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@@ -1,14 +1,57 @@
-FROM rocker/r-base:latest
+# Dockerfile for Hugging Face Spaces - R Shiny App
+# Quality Growth Strategy Dashboard
 
-WORKDIR /code
+FROM rocker/shiny:4.3.2
 
-RUN install2.r --error \
-    shiny \
-    dplyr \
-    ggplot2 \
-    readr \
-    ggExtra
-    
-COPY . .
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    libcurl4-gnutls-dev \
+    libssl-dev \
+    libxml2-dev \
+    libfontconfig1-dev \
+    libfreetype6-dev \
+    libpng-dev \
+    libtiff5-dev \
+    libjpeg-dev \
+    libharfbuzz-dev \
+    libfribidi-dev \
+    pandoc \
+    && rm -rf /var/lib/apt/lists/*
 
-CMD ["R", "--quiet", "-e", "shiny::runApp(host='0.0.0.0', port=7860)"]
+# Install R packages
+RUN R -e "install.packages(c(\
+    'shiny', \
+    'shinydashboard', \
+    'ggplot2', \
+    'dplyr', \
+    'tidyr', \
+    'plotly', \
+    'DT', \
+    'scales' \
+), repos='https://cloud.r-project.org/')"
+
+# Create app directory
+RUN mkdir -p /srv/shiny-server/app
+
+# Copy app files
+COPY app.R /srv/shiny-server/app/
+
+# Set permissions
+RUN chown -R shiny:shiny /srv/shiny-server/app
+
+# Expose port (Hugging Face uses 7860)
+EXPOSE 7860
+
+# Configure shiny-server to use port 7860
+RUN echo "run_as shiny;\n\
+server {\n\
+  listen 7860;\n\
+  location / {\n\
+    site_dir /srv/shiny-server/app;\n\
+    log_dir /var/log/shiny-server;\n\
+    directory_index on;\n\
+  }\n\
+}" > /etc/shiny-server/shiny-server.conf
+
+# Start shiny-server
+CMD ["/usr/bin/shiny-server"]

app.R

@@ -1,58 +1,838 @@
+# ============================================================================
+# Quality Growth Strategy Dashboard
+# グローバルマーケティング戦略及びソートリーダーシップの高度化
+# データドリブンな経営ナラティブによる「Quality Growth」の実現
+# ============================================================================
+
 library(shiny)
-library(bslib)
-library(dplyr)
+library(shinydashboard)
 library(ggplot2)
+library(dplyr)
+library(tidyr)
+library(plotly)
+library(DT)
+library(scales)
+
+# ============================================================================
+# データ生成（架空データ）
+# ============================================================================
+
+set.seed(2026)
+
+# 1. 海外ブランディング時系列データ（24ヶ月）
+create_branding_data <- function() {
+  n_months <- 24
+  dates <- seq(as.Date("2024-01-01"), by = "month", length.out = n_months)
+  
+  # トレンド + 季節性 + ノイズ
+  trend <- seq(200, 1000, length.out = n_months)
+  seasonal <- 100 * sin(seq(0, 4*pi, length.out = n_months))
+  noise <- rnorm(n_months, 0, 50)
+  
+  spend <- trend + seasonal + noise
+  search <- 0.8 * lag(spend, 1, default = 200) + 1.2 * lag(spend, 2, default = 200) + rnorm(n_months, 0, 30)
+  revenue <- 2000 + 3.5 * lag(spend, 2, default = 300) + 2.8 * lag(search, 1, default = 400) + rnorm(n_months, 0, 200)
+  
+  data.frame(
+    Date = dates,
+    Spend = round(spend, 0),
+    Search = round(pmax(search, 100), 0),
+    Revenue = round(pmax(revenue, 2000), 0)
+  )
+}
+
+# 2. 地域別ROIデータ
+create_regional_data <- function() {
+  regions <- c("US", "Europe", "Asia", "LATAM", "MEA")
+  n_per_region <- 12
+  
+  regional_data <- do.call(rbind, lapply(regions, function(region) {
+    base_spend <- switch(region,
+      "US" = 800, "Europe" = 600, "Asia" = 400, "LATAM" = 200, "MEA" = 150
+    )
+    roi_mult <- switch(region,
+      "US" = 3.99, "Europe" = 3.53, "Asia" = 6.48, "LATAM" = 4.2, "MEA" = 5.1
+    )
+    
+    spend <- base_spend + rnorm(n_per_region, 0, base_spend * 0.2)
+    search <- spend * runif(n_per_region, 1.2, 1.8) + rnorm(n_per_region, 0, 50)
+    revenue <- spend * roi_mult + rnorm(n_per_region, 0, spend * 0.3)
+    
+    data.frame(
+      Region = region,
+      Month = seq(as.Date("2025-01-01"), by = "month", length.out = n_per_region),
+      Spend = round(spend, 0),
+      Search = round(search, 0),
+      Revenue = round(revenue, 0),
+      Tech_Score = round(runif(n_per_region, 55, 90), 1),
+      Design_Score = round(runif(n_per_region, 55, 90), 1),
+      Trust_Score = round(runif(n_per_region, 55, 90), 1)
+    )
+  }))
+  
+  regional_data
+}
 
-df <- readr::read_csv("penguins.csv")
-# Find subset of columns that are suitable for scatter plot
-df_num <- df |> select(where(is.numeric), -Year)
-
-ui <- page_sidebar(
-  theme = bs_theme(bootswatch = "minty"),
-  title = "Penguins explorer",
-  sidebar = sidebar(
-    varSelectInput("xvar", "X variable", df_num, selected = "Bill Length (mm)"),
-    varSelectInput("yvar", "Y variable", df_num, selected = "Bill Depth (mm)"),
-    checkboxGroupInput("species", "Filter by species",
-      choices = unique(df$Species), selected = unique(df$Species)
+# 3. ソートリーダーシップデータ
+create_tl_data <- function() {
+  n_sessions <- 600
+  channels <- c("Organic", "Paid", "Social", "Direct", "Referral")
+  reports <- c(
+    "AI活用事例2025", "サステナビリティ報告", 
+    "DX推進白書", "次世代インフラ展望", "グローバル市場分析"
+  )
+  
+  channel_probs <- c(0.32, 0.19, 0.20, 0.19, 0.10)
+  
+  tl_data <- data.frame(
+    session_id = paste0("S", sprintf("%04d", 1:n_sessions)),
+    channel = sample(channels, n_sessions, replace = TRUE, prob = channel_probs),
+    report = sample(reports, n_sessions, replace = TRUE),
+    duration_sec = NA,
+    scrolled = NA,
+    pages_viewed = NA
+  )
+  
+  # チャネル別の行動パターン
+  for (i in 1:nrow(tl_data)) {
+    ch <- tl_data$channel[i]
+    if (ch == "Referral") {
+      tl_data$duration_sec[i] <- round(rnorm(1, 130, 40))
+      tl_data$scrolled[i] <- runif(1) < 0.85
+      tl_data$pages_viewed[i] <- rpois(1, 3) + 1
+    } else if (ch == "Organic") {
+      tl_data$duration_sec[i] <- round(rnorm(1, 105, 35))
+      tl_data$scrolled[i] <- runif(1) < 0.75
+      tl_data$pages_viewed[i] <- rpois(1, 2.5) + 1
+    } else if (ch == "Direct") {
+      tl_data$duration_sec[i] <- round(rnorm(1, 70, 30))
+      tl_data$scrolled[i] <- runif(1) < 0.60
+      tl_data$pages_viewed[i] <- rpois(1, 2) + 1
+    } else if (ch == "Social") {
+      tl_data$duration_sec[i] <- round(rnorm(1, 65, 35))
+      tl_data$scrolled[i] <- runif(1) < 0.50
+      tl_data$pages_viewed[i] <- rpois(1, 1.5) + 1
+    } else {  # Paid
+      tl_data$duration_sec[i] <- round(rnorm(1, 35, 20))
+      tl_data$scrolled[i] <- runif(1) < 0.30
+      tl_data$pages_viewed[i] <- rpois(1, 1) + 1
+    }
+  }
+  
+  tl_data$duration_sec <- pmax(tl_data$duration_sec, 5)
+  
+  # TL成功判定: 滞在60秒以上 + スクロール + 2ページ以上閲覧
+  tl_data$is_tl_success <- with(tl_data, 
+    duration_sec >= 60 & scrolled == TRUE & pages_viewed >= 2
+  )
+  
+  tl_data
+}
+
+# データ生成
+branding_data <- create_branding_data()
+regional_data <- create_regional_data()
+tl_data <- create_tl_data()
+
+# ============================================================================
+# UI定義
+# ============================================================================
+
+ui <- dashboardPage(
+  skin = "blue",
+  
+  dashboardHeader(
+    title = tags$span(
+      tags$img(src = "", height = "30px", style = "margin-right: 10px;"),
+      "Quality Growth Dashboard"
     ),
-    hr(), # Add a horizontal rule
-    checkboxInput("by_species", "Show species", TRUE),
-    checkboxInput("show_margins", "Show marginal plots", TRUE),
-    checkboxInput("smooth", "Add smoother"),
+    titleWidth = 350
   ),
-  plotOutput("scatter")
-)
-
-server <- function(input, output, session) {
-  subsetted <- reactive({
-    req(input$species)
-    df |> filter(Species %in% input$species)
-  })
-
-  output$scatter <- renderPlot(
-    {
-      p <- ggplot(subsetted(), aes(!!input$xvar, !!input$yvar)) +
-        theme_light() +
-        list(
-          theme(legend.position = "bottom"),
-          if (input$by_species) aes(color = Species),
-          geom_point(),
-          if (input$smooth) geom_smooth()
+  
+  dashboardSidebar(
+    width = 280,
+    sidebarMenu(
+      id = "sidebar",
+      menuItem("Executive Summary", tabName = "summary", icon = icon("chart-line")),
+      menuItem("海外ブランディング分析", tabName = "branding", icon = icon("globe")),
+      menuItem("地域別ROI分析", tabName = "regional", icon = icon("map")),
+      menuItem("ソートリーダーシップ", tabName = "thought_leadership", icon = icon("lightbulb")),
+      menuItem("統計モデル", tabName = "models", icon = icon("calculator")),
+      
+      hr(),
+      
+      tags$div(
+        style = "padding: 15px; color: #b8c7ce; font-size: 12px;",
+        tags$p(tags$strong("プロジェクト情報")),
+        tags$p("Client: NTT DATA Corporation"),
+        tags$p("期間: 2026年1月19日〜3月15日")
+      )
+    )
+  ),
+  
+  dashboardBody(
+    tags$head(
+      tags$style(HTML("
+        .content-wrapper { background-color: #f4f6f9; }
+        .box { border-radius: 8px; box-shadow: 0 2px 10px rgba(0,0,0,0.1); }
+        .box-header { border-bottom: 2px solid #3c8dbc; }
+        .info-box { border-radius: 8px; }
+        .small-box { border-radius: 8px; }
+        .nav-tabs-custom > .nav-tabs > li.active { border-top-color: #3c8dbc; }
+        .skin-blue .main-header .logo { background-color: #1a3a5c; }
+        .skin-blue .main-header .navbar { background-color: #1a3a5c; }
+        .metric-card { 
+          background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+          border-radius: 12px;
+          padding: 20px;
+          color: white;
+          margin-bottom: 15px;
+        }
+        .metric-card h3 { margin: 0; font-size: 2em; }
+        .metric-card p { margin: 5px 0 0; opacity: 0.9; }
+      "))
+    ),
+    
+    tabItems(
+      # Executive Summary タブ
+      tabItem(
+        tabName = "summary",
+        fluidRow(
+          column(12,
+            tags$div(
+              style = "background: linear-gradient(135deg, #1a3a5c 0%, #2d5a87 100%); 
+                       padding: 30px; border-radius: 12px; color: white; margin-bottom: 20px;",
+              tags$h2("グローバルマーケティング戦略及びソートリーダーシップの高度化", 
+                      style = "margin-top: 0;"),
+              tags$p("データドリブンな経営ナラティブによる「Quality Growth」の実現", 
+                     style = "font-size: 1.2em; opacity: 0.9;")
+            )
+          )
+        ),
+        
+        fluidRow(
+          valueBoxOutput("total_investment", width = 3),
+          valueBoxOutput("avg_roi", width = 3),
+          valueBoxOutput("tl_success_rate", width = 3),
+          valueBoxOutput("top_region", width = 3)
+        ),
+        
+        fluidRow(
+          box(
+            title = "Strategic Context: Quality Growthへの戦略的整合",
+            status = "primary", solidHeader = TRUE, width = 6,
+            tags$div(
+              style = "display: flex; gap: 20px;",
+              tags$div(
+                style = "flex: 1; padding: 15px; background: #f8f9fa; border-radius: 8px;",
+                tags$h4("Current Context (現状)", style = "color: #6c757d;"),
+                tags$ul(
+                  tags$li("Volume Expansion（量的拡大）"),
+                  tags$li("Technology-focused Context（技術文脈）")
+                ),
+                tags$p("課題：経営層への認知・顧客化に最適化されていない", 
+                       style = "color: #dc3545; font-size: 0.9em;")
+              ),
+              tags$div(
+                style = "flex: 1; padding: 15px; background: #e8f4f8; border-radius: 8px;",
+                tags$h4("Strategic Pivot (新方針)", style = "color: #007bff;"),
+                tags$ul(
+                  tags$li("Quality Growth（質的成長）"),
+                  tags$li("Management-focused Narrative（経営ナラティブ）"),
+                  tags$li("Key Themes: Powered & Next Gen Infrastructure")
+                )
+              )
+            )
+          ),
+          
+          box(
+            title = "Project Scope: 2つの並行ワークストリーム",
+            status = "primary", solidHeader = TRUE, width = 6,
+            tags$div(
+              style = "display: flex; gap: 20px;",
+              tags$div(
+                style = "flex: 1; padding: 15px; background: #fff3cd; border-radius: 8px;",
+                tags$h4("Region A: 思想リーダーシップ", style = "color: #856404;"),
+                tags$p("Focus: 基準の再定義と経営ナラティブ"),
+                tags$ul(
+                  tags$li("現状分析（回遊ログ・アクセス解析）"),
+                  tags$li("競合ベンチマーク調査"),
+                  tags$li("「質」を測る新KPIの定義")
+                )
+              ),
+              tags$div(
+                style = "flex: 1; padding: 15px; background: #d4edda; border-radius: 8px;",
+                tags$h4("Region B: グローバルマーケティング", style = "color: #155724;"),
+                tags$p("Focus: 標準化と投資対効果（ROI）"),
+                tags$ul(
+                  tags$li("拠点間横比較（市場規模 vs 成長率）"),
+                  tags$li("ROI分析と投資効率の可視化"),
+                  tags$li("レポートフォーマットの標準化")
+                )
+              )
+            )
+          )
+        ),
+        
+        fluidRow(
+          box(
+            title = "Schedule & Roadmap",
+            status = "info", solidHeader = TRUE, width = 12,
+            plotlyOutput("timeline_chart", height = "250px")
+          )
         )
-
-      if (input$show_margins) {
-        margin_type <- if (input$by_species) "density" else "histogram"
-        p <- p |> ggExtra::ggMarginal(
-          type = margin_type, margins = "both",
-          size = 8, groupColour = input$by_species, groupFill = input$by_species
+      ),
+      
+      # 海外ブランディング分析 タブ
+      tabItem(
+        tabName = "branding",
+        fluidRow(
+          box(
+            title = "海外ブランディング指標の推移（24ヶ月）",
+            status = "primary", solidHeader = TRUE, width = 12,
+            plotlyOutput("branding_timeseries", height = "400px")
+          )
+        ),
+        
+        fluidRow(
+          box(
+            title = "相互相関分析: Spend vs Revenue",
+            status = "info", solidHeader = TRUE, width = 6,
+            plotOutput("ccf_plot", height = "300px"),
+            tags$p("タイムラグの特定：予算投下から効果が最大化するまでの期間を可視化", 
+                   style = "font-size: 0.9em; color: #6c757d;")
+          ),
+          
+          box(
+            title = "回帰分析結果",
+            status = "info", solidHeader = TRUE, width = 6,
+            verbatimTextOutput("regression_summary"),
+            tags$div(
+              style = "margin-top: 15px; padding: 10px; background: #e8f4f8; border-radius: 8px;",
+              tags$h5("解釈ポイント:"),
+              tags$ul(
+                tags$li("lag(Spend, 2)の係数: 2ヶ月前の投資が現在の売上に寄与"),
+                tags$li("lag(Search, 1)の係数: 指名検索数が売上をドライブ"),
+                tags$li("R-squared: モデルの説明力")
+              )
+            )
+          )
+        ),
+        
+        fluidRow(
+          box(
+            title = "投資効率サマリー",
+            status = "success", solidHeader = TRUE, width = 12,
+            fluidRow(
+              column(4, 
+                tags$div(class = "metric-card",
+                  tags$h3(textOutput("total_spend_text")),
+                  tags$p("総投資額")
+                )
+              ),
+              column(4,
+                tags$div(class = "metric-card", style = "background: linear-gradient(135deg, #11998e 0%, #38ef7d 100%);",
+                  tags$h3(textOutput("total_revenue_text")),
+                  tags$p("総売上額")
+                )
+              ),
+              column(4,
+                tags$div(class = "metric-card", style = "background: linear-gradient(135deg, #ee0979 0%, #ff6a00 100%);",
+                  tags$h3(textOutput("simple_roi_text")),
+                  tags$p("単純ROI")
+                )
+              )
+            )
+          )
         )
-      }
-
-      p
-    },
-    res = 100
+      ),
+      
+      # 地域別ROI分析 タブ
+      tabItem(
+        tabName = "regional",
+        fluidRow(
+          box(
+            title = "地域別ROI比較",
+            status = "primary", solidHeader = TRUE, width = 6,
+            plotlyOutput("regional_roi_chart", height = "350px")
+          ),
+          
+          box(
+            title = "Brand Search vs Revenue",
+            status = "primary", solidHeader = TRUE, width = 6,
+            plotlyOutput("search_revenue_scatter", height = "350px")
+          )
+        ),
+        
+        fluidRow(
+          box(
+            title = "定性データ: ブランド認知スコア（0-100）",
+            status = "info", solidHeader = TRUE, width = 12,
+            plotlyOutput("qualitative_heatmap", height = "300px"),
+            tags$div(
+              style = "margin-top: 15px; padding: 10px; background: #fff3cd; border-radius: 8px;",
+              tags$h5("地域別インサイト:"),
+              tags$ul(
+                tags$li(tags$strong("Asia:"), " 技術力（Technology）が高評価 → 最新技術をアピール"),
+                tags$li(tags$strong("Europe:"), " 信頼度（Trust）が重要 → 認証取得・伝統の訴求"),
+                tags$li(tags$strong("US:"), " デザイン（Design）が先行指標 → クリエイティブ重視")
+              )
+            )
+          )
+        ),
+        
+        fluidRow(
+          box(
+            title = "地域別詳細データ",
+            status = "success", solidHeader = TRUE, width = 12,
+            DTOutput("regional_table")
+          )
+        )
+      ),
+      
+      # ソートリーダーシップ タブ
+      tabItem(
+        tabName = "thought_leadership",
+        fluidRow(
+          box(
+            title = "チャネル別TL成功率",
+            status = "primary", solidHeader = TRUE, width = 6,
+            plotlyOutput("tl_channel_chart", height = "350px"),
+            tags$p("TL成功条件: 滞在60秒以上 + スクロール発生 + 2ページ以上閲覧", 
+                   style = "font-size: 0.9em; color: #6c757d;")
+          ),
+          
+          box(
+            title = "コンテンツ別パフォーマンス",
+            status = "primary", solidHeader = TRUE, width = 6,
+            plotlyOutput("content_performance", height = "350px")
+          )
+        ),
+        
+        fluidRow(
+          box(
+            title = "チャネル別統計サマリー",
+            status = "info", solidHeader = TRUE, width = 12,
+            DTOutput("tl_summary_table")
+          )
+        ),
+        
+        fluidRow(
+          box(
+            title = "流入チャネルの「質」の評価",
+            status = "warning", solidHeader = TRUE, width = 6,
+            tags$div(
+              style = "padding: 15px;",
+              tags$h5("Referral / Organicの成功率が高い場合:"),
+              tags$p("外部メディアでの紹介や検索意図が、レポートの内容と合致しており、信頼獲得に成功している。"),
+              tags$hr(),
+              tags$h5("Paid / Socialの成功率が低い場合:"),
+              tags$p("「タイトル釣り」などで流入は稼げているが、中身が期待に沿っていない、あるいは「ながら読み」層が多い可能性がある。")
+            )
+          ),
+          
+          box(
+            title = "次のアクション提案",
+            status = "success", solidHeader = TRUE, width = 6,
+            tags$div(
+              style = "padding: 15px;",
+              tags$ul(
+                tags$li("スター・コンテンツ（High PV, High Success）の横展開"),
+                tags$li("ニッチ・リーダーシップコンテンツの広報強化"),
+                tags$li("Low Successコンテンツのトピック・ターゲット見直し"),
+                tags$li("GA4実データへの移行とスコアリングカスタマイズ")
+              )
+            )
+          )
+        )
+      ),
+      
+      # 統計モデル タブ
+      tabItem(
+        tabName = "models",
+        fluidRow(
+          box(
+            title = "Analytical Methodology: 統計的アプローチの採用",
+            status = "primary", solidHeader = TRUE, width = 12,
+            tags$div(
+              style = "display: flex; gap: 20px; flex-wrap: wrap;",
+              tags$div(
+                style = "flex: 1; min-width: 250px; padding: 20px; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); border-radius: 12px; color: white;",
+                tags$h4("Regression Analysis（回帰分析）"),
+                tags$p("成功に寄与している変数を統計的に切り分け、投資効率を算出する。")
+              ),
+              tags$div(
+                style = "flex: 1; min-width: 250px; padding: 20px; background: linear-gradient(135deg, #11998e 0%, #38ef7d 100%); border-radius: 12px; color: white;",
+                tags$h4("Weighted Average Models（加重平均モデル）"),
+                tags$p("各国の市場環境の違いを考慮に入れた、公平な評価モデルの設計。")
+              ),
+              tags$div(
+                style = "flex: 1; min-width: 250px; padding: 20px; background: linear-gradient(135deg, #ee0979 0%, #ff6a00 100%); border-radius: 12px; color: white;",
+                tags$h4("Statistical Tools（分析ツール）"),
+                tags$p("専門的な統計解析ソフトウェアを活用し、ブラックボックス化しない「説明可能なロジック」を構築。")
+              )
+            )
+          )
+        ),
+        
+        fluidRow(
+          box(
+            title = "地域別回帰分析結果",
+            status = "info", solidHeader = TRUE, width = 12,
+            selectInput("region_select", "地域を選択:", 
+                        choices = c("US", "Europe", "Asia", "LATAM", "MEA"),
+                        selected = "US"),
+            verbatimTextOutput("regional_regression")
+          )
+        ),
+        
+        fluidRow(
+          box(
+            title = "モデル診断プロット",
+            status = "warning", solidHeader = TRUE, width = 6,
+            plotOutput("residual_plot", height = "300px")
+          ),
+          
+          box(
+            title = "予測 vs 実績",
+            status = "warning", solidHeader = TRUE, width = 6,
+            plotlyOutput("predicted_vs_actual", height = "300px")
+          )
+        )
+      )
+    )
   )
+)
+
+# ============================================================================
+# Server定義
+# ============================================================================
+
+server <- function(input, output, session) {
+  
+  # --- Value Boxes ---
+  output$total_investment <- renderValueBox({
+    valueBox(
+      "¥10,500,000",
+      "Total Investment (税別)",
+      icon = icon("yen-sign"),
+      color = "blue"
+    )
+  })
+  
+  output$avg_roi <- renderValueBox({
+    roi_summary <- regional_data %>%
+      group_by(Region) %>%
+      summarise(ROI = sum(Revenue) / sum(Spend)) %>%
+      summarise(Avg_ROI = mean(ROI))
+    
+    valueBox(
+      sprintf("%.2fx", roi_summary$Avg_ROI),
+      "平均ROI（全地域）",
+      icon = icon("chart-line"),
+      color = "green"
+    )
+  })
+  
+  output$tl_success_rate <- renderValueBox({
+    success_rate <- mean(tl_data$is_tl_success) * 100
+    valueBox(
+      sprintf("%.1f%%", success_rate),
+      "TL成功率",
+      icon = icon("lightbulb"),
+      color = "yellow"
+    )
+  })
+  
+  output$top_region <- renderValueBox({
+    top <- regional_data %>%
+      group_by(Region) %>%
+      summarise(ROI = sum(Revenue) / sum(Spend)) %>%
+      arrange(desc(ROI)) %>%
+      slice(1)
+    
+    valueBox(
+      top$Region,
+      sprintf("Top ROI Region (%.2fx)", top$ROI),
+      icon = icon("trophy"),
+      color = "purple"
+    )
+  })
+  
+  # --- Timeline Chart ---
+  output$timeline_chart <- renderPlotly({
+    phases <- data.frame(
+      Phase = c("Phase 1: Mobilization", "Phase 2: Deep Analysis", "Phase 3: Reporting", "Phase 4: Close"),
+      Start = as.Date(c("2026-01-19", "2026-02-02", "2026-02-23", "2026-03-02")),
+      End = as.Date(c("2026-02-01", "2026-02-22", "2026-03-01", "2026-03-15")),
+      Color = c("#3498db", "#e74c3c", "#2ecc71", "#9b59b6")
+    )
+    
+    plot_ly() %>%
+      add_segments(
+        data = phases,
+        x = ~Start, xend = ~End,
+        y = ~Phase, yend = ~Phase,
+        color = ~Phase,
+        colors = phases$Color,
+        line = list(width = 20),
+        showlegend = FALSE
+      ) %>%
+      layout(
+        xaxis = list(title = "", tickformat = "%b %d"),
+        yaxis = list(title = "", categoryorder = "array", 
+                     categoryarray = rev(phases$Phase)),
+        margin = list(l = 150)
+      )
+  })
+  
+  # --- Branding Time Series ---
+  output$branding_timeseries <- renderPlotly({
+    df_long <- branding_data %>%
+      pivot_longer(cols = c(Spend, Search, Revenue), 
+                   names_to = "Metric", values_to = "Value")
+    
+    plot_ly(df_long, x = ~Date, y = ~Value, color = ~Metric, 
+            type = 'scatter', mode = 'lines+markers',
+            colors = c("Revenue" = "#2ecc71", "Search" = "#e74c3c", "Spend" = "#3498db")) %>%
+      layout(
+        xaxis = list(title = ""),
+        yaxis = list(title = "Value (Standardized Units)"),
+        legend = list(orientation = "h", y = -0.1),
+        hovermode = "x unified"
+      )
+  })
+  
+  # --- CCF Plot ---
+  output$ccf_plot <- renderPlot({
+    ccf_result <- ccf(branding_data$Spend, branding_data$Revenue, 
+                      lag.max = 10, plot = FALSE)
+    
+    df_ccf <- data.frame(
+      Lag = ccf_result$lag,
+      ACF = ccf_result$acf
+    )
+    
+    ggplot(df_ccf, aes(x = Lag, y = ACF)) +
+      geom_hline(yintercept = 0, color = "gray50") +
+      geom_hline(yintercept = c(-0.4, 0.4), linetype = "dashed", color = "blue", alpha = 0.5) +
+      geom_segment(aes(xend = Lag, yend = 0), color = "#3498db", size = 1) +
+      geom_point(color = "#3498db", size = 3) +
+      labs(title = "Cross-Correlation: Spend vs Revenue",
+           x = "Lag (months)", y = "ACF") +
+      theme_minimal(base_size = 14) +
+      theme(plot.title = element_text(hjust = 0.5, face = "bold"))
+  })
+  
+  # --- Regression Summary ---
+  output$regression_summary <- renderPrint({
+    df <- branding_data
+    model <- lm(Revenue ~ lag(Spend, 2) + lag(Search, 1), data = df)
+    summary(model)
+  })
+  
+  # --- Metric Cards ---
+  output$total_spend_text <- renderText({
+    paste0("¥", format(sum(branding_data$Spend) * 1000, big.mark = ","))
+  })
+  
+  output$total_revenue_text <- renderText({
+    paste0("¥", format(sum(branding_data$Revenue) * 1000, big.mark = ","))
+  })
+  
+  output$simple_roi_text <- renderText({
+    roi <- sum(branding_data$Revenue) / sum(branding_data$Spend)
+    sprintf("%.2fx", roi)
+  })
+  
+  # --- Regional ROI Chart ---
+  output$regional_roi_chart <- renderPlotly({
+    roi_data <- regional_data %>%
+      group_by(Region) %>%
+      summarise(ROI = sum(Revenue) / sum(Spend)) %>%
+      arrange(desc(ROI))
+    
+    plot_ly(roi_data, x = ~reorder(Region, ROI), y = ~ROI, type = 'bar',
+            marker = list(color = c("#2ecc71", "#3498db", "#e74c3c", "#9b59b6", "#f39c12"))) %>%
+      layout(
+        xaxis = list(title = "Region"),
+        yaxis = list(title = "ROI (Revenue / Spend)"),
+        title = list(text = "Revenue per 1 Unit of Spend")
+      )
+  })
+  
+  # --- Search vs Revenue Scatter ---
+  output$search_revenue_scatter <- renderPlotly({
+    agg_data <- regional_data %>%
+      group_by(Region) %>%
+      summarise(
+        Avg_Search = mean(Search),
+        Avg_Revenue = mean(Revenue)
+      )
+    
+    plot_ly(agg_data, x = ~Avg_Search, y = ~Avg_Revenue, color = ~Region,
+            type = 'scatter', mode = 'markers',
+            marker = list(size = 15),
+            text = ~Region) %>%
+      layout(
+        xaxis = list(title = "Brand Search Volume (Avg)"),
+        yaxis = list(title = "Revenue (Avg)")
+      )
+  })
+  
+  # --- Qualitative Heatmap ---
+  output$qualitative_heatmap <- renderPlotly({
+    qual_data <- regional_data %>%
+      group_by(Region) %>%
+      summarise(
+        Technology = mean(Tech_Score),
+        Design = mean(Design_Score),
+        Trust = mean(Trust_Score)
+      ) %>%
+      pivot_longer(cols = c(Technology, Design, Trust),
+                   names_to = "Attribute", values_to = "Score")
+    
+    plot_ly(qual_data, x = ~Attribute, y = ~Region, z = ~Score,
+            type = 'heatmap',
+            colorscale = list(c(0, "#f7fbff"), c(0.5, "#6baed6"), c(1, "#08306b")),
+            text = ~round(Score, 1),
+            texttemplate = "%{text}",
+            showscale = TRUE) %>%
+      layout(
+        xaxis = list(title = ""),
+        yaxis = list(title = "")
+      )
+  })
+  
+  # --- Regional Table ---
+  output$regional_table <- renderDT({
+    summary_data <- regional_data %>%
+      group_by(Region) %>%
+      summarise(
+        `Total Spend` = sum(Spend),
+        `Total Revenue` = sum(Revenue),
+        `ROI` = round(sum(Revenue) / sum(Spend), 2),
+        `Avg Tech Score` = round(mean(Tech_Score), 1),
+        `Avg Design Score` = round(mean(Design_Score), 1),
+        `Avg Trust Score` = round(mean(Trust_Score), 1)
+      )
+    
+    datatable(summary_data, 
+              options = list(pageLength = 10, dom = 't'),
+              rownames = FALSE) %>%
+      formatCurrency(c('Total Spend', 'Total Revenue'), currency = "¥", digits = 0)
+  })
+  
+  # --- TL Channel Chart ---
+  output$tl_channel_chart <- renderPlotly({
+    channel_summary <- tl_data %>%
+      group_by(channel) %>%
+      summarise(
+        sessions = n(),
+        success_count = sum(is_tl_success),
+        success_rate = mean(is_tl_success)
+      ) %>%
+      arrange(desc(success_rate))
+    
+    plot_ly(channel_summary, y = ~reorder(channel, success_rate), x = ~success_rate,
+            type = 'bar', orientation = 'h',
+            marker = list(color = c("#00bcd4", "#8bc34a", "#ff9800", "#e91e63", "#9c27b0"))) %>%
+      layout(
+        xaxis = list(title = "Success Rate", tickformat = ".0%"),
+        yaxis = list(title = "")
+      )
+  })
+  
+  # --- Content Performance ---
+  output$content_performance <- renderPlotly({
+    content_summary <- tl_data %>%
+      group_by(report) %>%
+      summarise(
+        sessions = n(),
+        success_rate = mean(is_tl_success),
+        avg_duration = mean(duration_sec)
+      )
+    
+    plot_ly(content_summary, x = ~sessions, y = ~success_rate, 
+            size = ~avg_duration, color = ~report,
+            type = 'scatter', mode = 'markers',
+            marker = list(sizemode = 'diameter', sizeref = 2),
+            text = ~paste(report, "<br>Sessions:", sessions, 
+                          "<br>Success Rate:", scales::percent(success_rate),
+                          "<br>Avg Duration:", round(avg_duration), "sec")) %>%
+      layout(
+        xaxis = list(title = "Sessions (PV)"),
+        yaxis = list(title = "TL Success Rate", tickformat = ".0%")
+      )
+  })
+  
+  # --- TL Summary Table ---
+  output$tl_summary_table <- renderDT({
+    tl_data %>%
+      group_by(channel) %>%
+      summarise(
+        `Sessions` = n(),
+        `Success Count` = sum(is_tl_success),
+        `Success Rate` = scales::percent(mean(is_tl_success), accuracy = 0.1),
+        `Avg Duration (sec)` = round(mean(duration_sec), 1),
+        `Avg Pages Viewed` = round(mean(pages_viewed), 2)
+      ) %>%
+      arrange(desc(`Success Count`)) %>%
+      datatable(options = list(pageLength = 10, dom = 't'), rownames = FALSE)
+  })
+  
+  # --- Regional Regression ---
+  output$regional_regression <- renderPrint({
+    region_df <- regional_data %>% filter(Region == input$region_select)
+    model <- lm(Revenue ~ Tech_Score + Design_Score + Trust_Score, data = region_df)
+    summary(model)
+  })
+  
+  # --- Residual Plot ---
+  output$residual_plot <- renderPlot({
+    df <- branding_data %>% filter(!is.na(lag(Spend, 2)) & !is.na(lag(Search, 1)))
+    model <- lm(Revenue ~ lag(Spend, 2) + lag(Search, 1), data = branding_data)
+    
+    residuals_df <- data.frame(
+      Fitted = fitted(model),
+      Residuals = residuals(model)
+    )
+    
+    ggplot(residuals_df, aes(x = Fitted, y = Residuals)) +
+      geom_point(color = "#3498db", size = 3, alpha = 0.7) +
+      geom_hline(yintercept = 0, linetype = "dashed", color = "red") +
+      geom_smooth(method = "loess", se = FALSE, color = "#e74c3c") +
+      labs(title = "Residuals vs Fitted", x = "Fitted Values", y = "Residuals") +
+      theme_minimal(base_size = 14)
+  })
+  
+  # --- Predicted vs Actual ---
+  output$predicted_vs_actual <- renderPlotly({
+    model <- lm(Revenue ~ lag(Spend, 2) + lag(Search, 1), data = branding_data)
+    
+    pred_df <- data.frame(
+      Actual = branding_data$Revenue[3:24],
+      Predicted = predict(model, branding_data)[3:24]
+    )
+    
+    plot_ly(pred_df) %>%
+      add_trace(x = ~Actual, y = ~Predicted, type = 'scatter', mode = 'markers',
+                marker = list(color = "#3498db", size = 10),
+                name = "Data Points") %>%
+      add_trace(x = c(min(pred_df$Actual), max(pred_df$Actual)),
+                y = c(min(pred_df$Actual), max(pred_df$Actual)),
+                type = 'scatter', mode = 'lines',
+                line = list(color = "red", dash = "dash"),
+                name = "Perfect Fit") %>%
+      layout(
+        xaxis = list(title = "Actual Revenue"),
+        yaxis = list(title = "Predicted Revenue")
+      )
+  })
 }
 
-shinyApp(ui, server)
+# ============================================================================
+# アプリ起動
+# ============================================================================
+
+shinyApp(ui = ui, server = server)