Update app.R

app.R

@@ -1,58 +1,158 @@
 library(shiny)
-library(bslib)
-library(dplyr)
 library(ggplot2)
+library(dplyr)
+library(tidyr)
+library(showtext)
 
-df <- readr::read_csv("penguins.csv")
-# Find subset of columns that are suitable for scatter plot
-df_num <- df |> select(where(is.numeric), -Year)
+# 日本語フォント設定
+font_add("Hiragino", "/System/Library/Fonts/ヒラギノ角ゴシック W3.ttc")
+showtext_auto()
 
-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)
+ui <- fluidPage(
+  titlePanel("クライアント型電子計算機の分析可視化"),
+  sidebarLayout(
+    sidebarPanel(
+      sliderInput("alpha", "残存関数のパラメータ alpha:", min = 1, max = 4, value = 2.58, step = 0.01),
+      sliderInput("lambda", "残存関数のパラメータ lambda:", min = 5, max = 15, value = 8.0, step = 0.1),
+      # TEC分布インタラクティブ追加
+      conditionalPanel(
+        condition = "$('li.active a').text() === 'TEC分布（ノートPC）'",
+        sliderInput("screen_min", "画面サイズ（最小）", min = 8, max = 14.9, value = 8, step = 0.1),
+        sliderInput("screen_max", "画面サイズ（最大）", min = 15.1, max = 18, value = 18, step = 0.1),
+        numericInput("mean_tec", "TEC平均値", value = 20, min = 0),
+        numericInput("sd_tec", "TEC標準偏差", value = 5, min = 0.1),
+        checkboxInput("show_medians", "中央値ラベルを表示", value = TRUE),
+        checkboxInput("show_vline", "15インチ区切り線を表示", value = TRUE)
+      )
     ),
-    hr(), # Add a horizontal rule
-    checkboxInput("by_species", "Show species", TRUE),
-    checkboxInput("show_margins", "Show marginal plots", TRUE),
-    checkboxInput("smooth", "Add smoother"),
-  ),
-  plotOutput("scatter")
+    mainPanel(
+      tabsetPanel(
+        tabPanel("残存関数", plotOutput("residualPlot")),
+        tabPanel("ストック推計", plotOutput("stockPlot")),
+        tabPanel("TEC分布（ノートPC）", plotOutput("tecNotePlot")),
+        tabPanel("TEC分布（Pスコア）", plotOutput("tecPscorePlot")),
+        tabPanel("TEC分布（デスクトップ）", plotOutput("tecDesktopPlot")),
+        tabPanel("TEC分布（分離型PC）", plotOutput("tecSplitPlot")),
+        tabPanel("エネルギー消費量", plotOutput("energyPlot"))
+      )
+    )
+  )
 )
 
-server <- function(input, output, session) {
-  subsetted <- reactive({
-    req(input$species)
-    df |> filter(Species %in% input$species)
+server <- function(input, output) {
+  
+  output$residualPlot <- renderPlot({
+    n <- 0:30
+    residual_rate <- exp(- (n / input$lambda)^input$alpha)
+    df <- data.frame(Year = n, ResidualRate = residual_rate)
+    
+    ggplot(df, aes(x = Year, y = ResidualRate)) +
+      geom_line(color = "darkorange", size = 1.2) +
+      labs(title = "残存関数", subtitle = paste("alpha=", input$alpha, ", lambda=", input$lambda),
+           x = "年数 (n)", y = "残存率") +
+      theme_minimal() + ylim(0, 1.2)
+  })
+  
+  output$stockPlot <- renderPlot({
+    df <- data.frame(
+      年度 = 2000:2030,
+      実績 = c(55000000, 61000000, 67000000, 73000000, 80000000, 86000000, 91000000, 96000000, 101000000,
+             106000000, 111000000, 115000000, 118000000, 120000000, 121000000, 120000000, 116000000, 113000000,
+             112000000, 111000000, 108000000, 107000000, 106000000, rep(NA, 8)),
+      推計 = c(rep(NA, 23), 104000000, 102000000, 101000000, 99000000, 98000000, 97000000, 96000000, 95000000)
+    )
+    df_long <- df %>% mutate(年度 = as.character(年度)) %>%
+      pivot_longer(cols = c(実績, 推計), names_to = "区分", values_to = "ストック数") %>%
+      filter(!is.na(ストック数))
+    ggplot(df_long, aes(x = 年度, y = ストック数, fill = 区分)) +
+      geom_col(position = "dodge") +
+      scale_fill_manual(values = c("実績" = "steelblue", "推計" = "darkorange")) +
+      theme_minimal() + theme(axis.text.x = element_text(angle = 90)) +
+      labs(title = "ストック量の推計", x = "年度", y = "ストック数（台）")
+  })
+  
+  output$tecNotePlot <- renderPlot({
+    set.seed(123)
+    screen_size <- c(runif(25, input$screen_min, min(14.9, input$screen_max)),
+                     runif(15, max(15.1, input$screen_min), input$screen_max))
+    tec_value <- c(rnorm(25, input$mean_tec - 5, input$sd_tec),
+                   rnorm(15, input$mean_tec + 5, input$sd_tec))
+    df1 <- data.frame(ScreenSize = screen_size, TEC = tec_value)
+    medians1 <- data.frame(
+      ScreenSize = c(mean(c(input$screen_min, 14.9)), mean(c(15.1, input$screen_max))),
+      TEC = c(input$mean_tec - 5, input$mean_tec + 5),
+      Label = sprintf("%.2f", c(input$mean_tec - 5, input$mean_tec + 5))
+    )
+    
+    p <- ggplot(df1, aes(x = ScreenSize, y = TEC)) +
+      geom_point(color = "blue") +
+      labs(title = "TEC分布：ノートPC（区分J・K）", x = "画面サイズ", y = "TEC[kWh]") +
+      theme_minimal(base_family = "Hiragino")
+    
+    if (input$show_vline) {
+      p <- p + geom_vline(xintercept = 15, linetype = "dashed")
+    }
+    if (input$show_medians) {
+      p <- p + geom_text(data = medians1, aes(label = Label), vjust = -1, color = "red", size = 4)
+    }
+    p
+  })
+  
+  output$tecPscorePlot <- renderPlot({
+    set.seed(124)
+    df2 <- data.frame(P_score = runif(30, 8, 13), TEC = rnorm(30, 20, 5))
+    ggplot(df2, aes(x = P_score, y = TEC)) + geom_point(color = "darkgreen") +
+      geom_segment(aes(x = 10.5, xend = 12.5, y = 11.34, yend = 11.34), color = "red") +
+      annotate("text", x = 12.7, y = 11.34, label = "11.34") +
+      labs(title = "TEC分布：Pスコア8以上（区分L）", x = "Pスコア", y = "TEC[kWh]") +
+      theme_minimal(base_family = "Hiragino")
+  })
+  
+  output$tecDesktopPlot <- renderPlot({
+    set.seed(125)
+    df3 <- data.frame(P_score = runif(40, 2, 15), TEC = runif(40, 30, 180))
+    ggplot(df3, aes(x = P_score, y = TEC)) + geom_point() +
+      geom_vline(xintercept = 8, linetype = "dashed") +
+      annotate("text", x = 5, y = 180, label = "区分M") +
+      annotate("text", x = 12, y = 180, label = "区分N") +
+      geom_segment(aes(x = 3, xend = 7, y = 39.87, yend = 39.87), color = "red") +
+      geom_segment(aes(x = 10, xend = 14, y = 53.32, yend = 53.32), color = "red") +
+      annotate("text", x = 7.1, y = 39.87, label = "39.87") +
+      annotate("text", x = 14.1, y = 53.32, label = "53.32") +
+      labs(title = "TEC分布：一体型デスクトップ（M/N）", x = "Pスコア", y = "TEC[kWh]") +
+      theme_minimal(base_family = "Hiragino")
+  })
+  
+  output$tecSplitPlot <- renderPlot({
+    set.seed(126)
+    df4 <- data.frame(Volume = runif(40, 0, 20), TEC = runif(40, 20, 140))
+    ggplot(df4, aes(x = Volume, y = TEC)) + geom_point() +
+      geom_vline(xintercept = 10, linetype = "dashed") +
+      annotate("text", x = 4, y = 140, label = "区分O") +
+      annotate("text", x = 14, y = 140, label = "区分P") +
+      geom_segment(aes(x = 3, xend = 8, y = 29.55, yend = 29.55), color = "red") +
+      geom_segment(aes(x = 11, xend = 15, y = 31.33, yend = 31.33), color = "red") +
+      annotate("text", x = 8.1, y = 29.55, label = "29.55") +
+      annotate("text", x = 15.1, y = 31.33, label = "31.33") +
+      labs(title = "TEC分布：分離型PC（O/P）", x = "容量[L]", y = "TEC[kWh]") +
+      theme_minimal(base_family = "Hiragino")
+  })
+  
+  output$energyPlot <- renderPlot({
+    data <- tibble(
+      年度 = 2006:2030,
+      実績 = c(10000, 10300, 10400, 10200, 10100, 10000, 9800, 9400, 9200, 8900,
+             8500, 8100, 7700, 7200, 6800, 6300, 6000, rep(NA, 8)),
+      推計 = c(rep(NA, 17), 5800, 5600, 5400, 5200, 5000, 4800, 4700, 4600)
+    )
+    data_long <- data %>% pivot_longer(cols = c("実績", "推計"), names_to = "区分", values_to = "消費量")
+    ggplot(data_long, aes(x = 年度, y = 消費量, fill = 区分)) +
+      geom_col(position = "dodge") +
+      scale_fill_manual(values = c("実績" = "steelblue", "推計" = "darkorange")) +
+      scale_y_continuous(labels = scales::comma) +
+      labs(title = "エネルギー消費量の推移", x = "年度", y = "GWh/年") +
+      theme_minimal() + theme(axis.text.x = element_text(angle = 45, hjust = 1))
   })
-
-  output$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()
-        )
-
-      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
-        )
-      }
-
-      p
-    },
-    res = 100
-  )
 }
 
-shinyApp(ui, server)
+shinyApp(ui = ui, server = server)