- clean up server.R

CeMOS-Mannheim · May 23, 2024 · 7b783c8 · 7b783c8
1 parent 3d55480
commit 7b783c8
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Showing 5 changed files with 56 additions and 67 deletions.
diff --git a/components/server.R b/components/server.R
@@ -16,7 +16,6 @@ server <- function(input, output, session) {
   if (!is.null(defaults$dir)) {
     appData$selected_dir <- defaults$dir
     message("Dir set from loaded default value.\n")
-    #info_state("dir_set")
     appData$info_state <- "dir_set"
   }
 
@@ -264,7 +263,7 @@ server <- function(input, output, session) {
   ### curve and peak plots ####
   output$curve <- renderPlotly({
     if (appData$show_plot) {
-      p_curve <<- plotCurves(appData$res,
+      p_curve <- plotCurves(appData$res,
                              mzIdx = input$mzTable_rows_selected[1],
                              errorbars = input$errorbars) +
         labs(title = paste0("m/z = ",
@@ -275,38 +274,30 @@ server <- function(input, output, session) {
 
       ggplotly(p_curve)
     } else {
-      # dummy plot
-      p_curve <- dummyPlot()
-
-      ggplotly(p_curve)
+      dummyPlot()
     }
 
   })
 
   output$peak <- renderPlotly({
     if (appData$show_plot) {
-      p_peak <<- plotPeak(appData$res,
+      p_peak <- plotPeak(appData$res,
                           mzIdx = input$mzTable_rows_selected[1],
                           tol = input$zoom) +
         labs(title = NULL)
       ggplotly(p_peak)
     } else {
-      # dummy plot
-      p_peak <- dummyPlot()
-      ggplotly(p_peak)
+      dummyPlot()
     }
 
   })
 
   ### score plot ####
   output$scorePlot <- renderPlotly({
     if (appData$show_plot) {
-      p_score <- scorePlot(appData$stats, metric = input$metric)
-      ggplotly(p_score)
+      scorePlot(appData$stats, metric = input$metric)
     } else {
-      # dummy plot
-      p_score <- dummyPlot()
-      ggplotly(p_score)
+      dummyPlot()
     }
   })
 
@@ -318,8 +309,8 @@ server <- function(input, output, session) {
                               idx = seq_along(getAvgSpectra(appData$res)))
       ggplotly(p)
     } else {
-      p <- dummyPlot()
-      ggplotly(p)
+      dummyPlot()
+
     }
 
   })
@@ -352,8 +343,7 @@ server <- function(input, output, session) {
   #### PCA tab ####
   # default plot for PCA
   output$pca <- renderPlotly({
-    p <- dummyPlot()
-    ggplotly(p)
+    dummyPlot()
   })
 
   observeEvent(input$doPca, {
@@ -369,54 +359,43 @@ server <- function(input, output, session) {
   observeEvent(input$doPca, {
     output$pca <- renderPlotly({
       if (!is.null(appData$pca)) {
-        p <- pcaPlot(pca = appData$pca,
-                     conc = factor(getConc(appData$res)),
-                     x = input$pcaX,
-                     y = input$pcaY,
-                     ellipseLevel = as.numeric(input$pcaEllipse),
-                     spots = getSpots(appData$res))
-
+        pcaPlot(pca = appData$pca,
+                conc = factor(getConc(appData$res)),
+                x = input$pcaX,
+                y = input$pcaY,
+                ellipseLevel = as.numeric(input$pcaEllipse),
+                spots = getSpots(appData$res))
       } else {
-        p <- dummyPlot()
+        dummyPlot()
       }
-      ggplotly(p)
     })
   })
 
   output$pcaLoading1 <- renderPlotly({
     if (appData$show_plot & !is.null(appData$pca)) {
+      browser()
       p <- loadingsPlot(appData$pca,
                         pc = input$pcaX,
-                        simple = input$simpleLoadings) +
-        labs(title = paste("Feature importance for", input$pcaX))
-      if (input$simpleLoadings) {
-        p <- p +
-          labs(title = paste("Top-Features for", input$pcaX))
-      }
+                        simple = input$simpleLoadings)
 
-      ggplotly(p)
+      return(p)
 
     } else {
-      p <- dummyPlot()
-      ggplotly(p)
+      dummyPlot()
+
     }
   })
 
   output$pcaLoading2 <- renderPlotly({
     if (appData$show_plot & !is.null(appData$pca)) {
       p <- loadingsPlot(appData$pca,
                         pc = input$pcaY,
-                        simple = input$simpleLoadings) +
-        labs(title = paste("Feature importance for", input$pcaY))
-      if (input$simpleLoadings) {
-        p <- p + labs(title = paste("Top-Features for", input$pcaY))
-      }
+                        simple = input$simpleLoadings)
 
-      ggplotly(p)
+      return(p)
 
     } else {
-      p <- dummyPlot()
-      ggplotly(p)
+      dummyPlot()
     }
   })
 
@@ -443,35 +422,29 @@ server <- function(input, output, session) {
   })
   output$hclustPlot <- renderPlotly({
     if (appData$show_plot & !is.null(appData$hc)) {
-      p <- plotClusters(appData$hc, k = input$num_cluster) +
-        labs(y = "rel. Intensity [arb. u.]",
-             x = "Log10 Concentration",
-             title = NULL)
+      plotClusters(appData$hc, k = input$num_cluster)
 
-      return(ggplotly(p))
     }
   })
 
   output$clustCurvesPlot <- renderPlotly({
     if (appData$show_plot & !is.null(appData$hc)) {
       show_spinner()
 
-      p <- plotTraj(appData$hc, k = input$num_cluster) +
-        labs(y = "rel. Intensity [arb. u.]",
-             x = "Log10 Concentration",
-             title = "Average Trajectories")
+      p <- plotTraj(appData$hc, k = input$num_cluster)
 
       hide_spinner()
-      return(ggplotly(p))
+      return(p)
     }
   })
 
   output$optNumClust <- renderPlotly({
     if (appData$show_plot & !is.null(appData$hc)) {
       show_spinner()
-      p <- plotClusterMetrics(appData$hc) +
+      p <- plotClusterMetrics(appData$hc)
+
       hide_spinner()
-      return(ggplotly(p))
+      return(p)
     }
   })
 

diff --git a/functions/dummyPlot.R b/functions/dummyPlot.R
@@ -4,5 +4,7 @@ dummyPlot <- function(label = "Load data\nto display plot") {
                      y = 1),
               aes(x = x, y = y, label = label)) +
     geom_text(size = 5)
+
+  p <- ggplotly(p)
   return(p)
 }
diff --git a/functions/hclust.R b/functions/hclust.R
@@ -47,15 +47,22 @@ extractLaClusters <- function(models, k = 2) {
 plotClusters <- function(models, k) {
   model <- models[[k-1]]
 
-  p <- latrend::plot(model)
-
+  p <- latrend::plot(model) +
+    labs(y = "rel. Intensity [arb. u.]",
+         x = "Log10 Concentration",
+         title = NULL)
+  p <- ggplotly(p)
   return(p)
 }
 
 plotTraj <- function(models, k) {
   model <- models[[k-1]]
 
-  p <- latrend::plotClusterTrajectories(model)
+  p <- latrend::plotClusterTrajectories(model) +
+    labs(y = "rel. Intensity [arb. u.]",
+         x = "Log10 Concentration",
+         title = "Average Trajectories")
+  p <- ggplotly(p)
 
   return(p)
 }
@@ -66,5 +73,6 @@ plotClusterMetrics <- function(models) {
                scales = "free_y",
                nrow = 1) +
     scale_x_continuous(breaks = c(2, 5, 10, 15))
+  p <- ggplotly(p)
   return(p)
 }
diff --git a/functions/plotFunctions.R b/functions/plotFunctions.R
@@ -43,7 +43,7 @@ pcaPlot <- function(pca, conc, x, y, ellipseLevel, spots) {
     spot = spots
   )
 
-  df %>%
+  p <- df %>%
     ggplot(aes(x = xax,
                y = yax,
                col = c,
@@ -54,6 +54,10 @@ pcaPlot <- function(pca, conc, x, y, ellipseLevel, spots) {
     labs(col = "Conc. [M]",
          x = paste0(x, " (", exp[xnum], "% expl. var.)"),
          y = paste0(y, " (", exp[ynum], "% expl. var.)"))
+
+  p <- ggplotly(p)
+
+  return(p)
 }
 
 loadingsPlot <- function(pca, pc, simple = TRUE, n = 10) {
@@ -78,21 +82,23 @@ loadingsPlot <- function(pca, pc, simple = TRUE, n = 10) {
       geom_hline(yintercept = 0, alpha = 0.75, linetype = "dashed") +
       coord_flip() +
       labs(x = "m/z [Da]",
-           y = paste0(pc, " Loading")) +
+           y = paste0(pc, " Loading"),
+           title = paste("Top-Features for", pc)) +
       theme(legend.position = "none")
 
-    return(p)
   } else {
     p <- df %>%
       ggplot(aes(x = mz,
                  y = val,
                  col = sign)) +
       geom_linerange(aes(ymin = 0, ymax = val), show.legend = FALSE) +
       labs(x = "m/z [Da]",
-           y = paste0(pc, " Loading")) +
+           y = paste0(pc, " Loading"),
+           title = paste("Feature importance for", pc)) +
       theme(legend.position = "none")
-    return(p)
   }
+  p <- ggplotly(p)
+  return(p)
 }
 
 glmRegPlot <- function(model, penalty) {
@@ -379,6 +385,8 @@ scorePlot <- function(stats, metric = c("CRS", "V'", "Z'", "log2FC", "pEC50", "S
                          labels = c(100, 50, 0, 50 , 100))
   }
 
+  p <- ggplotly(p)
+
   return(p)
 }
 
diff --git a/settings.csv b/settings.csv