Merge

.hgignore

@@ -4,3 +4,4 @@ syntax: glob
 archive
 data/tmp/
 data/new/
+node_modules/

Parses.R

@@ -1,27 +1,27 @@
 
-modules::import(openxlsx)
 modules::import(bnlearn)
+modules::import(openxlsx)
 modules::import(stringr)
-modules::import(graph)
-modules::import(ggplot2)
 modules::import(stats)
-modules::import(plotly)
-modules::import(utils)
 
 
 #Improvements needed: make the selection of first row/column of nodes programmatic
 FIRST_NODE_COL <- 3
 
-mappings <- c('TestScenario', 'Map_P_BA', 'Map_BA_OP', 'Map_OP_ES')
-nodeTypes <- c('Input.Nodes', 'Internal.Nodes', 'Published.Nodes')
-states <- c('impact', 'confidence', 'growth', 'recovery', 'layer')
-refs <-c(1:length(mappings))
+mappings <- c("TestScenario", "Map_P_BA", "Map_BA_OP", "Map_OP_ES")
+nodeTypes <- c("Input.Nodes", "Internal.Nodes", "Published.Nodes")
+states <- c("impact", "confidence", "growth", "recovery", "layer")
+refs <- c(1:length(mappings))
 
 setEmpties <- function(val) {
-  if (is.na(val)) return(0) else return(val)
+  if (is.na(val)) {
+    return(0)
+  } else {
+    return(val)
+  }
 }
 
-readXL <- function(fName, sheetN, startRow=1) {
+readXL <- function(fName, sheetN, startRow = 1) {
   xl <- read.xlsx(fName, sheet = sheetN, startRow)    #, rowNames = import)
   return(data.frame(xl, stringsAsFactors = FALSE, row.names = NULL))
 }
@@ -32,73 +32,87 @@ delNA <- function(vec) {
 
 buildExpr <- function(pressStatus) {
   #pressStatus is a two column DF of name of pressure and status Ii.e. on or off)
-  MEANPRESS = 0
+  MEANPRESS <- 0
   expr <- "("
   for (p in 1:nrow(pressStatus)) {
-    if (pressStatus$status[p] == 'On') symbol='>=' else symbol='<='
+    if (pressStatus$status[p] == "On") {
+      symbol <- ">="
+    } else {
+      symbol <- "<="
+    }
+
     expr <- paste0(expr, "(\"", pressStatus$code[p], "\"", symbol, MEANPRESS, ") & ")
   }
-  expr<-substr(expr, 1, nchar(expr)-2)
-  expr<-paste0(expr, ')')
+  expr <- substr(expr, 1, nchar(expr) - 2)
+  expr <- paste0(expr, ")")
 
   return(expr)
 }
 
-parseScenario <- function(press, prefix = 'p') {
+parseScenario <- function(press, prefix = "p") {
   pressNames <- colnames(press)[2:length(colnames(press))]
-  coefs <- matrix(data=NA, nrow=length(pressNames), ncol=3, dimnames=list(NULL, c('growth', 'confidence', 'layer')))
+  coefs <- matrix(
+    data = NA,
+    nrow = length(pressNames),
+    ncol = 3,
+    dimnames = list(NULL, c("growth", "confidence", "layer"))
+  )
   for (col in 2:ncol(press)) {
-    coefs[col-1,] <-  as.numeric(split(press[1, col]))[match(c('growth', 'confidence', 'layer'), states)]
+    coefs[col-1,] <-  as.numeric(split(press[1, col]))[match(c("growth", "confidence", "layer"), states)]
   }
   press[is.na(press)] <- 0
-  if (sum(duplicated(pressNames))>0) {
-    cat('Duplicated pressure node names found')
+  if (sum(duplicated(pressNames)) > 0) {
+    cat("Duplicated pressure node names found")
     print(pressNodes[duplicated(pressNames)])
   }
 
   return(list(
-    timeSeq=press, 
-    nodes=data.frame(name = pressNames, 
-                     code=paste0(prefix, seq(1:length(pressNames))), 
-                     growth = coefs[,'growth'], 
-                     confidence=coefs[,'confidence'], 
-                     layer=coefs[,'layer'], 
-                     stringsAsFactors = FALSE),
-    edges=data.frame(input=NULL, output=NULL, impact=NULL)
+    timeSeq = press,
+    nodes = data.frame(
+      name = pressNames,
+      code = paste0(prefix, seq(1:length(pressNames))),
+      growth = coefs[,"growth"],
+      confidence = coefs[,"confidence"],
+      layer = coefs[,"layer"],
+      stringsAsFactors = FALSE
+    ),
+    edges = data.frame(input = NULL, output = NULL, impact = NULL)
   ))
 }
 
 getInitial <- function(string, letter) {
-  return(tolower(substr(string, start=1, stop=1)))
+  return(tolower(substr(string, start = 1, stop = 1)))
 }
 
 split <- function(cell) {
-  params <- unlist(strsplit(cell, ','))
+  params <- unlist(strsplit(cell, ","))
   values <- rep(0, length(states))
 
   for (n in 1:length(params)) {
-     kvp <- unlist(strsplit(params[n], '='))
+     kvp <- unlist(strsplit(params[n], "="))
      ref <- match(getInitial(trimws(kvp[1])), getInitial(states))
-     if ((ref>0)  & (ref<=length(values))) {
+
+     if ((ref > 0) & (ref <= length(values))) {
        values[ref] <- kvp[2]
      } else {
-       print(paste('Unrecognised parameter(s):',params[n]))
+       print(paste("Unrecognised parameter(s):",params[n]))
      }
-
   }
-  return(values)
 
+  return(values)
 }
 
 cleanTitles <- function(titleV) {
-  return(str_replace_all(titleV, c(' ' = '.', '-' = '')))
+  return(str_replace_all(titleV, c(" " = ".", "-" = "")))
 }
 
 getOutNodes <- function(codes, codeList) {
-  v <- vector(mode='logical', length=length(codes))
+  v <- vector(mode = "logical", length = length(codes))
+
   for (idx in 1:length(codes)) {
-    v[idx] <- (sum(startsWith(codes[idx], codeList))>0)
+    v[idx] <- (sum(startsWith(codes[idx], codeList)) > 0)
   }
+
   return(v)
 }
 
@@ -112,38 +126,38 @@ buildGraph <- function(model, desc) {
   #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 ="")
+  inputText <- paste0("[", inputNodes, "]", collapse = "")
 
   #do the internal nodes
   edges <- ""
 
   outNodes <- model$nodes$code[getOutNodes(model$nodes$code, desc$outputCodes)]
-  outDist <- vector(mode="list", length=length(outNodes))
+  outDist <- vector(mode = "list", length = length(outNodes))
 
   for (idx in 1:length(outNodes)) {
     nodeRef <- match(outNodes[idx], model$nodes$code)
 
     rows <- which(model$edges$output == outNodes[idx])
-    inputsStr <- paste0(model$edges$input[which(model$edges$output == outNodes[idx])], sep=":", collapse="")
-    edges <- paste0(edges, paste0("[", outNodes[idx], "|", substr(inputsStr, start=1, stop=(nchar(inputsStr)-1)), "]"))
+    inputsStr <- paste0(model$edges$input[which(model$edges$output == outNodes[idx])], sep = ":", collapse = "")
+    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]), 
-                        c("(Intercept)", model$edges$input[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, 
-                         sd = model$nodes$confidence[nodeRef])
+    outDist[[idx]] <- list(coef = coefVal, sd = model$nodes$confidence[nodeRef])
   }
 
-  print('about to build network')
+  print("about to build network")
   print(paste0(inputText, edges))
 
-  net <- model2network(paste0(inputText, edges), debug=TRUE)
+  net <- model2network(paste0(inputText, edges), debug = TRUE)
 
-  print('network build successful')
+  print("network build successful")
 
-  inDist <- vector(mode="list", length=length(inputNodes))
+  inDist <- vector(mode = "list", length = length(inputNodes))
 
   for (idx in 1:length(inputNodes)) {
     inRef <- match(inputNodes[idx], model$nodes$code)
@@ -151,17 +165,17 @@ buildGraph <- function(model, desc) {
     inDist[[idx]] <- list(coef = coefVal, sd = model$nodes$confidence[inRef])
   }
 
-  allDists = as.list(setNames(c(inDist, outDist), c(inputNodes, outNodes)))
-  cfit = custom.fit(net, allDists)
+  allDists <- as.list(setNames(c(inDist, outDist), c(inputNodes, outNodes)))
+  cfit <- custom.fit(net, allDists)
 
-  cat('about to calculate sample distributions')
+  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')
+  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)]
@@ -185,58 +199,62 @@ getValidNodes <- function(mapping, prevOutputs, prefix) {
   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) {
-    if (sum(inputNodes %in% prevOutputs$name)<length(inputNodes)) {
-      cat('Missing entries for input nodes in previous output columns')
+  inputNodes <- delNA(mapping[mapping[,"Node.Type"] == "input", "Nodes"])
+  if (length(inputNodes) > 0) {
+    if (sum(inputNodes %in% prevOutputs$name) < length(inputNodes)) {
+      cat("Missing entries for input nodes in previous output columns")
       print(inputNodes[!inputNodes %in% prevOutputs$name])
     }
-  } else print('Invalid sheet - table must have at least one input row containing names from previous table')
+  } 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')
+  if (length(validInputs) == 0) {
+    print("Invalid sheet - table must have at least one input row containing names from previous table")
+  }
 
 
-  inputInts <- delNA(inputNodes[mapping$Node.Type!='link'])
+  inputInts <- delNA(inputNodes[mapping$Node.Type != "link"])
 
   if (sum(duplicated(inputInts))>0) {
-    cat('Duplicated input node names found')
+    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')
+  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) {
+  intNodes <- delNA(mapping[mapping[,"Node.Type"] == "internal", "Nodes"])
+  if (length(intNodes) > 0) {
     if (sum(intNodes %in% outNodes)<length(intNodes)) {
-      cat('Missing entries for internal nodes in output columns')
-      print(intNodes[!intNodes %in% outNodes])
+      cat("Missing entries for internal nodes in output columns")
+      print(intNodes[!(intNodes %in% outNodes)])
     }
   }
 
-  coefs <- matrix(data=NA, nrow=length(outNodes), ncol=3, dimnames=list(NULL, c('growth', 'confidence', 'layer')))
+  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)]
+    coefs[idx,] <-  as.numeric(split(mapping[1, col]))[match(c("growth", "confidence", "layer"), states)]
   }
 
   print(coefs)
 
   return(data.frame(
-    code=c(prevOutputs$code, paste0(prefix, seq(1:length(outNodes)))), 
-    name=c(prevOutputs$name, outNodes),
-    growth=c(prevOutputs$growth, coefs[,"growth"]),
-    confidence=c(prevOutputs$confidence, coefs[,"confidence"]),
-    layer=c(prevOutputs$layer, coefs[,"layer"]),
-    stringsAsFactors=FALSE
+    code = c(prevOutputs$code, paste0(prefix, seq(1:length(outNodes)))),
+    name = c(prevOutputs$name, outNodes),
+    growth = c(prevOutputs$growth, coefs[,"growth"]),
+    confidence = c(prevOutputs$confidence, coefs[,"confidence"]),
+    layer = c(prevOutputs$layer, coefs[,"layer"]),
+    stringsAsFactors = FALSE
   ))
 }
 
@@ -244,14 +262,15 @@ getCode <- function(name, nodeDF) {
   nodeDF$code[match(name, nodeDF$name)]
 }
 
-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))
+getValidEdges <- function(mapping, nodeDF, prevEdge = NULL, prefix) {
+  utils::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
+    count <- 0
 
     for (row in 2:nrow(mapping)) {
 
@@ -259,76 +278,74 @@ getValidEdges <- function(mapping, nodeDF, prevEdge=NULL, prefix) {
         edgeM <- rbind(edgeM,
           c(getCode(mapping[row, 1], nodeDF),
             getCode(colnames(mapping)[col], nodeDF),
-            split(mapping[row,col])[match('impact', states)]
-           )
+            split(mapping[row,col])[match("impact", states)]
+          )
         )
-        count=count+1
+        count <- count + 1
       }
-      #if (count==0) print(paste('No edges found for output', colnames(mapping)[col]))
+      #if (count == 0) print(paste("No edges found for output", colnames(mapping)[col]))
     }
   }
-  if (is.null(prevEdge)) return (
-    data.frame(
+  if (is.null(prevEdge)) {
+    return (data.frame(
       input = edgeM[,"inputNode"],
       output = edgeM[,"outputNode"],
       impact = edgeM[,"impact"],
       stringsAsFactors = FALSE
-    )
-  ) else return (
-    data.frame(
+    ))
+  } else {
+    return (data.frame(
       input = c(prevEdge$input, edgeM[,"inputNode"]),
       output = c(prevEdge$output, edgeM[,"outputNode"]),
       impact = c(prevEdge$impact, edgeM[,"impact"]),
       stringsAsFactors = FALSE
-    )
-  )
+    ))
+  }
 }
 
 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)
+  edgeDF <- getValidEdges(mapping, nodeDF, prevEdge = prevOutputs$edges, prefix)
 
   return(list(
     #New structure
-    nodes=nodeDF,
-    edges=edgeDF
+    nodes = nodeDF,
+    edges = edgeDF
   ))
-
 }
 
 parseSheet <- function(fName) {
   #get sheet names
 
-  print(paste('starting sheet load', fName))
+  print(paste("starting sheet load", fName))
 
   if (file.exists(fName)) {
     names <- openxlsx::getSheetNames(fName)
 
-    if (length(names)>0) {
+    if (length(names) > 0) {
 
       sheets <- sort(delNA(match(names, mappings)))
 
-      cat('starting sheet parse')
+      cat("starting sheet parse")
       print(sheets)
 
-      if (sum(sheets==refs)==length(refs)) {
+      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')
+        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')
+        #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')))
+        #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')
+        print("sheet load completed")
         return(
           #list(
             #pressBioAss = p_baNet,
@@ -339,8 +356,8 @@ parseSheet <- function(fName) {
         )
 
       } else {
-        print(paste('Sheets found include', mappings[sheets]))
-        cat('Missing sheets are:')
+        print(paste("Sheets found include", mappings[sheets]))
+        cat("Missing sheets are:")
         print(refs[-sheets])
       }
     }

README.md

@@ -0,0 +1,56 @@
+## Installation
+
+#### Required R libraries:
+- bnlearn
+- DT
+- ggplot2
+- graph
+- htmltools
+- kableExtra
+- knitr
+- magrittr
+- openxlsx
+- plotly
+- processx
+- RColorBrewer
+- shiny
+- shinyBS
+- shinycssloaders
+- shinydashboard
+- shinydashboardPlus
+- shinyjs
+- stringr
+- vizNetwork
+- zip
+```
+install.packages(c("bnlearn", "DT", "ggplot2", "graph", "htmltools", "kableExtra", "knitr", "magrittr", "openxlsx", "plotly", "processx", "RColorBrewer", "shiny", "shinyBS", "shinycssloaders", "shinydashboard", "shinydashboardPlus", "shinyjs", "stringr", "vizNetwork", "zip", "devtools"))
+devtools::install_github("ropensci/plotly")
+```
+
+#### ORCA for downloads:
+- NodeJs (v8)
+- electron
+- orca
+```
+npm install 
+export PATH=`pwd`/node_modules/.bin:$PATH
+```
+NOTE: remember to export the path when running the application so that R can find orca
+
+#### Start script (optional)
+Assumes application runs under the `shiny` account
+```
+#!/bin/bash
+
+if [ "$(whoami)" != "shiny" ]; then
+    sudo -u shiny $0
+    exit 1
+fi
+
+export NVM_DIR="$HOME/.nvm"
+[ -s "$NVM_DIR/nvm.sh" ] && \. "$NVM_DIR/nvm.sh"  # This loads nvm
+
+export PATH=/srv/shiny/bin:$PATH
+
+screen -dmS MESO R --vanilla -e "shiny::runApp('app.R', host = '0.0.0.0', port = 6376)"
+```

app.R

@@ -1,47 +1,40 @@
-modules::import(DT)
 modules::import(shiny)
-modules::import(shinyBS)
-modules::import(shinyjs)
 modules::import(shinydashboard)
 modules::import(shinydashboardPlus)
-modules::import(htmltools)
-#modules::import(DiagrammeR)
-modules::import(magrittr)
-modules::import(plotly)
-modules::import(kableExtra)
-#modules::import(Rgraphviz)
-modules::import(knitr)
 modules::import(shinycssloaders)
-#modules::import(googleway)
+modules::import(shinyjs)
+
 modules::import(bnlearn)
 modules::import(visNetwork)
 modules::import(RColorBrewer)
-modules::import(zip)
-modules::import(processx)
+modules::import(plotly)
 modules::import(openxlsx)
+modules::import(zip)
+modules::import(DT)
 
+parser <- modules::use("Parses.R")
 
-parser <- modules::use('Parses.R')
-
-layers <- c("Pressures to Bio-Assemblages", "Bio-Assemblages to Output Processes", "Output Processes to Ecosystem services")
-transitions <- c("Pressures to Bio-Assemblages", "Pressures to Output Processes", "Pressures to Ecosystem services")
-impacts <- c('Very High', '>=High', '>=Medium', '>=Low', 'All')
-thresholds <- c(0.97, 0.9, 0.45, 0.17, 0)
-impLabels <- c('Very High', 'High', 'Medium', 'Low', 'Very Low')
-
-legends <- c('Pressures',
-             'Suspension feeders',
-             'Mobile and burrow dwellers',
-             'Predators',
-             'Epifauna and algae',
-             'Functional groups',
-             'Output processes',
-             'Output enablers',
-             'Ecosystem services')
 
 addResourcePath("js", "./www/js")
 
-ui<-dashboardPage(
+
+layers <- c("Pressures to Bio-Assemblages", "Bio-Assemblages to Output Processes", "Output Processes to Ecosystem services")
+transitions <- c("Pressures to Bio-Assemblages", "Pressures to Output Processes", "Pressures to Ecosystem services")
+impacts <- c("Very High", ">= High", ">= Medium", ">= Low", "All")
+thresholds <- c(0.97, 0.9, 0.45, 0.17, 0)
+impLabels <- c("Very High", "High", "Medium", "Low", "Very Low")
+
+legends <- c("Pressures",
+             "Suspension feeders",
+             "Mobile and burrow dwellers",
+             "Predators",
+             "Epifauna and algae",
+             "Functional groups",
+             "Output processes",
+             "Output enablers",
+             "Ecosystem services")
+
+ui <- dashboardPage(
       dashboardHeader(title = "JNCC MESO online",
         tags$li(
           id = "dropdownHelp",
@@ -95,52 +88,52 @@ ui<-dashboardPage(
             menuItem("Bayesian Network", tabName = "2", icon = icon("atom")),
             #menuItem("Habitats", tabName = "3", icon = icon("atlas")),
             #menuItem("Ingestion", tabName = "3", icon = icon("utensils")),
-            selectInput("modelSelect", "Select MESO model", choices=c(""), selected=NULL, multiple=FALSE),
-            downloadButton("download", "", icon=icon("download")),
+            selectInput("modelSelect", "Select MESO model", choices = c(""), selected = NULL, multiple = FALSE),
+            downloadButton("download", "", icon = icon("download")),
             uiOutput("pressureList")
             #selectInput("layerSelect", "Select Transition",
-            #            choices=transitions, 
-            #            selected=NULL, multiple=FALSE)
+            #            choices = transitions,
+            #            selected = NULL, multiple = FALSE)
         )
       ),
       dashboardBody(
         tabItems(
-          tabItem(tabName = "1", h2('Impact Distribution'),
+          tabItem(tabName = "1", h2("Impact Distribution"),
             fluidRow(
               column(
-                width=6, 
-                h4('Effect on bio-assemblage')
+                width = 6,
+                h4("Effect on bio-assemblage")
               ),
               column(
-                width=1,
-                actionButton("layer1Slider", "1", icon=icon("sliders-h"))
+                width = 1,
+                actionButton("layer1Slider", "1", icon = icon("sliders-h"))
               ),
               column(
-                width=5,
+                width = 5,
                 strong("Customise sensitivity weightings")
               )
             ),
-            plotlyOutput("layer1", height="270px") %>% withSpinner(),
-            h4('Effect on Output Processes'),
-            plotlyOutput("layer2", height="270px") %>% withSpinner(),
-            h4('Effect on Ecosystem services'),
-            plotlyOutput("layer3", height="270px") %>% withSpinner()
+            plotlyOutput("layer1", height = "270px") %>% withSpinner(),
+            h4("Effect on Output Processes"),
+            plotlyOutput("layer2", height = "270px") %>% withSpinner(),
+            h4("Effect on Ecosystem services"),
+            plotlyOutput("layer3", height = "270px") %>% withSpinner()
           ),
           tabItem(tabName = "2",h2("Bayesian Network"),
             fluidPage(
-              p('Graphical output of the Bayesian Network.  Note: The graph will only draw if pressures are applied!'),
+              p("Graphical output of the Bayesian Network.  Note: The graph will only draw if pressures are applied!"),
               fluidRow(
                 column(
-                  width=4,
-                  checkboxInput("bbnDisplayNames", "Display Node names", value=FALSE)
+                  width = 4,
+                  checkboxInput("bbnDisplayNames", "Display Node names", value = FALSE)
                 ),
                 column(
-                  width=4,
-                  checkboxInput("bbnDisplayEdges", "Display edge status", value=FALSE)
+                  width = 4,
+                  checkboxInput("bbnDisplayEdges", "Display edge status", value = FALSE)
                 ),
                 column(
-                  width=4,
-                  selectInput("bbnImpactSelect", "Impact Threshold", choices=impacts, selected='All')
+                  width = 4,
+                  selectInput("bbnImpactSelect", "Impact Threshold", choices = impacts, selected = "All")
                 )
               ),
               fluidRow(
@@ -148,14 +141,14 @@ ui<-dashboardPage(
               ),
               fluidRow(
                column(
-                 width=6,
-                 h4('Ecoservice nodes'),
-                 DT::dataTableOutput('nodeTable')
+                 width = 6,
+                 h4("Ecoservice nodes"),
+                 DT::dataTableOutput("nodeTable")
                ),
                column(
-                 width=6,
-                 h4('Ecoservice influences'),
-                 DT::dataTableOutput('edgeTable')
+                 width = 6,
+                 h4("Ecoservice influences"),
+                 DT::dataTableOutput("edgeTable")
                )
               )
             )
@@ -169,8 +162,8 @@ ui<-dashboardPage(
              fluidPage(
                p("Select a spreadsheet from your network for input into the JNCC Bayesian Network Analyser:"),
                fileInput("fileSelect", "Choose Excel Spreadsheet File (xlsx format)", multiple = FALSE, accept = "xlsx"),
-               fluidRow(renderUI('status')),
-               actionButton('loadAB', 'Load') # icon='upload')
+               fluidRow(renderUI("status")),
+               actionButton("loadAB", "Load") # icon = "upload")
              )
            )
         )
@@ -180,20 +173,20 @@ ui<-dashboardPage(
 server <- function(input, output, session) {
   #SERVER Constants
 
-  print('Loading data')
+  print("Loading data")
 
   #set_key("AIzaSyAw8_btgGN1drf8qhCxNcotP6r11qEXA_M")
-  dataStorage <- 'data/'
+  dataStorage <- "data/"
 
-  models<-NULL
+  models <- NULL
   pressures <- NULL
 
   .loadStatus <- reactiveValues(
-    valid = c(p=FALSE, ba=FALSE, op=FALSE, es=FALSE),
+    valid = c(p = FALSE, ba = FALSE, op = FALSE, es = FALSE),
     msgs = NULL
   )
 
-  .likelihoods <-reactiveValues(
+  .likelihoods <- reactiveValues(
     p_ba = NULL,
     ba_os = NULL,
     os_es = NULL,
@@ -206,7 +199,7 @@ server <- function(input, output, session) {
 
 
   .resistanceScores <- c(
-    ins= -0.01,
+    ins = -0.01,
     hr = -0.2,
     mr = -0.75,
     lr = -0.95,
@@ -216,11 +209,11 @@ server <- function(input, output, session) {
   )
 
   .selections <- reactiveValues(
-    model=1, 
-    bbnImpact=1, 
-    bbnNames=FALSE,
-    bbnEdges=FALSE,
-    pressStatus=NULL
+    model = 1,
+    bbnImpact = 1,
+    bbnNames = FALSE,
+    bbnEdges = FALSE,
+    pressStatus = NULL
   )
 
   getImpact <- function(v) {
@@ -234,13 +227,13 @@ server <- function(input, output, session) {
   }
 
   getAvailableModels <- function() {
-    fileList <- list.files(dataStorage, pattern='.xlsx')
+    fileList <- list.files(dataStorage, pattern = ".xlsx")
 
     modelList <- list()
-    cnt<-1
+    cnt <- 1
 
     for (idx in 1:length(fileList)) {
-      print(paste('attempting to load', paste0(dataStorage, fileList[idx])))
+      print(paste("attempting to load", paste0(dataStorage, fileList[idx])))
 
       tmp <- parser$parseSheet(paste0(dataStorage, fileList[idx]))
       print(tmp)
@@ -249,12 +242,12 @@ server <- function(input, output, session) {
       if (!is.null(tmp)) {
         modelList[[cnt]] <- tmp
         models <<- c(models, substr(fileList[idx], 1, (nchar(fileList[idx])-5)))
-        print(paste('Model file successfully loaded', fileList[idx]))
-        #save(tmp, file='tmp.RData')
-        cnt=cnt+1
+        print(paste("Model file successfully loaded", fileList[idx]))
+        #save(tmp, file = "tmp.RData")
+        cnt <- cnt+1
       }
     }
-    updateSelectInput(session, "modelSelect", choices=models)
+    updateSelectInput(session, "modelSelect", choices = models)
     return(modelList)
   }
 
@@ -266,41 +259,41 @@ server <- function(input, output, session) {
 
     isolate({
 
-      #if (layer==1) layerStr='ba' else if (layer==2) layerStr='op' else if (layer==3) layerStr='es'
+      #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]
+      #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$confidence <<- .resistanceScores['pressSD']
+      modelList[[.selections$model]]$nodes$growth <<- .resistanceScores["ssgr"]
+      modelList[[.selections$model]]$nodes$confidence <<- .resistanceScores["pressSD"]
 
       thisModel <- modelList[[.selections$model]]
 
 
-      MEANPOS=1
-      MEANNEG=0
+      MEANPOS <- 1
+      MEANNEG <- 0
 
       expr <- "list("
       for (p in 1:nrow(pressStatus)) {
-        if (pressStatus$status[p] == 'On') {
-          threshold = MEANPOS
+        if (pressStatus$status[p] == "On") {
+          threshold <- MEANPOS
         } else {
-          threshold = MEANNEG
+          threshold <- MEANNEG
         }
 
-        expr <- paste0(expr, "\"", pressStatus$code[p], "\"=", threshold, ", ")
+        expr <- paste0(expr, "\"", pressStatus$code[p], "\" = ", threshold, ", ")
       }
-      expr <-substr(expr, 1, nchar(expr)-2)
-      expr<-paste0(expr, ')')
+      expr <- substr(expr, 1, nchar(expr)-2)
+      expr <- paste0(expr, ")")
 
-      thisNet <- parser$buildGraph(thisModel, desc=list(inputCode='p', outputCodes=c('ba', 'op', 'es')))
+      thisNet <- parser$buildGraph(thisModel, desc = list(inputCode = "p", outputCodes = c("ba", "op", "es")))
 
       sampleDists <- cpdist(
         fitted = thisNet$cfit,
@@ -308,7 +301,7 @@ server <- function(input, output, session) {
         evidence = eval(parse(text = expr)),
         method = "lw",
         n = 10000,
-        debug=TRUE
+        debug = FALSE
       )
     })
 
@@ -319,23 +312,23 @@ server <- function(input, output, session) {
     means <- apply(sampleDists, 2, mean)
     stdDev <- apply(sampleDists, 2, sd)
 
-    print(paste('Building likelihoods from model, sample dists', length(thisModel$nodes$name), length(sampleDists)))
+    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,
-           layer = thisModel$nodes$layer,
-           range = c(
-                  apply(sampleDists, 2, min),
-                  means - 2*stdDev,
-                  means - stdDev,
-                  means,
-                  means + stdDev,
-                  means + 2*stdDev,
-                  apply(sampleDists, 2, max)
-           ),
-           stringsAsFactors=FALSE
-         ))
+      name = thisModel$nodes$name,
+      code = thisModel$nodes$code,
+      layer = thisModel$nodes$layer,
+      range = c(
+        apply(sampleDists, 2, min),
+        means - 2*stdDev,
+        means - stdDev,
+        means,
+        means + stdDev,
+        means + 2*stdDev,
+        apply(sampleDists, 2, max)
+      ),
+      stringsAsFactors = FALSE
+    ))
   }
 
 
@@ -347,19 +340,21 @@ server <- function(input, output, session) {
     isolate(myList <- reactiveValuesToList(input))
     matches <- match(pressures$code,  names(myList))
 
-    if (length(matches)>0) {
-      status <-NULL
-      for (n in 1:length(matches))  status[n] = myList[[matches[n]]]
+    if (length(matches) > 0) {
+      status <- NULL
+      for (n in 1:length(matches)) {
+        status[n] <- myList[[matches[n]]]
+      }
 
-      newStatus <- data.frame(code=pressures$code, status=status, stringsAsFactors = FALSE)
+      newStatus <- data.frame(code = pressures$code, status = status, stringsAsFactors = FALSE)
 
       if (!identical(newStatus, .selections$pressStatus)) {
-        print('Running calc')
+        print("Running calc")
         #.likelihoods$p_ba <<- calcLikelihood(1,  newStatus)
         #.likelihoods$ba_os <<- calcLikelihood(2, newStatus)
         #.likelihoods$os_es <<- calcLikelihood(3, newStatus)
         .likelihoods$p_es <<- calcLikelihood(0, newStatus)
-        #write.xlsx(.likelihoods$p_es, 'tmp.xlsx')
+        #write.xlsx(.likelihoods$p_es, "tmp.xlsx")
         .selections$pressStatus <<- newStatus
       }
 
@@ -367,15 +362,18 @@ server <- function(input, output, session) {
   })
 
   makeRadioButtons <- function(row) {
-    radioButtons(row['code'], row['name'], choices=c('Off', 'On'), selected='Off', inline=TRUE)
+    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$code[pressCodes], 
-                     name = modelList[[.selections$model]]$nodes$name[pressCodes], stringsAsFactors=FALSE)
+      pressCodes <- which(startsWith(modelList[[.selections$model]]$nodes$code, "p"))
+      pressures <- data.frame(
+        code = modelList[[.selections$model]]$nodes$code[pressCodes],
+        name = modelList[[.selections$model]]$nodes$name[pressCodes],
+        stringsAsFactors = FALSE
+      )
       setPressures(pressures)
       btnList <- apply(pressures, 1, makeRadioButtons)
     }
@@ -402,36 +400,36 @@ server <- function(input, output, session) {
     showModal(
       modalDialog({
         tagList(
-          sliderInput("l1VL", "Insensitive", 0.01, 0.2, abs(.resistanceScores[1]), step=0.01),
-          sliderInput("l1L", "Low Sensitivity/High resistance", 0.15, 0.5, abs(.resistanceScores[2]), step=0.01),
-          sliderInput("l1M", "Medium Sensitivity/Med resistance", 0.5, 0.75, abs(.resistanceScores[3]), step=0.01),
-          sliderInput("l1H", "High Sensitivity/Low resistance", 0.75, 1.0, abs(.resistanceScores[4]), step=0.01),
-          sliderInput("l1VH", "Very High Sensitivity/No resistance", 0.9, 1.0, abs(.resistanceScores[5]), step=0.01),
-          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)
+          sliderInput("l1VL", "Insensitive", 0.01, 0.2, abs(.resistanceScores[1]), step = 0.01),
+          sliderInput("l1L", "Low Sensitivity/High resistance", 0.15, 0.5, abs(.resistanceScores[2]), step = 0.01),
+          sliderInput("l1M", "Medium Sensitivity/Med resistance", 0.5, 0.75, abs(.resistanceScores[3]), step = 0.01),
+          sliderInput("l1H", "High Sensitivity/Low resistance", 0.75, 1.0, abs(.resistanceScores[4]), step = 0.01),
+          sliderInput("l1VH", "Very High Sensitivity/No resistance", 0.9, 1.0, abs(.resistanceScores[5]), step = 0.01),
+          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', 
-      footer=tagList(
+      title = "Layer 1 controls",
+      footer = tagList(
         modalButton("Cancel"),
         actionButton("modalOK", "OK")
       ),
-      size='s')
+      size = "s")
     )
   })
 
   observeEvent(input$modalOK, {
-    print('Modal ok pressed')
+    print("Modal ok pressed")
 
-    .resistanceScores['nr'] <<- -input$l1VH
-    .resistanceScores['lr'] <<- -input$l1H
-    .resistanceScores['mr'] <<- -input$l1M
-    .resistanceScores['hr'] <<- -input$l1L
-    .resistanceScores['ins'] <<- -input$l1VL
-    .resistanceScores['ssgr'] <<- input$ssgr
-    .resistanceScores['pressSD'] <<- input$l1PressSD
+    .resistanceScores["nr"] <<- -input$l1VH
+    .resistanceScores["lr"] <<- -input$l1H
+    .resistanceScores["mr"] <<- -input$l1M
+    .resistanceScores["hr"] <<- -input$l1L
+    .resistanceScores["ins"] <<- -input$l1VL
+    .resistanceScores["ssgr"] <<- input$ssgr
+    .resistanceScores["pressSD"] <<- input$l1PressSD
 
-    print('Running calc')
+    print("Running calc")
     #.likelihoods$p_ba <<- calcLikelihood(1,  .selections$pressStatus)
     #.likelihoods$ba_os <<- calcLikelihood(2, .selections$pressStatus)
     #.likelihoods$os_es <<- calcLikelihood(3, .selections$pressStatus)
@@ -442,38 +440,52 @@ server <- function(input, output, session) {
 
 
   output$nodeTable <- DT::renderDataTable(
-    
     modelList[[.selections$model]]$nodes,
-      selection = 'single',options = list(searching = TRUE, pageLength = 10, editable=TRUE),server = TRUE, escape = FALSE,rownames= TRUE
-    )
+    selection = "single",
+    server = TRUE,
+    escape = FALSE,
+    rownames = TRUE,
+    options = list(searching = TRUE, pageLength = 10, editable = 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
+    selection = "single",
+    server = TRUE,
+    escape = FALSE,
+    rownames = TRUE,
+    options = list(searching = TRUE, pageLength = 10, editable = TRUE)
   )
 
   getLabel <- function(value) {
-    sign <- ifelse(value<0, "-", "+")
-    idx <- min(which((abs(value)>=thresholds)==TRUE))
+    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))}
+    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),
-      to=match(model$edges$output, nodes$code),
-      values=model$edges$values,
-      label=labels,
-      arrows="to",
-      stringsAsFactors=FALSE
+      from = match(model$edges$input, nodes$code),
+      to = match(model$edges$output, nodes$code),
+      values = model$edges$values,
+      label = labels,
+      arrows = "to",
+      stringsAsFactors = FALSE
     )
-    if (.selections$bbnNames) {labels <- nodes$name} else {labels <- nodes$code}
+    if (.selections$bbnNames) {
+      labels <- nodes$name
+    } else {
+      labels <- nodes$code
+    }
 
     nodeSpacing <- ifelse(.selections$bbnNames, 600, 150)
 
@@ -486,19 +498,19 @@ server <- function(input, output, session) {
       group = nodes$layer,
       color = palette[as.integer(nodes$layer)],
       code = nodes$code,
-      stringsAsFactors=FALSE
+      stringsAsFactors = FALSE
     )
 
-    edges <- edges[(abs(edges$values)>=.selections$bbnImpact),]
+    edges <- edges[(abs(edges$values) >= .selections$bbnImpact),]
 
-    nodeNet <- nodes[(nodes$code %in% .selections$pressStatus$code[.selections$pressStatus$status %in% c('On')]),]
+    nodeNet <- nodes[(nodes$code %in% .selections$pressStatus$code[.selections$pressStatus$status %in% c("On")]),]
 
-    #save(nodes, edges, nodeNet, file = 'tmp.RData')
+    #save(nodes, edges, nodeNet, file = "tmp.RData")
 
-    if (nrow(nodeNet)>0) {
+    if (nrow(nodeNet) > 0) {
       #do pressures
       edgeNet <- edges[edges$from %in% nodeNet$id, ]
-      idx = 1
+      idx <- 1
       repeat {
         nodesToAdd <- nodes[nodes$id %in% edgeNet$to, ]
         nodesToAdd <- nodesToAdd[!(nodesToAdd$id %in% nodeNet$id),]
@@ -507,12 +519,14 @@ server <- function(input, output, session) {
         edgesToAdd <- edgesToAdd[!(edgesToAdd$id %in% edgeNet$id),]
 
         idx <- idx + 1
-        if ((idx>20) || ((nrow(nodesToAdd)==0) && (nrow(edgesToAdd)==0))) break
+        if ((idx > 20) || ((nrow(nodesToAdd) == 0) && (nrow(edgesToAdd) == 0))) break
         nodeNet <- rbind(nodeNet, nodesToAdd)
         edgeNet <- rbind(edgeNet, edgesToAdd)
 
       } #until finished
-    } else edgeNet <- edges
+    } else {
+      edgeNet <- edges
+    }
 
     legendDF <- data.frame(
       id = 1:length(legends),
@@ -521,10 +535,10 @@ server <- function(input, output, session) {
       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') %>%
+      visLegend(useGroups = FALSE, addNodes = legendDF) %>%
+      visHierarchicalLayout(nodeSpacing = nodeSpacing, direction = "LR") %>%
       visOptions(highlightNearest = TRUE) #%>%
       #visInteraction(navigationButtons = TRUE, dragNodes = TRUE, dragView = TRUE, zoomView = TRUE)
   }
@@ -535,39 +549,37 @@ server <- function(input, output, session) {
 
   #observe({
   #  visNetworkProxy("bbnGraphPlot") %>%
-  #    visStabilize(iterations=10)
+  #    visStabilize(iterations = 10)
   #})
 
   getModelName <- function() {
-    paste0('data/', input$modelSelect, '.xlsx')
+    paste0("data/", input$modelSelect, ".xlsx")
   }
 
   genPlot <- function(boxPlot, title) {
-    if (nrow(boxPlot)>0) {
+    if (nrow(boxPlot) > 0) {
 
       palette <- brewer.pal(length(legends), "RdYlGn")
-      #print(palette)
+      names(palette) <- 1:length(legends)
 
-      colours <- palette[as.integer(boxPlot$Group)]
-      
-      #print(paste('Box plot, colours', nrow(boxPlot), length(colours)))
+      #print(paste("Box plot, colours", nrow(boxPlot), length(colours)))
       #cat(colours)
       xform <- list(categoryorder = "array",
                     categoryarray = boxPlot[,1],
-                    zerolinewidth=10)
+                    zerolinewidth = 10)
       #
-      plot_ly(boxPlot, x = boxPlot[,1], y = ~Range,   color = colours,  colors = palette, type = "box") %>%
-        layout(xaxis = xform, showlegend=FALSE, title=title)
+      plot_ly(boxPlot, x = boxPlot[,1], y = ~Range, color = as.character(boxPlot$Group), colors = palette, type = "box") %>%
+        layout(xaxis = xform, showlegend = FALSE, title = title)
 
     }
   }
 
-  prepPlot <- function(code="ba", name="Bio-Assemblage") {
+  prepPlot <- function(code = "ba", name = "Bio-Assemblage") {
     if (!is.null(.likelihoods$p_es)) {
       inScope <- startsWith(.likelihoods$p_es$code, code)
       thisPlot <- .likelihoods$p_es[inScope, c(1,3,4)]
       colnames(thisPlot) <- c(name, "Group", "Range")
-      title <- paste(input$modelSelect, name, 'Box Plot')
+      title <- paste(input$modelSelect, name, "Box Plot")
       genPlot(thisPlot, title)
     }
   }
@@ -588,33 +600,30 @@ server <- function(input, output, session) {
   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')
-    l3 <- orca(prepPlot("es", "Ecosystem Services"), 'tmp/layer3.png')
+    l1 <- orca(prepPlot("ba", "Bio-Assemblage"), "tmp/layer1.png")
+    l2 <- orca(prepPlot("op", "Output Processes"),"tmp/layer2.png")
+    l3 <- orca(prepPlot("es", "Ecosystem Services"), "tmp/layer3.png")
 
     #Save pressure list, confidence levels, node and edge tables in xlsx
     l <- list(
       pressures = .selections$pressStatus,
       nodes = model$nodes,
       edges = model$edges,
-      settings = as.data.frame(cbind(names(.resistanceScores), .resistanceScores), stringsAsFactors=FALSE)
+      settings = as.data.frame(cbind(names(.resistanceScores), .resistanceScores), stringsAsFactors = FALSE)
     )
 
-    xl <- write.xlsx(l, 'tmp/dataset.xlsx')
+    xl <- write.xlsx(l, "tmp/dataset.xlsx")
 
-    print('saving xlsx file export 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'))
+    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))
+    print(paste("zip file complete", zipFile))
 
     return(zipFile)
   }
 
 
-  
-  
-  
   output$linkBackgroundData <- downloadHandler(
     filename = getModelName(),
     content = function(file) {
@@ -623,8 +632,8 @@ server <- function(input, output, session) {
     contentType = "application/xlsx"
   )
 
-  output$download <-downloadHandler(
-    filename = paste0('MESO-', format(Sys.time(), "%m%d_%H%M"), '.zip'),
+  output$download <- downloadHandler(
+    filename = paste0("MESO-", format(Sys.time(), "%m%d_%H%M"), ".zip"),
     content = function(file) {
       fName <- export(modelList[[.selections$model]])
       file.copy(fName, file)

package.json

@@ -0,0 +1,15 @@
+{
+  "name": "jncc-meso",
+  "version": "1.0.0",
+  "description": "A Bayesian Belief Network to estimate the impacts of pressure of marine environments",
+  "dependencies": {
+    "electron": "^4.1.4",
+    "orca": "^1.2.1"
+  },
+  "devDependencies": {},
+  "scripts": {
+    "test": "echo \"Error: no test specified\" && exit 1"
+  },
+  "author": "AVS Developments Ltd",
+  "license": "MIT"
+}