Update app.R

app.R

@@ -6,29 +6,6 @@ library(corrplot)
 library(plotly)
 library(gridExtra)
 
-# データの読み込みと前処理
-load_and_prepare_data <- function(file_path) {
-  # CSVファイルを読み込み
-  df <- read.csv(file_path, row.names = 1, check.names = FALSE)
-  
-  # 行名を列に追加
-  df <- df %>%
-    mutate(年月 = rownames(df), .before = 1) %>%
-    rownames_to_column(var = "index") %>%
-    select(-index)
-  
-  return(df)
-}
-
-# データを準備
-data_file <- "SampleData.csv"
-
-# アプリが実行されるディレクトリを確認して適切なパスを設定
-if (!file.exists(data_file)) {
-  # Shinyサーバーで実行される場合のパス
-  data_file <- file.path(dirname(rstudioapi::getSourceEditorContext()$path), "SampleData.csv")
-}
-
 ui <- fluidPage(
   # タイトルと概要
   titlePanel("経営指標分析ダッシュボード"),
@@ -48,150 +25,148 @@ ui <- fluidPage(
   tabsetPanel(
     # タブ1: 相関分析
     tabPanel("相関分析",
-             fluidRow(
-               column(12,
-                      h3("指標間の相関係数（ヒートマップ）"),
-                      p("各ビジネス部門の売上、原価、粗利、営業利益、KPIなど全指標間の相関を表示します。")
-               )
-             ),
-             fluidRow(
-               column(8, plotOutput("correlation_heatmap", height = "600px")),
-               column(4,
-                      wellPanel(
-                        h4("相関分析の概要"),
-                        verbatimTextOutput("correlation_summary")
-                      )
-               )
-             )
+      fluidRow(
+        column(12,
+          h3("指標間の相関係数（ヒートマップ）"),
+          p("各ビジネス部門の売上、原価、粗利、営業利益、KPIなど全指標間の相関を表示します。")
+        )
+      ),
+      fluidRow(
+        column(8, plotOutput("correlation_heatmap", height = "600px")),
+        column(4,
+          wellPanel(
+            h4("相関分析の概要"),
+            verbatimTextOutput("correlation_summary")
+          )
+        )
+      )
     ),
     
     # タブ2: 回帰分析
     tabPanel("回帰分析",
-             fluidRow(
-               column(12, h3("KPIと売上・利益の関係分析"))
-             ),
-             fluidRow(
-               column(6,
-                      wellPanel(
-                        h4("回帰分析の設定"),
-                        selectInput("x_var", "説明変数（X軸）", 
-                                    c("整備_車検件数" = "x25", 
-                                      "整備_整備・板金数" = "x31",
-                                      "運送_自社ドライバー数" = "x67",
-                                      "運送_他社ドライバー数" = "x73",
-                                      "レンタル_レンタル台数" = "x91")),
-                        selectInput("y_var", "被説明変数（Y軸）",
-                                    c("整備_売上" = "x2",
-                                      "整備_一般車検_売上" = "x3",
-                                      "整備_板金_売上" = "x4",
-                                      "整備_営業利益" = "x9",
-                                      "商品_売上" = "x37",
-                                      "商品_営業利益" = "x41",
-                                      "運送_売上" = "x52",
-                                      "運送_営業利益" = "x56",
-                                      "レンタル_売上" = "x76",
-                                      "レンタル_営業利益" = "x80")),
-                        actionButton("update_regression", "更新", class = "btn-primary")
-                      )
-               )
-             ),
-             fluidRow(
-               column(6, plotlyOutput("regression_scatter")),
-               column(6, 
-                      wellPanel(
-                        h4("回帰分析結果"),
-                        verbatimTextOutput("regression_results")
-                      )
-               )
-             )
+      fluidRow(
+        column(12, h3("KPIと売上・利益の関係分析"))
+      ),
+      fluidRow(
+        column(6,
+          wellPanel(
+            h4("回帰分析の設定"),
+            selectInput("x_var", "説明変数（X軸）", 
+              c("整備_車検件数" = "x25", 
+                "整備_整備・板金数" = "x31",
+                "運送_自社ドライバー数" = "x67",
+                "運送_他社ドライバー数" = "x73",
+                "レンタル_レンタル台数" = "x91")),
+            selectInput("y_var", "被説明変数（Y軸）",
+              c("整備_売上" = "x2",
+                "整備_営業利益" = "x9",
+                "商品_売上" = "x37",
+                "商品_営業利益" = "x41",
+                "運送_売上" = "x52",
+                "運送_営業利益" = "x56",
+                "レンタル_売上" = "x76",
+                "レンタル_営業利益" = "x80")),
+            actionButton("update_regression", "更新", class = "btn-primary")
+          )
+        )
+      ),
+      fluidRow(
+        column(6, plotlyOutput("regression_scatter")),
+        column(6, 
+          wellPanel(
+            h4("回帰分析結果"),
+            verbatimTextOutput("regression_results")
+          )
+        )
+      )
     ),
     
     # タブ3: 時系列分析
     tabPanel("時系列分析",
-             fluidRow(
-               column(12, h3("各部門の営業利益推移"))
-             ),
-             fluidRow(
-               column(12, plotlyOutput("timeseries_profit", height = "500px"))
-             ),
-             fluidRow(
-               column(12, h3("売上高の推移"))
-             ),
-             fluidRow(
-               column(12, plotlyOutput("timeseries_sales", height = "500px"))
-             ),
-             fluidRow(
-               column(12, h3("KPI推移"))
-             ),
-             fluidRow(
-               column(12, plotlyOutput("timeseries_kpi", height = "500px"))
-             )
+      fluidRow(
+        column(12, h3("各部門の営業利益推移"))
+      ),
+      fluidRow(
+        column(12, plotlyOutput("timeseries_profit", height = "500px"))
+      ),
+      fluidRow(
+        column(12, h3("売上高の推移"))
+      ),
+      fluidRow(
+        column(12, plotlyOutput("timeseries_sales", height = "500px"))
+      ),
+      fluidRow(
+        column(12, h3("KPI推移"))
+      ),
+      fluidRow(
+        column(12, plotlyOutput("timeseries_kpi", height = "500px"))
+      )
     ),
     
     # タブ4: 統計サマリー
     tabPanel("統計サマリー",
-             fluidRow(
-               column(12, h3("基本統計量"))
-             ),
-             fluidRow(
-               column(6,
-                      wellPanel(
-                        h4("整備部門"),
-                        verbatimTextOutput("summary_seisetsubi")
-                      )
-               ),
-               column(6,
-                      wellPanel(
-                        h4("商品部門"),
-                        verbatimTextOutput("summary_shohin")
-                      )
-               )
-             ),
-             fluidRow(
-               column(6,
-                      wellPanel(
-                        h4("運送部門"),
-                        verbatimTextOutput("summary_unsou")
-                      )
-               ),
-               column(6,
-                      wellPanel(
-                        h4("レンタル部門"),
-                        verbatimTextOutput("summary_rental")
-                      )
-               )
-             )
+      fluidRow(
+        column(12, h3("基本統計量"))
+      ),
+      fluidRow(
+        column(6,
+          wellPanel(
+            h4("整備部門"),
+            verbatimTextOutput("summary_seisetsubi")
+          )
+        ),
+        column(6,
+          wellPanel(
+            h4("商品部門"),
+            verbatimTextOutput("summary_shohin")
+          )
+        )
+      ),
+      fluidRow(
+        column(6,
+          wellPanel(
+            h4("運送部門"),
+            verbatimTextOutput("summary_unsou")
+          )
+        ),
+        column(6,
+          wellPanel(
+            h4("レンタル部門"),
+            verbatimTextOutput("summary_rental")
+          )
+        )
+      )
     ),
     
     # タブ5: 詳細な散布図分析
     tabPanel("詳細分析",
-             fluidRow(
-               column(12, h3("主要指標の散布図行列"))
-             ),
-             fluidRow(
-               column(12,
-                      wellPanel(
-                        h4("分析対象指標の選択（複数選択可）"),
-                        checkboxGroupInput("scatter_vars", "変数を選択",
-                                           c("整備_売上" = "x2",
-                                             "整備_粗利" = "x7",
-                                             "整備_営業利益" = "x9",
-                                             "商品_売上" = "x37",
-                                             "商品_粗利" = "x39",
-                                             "商品_営業利益" = "x41",
-                                             "運送_売上" = "x52",
-                                             "運送_粗利" = "x54",
-                                             "運送_営業利益" = "x56",
-                                             "レンタル_売上" = "x76",
-                                             "レンタル_粗利" = "x78",
-                                             "レンタル_営業利益" = "x80"),
-                                           selected = c("x2", "x9", "x37", "x52"))
-                      )
-               )
-             ),
-             fluidRow(
-               column(12, plotOutput("scatter_matrix", height = "800px"))
-             )
+      fluidRow(
+        column(12, h3("主���指標の散布図行列"))
+      ),
+      fluidRow(
+        column(12,
+          wellPanel(
+            h4("分析対象指標の選択（複数選択可）"),
+            checkboxGroupInput("scatter_vars", "変数を選択",
+              c("整備_売上" = "x2",
+                "整備_粗利" = "x7",
+                "整備_営業利益" = "x9",
+                "商品_売上" = "x37",
+                "商品_粗利" = "x39",
+                "商品_営業利益" = "x41",
+                "運送_売上" = "x52",
+                "運送_粗利" = "x54",
+                "運送_営業利益" = "x56",
+                "レンタル_売上" = "x76",
+                "レンタル_粗利" = "x78",
+                "レンタル_営業利益" = "x80"),
+              selected = c("x2", "x9", "x37", "x52"))
+          )
+        )
+      ),
+      fluidRow(
+        column(12, plotOutput("scatter_matrix", height = "800px"))
+      )
     )
   )
 )
@@ -200,11 +175,12 @@ server <- function(input, output, session) {
   
   # データの読み込み
   df <- reactive({
-    # ファイルパスを設定（MacOS対応）
+    # ファイルパスを設定（Docker対応）
     possible_paths <- c(
-      "SampleData.csv",
-      file.path(getwd(), "SampleData.csv"),
-      "/mnt/user-data/uploads/SampleData.csv"
+      "DummyData.csv",          # Docker環境
+      "SampleData.csv",         # ローカル環境
+      file.path(getwd(), "DummyData.csv"),
+      file.path(getwd(), "SampleData.csv")
     )
     
     for (path in possible_paths) {
@@ -227,7 +203,7 @@ server <- function(input, output, session) {
     }
     
     # ファイルが見つからない場合は警告
-    showNotification("SampleData.csvが見つかりません", type = "error")
+    showNotification("DummyData.csv または SampleData.csv が見つかりません", type = "error")
     return(NULL)
   })
   
@@ -293,9 +269,9 @@ server <- function(input, output, session) {
       
       # ヒートマップを作成
       corrplot(corr_matrix, method = "color", type = "full",
-               tl.cex = 0.7, tl.col = "black",
-               addCoef.col = "white", number.cex = 0.6,
-               title = "相関係数ヒートマップ", mar = c(0, 0, 2, 0))
+        tl.cex = 0.7, tl.col = "black",
+        addCoef.col = "white", number.cex = 0.6,
+        title = "相関係数ヒートマップ", mar = c(0, 0, 2, 0))
     }, error = function(e) {
       plot(1, 1, type="n", xlab="", ylab="", main="エラー")
       text(1, 1, paste("エラー:", e$message), cex=1.2, col="red")
@@ -333,11 +309,11 @@ server <- function(input, output, session) {
       cat("相関係数が強い上位10の組み合わせ：\n\n")
       for (i in 1:nrow(corr_pairs)) {
         cat(sprintf("%s (%s) と %s (%s): %.3f\n",
-                    corr_pairs$var1_label[i],
-                    corr_pairs$var1_code[i],
-                    corr_pairs$var2_label[i],
-                    corr_pairs$var2_code[i],
-                    corr_pairs$correlation[i]))
+          corr_pairs$var1_label[i],
+          corr_pairs$var1_code[i],
+          corr_pairs$var2_label[i],
+          corr_pairs$var2_code[i],
+          corr_pairs$correlation[i]))
       }
     }, error = function(e) {
       cat("エラーが発生しました：", e$message)
@@ -376,9 +352,9 @@ server <- function(input, output, session) {
       geom_point(size = 3, color = "steelblue", alpha = 0.6) +
       geom_smooth(method = "lm", se = TRUE, color = "red", fill = "red", alpha = 0.2) +
       labs(title = paste(y_label, "vs", x_label),
-           subtitle = sprintf("(%s) vs (%s)", input$y_var, input$x_var),
-           x = x_label,
-           y = y_label) +
+        subtitle = sprintf("(%s) vs (%s)", input$y_var, input$x_var),
+        x = x_label,
+        y = y_label) +
       theme_minimal() +
       theme(plot.title = element_text(hjust = 0.5, size = 14, face = "bold"))
     
@@ -410,9 +386,9 @@ server <- function(input, output, session) {
     data <- df() %>%
       select(年月, x9, x41, x56, x80) %>%
       rename("整備営業利益" = x9,
-             "商品営業利益" = x41,
-             "運送営業利益" = x56,
-             "レンタル営業利益" = x80) %>%
+        "商品営業利益" = x41,
+        "運送営業利益" = x56,
+        "レンタル営業利益" = x80) %>%
       gather(key = "部門", value = "営業利益", -年月)
     
     p <- ggplot(data, aes(x = factor(年月, levels = unique(年月)), 
@@ -422,12 +398,12 @@ server <- function(input, output, session) {
       geom_line(size = 1) +
       geom_point(size = 2) +
       labs(title = "各部門の営業利益推移",
-           x = "年月",
-           y = "営業利益（円）",
-           color = "部門") +
+        x = "年月",
+        y = "営業利益（円）",
+        color = "部門") +
       theme_minimal() +
       theme(axis.text.x = element_text(angle = 45, hjust = 1),
-            plot.title = element_text(hjust = 0.5, size = 14, face = "bold"))
+        plot.title = element_text(hjust = 0.5, size = 14, face = "bold"))
     
     ggplotly(p)
   })
@@ -438,9 +414,9 @@ server <- function(input, output, session) {
     data <- df() %>%
       select(年月, x2, x37, x52, x76) %>%
       rename("整備売上" = x2,
-             "商品売上" = x37,
-             "運送売上" = x52,
-             "レンタル売上" = x76) %>%
+        "商品売上" = x37,
+        "運送売上" = x52,
+        "レンタル売上" = x76) %>%
       gather(key = "部門", value = "売上", -年月)
     
     p <- ggplot(data, aes(x = factor(年月, levels = unique(年月)), 
@@ -450,12 +426,12 @@ server <- function(input, output, session) {
       geom_line(size = 1) +
       geom_point(size = 2) +
       labs(title = "各部門の売上高推移",
-           x = "年月",
-           y = "売上（円）",
-           color = "部門") +
+        x = "年月",
+        y = "売上（円）",
+        color = "部門") +
       theme_minimal() +
       theme(axis.text.x = element_text(angle = 45, hjust = 1),
-            plot.title = element_text(hjust = 0.5, size = 14, face = "bold"))
+        plot.title = element_text(hjust = 0.5, size = 14, face = "bold"))
     
     ggplotly(p)
   })
@@ -466,10 +442,10 @@ server <- function(input, output, session) {
     data <- df() %>%
       select(年月, x25, x31, x67, x73, x91) %>%
       rename("車検件数" = x25,
-             "整備・板金数" = x31,
-             "自社ドライバー数" = x67,
-             "他社ドライバー数" = x73,
-             "レンタル台数" = x91) %>%
+        "整備・板金数" = x31,
+        "自社ドライバー数" = x67,
+        "他社ドライバー数" = x73,
+        "レンタル台数" = x91) %>%
       gather(key = "KPI", value = "件数", -年月)
     
     p <- ggplot(data, aes(x = factor(年月, levels = unique(年月)), 
@@ -479,12 +455,12 @@ server <- function(input, output, session) {
       geom_line(size = 1) +
       geom_point(size = 2) +
       labs(title = "KPI推移",
-           x = "年月",
-           y = "件数・台数",
-           color = "KPI") +
+        x = "年月",
+        y = "件数・台数",
+        color = "KPI") +
       theme_minimal() +
       theme(axis.text.x = element_text(angle = 45, hjust = 1),
-            plot.title = element_text(hjust = 0.5, size = 14, face = "bold"))
+        plot.title = element_text(hjust = 0.5, size = 14, face = "bold"))
     
     ggplotly(p)
   })
@@ -582,11 +558,11 @@ server <- function(input, output, session) {
       
       # 散布図行列を作成
       pairs(data_clean, 
-            panel = function(x, y, ...) {
-              points(x, y, col = "steelblue", cex = 0.7)
-              abline(lm(y ~ x), col = "red", lty = 2)
-            },
-            main = "変数間の散布図行列")
+        panel = function(x, y, ...) {
+          points(x, y, col = "steelblue", cex = 0.7)
+          abline(lm(y ~ x), col = "red", lty = 2)
+        },
+        main = "変数間の散布図行列")
     }, error = function(e) {
       plot(1, 1, type="n", xlab="", ylab="")
       text(1, 1, paste("エラー:", e$message), cex=1.2, col="red")