Fixed:

- trajPlot.R; cluster colours displayed properly when manually selecting clusters to display

modules/auxfunc.R

@@ -111,6 +111,8 @@ myGgplotTraj = function(dt.arg, # data table
     loc.dt.cl = data.table(xx = 1:length(facet.color.arg), yy = loc.y.max)
     setnames(loc.dt.cl, 'xx', facet.arg)
     
+    # adjust facet.color.arg to plot
+    
     p.tmp = p.tmp +
       geom_hline(data = loc.dt.cl, colour = facet.color.arg, yintercept = loc.y.max, size = 4) +
       scale_colour_manual(values = facet.color.arg,
@@ -256,23 +258,23 @@ myNorm = function(in.dt,
   
   if (is.null(in.by.cols)) {
     if (in.robust)
-      loc.dt.pre.aggr = loc.dt[get(in.rt.col) > in.rt.min &
-                                 get(in.rt.col) < in.rt.max, .(meas.md = median(get(in.meas.col), na.rm = TRUE),
+      loc.dt.pre.aggr = loc.dt[get(in.rt.col) >= in.rt.min &
+                                 get(in.rt.col) <= in.rt.max, .(meas.md = median(get(in.meas.col), na.rm = TRUE),
                                                                meas.mad = mad(get(in.meas.col), na.rm = TRUE))]
     else
-      loc.dt.pre.aggr = loc.dt[get(in.rt.col) > in.rt.min &
-                                 get(in.rt.col) < in.rt.max, .(meas.md = mean(get(in.meas.col), na.rm = TRUE),
+      loc.dt.pre.aggr = loc.dt[get(in.rt.col) >= in.rt.min &
+                                 get(in.rt.col) <= in.rt.max, .(meas.md = mean(get(in.meas.col), na.rm = TRUE),
                                                                meas.mad = sd(get(in.meas.col), na.rm = TRUE))]
     
     loc.dt = cbind(loc.dt, loc.dt.pre.aggr)
   }  else {
     if (in.robust)
-      loc.dt.pre.aggr = loc.dt[get(in.rt.col) > in.rt.min &
-                                 get(in.rt.col) < in.rt.max, .(meas.md = median(get(in.meas.col), na.rm = TRUE),
+      loc.dt.pre.aggr = loc.dt[get(in.rt.col) >= in.rt.min &
+                                 get(in.rt.col) <= in.rt.max, .(meas.md = median(get(in.meas.col), na.rm = TRUE),
                                                                meas.mad = mad(get(in.meas.col), na.rm = TRUE)), by = in.by.cols]
     else
-      loc.dt.pre.aggr = loc.dt[get(in.rt.col) > in.rt.min &
-                                 get(in.rt.col) < in.rt.max, .(meas.md = mean(get(in.meas.col), na.rm = TRUE),
+      loc.dt.pre.aggr = loc.dt[get(in.rt.col) >= in.rt.min &
+                                 get(in.rt.col) <= in.rt.max, .(meas.md = mean(get(in.meas.col), na.rm = TRUE),
                                                                meas.mad = sd(get(in.meas.col), na.rm = TRUE)), by = in.by.cols]
     
     loc.dt = merge(loc.dt, loc.dt.pre.aggr, by = in.by.cols)

modules/tabBoxPlot.R

@@ -40,6 +40,8 @@ tabBoxPlotUI =  function(id, label = "Comparing t-points") {
         ),
         uiOutput(ns('uiPlotBoxNotches')),
         uiOutput(ns('uiPlotBoxOutliers')),
+        uiOutput(ns('uiPlotBoxDodge')),
+        #uiOutput(ns('uiPlotBoxWidth')),
         uiOutput(ns('uiPlotBoxAlpha')),
         uiOutput(ns('uiPlotDotNbins'))
       ),
@@ -153,13 +155,31 @@ tabBoxPlot = function(input, output, session, in.data) {
       checkboxInput(ns('inPlotBoxOutliers'), 'Box plot outliers?', FALSE)
   })
   
+  output$uiPlotBoxDodge = renderUI({
+    cat(file = stderr(), 'UI uiPlotBoxDodge\n')
+    
+    ns <- session$ns
+    
+    if(!( input$inPlotType %in% 'line' ))
+      sliderInput(ns('inPlotBoxDodge'), 'Dodge series:', min = 0, max = 1, value = .4, step = 0.05)
+  })
+
+  output$uiPlotBoxWidth = renderUI({
+    cat(file = stderr(), 'UI uiPlotBoxWidth\n')
+    
+    ns <- session$ns
+    
+    if('box' %in% input$inPlotType)
+      sliderInput(ns('inPlotBoxWidth'), 'Box plot width:', min = 0, max = 1, value = .2, step = 0.1)
+  })
+  
   output$uiPlotBoxAlpha = renderUI({
     cat(file = stderr(), 'UI uiPlotBoxAlpha\n')
     
     ns <- session$ns
     
     if('box' %in% input$inPlotType)
-      sliderInput(ns('inPlotBoxAlpha'), 'Box plot transparency:', min = 0, max = 1, value = 1, step = 0.1)
+      sliderInput(ns('inPlotBoxAlpha'), 'Box plot transparency:', min = 0, max = 1, value = 1, step = 0.05)
   })
   
   output$uiPlotDotNbins = renderUI({
@@ -308,11 +328,16 @@ tabBoxPlot = function(input, output, session, in.data) {
     cat(file = stderr(), 'plotBox:dt not NULL\n')
     
     
-    loc.par.dodge <- position_dodge(width = 0.4)
+    loc.par.dodge <- position_dodge(width = input$inPlotBoxDodge)
     p.out = ggplot(loc.dt, aes(x = as.factor(realtime), y = y)) 
     
     if('dot' %in% input$inPlotType)
-      p.out = p.out + geom_dotplot(aes(fill = group), binaxis = "y", stackdir = "center", position = loc.par.dodge, binwidth = 10^(input$inPlotDotNbins), method = 'histodot')
+      p.out = p.out + geom_dotplot(aes(fill = group), 
+                                   binaxis = "y", 
+                                   stackdir = "center", 
+                                   position = loc.par.dodge, 
+                                   binwidth = 10^(input$inPlotDotNbins), 
+                                   method = 'histodot')
     
     if('viol' %in% input$inPlotType)
       p.out = p.out + geom_violin(aes(fill = group),
@@ -328,7 +353,7 @@ tabBoxPlot = function(input, output, session, in.data) {
       p.out = p.out + geom_boxplot(
         aes(fill = group), 
         position = loc.par.dodge,
-        width = 0.2,
+        #width = 0.2, #input$inPlotBoxWidth,
         notch = input$inPlotBoxNotches, 
         alpha = input$inPlotBoxAlpha,
         outlier.colour = if (input$inPlotBoxOutliers)

modules/trajPlot.R

@@ -138,6 +138,28 @@ modTrajPlot = function(input, output, session, in.data, in.facet = 'group', in.f
     else
       locPos = FALSE
     
+    # If in.facet.color present,
+    # make sure to include the same number of colours in the palette,
+    # as the number of groups in dt.
+    # in.facet.color is typically used when plotting time series within clusters.
+    # Then, the number of colours in the palette has to be equal to the number of clusters (facetted according to in.facet variable).
+    # This might differ if the user selects manually clusters to display.
+    if (is.null(in.facet.color)) 
+      loc.facet.col = NULL 
+    else {
+      # get group numbers in dt; 
+      # loc.dt[, c(in.facet), with = FALSE] returns a data table with a single column
+      # [[1]] at the end extracts the first column and returns as a vector
+      loc.groups = unique(loc.dt[, c(in.facet), with = FALSE][[1]])
+
+      # get colour palette
+      # the length is equal to the number of groups in the original dt.
+      # When plotting time series within clusters, the length equals the number of clusters.
+      loc.facet.col = in.facet.color()$cl.col
+      loc.facet.col = loc.facet.col[loc.groups]
+    }
+    
+    
     p.out = myGgplotTraj(
       dt.arg = loc.dt,
       x.arg = 'realtime',
@@ -145,7 +167,7 @@ modTrajPlot = function(input, output, session, in.data, in.facet = 'group', in.f
       group.arg = "id",
       facet.arg = in.facet,
       facet.ncol.arg = input$inPlotTrajFacetNcol,
-      facet.color.arg = if (is.null(in.facet.color)) NULL else in.facet.color()$cl.col,
+      facet.color.arg = loc.facet.col,
       xlab.arg = 'Time (min)',
       line.col.arg = loc.line.col.arg,
       aux.label1 = if (locPos) 'pos.x' else NULL,

server.R

@@ -285,7 +285,7 @@ shinyServer(function(input, output, session) {
         label = 'Percentage of middle data',
         min = 90,
         max = 100,
-        value = 99, 
+        value = 99.5, 
         step = 0.1
       )
       
@@ -1166,7 +1166,7 @@ shinyServer(function(input, output, session) {
   callModule(downPlot, "downPlotHierSparHM",       paste0('clust_hierchSparse_heatMap_',
                                                           s.cl.spar.diss[as.numeric(input$selectPlotHierSparDiss)],
                                                           '_',
-                                                          s.cl.spar.linkage[as.numeric(input$selectPlotHierSparLinkage)], '.pdf'), plotHierSpar)
+                                                          s.cl.spar.linkage[as.numeric(input$selectPlotHierSparLinkage)], '.png'), plotHierSpar)
   
 
   # Sparse Hierarchical clustering (sparcl) interactive version