R 如何使用按钮切换轨迹

我的R Shiny应用程序目前生成动态图，并将其组织成类别.当input$regression为真时，所有的output$continuous_plots都被重新呈现为包含一条回归线.这些图根据input$group_var、input$regression(布尔值)和input$group_var_values中的变化动态更新.我不需要每次重新渲染图以包含回归线或恢复到原始散点图，而是动态添加表示input$regression为真时回归线的迹线.当input$regression为假时，图应重置为原始散点图.我的代码可以吗？

output$binary_plots <- renderUI({
      binary_x_vars <- Filter(function(x) get_variable_type(global_dat[[x]]) == "binary", input$x_sel)  # Filter binary variables
      plot_output_list <- lapply(binary_x_vars, function(x_var) {
        plotname <- paste("plot", x_var, sep = "_")
        plot_output <- plotlyOutput(plotname, height = '300px',width = '100%')  # Create plot output for each continuous variable
        div(style = "margin-bottom: 10px;", plot_output)
      })
      
      do.call(tagList, plot_output_list)  # Combine plot outputs into a tag list
    })
    
    output$continuous_plots <-  renderUI({
      continuous_x_vars <- Filter(function(x) get_variable_type(global_dat[[x]]) == "continuous", input$x_sel)  # Filter continuous variables
      plot_output_list <- lapply(continuous_x_vars, function(x_var) {
        plotname <- paste("plot", x_var, sep = "_")
        plot_output <- plotlyOutput(plotname, height = '300px',width = '100%')  # Create plot output for each continuous variable
        div(style = "margin-bottom: 20px;", plot_output)
      })
      
      do.call(tagList, plot_output_list)  # Combine plot outputs into a tag list
    })
    
    output$string_plots <- renderUI({
      string_x_vars <- Filter(function(x) get_variable_type(global_dat[[x]]) == "string", input$x_sel)  # Filter string variables
      plot_output_list <- lapply(string_x_vars, function(x_var) {
        plotname <- paste("plot", x_var, sep = "_")
        plot_output <- plotlyOutput(plotname, height = '300px',width = '100%')  # Create plot output for each continuous variable
        div(style = "margin-bottom: 5px;", plot_output)
      })
      
      do.call(tagList, plot_output_list)  # Combine plot outputs into a tag list
    })
    
    
    
    
    observe({
      req(input$y_sel, input$x_sel)  # Require selection of y and x variables

      lapply(input$x_sel, function(x_var) {
        output[[paste("plot", x_var, sep = "_")]] <- renderPlotly({
          filtered_dat <- global_dat

          # Apply filter based on selected group values
          if (!is.null(input$group_var_values) && length(input$group_var_values) > 0) {
            filtered_dat <- filtered_dat %>% filter(filtered_dat[[input$group_var]] %in% as.list(input$group_var_values))
          }

          # Define plot name for this iteration
          plot_name <- glue::glue('{input$y_sel}_vs_{x_var}')

          # Reset input values so the donwload csv names are unique to every input$y_sel and input$x_sel combination
          isolate({
            updateSelectInput(session, "y_sel", selected = NULL)
            updateSelectInput(session, "x_sel", selected = NULL)
          })

          # Generate plot
          p <- if (is.factor(filtered_dat[[x_var]]) || is.factor(filtered_dat[[input$y_sel]])) {
            if (input$group_var == 'None selected') {
              ggplot(filtered_dat, aes_string(x = x_var, y = input$y_sel)) +
                geom_boxplot() +
                ggtitle(paste("Boxplot of", x_var, "vs", input$y_sel)) +
                theme_bw()
            } else {
              ggplot(filtered_dat, aes_string(x = x_var, y = input$y_sel, color = input$group_var,customdata = 'row_id')) +
                geom_boxplot() +
                ggtitle(paste("Boxplot of", x_var, "vs", input$y_sel, "with Group Coloring")) +
                theme_bw()
            }
          } else {
            if (input$group_var == 'None selected') {
              ggplot(filtered_dat, aes_string(x = x_var, y = input$y_sel)) +
                geom_point() +
                {
                  if (input$regression)
                    stat_smooth(
                      method = "lm",se = F,
                      linetype = "dashed",
                      color = "red"
                    )
                } +
                ggtitle(paste("Scatter Plot of", x_var, "vs", input$y_sel)) +
                theme_bw()
            } else {
              ggplot(filtered_dat, aes_string(x = x_var, y = input$y_sel, color = as.character(input$group_var),customdata = 'row_id')) +
                geom_point(alpha = .5) +
                {
                  if (input$regression)
                    stat_smooth(method = "lm", se = F,linetype = 'dashed')
                } +
                ggtitle(paste("Scatter Plot of", x_var, "vs", input$y_sel, "with Group Coloring")) +
                theme_bw()
            }
          }

          # Convert ggplot to plotly
          p <- ggplotly(p, source = "plot1") %>%  layout(clickmode = "event+select", dragmode = 'select')

          # Configure the plot with the download button
          p <- config(
            p,
            scrollZoom = TRUE,
            modeBarButtonsToAdd = list(
              list(button_fullscreen(), button_download(data = p[["x"]][["visdat"]][[p[["x"]][["cur_data"]]]](), plot_name = plot_name))
            ),
            modeBarButtonsToRemove = c("toImage", "hoverClosest", "hoverCompare"),
            displaylogo = FALSE
          )

          # Return the plot
          p %>% toWebGL()
        })
      })
    })

这是我得到的最接近的例子，它将根据 colored颜色 组生成回归，但我不知道如何删除轨迹，并保留散点图，如果复选标记输入值为false:

library(shiny)
library(plotly)


# Generate 100,000 observations from 2 correlated random variables
s <- matrix(c(1, 0.5, 0.5, 1), 2, 2)
d <- MASS::mvrnorm(300, mu = c(0, 0), Sigma = s)
d <- setNames(as.data.frame(d), c("x", "y"))

# Introduce a grouping variable
set.seed(123)  # for reproducibility
d$group <- sample(letters[1:3], nrow(d), replace = TRUE)

# fit separate linear models for each group
models <- lapply(unique(d$group), function(g) {
  lm(y ~ x, data = subset(d, group == g))
})

# generate y predictions over a grid of 10 x values for each group
dpred <- lapply(models, function(model) {
  data.frame(
    x = seq(min(d$x), max(d$x), length.out = 10),
    yhat = predict(model, newdata = data.frame(x = seq(min(d$x), max(d$x), length.out = 10)))
  )
})

# Define colors for each group
group_colors <- c("red", "blue", "green")

ui <- fluidPage(
  plotlyOutput("scatterplot"),
  checkboxInput(
    "smooth", 
    label = "Overlay fitted lines?", 
    value = FALSE
  )
)

server <- function(input, output, session) {
  
  added_traces <- list()  # Initialize list to store added traces indices
  
  output$scatterplot <- renderPlotly({
    p <- plot_ly()  # Initialize plot object
    
    # Add markers for each group
    for (i in seq_along(models)) {
      group_data <- subset(d, group == unique(d$group)[i])
      p <- p %>% add_markers(
        data = group_data,
        x = ~x, y = ~y,
        color = I(group_colors[i]),
        alpha = 0.5
      )
    }
    
    p %>% toWebGL()
  })
  
  observeEvent(input$smooth, {
    if (input$smooth) {
      # Add lines for each group's regression line
      for (i in seq_along(dpred)) {
        trace <- plotlyProxy("scatterplot", session) %>%
          plotlyProxyInvoke(
            "addTraces",
            list(
              x = dpred[[i]]$x,
              y = dpred[[i]]$yhat,
              type = "scattergl",
              mode = "lines",
              line = list(color = group_colors[i])
            )
          )
        added_traces <- c(added_traces, trace)  # Store the index of added trace
      }
    } else {
      # Remove all traces if checkbox is unchecked
      plotlyProxy("scatterplot", session) %>%
        plotlyProxyInvoke("deleteTraces",1)
      
    }
  }, ignoreInit = TRUE)
  
}

shinyApp(ui, server)