutput(\"price_chart\", height = \"400px\")\n )\n )\n ),\n # Stats panel\n column(4,\n card(\n card_header(\"Statistics\"),\n card_body(\n tableOutput(\"stats_table\")\n )\n )\n )\n ),\n \n # Technical analysis\n fluidRow(\n column(6,\n card(\n card_header(\"Moving Averages\"),\n card_body(\n plotOutput(\"ma_chart\", height = \"300px\")\n )\n )\n ),\n column(6,\n card(\n card_header(\"Volume Analysis\"),\n card_body(\n plotOutput(\"volume_chart\", height = \"300px\")\n )\n )\n )\n )\n)", "install.R": "install.packages('bslib')\ninstall.packages('dplyr')\ninstall.packages('ggplot2')\ninstall.packages('magrittr')\ninstall.packages('shiny')", "startApp.R": "\nlibrary(shiny)\noptions(shiny.host = '0.0.0.0')\noptions(shiny.port = 80)\nrunApp('app.R')\n"}