Whitespace

Parses.R

@@ -40,7 +40,7 @@ buildExpr <- function(pressStatus) {
   }
   expr<-substr(expr, 1, nchar(expr)-2)
   expr<-paste0(expr, ')')
-  
+
   return(expr)
 }
 
@@ -55,14 +55,14 @@ parseScenario <- function(press, prefix = 'p') {
     cat('Duplicated pressure node names found')
     print(pressNodes[duplicated(pressNames)])
   }
-  
+
   return(list(
-    timeSeq=press, 
+    timeSeq=press,
-    nodes=data.frame(name = pressNames, 
+    nodes=data.frame(name = pressNames,
-                     code=paste0(prefix, seq(1:length(pressNames))), 
+                     code=paste0(prefix, seq(1:length(pressNames))),
-                     growth = coefs[,'growth'], 
+                     growth = coefs[,'growth'],
-                     confidence=coefs[,'confidence'], 
+                     confidence=coefs[,'confidence'],
-                     layer=coefs[,'layer'], 
+                     layer=coefs[,'layer'],
                      stringsAsFactors = FALSE),
     edges=data.frame(input=NULL, output=NULL, impact=NULL)
   ))
@@ -75,7 +75,7 @@ getInitial <- function(string, letter) {
 split <- function(cell) {
   params <- unlist(strsplit(cell, ','))
   values <- rep(0, length(states))
-  
+
   for (n in 1:length(params)) {
      kvp <- unlist(strsplit(params[n], '='))
      ref <- match(getInitial(trimws(kvp[1])), getInitial(states))
@@ -106,11 +106,11 @@ buildGraph <- function(model, desc) {
 
   #model contains the following
   # node table, edge table
-  
+
   #descriptor (desc) contains:
   #inputCode - the top layer of the model
   #outputCodes - all subsequent layers to be included in the model
-  
+
   inputNodes <- model$nodes$code[which(startsWith(model$nodes$code, desc$inputCode))]
   inputText <- paste0("[", inputNodes, "]", collapse ="")
 
@@ -128,19 +128,19 @@ buildGraph <- function(model, desc) {
     edges <- paste0(edges, paste0("[", outNodes[idx], "|", substr(inputsStr, start=1, stop=(nchar(inputsStr)-1)), "]"))
 
     #Make the coefficient of the distribution
-    coefVal <- setNames(c(model$nodes$growth[nodeRef], model$edges$values[rows]), 
+    coefVal <- setNames(c(model$nodes$growth[nodeRef], model$edges$values[rows]),
                         c("(Intercept)", model$edges$input[rows])
                         )
     #str(coefVal)
-    outDist[[idx]] <- list(coef = coefVal, 
+    outDist[[idx]] <- list(coef = coefVal,
                          sd = model$nodes$confidence[nodeRef])
   }
-  
+
   print('about to build network')
   print(paste0(inputText, edges))
 
   net <- model2network(paste0(inputText, edges), debug=TRUE)
-  
+
   print('network build successful')
 
   inDist <- vector(mode="list", length=length(inputNodes))
@@ -153,19 +153,19 @@ buildGraph <- function(model, desc) {
 
   allDists = as.list(setNames(c(inDist, outDist), c(inputNodes, outNodes)))
   cfit = custom.fit(net, allDists)
-  
+
   cat('about to calculate sample distributions')
   print(outNodes)
-  
+
   sampleDists <- cpdist(cfit, nodes = outNodes, evidence = TRUE, n = 10000, method = "lw")
   summDists <- summary(sampleDists)
   #stdDev <- sd(sampleDists)
-  
+
   print('sample distribution build successful')
-  
+
   model$edges$input <- model$nodes$name[match(model$edges$input, model$nodes$code)]
   model$edges$output <- model$nodes$name[match(model$edges$output, model$nodes$code)]
-  
+
   return(
     list(
       nodes = model$nodes,
@@ -180,10 +180,10 @@ buildGraph <- function(model, desc) {
 
 
 getValidNodes <- function(mapping, prevOutputs, prefix) {
-  
+
   #Find row id for input nodes, internal and published
   inputNodes <- mapping[2:nrow(mapping),1]
-  
+
   #check that all input nodes are in the previous table
   inputNodes <- delNA(mapping[mapping[,"Node.Type"] == 'input', "Nodes"])
   if (length(inputNodes)>0) {
@@ -192,27 +192,27 @@ getValidNodes <- function(mapping, prevOutputs, prefix) {
       print(inputNodes[!inputNodes %in% prevOutputs$name])
     }
   } else print('Invalid sheet - table must have at least one input row containing names from previous table')
-  
+
 
   #Check the row headings concur with previous names
   validInputs <- delNA(inputNodes[which(unique(inputNodes) %in% prevOutputs$name)])
   if (length(validInputs)==0) print('Invalid sheet - table must have at least one input row containing names from previous table')
-  
+
-  
+
   inputInts <- delNA(inputNodes[mapping$Node.Type!='link'])
 
   if (sum(duplicated(inputInts))>0) {
     cat('Duplicated input node names found')
     print(inputNodes[duplicated(inputNodes)])
   }
-  
+
   outNodes <- delNA(colnames(mapping)[FIRST_NODE_COL:ncol(mapping)])
   if (sum(duplicated(outNodes))>0) {
     cat('Duplicated output node names found')
     print(outNodes[duplicated(outNodes)])
   }
-  
+
-  
+
   #check that all internal nodes are in the columns
   intNodes <- delNA(mapping[mapping[,"Node.Type"] == 'internal', "Nodes"])
   if (length(intNodes)>0) {
@@ -221,17 +221,17 @@ getValidNodes <- function(mapping, prevOutputs, prefix) {
       print(intNodes[!intNodes %in% outNodes])
     }
   }
-  
+
   coefs <- matrix(data=NA, nrow=length(outNodes), ncol=3, dimnames=list(NULL, c('growth', 'confidence', 'layer')))
   for (idx in 1:length(outNodes)) {
     col <- match(outNodes[idx], colnames(mapping))
     coefs[idx,] <-  as.numeric(split(mapping[1, col]))[match(c('growth', 'confidence', 'layer'), states)]
   }
-  
+
   print(coefs)
 
   return(data.frame(
-    code=c(prevOutputs$code, paste0(prefix, seq(1:length(outNodes)))), 
+    code=c(prevOutputs$code, paste0(prefix, seq(1:length(outNodes)))),
     name=c(prevOutputs$name, outNodes),
     growth=c(prevOutputs$growth, coefs[,"growth"]),
     confidence=c(prevOutputs$confidence, coefs[,"confidence"]),
@@ -248,17 +248,17 @@ getValidEdges <- function(mapping, nodeDF, prevEdge=NULL, prefix) {
   str(nodeDF)
   edgeCols <- c('inputNode', 'outputNode', 'impact')
   edgeM <- matrix(data=NA, nrow=0, ncol=length(edgeCols), dimnames=list(NULL, edgeCols))
-  
+
   #to start let just get the statements and print them out....
   for (col in FIRST_NODE_COL:ncol(mapping)) {
     count=0
-    
+
     for (row in 2:nrow(mapping)) {
 
       if (!is.na(mapping[row, col])) {
-        edgeM <- rbind(edgeM, 
+        edgeM <- rbind(edgeM,
-          c(getCode(mapping[row, 1], nodeDF), 
+          c(getCode(mapping[row, 1], nodeDF),
-            getCode(colnames(mapping)[col], nodeDF), 
+            getCode(colnames(mapping)[col], nodeDF),
             split(mapping[row,col])[match('impact', states)]
            )
         )
@@ -285,10 +285,10 @@ getValidEdges <- function(mapping, nodeDF, prevEdge=NULL, prefix) {
 }
 
 parseMapping <- function(mapping, prevOutputs, prefix) {
-  
+
   mapping <- mapping[,-1]
   mapping[,1] <- cleanTitles(mapping[,1])
-  
+
   nodeDF <- getValidNodes(mapping, prevOutputs$nodes, prefix)
   edgeDF <- getValidEdges(mapping, nodeDF, prevEdge=prevOutputs$edges, prefix)
 
@@ -302,32 +302,32 @@ parseMapping <- function(mapping, prevOutputs, prefix) {
 
 parseSheet <- function(fName) {
   #get sheet names
-  
+
   print(paste('starting sheet load', fName))
-  
+
   if (file.exists(fName)) {
     names <- openxlsx::getSheetNames(fName)
-    
+
     if (length(names)>0) {
-      
+
       sheets <- sort(delNA(match(names, mappings)))
-      
+
       cat('starting sheet parse')
       print(sheets)
-      
+
       if (sum(sheets==refs)==length(refs)) {
         #read all mapping tables
         scenario <-  parseScenario(readXL(fName,mappings[1], startRow=1), prefix='p')
         p_ba <-  parseMapping(readXL(fName,mappings[2], startRow=1), scenario, prefix='ba')
         p_op <- parseMapping(readXL(fName,mappings[3], startRow=1), p_ba, prefix='op')
         p_es <- parseMapping(readXL(fName,mappings[4], startRow=1), p_op, prefix='es')
-        
+
         #print('building graphs')
 
         #p_baNet <- buildGraph(p_ba, desc=list(inputCode='p', outputCodes='ba'))
         #p_opNet <- buildGraph(p_op, desc=list(inputCode='p', outputCodes=c('ba', 'op')))
         #p_esNet <- buildGraph(p_es, desc=list(inputCode='p', outputCodes=c('ba', 'op', 'es')))
-        
+
         print('sheet load completed')
         return(
           #list(

app.R

@@ -42,7 +42,7 @@ legends <- c('Pressures',
 addResourcePath("js", "./www/js")
 
 ui<-dashboardPage(
-      dashboardHeader(title = "JNCC MESO online", 
+      dashboardHeader(title = "JNCC MESO online",
         tags$li(
           id = "dropdownHelp",
           class = "dropdown",
@@ -98,8 +98,8 @@ ui<-dashboardPage(
             selectInput("modelSelect", "Select MESO model", choices=c(""), selected=NULL, multiple=FALSE),
             downloadButton("download", "", icon=icon("download")),
             uiOutput("pressureList")
-            #selectInput("layerSelect", "Select Transition", 
+            #selectInput("layerSelect", "Select Transition",
-            #            choices=transitions, 
+            #            choices=transitions,
             #            selected=NULL, multiple=FALSE)
         )
       ),
@@ -108,7 +108,7 @@ ui<-dashboardPage(
           tabItem(tabName = "1", h2('Impact Distribution'),
             fluidRow(
               column(
-                width=6, 
+                width=6,
                 h4('Effect on bio-assemblage')
               ),
               column(
@@ -164,7 +164,7 @@ ui<-dashboardPage(
           #    fluidPage(
           #      google_mapOutput(outputId = "map", width = "100%", height = "750px")
           #    )
-          #), 
+          #),
           tabItem(tabName = "3",h4("Ingestion"),
              fluidPage(
                p("Select a spreadsheet from your network for input into the JNCC Bayesian Network Analyser:"),
@@ -179,32 +179,32 @@ ui<-dashboardPage(
 
 server <- function(input, output, session) {
   #SERVER Constants
- 
+
   print('Loading data')
-  
+
   #set_key("AIzaSyAw8_btgGN1drf8qhCxNcotP6r11qEXA_M")
   dataStorage <- 'data/'
-  
+
   models<-NULL
   pressures <- NULL
-  
+
   .loadStatus <- reactiveValues(
     valid = c(p=FALSE, ba=FALSE, op=FALSE, es=FALSE),
     msgs = NULL
   )
-  
+
   .likelihoods <-reactiveValues(
     p_ba = NULL,
     ba_os = NULL,
     os_es = NULL,
     p_es = NULL
   )
-  
+
   setPressures <- function(newPressures) {
     pressures <<- newPressures
   }
 
- 
+
   .resistanceScores <- c(
     ins= -0.01,
     hr = -0.2,
@@ -214,15 +214,15 @@ server <- function(input, output, session) {
     ssgr = 0,
     pressSD = 0.5
   )
-  
+
   .selections <- reactiveValues(
-    model=1, 
+    model=1,
-    bbnImpact=1, 
+    bbnImpact=1,
     bbnNames=FALSE,
     bbnEdges=FALSE,
     pressStatus=NULL
   )
-  
+
   getImpact <- function(v) {
     print(v)
     if ((v == "INS") || (v == "IV")) return(.resistanceScores[1])
@@ -232,20 +232,20 @@ server <- function(input, output, session) {
     if (v == "NR") return(.resistanceScores[5])
     as.numeric(v)
   }
-  
+
   getAvailableModels <- function() {
     fileList <- list.files(dataStorage, pattern='.xlsx')
-    
+
     modelList <- list()
     cnt<-1
-    
+
     for (idx in 1:length(fileList)) {
       print(paste('attempting to load', paste0(dataStorage, fileList[idx])))
-      
+
       tmp <- parser$parseSheet(paste0(dataStorage, fileList[idx]))
       print(tmp)
       tmp$edges$values <- sapply(tmp$edges$impact, getImpact)
-      
+
       if (!is.null(tmp)) {
         modelList[[cnt]] <- tmp
         models <<- c(models, substr(fileList[idx], 1, (nchar(fileList[idx])-5)))
@@ -257,36 +257,36 @@ server <- function(input, output, session) {
     updateSelectInput(session, "modelSelect", choices=models)
     return(modelList)
   }
-  
+
   #parse on load sheets in the input sheet folder - replace with R Data
   modelList <- getAvailableModels()
-  
+
-  
+
   calcLikelihood <- function(layer, pressStatus) {
-    
+
     isolate({
 
       #if (layer==1) layerStr='ba' else if (layer==2) layerStr='op' else if (layer==3) layerStr='es'
 
-      
+
-      
+
       #layerRange <- which(startsWith(thisModel$nodes$code, layerStr))
-   
+
       #nodeCodes <-thisModel$nodes$code[layerRange]
       #nodeNames <- thisModel$nodes$name[layerRange]
-      
+
       thisModel <- modelList[[.selections$model]]
-      
+
       modelList[[.selections$model]]$edges$values <<- sapply(thisModel$edges$impact, getImpact)
       modelList[[.selections$model]]$nodes$growth <<- .resistanceScores['ssgr']
       modelList[[.selections$model]]$nodes$confidence <<- .resistanceScores['pressSD']
-      
+
       thisModel <- modelList[[.selections$model]]
-      
+
 
       MEANPOS=1
       MEANNEG=0
-      
+
       expr <- "list("
       for (p in 1:nrow(pressStatus)) {
         if (pressStatus$status[p] == 'On') {
@@ -294,12 +294,12 @@ server <- function(input, output, session) {
         } else {
           threshold = MEANNEG
         }
-          
+
         expr <- paste0(expr, "\"", pressStatus$code[p], "\"=", threshold, ", ")
       }
       expr <-substr(expr, 1, nchar(expr)-2)
       expr<-paste0(expr, ')')
-      
+
       thisNet <- parser$buildGraph(thisModel, desc=list(inputCode='p', outputCodes=c('ba', 'op', 'es')))
 
       sampleDists <- cpdist(
@@ -311,16 +311,16 @@ server <- function(input, output, session) {
         debug=TRUE
       )
     })
-    
+
     print(sampleDists)
-    
+
     #displayCols <- match(nodeCodes, colnames(sampleDists))
     sampleDists <- sampleDists[,match(thisModel$nodes$code, colnames(sampleDists))]
     means <- apply(sampleDists, 2, mean)
     stdDev <- apply(sampleDists, 2, sd)
-    
+
     print(paste('Building likelihoods from model, sample dists', length(thisModel$nodes$name), length(sampleDists)))
-    
+
     return(data.frame(
            name = thisModel$nodes$name,
            code = thisModel$nodes$code,
@@ -337,12 +337,12 @@ server <- function(input, output, session) {
            stringsAsFactors=FALSE
          ))
   }
-  
+
-    
+
   observeEvent(input$modelSelect, {
     .selections$model <<- match(input$modelSelect, models)
   })
-  
+
   observeEvent(reactiveValuesToList(input), {
     isolate(myList <- reactiveValuesToList(input))
     matches <- match(pressures$code,  names(myList))
@@ -350,9 +350,9 @@ server <- function(input, output, session) {
     if (length(matches)>0) {
       status <-NULL
       for (n in 1:length(matches))  status[n] = myList[[matches[n]]]
-      
+
       newStatus <- data.frame(code=pressures$code, status=status, stringsAsFactors = FALSE)
-      
+
       if (!identical(newStatus, .selections$pressStatus)) {
         print('Running calc')
         #.likelihoods$p_ba <<- calcLikelihood(1,  newStatus)
@@ -362,42 +362,42 @@ server <- function(input, output, session) {
         #write.xlsx(.likelihoods$p_es, 'tmp.xlsx')
         .selections$pressStatus <<- newStatus
       }
-      
+
     }
   })
-  
+
   makeRadioButtons <- function(row) {
     radioButtons(row['code'], row['name'], choices=c('Off', 'On'), selected='Off', inline=TRUE)
   }
-  
+
   output$pressureList <- renderUI({
     #isolate({
     if (!is.null(modelList[[.selections$model]]$nodes)) {
       pressCodes <- which(startsWith(modelList[[.selections$model]]$nodes$code, 'p'))
-      pressures <- data.frame(code = modelList[[.selections$model]]$nodes$code[pressCodes], 
+      pressures <- data.frame(code = modelList[[.selections$model]]$nodes$code[pressCodes],
                      name = modelList[[.selections$model]]$nodes$name[pressCodes], stringsAsFactors=FALSE)
       setPressures(pressures)
       btnList <- apply(pressures, 1, makeRadioButtons)
     }
   })
-  
+
   observeEvent(input$bbnImpactSelect, {
-    #filter nodes and edges to 
+    #filter nodes and edges to
     .selections$bbnImpact <- thresholds[match(input$bbnImpactSelect, impacts)]
     print(paste("Setting bbn impact", .selections$bbnImpact))
   })
-  
+
   observeEvent(input$bbnDisplayNames, {
     .selections$bbnNames <- input$bbnDisplayNames
     print(.selections$bbnNames)
   })
-  
+
   observeEvent(input$bbnDisplayEdges, {
     .selections$bbnEdges <- input$bbnDisplayEdges
 
   })
-  
+
-  
+
   observeEvent(input$layer1Slider, {
     showModal(
       modalDialog({
@@ -410,8 +410,8 @@ server <- function(input, output, session) {
           sliderInput("ssgr", "Steady state growth rate", -0.1, 0.1,.resistanceScores[6], step=0.01),
           sliderInput("l1PressSD", "Pressure Std Dev", 0.1, 1.0, .resistanceScores[7], step=0.01)
         )
-      }, 
+      },
-      title='Layer 1 controls', 
+      title='Layer 1 controls',
       footer=tagList(
         modalButton("Cancel"),
         actionButton("modalOK", "OK")
@@ -419,7 +419,7 @@ server <- function(input, output, session) {
       size='s')
     )
   })
-  
+
   observeEvent(input$modalOK, {
     print('Modal ok pressed')
 
@@ -430,7 +430,7 @@ server <- function(input, output, session) {
     .resistanceScores['ins'] <<- -input$l1VL
     .resistanceScores['ssgr'] <<- input$ssgr
     .resistanceScores['pressSD'] <<- input$l1PressSD
-    
+
     print('Running calc')
     #.likelihoods$p_ba <<- calcLikelihood(1,  .selections$pressStatus)
     #.likelihoods$ba_os <<- calcLikelihood(2, .selections$pressStatus)
@@ -439,31 +439,31 @@ server <- function(input, output, session) {
     removeModal()
 
   })
-  
+
-  
+
   output$nodeTable <- DT::renderDataTable(
-    
+
     modelList[[.selections$model]]$nodes,
       selection = 'single',options = list(searching = TRUE, pageLength = 10, editable=TRUE),server = TRUE, escape = FALSE,rownames= TRUE
     )
 
   output$edgeTable <- DT::renderDataTable(
-    
+
     modelList[[.selections$model]]$edges,
       selection = 'single',options = list(searching = TRUE, pageLength = 10, editable=TRUE),server = TRUE, escape = FALSE,rownames= TRUE
   )
-  
+
   getLabel <- function(value) {
     sign <- ifelse(value<0, "-", "+")
     idx <- min(which((abs(value)>=thresholds)==TRUE))
     return(paste0(sign, impLabels[idx]))
   }
-  
+
   makeBbnGraph <- function(model) {
     nodes <- model$nodes
-    
+
     if (.selections$bbnEdges) {labels <- sapply(model$edges$values, getLabel)} else {labels <- rep("", nrow(model$edges))}
-    
+
     edges <- data.frame(
       id = rownames(model$edges),
       from=match(model$edges$input, nodes$code),
@@ -472,13 +472,13 @@ server <- function(input, output, session) {
       label=labels,
       arrows="to",
       stringsAsFactors=FALSE
-    ) 
+    )
     if (.selections$bbnNames) {labels <- nodes$name} else {labels <- nodes$code}
-    
+
     nodeSpacing <- ifelse(.selections$bbnNames, 600, 150)
-    
+
     palette <- brewer.pal(length(legends), "RdYlGn")
-    
+
     nodes <- data.frame(
       id = rownames(nodes),
       label = labels,
@@ -487,81 +487,81 @@ server <- function(input, output, session) {
       color = palette[as.integer(nodes$layer)],
       code = nodes$code,
       stringsAsFactors=FALSE
-    ) 
+    )
-    
+
     edges <- edges[(abs(edges$values)>=.selections$bbnImpact),]
-    
+
     nodeNet <- nodes[(nodes$code %in% .selections$pressStatus$code[.selections$pressStatus$status %in% c('On')]),]
-    
+
     #save(nodes, edges, nodeNet, file = 'tmp.RData')
-    
+
     if (nrow(nodeNet)>0) {
-      #do pressures 
+      #do pressures
       edgeNet <- edges[edges$from %in% nodeNet$id, ]
       idx = 1
       repeat {
         nodesToAdd <- nodes[nodes$id %in% edgeNet$to, ]
         nodesToAdd <- nodesToAdd[!(nodesToAdd$id %in% nodeNet$id),]
-        
+
         edgesToAdd <- edges[edges$from %in% nodesToAdd$id, ]
         edgesToAdd <- edgesToAdd[!(edgesToAdd$id %in% edgeNet$id),]
-        
+
         idx <- idx + 1
         if ((idx>20) || ((nrow(nodesToAdd)==0) && (nrow(edgesToAdd)==0))) break
         nodeNet <- rbind(nodeNet, nodesToAdd)
         edgeNet <- rbind(edgeNet, edgesToAdd)
-        
+
       } #until finished
     } else edgeNet <- edges
-    
+
     legendDF <- data.frame(
       id = 1:length(legends),
       label = legends,
       color = palette,
       stringsAsFactors = FALSE
     )
-    
+
-    visNetwork(nodeNet, edgeNet, width = "100%", main='Bayesian Belief Network', submain=input$modelSelect) %>% 
+    visNetwork(nodeNet, edgeNet, width = "100%", main='Bayesian Belief Network', submain=input$modelSelect) %>%
       visExport() %>%
       visLegend(useGroups=FALSE, addNodes=legendDF) %>%
       visHierarchicalLayout(nodeSpacing=nodeSpacing, direction='LR') %>%
       visOptions(highlightNearest = TRUE) #%>%
       #visInteraction(navigationButtons = TRUE, dragNodes = TRUE, dragView = TRUE, zoomView = TRUE)
   }
-  
+
   output$bbnGraphPlot <- renderVisNetwork({
     makeBbnGraph(modelList[[.selections$model]])
   })
-  
+
   #observe({
   #  visNetworkProxy("bbnGraphPlot") %>%
   #    visStabilize(iterations=10)
   #})
-  
+
   getModelName <- function() {
     paste0('data/', input$modelSelect, '.xlsx')
   }
-  
+
   genPlot <- function(boxPlot, title) {
     if (nrow(boxPlot)>0) {
-      
+
       palette <- brewer.pal(length(legends), "RdYlGn")
       #print(palette)
-      
+
       colours <- palette[as.integer(boxPlot$Group)]
-      
+
       #print(paste('Box plot, colours', nrow(boxPlot), length(colours)))
       #cat(colours)
       xform <- list(categoryorder = "array",
                     categoryarray = boxPlot[,1],
                     zerolinewidth=10)
-      # 
+      #
       plot_ly(boxPlot, x = boxPlot[,1], y = ~Range,   color = colours,  colors = palette, type = "box") %>%
         layout(xaxis = xform, showlegend=FALSE, title=title)
-      
+
-    } 
+    }
   }
-  
+
   prepPlot <- function(code="ba", name="Bio-Assemblage") {
     if (!is.null(.likelihoods$p_es)) {
       inScope <- startsWith(.likelihoods$p_es$code, code)
@@ -571,22 +571,22 @@ server <- function(input, output, session) {
       genPlot(thisPlot, title)
     }
   }
-  
+
   output$layer1 <- renderPlotly({
     prepPlot("ba", "Bio-Assemblage")
   })
-  
+
   output$layer2 <- renderPlotly({
     prepPlot("op", "Output Processes")
   })
-  
+
   output$layer3 <- renderPlotly({
     prepPlot("es", "Ecosystem Services")
   })
-  
+
-  
+
   export <- function(model) {
-    
+
     #Get the network graph
     l1 <- orca(prepPlot("ba", "Bio-Assemblage"), 'tmp/layer1.png')
     l2 <- orca(prepPlot("op", "Output Processes"),'tmp/layer2.png')
@@ -594,27 +594,27 @@ server <- function(input, output, session) {
 
     #Save pressure list, confidence levels, node and edge tables in xlsx
     l <- list(
-      pressures = .selections$pressStatus, 
+      pressures = .selections$pressStatus,
       nodes = model$nodes,
       edges = model$edges,
       settings = as.data.frame(cbind(names(.resistanceScores), .resistanceScores), stringsAsFactors=FALSE)
     )
-    
+
     xl <- write.xlsx(l, 'tmp/dataset.xlsx')
-    
+
     print('saving xlsx file export tmp/dataset.xlsx')
-   
+
     zipFile <- zipr(paste0('tmp/MESO-', format(Sys.time(), "%m%d_%H%M"), '.zip'), c('tmp/layer1.png', 'tmp/layer2.png', 'tmp/layer3.png', 'tmp/dataset.xlsx'))
-    
+
     print(paste('zip file complete', zipFile))
-    
+
     return(zipFile)
   }
-  
+
-  
+
-  
+
-  
+
-  
+
   output$linkBackgroundData <- downloadHandler(
     filename = getModelName(),
     content = function(file) {
@@ -622,7 +622,7 @@ server <- function(input, output, session) {
     },
     contentType = "application/xlsx"
   )
-  
+
   output$download <-downloadHandler(
     filename = paste0('MESO-', format(Sys.time(), "%m%d_%H%M"), '.zip'),
     content = function(file) {
@@ -632,7 +632,7 @@ server <- function(input, output, session) {
     contentType = "application/zip"
   )
 
-  
+
 }
 
 shinyApp(ui, server)