Reorganized: split into ui, server, and global files.

global.R

+source("modules/hierclustfunction.R")
+source("modules/interpolCompleteFunc.R")
+source("modules/reacRollWinMODULE.R")
+source("modules/reacHierClustMODULE.R")
+source('modules/rolling_window_loop.R')

reacNewApproach.R → server.R

@@ -3,7 +3,6 @@
 #' Author: Mauro Gwerder
 #' 
 #' In this main file, the input data is processed for feeding it into the modules.
-#' UI & Server are combined in this file
 #' 
 #' package requirements:
 
@@ -20,68 +19,6 @@ library(imputeTS) # for interpolating NAs
 
 options(shiny.maxRequestSize = 400 * 1024 ^ 2)
 
-source("hierclustfunction.R")
-source("interpolCompleteFunc.R")
-source("reacRollWinMODULE.R")
-source("reacHierClustMODULE.R")
-source('rolling_window_loop.R')
-
-ui <- dashboardPage( # starts shiny in dashboard
-  
-  dashboardHeader(
-    
-    # Application title
-    title ="Outlier Detection" 
-    
-  ),
-  dashboardSidebar(
-    
-    # dashboard-equivalent to "tabs" in normal shiny
-    sidebarMenu(
-      
-      # Every item stands for one tab. "Rolling Window" and "hierarchical clustering" will show outputs of the
-      # correspondent modules, whereas "Generate synthetic Data" & "Load Data" represent dropdown-menus that load
-      # or generate datasets.
-      menuItem("Rolling Window", tabName = "rollwindow", icon = icon("windows")),
-      
-      menuItem("hierarchical clustering", tabName = "hiercluster", icon = icon("tree")),
-      
-      menuItem("Generate synthetic Data", tabName = "synDataOpt", icon = icon("random"), # tab for synthetic Data options
-               sliderInput("slider.syn", "amount of outliers", 0, 30, 1),
-               checkboxInput("check.super", "add innovative outliers"),
-               actionButton("b.syndata", "generate synthetic data"),
-               br()),
-      
-      menuItem("Load Data", tabName = " ownDataOpt", icon = icon("file"),
-               fileInput("file.name", "file name:",
-                         accept = c('text/csv', 'text/comma-separated-values,text/plain')
-                         ),
-               # extracted columns from the loaded dataset will be displayed for selection
-               uiOutput("uiOut.ID"),
-               uiOutput("uiOut.time"),
-               uiOutput("uiOut.meas"),
-               uiOutput("uiOut.FOV"),
-               actionButton("b.loaddata", "load File"),
-               br())
-    )
-  ),
-  dashboardBody(
-    
-    # Includes the usage of ShinyJS - not used in the app for now
-    useShinyjs(),
-    tabItems(
-      
-      # references to the modules "RollWin" & "HierCluster"
-      tabItem(tabName = "rollwindow",
-              RollWinInput("RollWin")
-      ),
-      tabItem(tabName = "hiercluster",
-              HierClusterInput("HierCluster")
-      )
-    )
-  )
-)
-
 server <- function(input, output) {
   
   # clustering and generation of heatmap
@@ -190,7 +127,7 @@ server <- function(input, output) {
     return(dm.out)
     
   })
-
+  
   # generates synthetic dataset. 
   # Function "LOCgenTraj" taken from Maciej Dobrzynski from his application "Timecourse inspector"
   synData <- eventReactive(input$b.syndata, {
@@ -213,7 +150,7 @@ server <- function(input, output) {
   # To enable this, button clicks will be registered so that only the last click decides on
   # which source is piloted.
   dataInBoth <- reactive({
-
+    
     InLoadData <- input$b.loaddata
     InSynData <- input$b.syndata
     
@@ -229,10 +166,10 @@ server <- function(input, output) {
     
     # isolate the checks of counter reactiveValues
     # as we set the values in this same reactive
-   if (InLoadData != isolate(counter$loadData)) {
+    if (InLoadData != isolate(counter$loadData)) {
       cat("dataInBoth if InLoadData\n")
-       dm = loadColData()
-       
+      dm = loadColData()
+      
       counter$loadData <- InLoadData
       
     } else if (InSynData != isolate(counter$synData)) {
@@ -250,5 +187,3 @@ server <- function(input, output) {
   
 }
 
-  shinyApp(ui, server)
-

ui.R

+ui <- dashboardPage( # starts shiny in dashboard
+  
+  dashboardHeader(
+    
+    # Application title
+    title ="Outlier Detection" 
+    
+  ),
+  dashboardSidebar(
+    
+    # dashboard-equivalent to "tabs" in normal shiny
+    sidebarMenu(
+      
+      # Every item stands for one tab. "Rolling Window" and "hierarchical clustering" will show outputs of the
+      # correspondent modules, whereas "Generate synthetic Data" & "Load Data" represent dropdown-menus that load
+      # or generate datasets.
+      menuItem("Rolling Window", tabName = "rollwindow", icon = icon("windows")),
+      
+      menuItem("hierarchical clustering", tabName = "hiercluster", icon = icon("tree")),
+      
+      menuItem("Generate synthetic Data", tabName = "synDataOpt", icon = icon("random"), # tab for synthetic Data options
+               sliderInput("slider.syn", "amount of outliers", 0, 30, 1),
+               checkboxInput("check.super", "add innovative outliers"),
+               actionButton("b.syndata", "generate synthetic data"),
+               br()),
+      
+      menuItem("Load Data", tabName = " ownDataOpt", icon = icon("file"),
+               fileInput("file.name", "file name:",
+                         accept = c('text/csv', 'text/comma-separated-values,text/plain')
+               ),
+               # extracted columns from the loaded dataset will be displayed for selection
+               uiOutput("uiOut.ID"),
+               uiOutput("uiOut.time"),
+               uiOutput("uiOut.meas"),
+               uiOutput("uiOut.FOV"),
+               actionButton("b.loaddata", "load File"),
+               br())
+    )
+  ),
+  dashboardBody(
+    
+    # Includes the usage of ShinyJS - not used in the app for now
+    useShinyjs(),
+    tabItems(
+      
+      # references to the modules "RollWin" & "HierCluster"
+      tabItem(tabName = "rollwindow",
+              RollWinInput("RollWin")
+      ),
+      tabItem(tabName = "hiercluster",
+              HierClusterInput("HierCluster")
+      )
+    )
+  )
+)