plots in OPTIMIZE improved

dev/app.R

@@ -15,10 +15,13 @@ library(purrr)
 library(gslnls)
 library(tidyverse)
 library(ggplot2)
+library(ggExtra)
+library(gtable)
 library(reshape2)
 library(openxlsx)
 library(DT)
 library(ggpubr)
+library(grid)
 library(gridExtra)
 library(drc)
 library(twopartm)
@@ -583,17 +586,16 @@ server <- function(input, output, session) {
                 box(
                   title = "Upload multiple worksheets", status = "warning", solidHeader = TRUE, width = 12, "Please upload your EXCEL file here",
                   fileInput("MiFile", "", accept = ".xlsx")
-              )
+              ),
+              sliderInput("dilslider", "Adjust the dilutions(+-change in %)", min = -100,max=100, value=0, step=1, round=0),
+              checkboxInput("fixupper","Fix highest concentration (if unticked, the center is fixed)",FALSE)
             )
           ),
           mainPanel(
             tabsetPanel(
               id = "tabs",
               tabPanel("4pl",
-                box(
-                  title = "ANOVA table", status = "primary", solidHeader = TRUE, width = 12,
-                  tableOutput("Anovatab")
-                ),
+                
 
                 column(12,
                   # h3("Confidence intervals"),
@@ -605,41 +607,43 @@ server <- function(input, output, session) {
                   plotOutput("sigPlotTEST"),
                   #selectInput(inputId = "scenario", label = "Select an 'optimal' scenario:", choices = c("scenario 2", "scenario 3", "scenario 6", "steep slope"))
 
-                   plotOutput("plotfordilutions"),
-                  # h4("in grey: most extreme bend point lines of theoretical samples with 50% and 200% potency"),
-                  # sliderInput("dilslider", "Adjust the dilutions(+-change in %)", min = -100, max = 100, value = 0, step = 1, round = 0),
-                  # checkboxInput("fixupper", "Fix highest concentration (if unticked, the center is fixed)", FALSE),
-                  # h5("Dilution factors"),
-                  # tableOutput("adjlogdil"),
-                  # "Short guidance: wider dilution ranges increase the CIs of rel. potency, and decrease the CIs of upper and lower asymptote ratios, as well as Hill's slope ratios", br(),
-                  # "Narrower dilution ranges decrease the CIs of rel. potency, and increase the CIs of upper and lower asymptote ratios, ands Hill's slope ratios",
+              )),
+              tabPanel("Dilution slider",
+                       h4("Adjust the dilutions"),
+                       plotOutput("plotfordilutions"),
+                       h5("Dilution factors"),
+                       box(
+                         title = "Adjusted dilutions", status = "primary", solidHeader = TRUE, width = 12, collapsible = TRUE,
+                         tableOutput("adjlogdil")
+                       ),
+                       "Short guidance: wider dilution ranges increase the CIs of rel. potency, and decrease the CIs of upper and lower asymptote ratios, as well as Hill's slope ratios", br(),
+                       "Narrower dilution ranges decrease the CIs of rel. potency, and increase the CIs of upper and lower asymptote ratios, ands Hill's slope ratios",
 
-                ),
-                column(
-                  3,
-                  h3("Bend points"),
-                  tableOutput("bps"),
-                  tableOutput("extremebps"),
-                  h4("Explanation of the plots")
-                )
               ),
               tabPanel("Histograms",
                 h4("Histograms of parameters"),
                 plotOutput("histCIs"),
-                plotOutput("histEC50REF"), 
-                plotOutput("histLasREF"),
-                plotOutput("histUasREF"),
+                column(6,
+                      plotOutput("histEC50REF"), 
+                      plotOutput("histLasREF"),
+                      plotOutput("histUasREF")
+                ),
+                column(6,
+                       plotOutput("histEC50TEST"), 
+                       plotOutput("histLasTEST"),
+                       plotOutput("histUasTEST")
+                )
                 
               ),
               tabPanel(
                 "Report",
-                h4("Settings for report")
-              )
+                h4("Settings for report"))
+              
             )
-          )
-        )
-      )
-    )
+          ) # main panel
+        ) # sidebar Layout
+      ) # tabpanel
+    ) # navbarPage
   })
 
 
@@ -696,7 +700,7 @@ server <- function(input, output, session) {
     reset(id = "") # from shinyjs package
   })
 
-
+#### input optim XL file ----
   observe({
     if (!is.null(input$MiFile)) {
       MinFile <- input$MiFile
@@ -2114,7 +2118,7 @@ server <- function(input, output, session) {
 
 
   #### Dilutions Simulator ----
-  output$plotfordilutions <- renderPlot({
+  observe({ 
     if (!is.null(Dat$Mws)) {
 
 
@@ -2125,55 +2129,55 @@ server <- function(input, output, session) {
     
     for (N_WS in 1:length(AllXL)) {
    
-    datWS <- as.data.frame(AllXL[[N_WS]])
+      datWS <- as.data.frame(AllXL[[N_WS]])
       
-    cn <- colnames(datWS)
-    logI <- grep("log|ln", cn)
-    logDoseI <- grep("log_dose", cn)
-    if (length(logI) > 0 & length(logDoseI) == 0) {
-      datWS$log_dose <- datWS[, logI]
-      datWS2 <- datWS[, -logI]
-      CORro <- cor(datWS$log_dose, datWS[, 3])
-    } else if (length(logI) == 0 & length(logDoseI) == 0) {
-      Ind <- grep(".ilution|.ose|.onc", cn)
-      datWS$log_dose <- log(datWS[, Ind])
-      CORro <- cor(datWS[, Ind], datWS[, 3])
-      datWS2 <- datWS[, -Ind]
-    } else if (length(logI) > 0 & length(logDoseI) > 0) {
-      datWS2 <- datWS
-      CORro <- cor(datWS[, logI], datWS[, 3])
-    }
-    Dat$datWS2 <- datWS2
+      cn <- colnames(datWS)
+      logI <- grep("log|ln", cn)
+      logDoseI <- grep("log_dose", cn)
+      if (length(logI) > 0 & length(logDoseI) == 0) {
+        datWS$log_dose <- datWS[, logI]
+        datWS2 <- datWS[, -logI]
+        CORro <- cor(datWS$log_dose, datWS[, 3])
+      } else if (length(logI) == 0 & length(logDoseI) == 0) {
+        Ind <- grep(".ilution|.ose|.onc", cn)
+        datWS$log_dose <- log(datWS[, Ind])
+        CORro <- cor(datWS[, Ind], datWS[, 3])
+        datWS2 <- datWS[, -Ind]
+      } else if (length(logI) > 0 & length(logDoseI) > 0) {
+        datWS2 <- datWS
+        CORro <- cor(datWS[, logI], datWS[, 3])
+      }
+      Dat$datWS2 <- datWS2
       
-    FITs <- Fitting_FUNC(datWS2, TransFlag = F)
+      FITs <- Fitting_FUNC(datWS2, TransFlag = F)
    
-    pot_est <- FITs[[3]]
-    potEstL[[N_WS]] <- pot_est
-    potU_est <- FITs[[4]]
-    # unrestricted
-    SU_mu <- FITs[[2]]
-    URMcoefs1 <- SU_mu$coefficients
-    URMcoefs <- t(matrix(unlist(URMcoefs1[,1])))
-    URMcoefs_ <- cbind(AllSheets[[N_WS]], URMcoefs)
-    URMcoefsL[[N_WS]] <- URMcoefs_
+      pot_est <- FITs[[3]]
+      potEstL[[N_WS]] <- pot_est
+      potU_est <- FITs[[4]]
+      # unrestricted
+      SU_mu <- FITs[[2]]
+      URMcoefs1 <- SU_mu$coefficients
+      URMcoefs <- t(matrix(unlist(URMcoefs1[,1])))
+      URMcoefs_ <- cbind(AllSheets[[N_WS]], URMcoefs)
+      URMcoefsL[[N_WS]] <- URMcoefs_
       
-    SU_mr <- FITs[[1]]
-    RMcoefs1 <- SU_mr$coefficients
-    RMcoefs <- t(matrix(unlist(RMcoefs1[,1])))
-    RMcoefs_ <- cbind(AllSheets[[N_WS]], RMcoefs)
-    RMcoefsL[[N_WS]] <- RMcoefs_
+      SU_mr <- FITs[[1]]
+      RMcoefs1 <- SU_mr$coefficients
+      RMcoefs <- t(matrix(unlist(RMcoefs1[,1])))
+      RMcoefs_ <- cbind(AllSheets[[N_WS]], RMcoefs)
+      RMcoefsL[[N_WS]] <- RMcoefs_
       
       
       
-    X <- seq(min(datWS2$log_dose), max(datWS2$log_dose), 0.1)
-    sigRef <- URMcoefs[1,1] + (URMcoefs[1,3]-URMcoefs[1,1])/(1+exp(URMcoefs[1,2]*(URMcoefs[1,4]-X)))
-    sigTest1 <- URMcoefs[1,5] + (URMcoefs[1,7]-URMcoefs[1,5])/(1+exp(URMcoefs[1,6]*(URMcoefs[1,4] - URMcoefs[1,8]-X)))
-    #browser()  
-    dfPlotsigRef <- data.frame(X=X, sigRef = sigRef, Sheet = AllSheets[[N_WS]])
-    dfPlotsigTest <- data.frame(X=X, sigTest = sigTest1, Sheet = AllSheets[[N_WS]])
+      X <- seq(min(datWS2$log_dose), max(datWS2$log_dose), 0.1)
+      sigRef <- URMcoefs[1,1] + (URMcoefs[1,3]-URMcoefs[1,1])/(1+exp(URMcoefs[1,2]*(URMcoefs[1,4]-X)))
+      sigTest1 <- URMcoefs[1,5] + (URMcoefs[1,7]-URMcoefs[1,5])/(1+exp(URMcoefs[1,6]*(URMcoefs[1,4] - URMcoefs[1,8]-X)))
+      #browser()  
+      dfPlotsigRef <- data.frame(X=X, sigRef = sigRef, Sheet = AllSheets[[N_WS]])
+      dfPlotsigTest <- data.frame(X=X, sigTest = sigTest1, Sheet = AllSheets[[N_WS]])
       
-    if (!exists("SIGrefDF")) SIGrefDF <- dfPlotsigRef else SIGrefDF <- rbind(SIGrefDF, dfPlotsigRef)
-    if (!exists("SIGtestDF")) SIGtestDF <- dfPlotsigTest else SIGtestDF <- rbind(SIGtestDF,dfPlotsigTest)
+      if (!exists("SIGrefDF")) SIGrefDF <- dfPlotsigRef else SIGrefDF <- rbind(SIGrefDF, dfPlotsigRef)
+      if (!exists("SIGtestDF")) SIGtestDF <- dfPlotsigTest else SIGtestDF <- rbind(SIGtestDF,dfPlotsigTest)
 
     } #for N_WS
     
@@ -2205,18 +2209,83 @@ server <- function(input, output, session) {
       x_UA <- max(X); x_LA <- min(X)
     } else { x_UA <- min(X); x_LA <- max(X) }
     
+    #browser()
+    BoxDF <- data.frame(EC50REF = EC50REF, EC50TEST = EC50TEST, LasREF = LasREF, UasREF = UasREF)
+    
     p1 <- ggplot(SIGrefDF, aes(x=X, y=sigRef, col=as.factor(Sheet))) +
       geom_line() +
       annotate("text", label="x", x=x_UA, y=UasREF, alpha=0.2) +
       annotate("text", label="o", x=x_LA, y=LasREF, alpha=0.2) +
       geom_vline(xintercept = EC50REF, alpha = 0.2) +
+      scale_x_continuous(expand = c(0, 0)) + 
+      scale_y_continuous(expand = c(0, 0)) + 
+      expand_limits(y = c(min(SIGrefDF$sigRef) - 0.1 * diff(range(SIGrefDF$sigRef)), 
+                          max(SIGrefDF$sigRef) + 0.1 * diff(range(SIGrefDF$sigRef)))) + 
+      expand_limits(x = c(min(SIGrefDF$X) - 0.1 * diff(range(SIGrefDF$X)), 
+                          max(SIGrefDF$X) + 0.1 * diff(range(SIGrefDF$X)))) + 
       xlab("dilutions") +
-      ggtitle("Plot of all calculated reference fits (unrestricted model, in gray vertical lines: EC50)") +
+      #ggtitle("Plot of all calculated reference fits (unrestricted model, in gray vertical lines: EC50)") +
       theme_bw() +
       theme(axis.text = element_text(face = "bold", size = 15),
-            plot.title = element_text(size = 15, face = "bold"))
+            plot.title = element_text(size = 15, face = "bold"),
+            plot.margin = unit(c(0.2, 0.2, 0.5, 0.5), "lines"))
+    # Horizontal marginal boxplot - to appear at the top of the chart
+    pBox_hor <- ggplot( BoxDF, aes(x = factor(1), y = EC50REF)) + 
+      geom_boxplot(outlier.colour = NA) + 
+      geom_jitter(position = position_jitter(width = 0.05)) + 
+      scale_y_continuous(expand = c(0, 0)) + 
+      expand_limits(y = c(min(SIGrefDF$X) - 0.1 * diff(range(SIGrefDF$X)), 
+                          max(SIGrefDF$X) + 0.1 * diff(range(SIGrefDF$X)))) + 
+      coord_flip() + 
+      theme_bw() +
+      theme(axis.text = element_blank(), 
+            axis.title = element_blank(), 
+            axis.ticks = element_blank(), 
+            plot.margin = unit(c(1, 0.2, -0.5, 0.5), "lines"))
     
-    output$sigPlotREF <- renderPlot({ p1 })
+    # Vertical marginal boxplot - to appear at the right of the chart
+    pBox_ver <- ggplot(BoxDF, aes(x = factor(1), y = UasREF)) + 
+      geom_boxplot(outlier.colour = NA) + 
+      geom_jitter(position = position_jitter(width = 0.05)) + 
+      scale_y_continuous(expand = c(0, 0)) + 
+      expand_limits(y = c(min(SIGrefDF$sigRef) - 0.1 * diff(range(SIGrefDF$sigRef)), 
+                          max(SIGrefDF$sigRef) + 0.1 * diff(range(SIGrefDF$sigRef)))) + 
+      theme_bw() +
+      theme(axis.text = element_blank(), 
+            axis.title = element_blank(), 
+            axis.ticks = element_blank(), 
+            plot.margin = unit(c(0.2, 1, 0.5, -0.5), "lines"))
+    
+    #browser()
+    gt1 <- ggplot_gtable(ggplot_build(p1))
+    gt2 <- ggplot_gtable(ggplot_build(pBox_hor))
+    gt3 <- ggplot_gtable(ggplot_build(pBox_ver))
+    
+    # Get maximum widths and heights
+    maxWidth <- unit.pmax(gt1$widths[2:3], gt2$widths[2:3])
+    maxHeight <- unit.pmax(gt1$heights[4:5], gt3$heights[4:5])
+    
+    # Set the maximums in the gtables for gt1, gt2 and gt3
+    gt1$widths[2:3] <- as.list(maxWidth)
+    gt2$widths[2:3] <- as.list(maxWidth)
+    
+    gt1$heights[4:5] <- as.list(maxHeight)
+    gt3$heights[4:5] <- as.list(maxHeight)
+    # Create a new gtable
+    gt <- gtable(widths = unit(c(7, 1), "null"), height = unit(c(1, 7), "null"))
+    
+    # Instert gt1, gt2 and gt3 into the new gtable
+    gt <- gtable_add_grob(gt, gt1, 2, 1)
+    gt <- gtable_add_grob(gt, gt2, 1, 1)
+    gt <- gtable_add_grob(gt, gt3, 2, 2)
+    
+    # grid.rect(x = 0.5, y = 0.5, height = 0.995, width = 0.995, default.units = "npc", 
+    #           gp = gpar(col = "black", fill = NA, lwd = 1))
+    # And render the plot
+    grid.newpage()
+#browser()
+    
+    output$sigPlotREF <- renderPlot({ grid.draw(gt) })
 
     Dat$sigPlotREF <- p1
     # 
@@ -2267,35 +2336,44 @@ server <- function(input, output, session) {
     output$histUasREF <- renderPlot({
       hist(UasREF, col="darkturquoise", border="white",main = 'Histogram of upper asymptotes REF')
     })
+    
+    output$histEC50TEST <- renderPlot({
+      hist(EC50TEST, col="steelblue", border="white", main = 'Histogram of EC50TEST')
+    })
+    output$histLasTEST <- renderPlot({
+      hist(LasTEST, col="violet", border="white",main = 'Histogram of lower asymptotes TEST')
+    })
+    output$histUasTEST <- renderPlot({
+      hist(UasTEST, col="darkturquoise", border="white",main = 'Histogram of upper asymptotes TEST')
+    })
     Dat$histEC50REF <- hist(EC50REF, col="steelblue", border="white", main = 'Histogram of EC50REF')
     Dat$histLasREF <- hist(LasREF, col="violet", border="white", main = 'Histogram of EC50REF')
     Dat$histUasREF <- hist(UasREF, col="darkturquoise", border="white", main = 'Histogram of EC50REF')
     
-    # dils <- tab$log_dose
-    # min_y <- min(tab[, 1:3])
-    # max_y <- max(tab[, 1:3])
-    # 
-    # if (input$fixupper) {
-    #   dils_av <- dils - max(dils)
-    #   dils_av_ <- dils_av * (input$dilslider / 100 + 1)
-    #   dils2 <- round(dils_av_ + max(dils), 4)
-    #   dilfactors <- 1 / exp(dils2 - lag(dils2))
-    # } else {
-    #   if (!is.null(Dat$cfordils)) {
-    #     av <- Dat$cfordils
-    #   } else {
-    #     av <- (min(dils) + max(dils)) / 2
-    #   }
-    #   dils_av <- dils - av
-    #   dils_avsc <- dils_av * (input$dilslider / 100 + 1)
-    #   dils2 <- dils_avsc + av
-    #   dilfactors <- 1 / exp(dils2 - lag(dils2))
-    # }
+    tab <- AllXL[[1]]
+    dils <- tab$log_dose
+    min_y <- min(tab[, 1:2])
+    max_y <- max(tab[, 1:2])
+
+    if (input$fixupper) {
+      dils_av <- dils - max(dils)
+      dils_av_ <- dils_av * (input$dilslider / 100 + 1)
+      dils2 <- round(dils_av_ + max(dils), 4)
+      dilfactors <- 1 / exp(dils2 - lag(dils2))
+    } else {
+      if (!is.null(EC50TEST)) {
+        av <- mean(EC50TEST, na.rm = TRUE)
+      } else {
+        av <- (min(dils) + max(dils)) / 2
+      }
+      dils_av <- dils - av
+      dils_avsc <- dils_av * (input$dilslider / 100 + 1)
+      dils2 <- dils_avsc + av
+      dilfactors <- 1 / exp(dils2 - lag(dils2))
+    }
  
     
-   
-    
-    #Dat$newDils <- dils2
+   Dat$newDils <- dils2
 
 
     #sigmoid <- sigmoid()
@@ -2333,111 +2411,65 @@ server <- function(input, output, session) {
     # 
     # pl_df <- cbind(dils2, SAMPLE50, SAMPLE200)
     # 
-    # 
-    # output$adjlogdil <- renderTable({
-    #   adjlogdilfactors <- round(dilfactors, 3)
-    #   adjlogdils <- round(dils2, 3)
-    #   adjdils <- round(exp(dils2), 3)
-    #   DilsTable <- data.frame(
-    #     "adjusted ln(dilutions)" = adjlogdils,
-    #     "adjusted ln_dilution_factors" = adjlogdilfactors,
-    #     "adjusted dilutions" = adjdils
-    #   )
-    #   DilsTable
-    # })
 
-    # if (!is.null(Dat$p2)) {
-    #   p2 <- Dat$p2
-    #   p_dil <- p2 +
-    #     annotate("pointrange", x = dils2, y = rep(min_y, length(dils2)), xmin = min(dils2), xmax = max(dils2)) +
-    #     annotate("text", x = dils2, y = rep(min_y + (max_y - min_y) * 0.05, length(dils2)), label = as.character(round(dils2, 3))) +
-    #     annotate("text",
-    #       x = dils2[-1] + (max(dils2) - min(dils2)) * 0.05,
-    #       y = rep(min_y + (max_y - min_y) * 0.1, length(dils2[-1])),
-    #       label = as.character(round(dilfactors[-1], 3))
-    #     ) +
-    #     geom_line(
-    #       data = as.data.frame(pl_df), aes(x = dils2, y = SAMPLE50), color = "grey15", linetype = 2,
-    #       inherit.aes = F
-    #     ) +
-    #     geom_line(
-    #       data = as.data.frame(pl_df), aes(x = dils2, y = SAMPLE200), color = "grey15", linetype = 2,
-    #       inherit.aes = F
-    #     ) +
-    #     geom_vline(xintercept = c(Xbend50, Xbend200), col = "grey15", linetype = 2) +
-    #     {
-    #       if (input$scenario == "scenario 6") {
-    #         annotate("pointrange",
-    #           x = optdils2, y = rep(min_y + (max_y - min_y) * 0.2, length(optdils2)),
-    #           xmin = min(optdils2), xmax = max(optdils2), color = "seagreen"
-    #         )
-    #       }
-    #     } +
-    #     {
-    #       if (input$scenario == "scenario 6") {
-    #         annotate("text",
-    #           x = optdils2, y = rep(min_y + (max_y - min_y) * 0.25, length(optdils2)),
-    #           label = as.character(round(optdils2, 3)), color = "seagreen"
-    #         )
-    #       }
-    #     } +
-    #     {
-    #       if (input$scenario == "scenario 2") {
-    #         annotate("pointrange",
-    #           x = optdils, y = rep(min_y + (max_y - min_y) * 0.2, length(optdils)),
-    #           xmin = min(optdils), xmax = max(optdils), color = "seagreen"
-    #         )
-    #       }
-    #     } +
-    #     {
-    #       if (input$scenario == "scenario 2") {
-    #         annotate("text",
-    #           x = optdils, y = rep(min_y + (max_y - min_y) * 0.25, length(optdils)),
-    #           label = as.character(round(optdils, 3)), color = "seagreen"
-    #         )
-    #       }
-    #     } +
-    #     {
-    #       if (input$scenario == "scenario 3") {
-    #         annotate("pointrange",
-    #           x = optdils_3, y = rep(min_y + (max_y - min_y) * 0.2, length(optdils_3)),
-    #           xmin = min(optdils_3), xmax = max(optdils_3), color = "seagreen"
-    #         )
-    #       }
-    #     } +
-    #     {
-    #       if (input$scenario == "scenario 3") {
-    #         annotate("text",
-    #           x = optdils_3, y = rep(min_y + (max_y - min_y) * 0.25, length(optdils_3)),
-    #           label = as.character(round(optdils_3, 3)), color = "seagreen"
-    #         )
-    #       }
-    #     } +
-    #     {
-    #       if (input$scenario == "steep slope") {
-    #         annotate("pointrange",
-    #           x = optdils3, y = rep(min_y + (max_y - min_y) * 0.2, length(optdils3)),
-    #           xmin = min(optdils3), xmax = max(optdils3), color = "seagreen"
-    #         )
-    #       }
-    #     } +
-    #     {
-    #       if (input$scenario == "steep slope") {
-    #         annotate("text",
-    #           x = optdils3, y = rep(min_y + (max_y - min_y) * 0.25, length(optdils3)),
-    #           label = as.character(round(optdils3, 3)), color = "seagreen"
-    #         )
-    #       }
-    #     } +
-    #     annotate("text",
-    #       x = optdils[1], y = (max_y + min_y) * 0.5,
-    #       label = paste("in green: optimal \n dilutions acc. to Whitepaper\n", input$scenario), color = "seagreen",
-    #       size = 14 / .pt, fontface = "bold"
-    #     )
-    # }
-    #print(p_dil)
-   # }
-    }
+    output$adjlogdil <- renderTable({
+      adjlogdilfactors <- round(dilfactors, 3)
+      adjlogdils <- round(dils2, 3)
+      adjdils <- round(exp(dils2), 3)
+      DilsTable <- data.frame(
+        "adjusted ln(dilutions)" = adjlogdils,
+        "adjusted ln_dilution_factors" = adjlogdilfactors,
+        "adjusted dilutions" = adjdils
+      )
+      DilsTable
+    })
+
+   if (!is.null(p2)) {
+     #p2 <- Dat$p2
+     p_dil <- p2 +
+       annotate("pointrange", x = dils2, y = rep(min_y, length(dils2)), xmin = min(dils2), xmax = max(dils2)) +
+       annotate("text", x = dils2, y = rep(min_y + (max_y - min_y) * 0.05, length(dils2)), label = as.character(round(dils2, 3))) +
+       annotate("text",
+         x = dils2[-1] + (max(dils2) - min(dils2)) * 0.05,
+         y = rep(min_y + (max_y - min_y) * 0.1, length(dils2[-1])),
+         label = as.character(round(dilfactors[-1], 3)))
+       # geom_line(
+       #   data = as.data.frame(pl_df), aes(x = dils2, y = SAMPLE50), color = "grey15", linetype = 2,
+       #   inherit.aes = F
+       # ) +
+       # geom_line(
+       #   data = as.data.frame(pl_df), aes(x = dils2, y = SAMPLE200), color = "grey15", linetype = 2,
+       #   inherit.aes = F
+       # ) +
+       # geom_vline(xintercept = c(Xbend50, Xbend200), col = "grey15", linetype = 2) +
+       # { if (input$scenario == "scenario 6") {
+       #     annotate("pointrange",
+       #       x = optdils2, y = rep(min_y + (max_y - min_y) * 0.2, length(optdils2)),
+       #       xmin = min(optdils2), xmax = max(optdils2), color = "seagreen"
+       #     )
+       #   }
+       # } +
+       # {
+       #   if (input$scenario == "scenario 6") {
+       #     annotate("text",
+       #       x = optdils2, y = rep(min_y + (max_y - min_y) * 0.25, length(optdils2)),
+       #       label = as.character(round(optdils2, 3)), color = "seagreen"
+       #     )
+       #   }
+       # } +
+      
+     
+       # annotate("text",
+       #   x = optdils[1], y = (max_y + min_y) * 0.5,
+       #   label = paste("in green: optimal \n dilutions acc. to Whitepaper\n", input$scenario), color = "seagreen",
+       #   size = 14 / .pt, fontface = "bold"
+       # )
+       output$plotfordilutions <- renderPlot({
+          print(p_dil)
+       })
+     
+      } # if (!is.null(p2))
+    } # if !is.null Dat$Mws
 
   })