Initial Script
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267
app.R
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267
app.R
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modules::import(DT)
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modules::import(shiny)
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modules::import(shinyBS)
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modules::import(shinyjs)
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modules::import(shinydashboard)
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modules::import(htmltools)
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modules::import(DiagrammeR)
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modules::import(magrittr)
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modules::import(plotly)
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modules::import(kableExtra)
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modules::import(Rgraphviz)
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modules::import(knitr)
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modules::import(shinycssloaders)
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modules::import(googleway)
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modules::import(Rgraphviz)
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modules::import(bnlearn)
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parser <- modules::use('Parses.R')
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layers <- c("Pressures to Bio-Assemblages", "Bio-Assemblages to Output Processes", "Output Processes to Eco-system services")
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transitions <- c("Pressures to Bio-Assemblages", "Pressures to Output Processes", "Pressures to Eco-system services")
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addResourcePath("js", "./www/js")
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ui<-dashboardPage(
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dashboardHeader(title = "JNCC MESO online"),
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dashboardSidebar(
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sidebarMenu(id = "tabs",
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menuItem("Pressure Test", tabName = "1", icon = icon("arrow-down")),
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menuItem("Bayesian Network", tabName = "2", icon = icon("atom")),
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menuItem("Habitats", tabName = "3", icon = icon("atlas")),
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selectInput("modelSelect", "Select MESO model", choices=c(""), selected=NULL, multiple=FALSE),
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selectInput("layerSelect", "Select Transition",
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choices=transitions,
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selected=NULL, multiple=FALSE)
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)
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),
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dashboardBody(
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tabItems(
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tabItem(tabName = "1",
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fluidRow(
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column(width=2,
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h4('Pressure Test'),
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radioButtons("pressure1", 'Sediment type', choices=c('On', 'Off'), inline=TRUE),
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radioButtons("pressure2", 'Seabed type', choices=c('On', 'Off'), inline=TRUE),
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radioButtons("pressure3", 'Material extraction', choices=c('On', 'Off'), inline=TRUE),
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radioButtons("pressure4", 'Abrasion of seabed', choices=c('On', 'Off'), inline=TRUE),
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radioButtons("pressure5", 'Penetration of seabed', choices=c('On', 'Off'), inline=TRUE),
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radioButtons("pressure6", 'Siltation', choices=c('On', 'Off'), inline=TRUE),
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radioButtons("pressure7", 'Wave exposure', choices=c('On', 'Off'), inline=TRUE),
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radioButtons("pressure8", 'Suspended sediment', choices=c('On', 'Off'), inline=TRUE),
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radioButtons("pressure9", 'Generic contamination', choices=c('On', 'Off'), inline=TRUE),
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radioButtons("pressure10", 'Deoxygenation', choices=c('On', 'Off'), inline=TRUE),
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radioButtons("pressure11", 'Removal of target species', choices=c('On', 'Off'), inline=TRUE),
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actionButton("calcAB", "Calc")
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),
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column(width=10,
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h4('Effect on bio-assemblage'),
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plotlyOutput("layer1", height="270px") %>% withSpinner(),
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h4('Effect on Output Processes'),
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plotlyOutput("layer2", height="270px") %>% withSpinner(),
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h4('Effect on Eco-system services'),
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plotlyOutput("layer3", height="270px") %>% withSpinner()
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)
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)
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),
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tabItem(tabName = "2",h4("Bayesian Network"),
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fluidPage(
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fluidRow(
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plotOutput("bbnGraphPlot")
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),
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fluidRow(
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column(
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width=6,
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h4('Ecoservice nodes'),
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DT::dataTableOutput('nodeTable')
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),
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column(
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width=6,
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h4('Ecoservice influences'),
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DT::dataTableOutput('edgeTable')
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)
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)
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)
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),
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tabItem(tabName = "3",h4("Habitats"),
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fluidPage(
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google_mapOutput(outputId = "map", width = "100%", height = "750px")
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)
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)
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)
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)
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)
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server <- function(input, output, session) {
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#SERVER Constants
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print('Loading data')
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set_key("AIzaSyAw8_btgGN1drf8qhCxNcotP6r11qEXA_M")
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dataStorage <- 'data/'
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models<-NULL
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getAvailableModels <- function() {
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fileList <- list.files(dataStorage, pattern='.xlsx')
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print(fileList)
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modelList <- list()
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cnt<-1
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for (idx in 1:length(fileList)) {
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print(paste('attempting to load', paste0(dataStorage, fileList[idx])))
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tmp <- parser$parseSheet(paste0(dataStorage, fileList[idx]))
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if (!is.null(tmp)) {
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modelList[[cnt]] <- tmp
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#tidy up the list for displaying
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models <<- c(models, substr(fileList[idx], 1, (nchar(fileList[idx])-5)))
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print(paste('Model file successfully loaded', fileList[idx]))
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cnt=cnt+1
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}
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}
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updateSelectInput(session, "modelSelect", choices=models)
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return(modelList)
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}
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.selections <- reactiveValues(model=1, layer=1)
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#parse on load sheets in the input sheet folder
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modelList <- getAvailableModels()
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calcLikelihood <- function(layer) {
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isolate({
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if (layer==1) layerStr='ba' else if (layer==2) layerStr='op' else layerStr ='es'
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nodeList <- modelList[[.selections$model]][[.selections$layer]]$nodes
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str(nodeList)
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nodeNames <- nodeList$name[startsWith(nodeList$code, layerStr)]
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mean = runif(length(nodeNames), min=-1, max=1)
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sd = runif(length(nodeNames), min=-0.25, max=0.25)
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df <- data.frame(
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nodeNames = nodeNames,
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range = c((mean - (3*sd)), (mean - (2*sd)), (mean - sd), mean,
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(mean + sd), (mean + (2*sd)), (mean + (3*sd))),
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stringsAsFactors=FALSE
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)
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print(df)
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})
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return(
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df
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)
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# isolate({
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#
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# if (layer==1) layerStr='ba' else if (layer==2) layerStr='op' else if (layer==3) layerStr='es'
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#
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# layerRange <- which(startsWith(modelList[[.selections$model]][[layer]]$nodes, layerStr))
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# distList <- modelList[[.selections$model]][[layer]]$summDist[,layerRange]
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# nodeNames <- modelList[[.selections$model]][[layer]]$nodes$name[layerRange]
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#
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# }
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# print(paste('Length of layer & node names',layer, length(nodeNames)))
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#
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# distList <- modelList[[.selections$model]][[layer]]$summDist
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# colNames <- c('min', 'q1', 'q1', 'mean', 'q3', 'q3', 'max')
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# distM <- matrix(data=NA, nrow=ncol(distList), ncol=length(colNames))
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#
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# print(paste('Length of distributions',nrow(distM)))
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# for (col in 1:ncol(distList)) {
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# valsAsStrs <- unlist(strsplit(distList[,col], ":"))
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# valIdxs <- seq(from=2, to=length(valsAsStrs), by=2)
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# distVals <- as.numeric(valsAsStrs[valIdxs])
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# distM[col,] <- c(distVals[1], distVals[2], distVals[2], distVals[4], distVals[5], distVals[5], distVals[6])
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# }
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# })
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#
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# df <- data.frame(
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# nodeNames = nodeNames,
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# dist = distM,
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# stringsAsFactors=FALSE
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# )
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# print(df)
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#
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# return(
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# df
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# )
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}
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.likelihoods <-reactiveValues(
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p_ba = calcLikelihood(1),
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ba_os = calcLikelihood(2),
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os_es = calcLikelihood(3)
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)
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observeEvent(input$modelSelect, {
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.selections$model <<- match(input$modelSelect, models)
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#print(.selections$model)
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})
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observeEvent(input$layerSelect, {
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.selections$layer <<- match(input$layerSelect, transitions)
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#print(.selections$layer)
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})
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observeEvent(input$calcAB, {
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#print(paste('Action button pressed', input$calcAB))
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.likelihoods$p_ba <<- calcLikelihood(1)
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.likelihoods$ba_os <<- calcLikelihood(2)
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.likelihoods$os_es <<- calcLikelihood(3)
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})
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output$map <- renderGoogle_map({
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google_map(location = c(55, 0), zoom = 7)
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})
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output$nodeTable <- DT::renderDataTable(
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modelList[[.selections$model]][[.selections$layer]]$nodes,
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selection = 'single',options = list(searching = TRUE, pageLength = 10),server = TRUE, escape = FALSE,rownames= TRUE
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)
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output$edgeTable <- DT::renderDataTable(
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modelList[[.selections$model]][[.selections$layer]]$edges,
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selection = 'single',options = list(searching = TRUE, pageLength = 10),server = TRUE, escape = FALSE,rownames= TRUE
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)
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output$bbnGraphPlot <- renderPlot({
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graphviz.plot(modelList[[.selections$model]][[.selections$layer]]$net)
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})
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output$layer1 <- renderPlotly({
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plot_ly(.likelihoods$p_ba, y = ~range, color = ~nodeNames, type = "box")
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})
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output$layer2 <- renderPlotly({
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#print(.likelihoods)
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if (.selections$layer>1) {
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plot_ly(.likelihoods$ba_os, y = ~range, color = ~nodeNames, type = "box")
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}
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})
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output$layer3 <- renderPlotly({
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if (.selections$layer>2) {
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plot_ly(.likelihoods$os_es, y = ~range, color = ~nodeNames, type = "box")
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}
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})
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}
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shinyApp(ui, server)
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