Выровнять весь geom_text над отдельными столбцами

У меня есть это приложение, которое должно показывать звездочки p-значений над столбцами или доверительными интервалами, если они выбраны. Это работает для одной категории, но не для другой (применяются только две из четырех категорий). Как я могу изменить свой код, чтобы исправить это? Я не уверен, в чем заключается ошибка и почему это работает для розничной торговли, но не для транспорта.

  ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      selectInput('cat','Select Category', unique(table_E.9_9$Ent_or_Rev)),
      checkboxInput("control_mean",label = "Show average for non-recipients", value = FALSE),
      checkboxInput("p_values",label = "Show p-value levels", value = FALSE),
      checkboxInput("error_bars",label = "Show 95% confidence intervals", value = FALSE),
      actionButton("Explain_p_values", "Explain p-values"),
      actionButton("Explain_error_bars", "Explain 95% confidence intervals")
    ),
    mainPanel(plotOutput('plot_overall'))
  )
)

server <- function(input, output, session) {
  observeEvent(input$Explain_p_values, {showModal(modalDialog(p_value_text))})
  observeEvent(input$Explain_error_bars, {showModal(modalDialog(CI_text))})
  
  output$plot_overall <- renderPlot({
    cbPalette_4 <- c("#999999",  "#F0E442", "#0072B2", "#D55E00")
    fun_select_cat <- function(table, cat) {
  table %>% 
    filter(Ent_or_Rev == cat)
}
    
     table_E.9_9_filtered <- fun_select_cat(table_E.9_9, input$cat) |> 
      ungroup()
    
     control_y <- table_E.9_9_filtered %>% pull(Control) |> unique()
     
     title <- if (input$cat == "Number of Enterprises") {
      input$cat
    } else {
      paste(input$cat, "(USD)", sep = " ")
    }

    layer_error <- if (input$error_bars) {
      geom_errorbar(aes(ymin = lower, ymax = higher), width = 0.25, position = position_dodge(width = 0.9))
    }
    
    layer_p <- if (input$p_values) {
      column_y_text <- if (input$error_bars) {   
        "higher"                                  #if p-values and error_bars checked then add stars at higher CI otherwise at the obs
      } else {                                    
        "new_est"
      }
      max_y_text <- table_E.9_9_filtered |>          # if asterisks column not NA then either put asterisks higher than error bars if error_bar checked
        filter(!is.na(Sig)) |>                   # or put it at bar height if not checked
        pull(column_y_text) |>                   # keep the height of tallest bar
        max()
      
      list(
        geom_text(aes(label = Sig, y = 1.05 * .data[[column_y_text]], group=variable), position = position_dodge(width = 0.9), na.rm = TRUE),   # asterisks go just above either bar or obs 
        if (!is.na(max_y_text)) expand_limits(y = c(0, max_y_text * 1.05))              # if tallest bar has asterisk then expand limit
      )
    }
    
    layer_control <- if (input$control_mean) {
      list(
        annotate("label",
                 x = 3.75, y = control_y,
                 label = "Control\nmean",
                 colour = "#CC79A7",
                 fontface = 2,
                 size = 4.2,
                 label.size = 0,
                 fill = NA,
                 vjust = 0
        ),
        geom_hline(aes(yintercept = Control), linetype = "dashed", col = "#CC79A7", size = 1.5),
        expand_limits(x = c(1, nlevels(table_E.9_9$Treatment) + 1.1))
      )
    }
 
 table_E.9_9_filtered |> 
  ggplot(aes(x = Treatment, y = new_est, fill = variable)) +
  geom_col(position = position_dodge(width = 0.9)) +
  scale_fill_manual(values = cbPalette_4) +
  scale_y_continuous(labels = label_comma(), expand = c(0, 0)) +
  theme_classic() +
  scale_x_discrete(drop = FALSE) +
  theme(
    plot.title = element_text(hjust = 0.5, size = 14, face = "bold"),
    axis.text = element_text(size = 12),
    legend.title = element_blank(),
    legend.text = element_text(size = 12)
  ) +
  layer_p +
  layer_error +
  layer_control +
  labs(title = title, x = NULL, y = NULL)
  
  })
}
shinyApp(ui = ui, server = server)

dput(таблица_E.9_9):

structure(list(Treatment = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L), levels = c("Long Term", "Short Term", "Lump Sum"), class = "factor"), 
    variable = c("Manufacturing", "Manufacturing", "Retail Trade", 
    "Retail Trade", "Services", "Services", "Transportation", 
    "Transportation", "Manufacturing", "Manufacturing", "Retail Trade", 
    "Retail Trade", "Services", "Services", "Transportation", 
    "Transportation", "Manufacturing", "Manufacturing", "Retail Trade", 
    "Retail Trade", "Services", "Services", "Transportation", 
    "Transportation"), Control = c(0.89, 185.99, 11.05, 1356.55, 
    1.56, 233.14, 0.94, 136.04, 0.89, 185.99, 11.05, 1356.55, 
    1.56, 233.14, 0.94, 136.04, 0.89, 185.99, 11.05, 1356.55, 
    1.56, 233.14, 0.94, 136.04), Estimate = c(0.02, 51.9, 3.89, 
    1601.42, 0.23, 198.64, 0.53, 100.76, 0.28, 254.11, 4.24, 
    770.01, 0.45, 718.68, 0.38, 101, 0.03, 17.82, 2.34, 464.6, 
    -0.04, 70.95, -0.12, -3.85), SE = c(0.27, 120.79, 1.28, 824.74, 
    0.33, 205.6, 0.29, 85.37, 0.23, 221.06, 1.03, 338.12, 0.38, 
    440.08, 0.29, 61.26, 0.21, 133.58, 0.95, 273.59, 0.29, 218.2, 
    0.18, 48.33), Sig = c(NA, NA, "�\u0088\u0097�\u0088\u0097�\u0088\u0097", 
    "�\u0088\u0097", NA, NA, "�\u0088\u0097", NA, NA, NA, "�\u0088\u0097�\u0088\u0097�\u0088\u0097", 
    "�\u0088\u0097�\u0088\u0097", NA, NA, NA, NA, NA, NA, "�\u0088\u0097�\u0088\u0097", 
    "�\u0088\u0097", NA, NA, NA, NA), Ent_or_Rev = c("Number of Enterprises", 
    "Net Revenues", "Number of Enterprises", "Net Revenues", 
    "Number of Enterprises", "Net Revenues", "Number of Enterprises", 
    "Net Revenues", "Number of Enterprises", "Net Revenues", 
    "Number of Enterprises", "Net Revenues", "Number of Enterprises", 
    "Net Revenues", "Number of Enterprises", "Net Revenues", 
    "Number of Enterprises", "Net Revenues", "Number of Enterprises", 
    "Net Revenues", "Number of Enterprises", "Net Revenues", 
    "Number of Enterprises", "Net Revenues"), new_est = c(0.91, 
    237.89, 14.94, 2957.97, 1.79, 431.78, 1.47, 236.8, 1.17, 
    440.1, 15.29, 2126.56, 2.01, 951.82, 1.32, 237.04, 0.92, 
    203.81, 13.39, 1821.15, 1.52, 304.09, 0.82, 132.19), lower = c(0.3808, 
    1.14160000000001, 12.4312, 1341.4796, 1.1432, 28.804, 0.9016, 
    69.4748, 0.7192, 6.82240000000002, 13.2712, 1463.8448, 1.2652, 
    89.2632, 0.7516, 116.9704, 0.5084, -58.0068, 11.528, 1284.9136, 
    0.9516, -123.582, 0.4672, 37.4632), higher = c(1.4392, 474.6384, 
    17.4488, 4574.4604, 2.4368, 834.756, 2.0384, 404.1252, 1.6208, 
    873.3776, 17.3088, 2789.2752, 2.7548, 1814.3768, 1.8884, 
    357.1096, 1.3316, 465.6268, 15.252, 2357.3864, 2.0884, 731.762, 
    1.1728, 226.9168)), class = c("grouped_df", "tbl_df", "tbl", 
"data.frame"), row.names = c(NA, -24L), groups = structure(list(
    Ent_or_Rev = c("Net Revenues", "Net Revenues", "Net Revenues", 
    "Net Revenues", "Number of Enterprises", "Number of Enterprises", 
    "Number of Enterprises", "Number of Enterprises"), variable = c("Manufacturing", 
    "Retail Trade", "Services", "Transportation", "Manufacturing", 
    "Retail Trade", "Services", "Transportation"), .rows = structure(list(
        c(2L, 10L, 18L), c(4L, 12L, 20L), c(6L, 14L, 22L), c(8L, 
        16L, 24L), c(1L, 9L, 17L), c(3L, 11L, 19L), c(5L, 13L, 
        21L), c(7L, 15L, 23L)), ptype = integer(0), class = c("vctrs_list_of", 
    "vctrs_vctr", "list"))), row.names = c(NA, -8L), .drop = TRUE, class = c("tbl_df", 
"tbl", "data.frame")))