Added: normalization

auxfunc.R

@@ -130,3 +130,63 @@ userDataGen <- function() {
   cat(colnames(dt.nuc))
   return(dt.nuc)
 }
+
+
+# Returns original dt with an additional column with normalized quantity.
+# The column to be normalised is given by 'in.meas.col'.
+# The name of additional column is the same as in.meas.col but with ".norm" suffix added.
+# Normalisation is based on part of the trajectory;
+# this is defined by in.rt.min and max, and the column with time in.rt.col.
+# Additional parameters:
+# in.by.cols - character vector with 'by' columns to calculate normalisation per group
+#              if NULL, no grouping is done
+# in.robust - whether robust measures should be used (median instead of mean, mad instead of sd)
+# in.type - type of normalization: z.score or mean (fi.e. old change w.r.t. mean)
+
+myNorm = function(in.dt,
+                  in.meas.col,
+                  in.rt.col = 'RealTime',
+                  in.rt.min = 10,
+                  in.rt.max = 20,
+                  in.by.cols = NULL,
+                  in.robust = TRUE,
+                  in.type = 'z.score') {
+  loc.dt <-
+    copy(in.dt) # copy so as not to alter original dt object w intermediate assignments
+  
+  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),
+                                                               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),
+                                                               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),
+                                                               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),
+                                                               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)
+  }
+  
+  
+  if (in.type == 'z.score') {
+    loc.dt[, meas.norm := (get(in.meas.col) - meas.md) / meas.mad]
+  } else {
+    loc.dt[, meas.norm := (get(in.meas.col) / meas.md)]
+  }
+  
+  setnames(loc.dt, 'meas.norm', paste0(in.meas.col, '.norm'))
+  
+  loc.dt[, c('meas.md', 'meas.mad') := NULL]
+  return(loc.dt)
+}
\ No newline at end of file

ui.R

@@ -19,17 +19,16 @@ shinyUI(fluidPage(
   
   sidebarLayout(
     sidebarPanel(
-      h4("Load data files"),
-      
       #Selector for file upload
       fileInput(
         'inFileLoadNuc',
-        'Select file (e.g. tCoursesSelected.csv) and press "Load Data"',
+        'Select data file (e.g. tCoursesSelected.csv) and press "Load Data"',
         accept = c('text/csv', 'text/comma-separated-values,text/plain')
       ),
       actionButton("inButLoadNuc", "Load Data"),
       actionButton("butReset", "Reset file input"),
       actionButton('inDataGen1', 'Generate artificial dataset'),
+
       tags$hr(),
       uiOutput('varSelSite'),
       uiOutput('varSelTrackLabel'),
@@ -48,7 +47,14 @@ shinyUI(fluidPage(
           '1 / X' = '1 / '
         )
       ),
-      uiOutput('varSelMeas2')
+      uiOutput('varSelMeas2'),
+      
+      tags$hr(),
+      checkboxInput('chBnorm', 'Normalization', FALSE),
+      uiOutput('uiChBnorm'),
+      uiOutput('uiSlNorm'),
+      uiOutput('uiChBnormRobust'),
+      uiOutput('uiChBnormGroup')
     ),
     
     mainPanel(tabsetPanel(