393 lines
12 KiB
R
393 lines
12 KiB
R
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(shinydashboardPlus)
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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(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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#tags$style(.times-circle {color:800000 }),
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#tags$style(.check-square {color:008000 }),
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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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menuItem("Ingestion", tabName = "4", icon = icon("utensils")),
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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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uiOutput("pressureList"),
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actionButton("calcAB", icon('calculator'))
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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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tabItem(tabName = "4",h4("Ingestion"),
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fluidPage(
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p("Select a spreadsheet from your network for input into the JNCC Bayesian Network Analyser:"),
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fileInput("fileSelect", "Choose Excel Spreadsheet File (xlsx format)", multiple = FALSE, accept = "xlsx"),
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fluidRow(renderUI('status')),
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actionButton('loadAB', 'Load') # icon='upload')
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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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pressures <- NULL
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#disable(input$loadAb)
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.loadStatus <- reactiveValues(
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valid = c(p=FALSE, ba=FALSE, op=FALSE, es=FALSE),
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msgs = NULL
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)
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.likelihoods <-reactiveValues(
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p_ba = NULL,
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ba_os = NULL,
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os_es = NULL
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)
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setPressures <- function(newPressures) {
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pressures <<- newPressures
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}
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validateSheets <- function() {
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req(inputs$selectFile)
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##TO DO - run parser on it and output the errors to
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}
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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$parseSheet(paste0(dataStorage, fileList[idx]))
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if (!is.null(tmp)) {
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modelList[[cnt]] <- tmp
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models <<- c(models, substr(fileList[idx], 1, (nchar(fileList[idx])-5)))
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print(paste('Model file successfully loaded', fileList[idx]))
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cnt=cnt+1
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}
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}
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updateSelectInput(session, "modelSelect", choices=models)
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return(modelList)
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}
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.selections <- reactiveValues(model=1, layer=1)
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#parse on load sheets in the input sheet folder - replace with R Data
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modelList <- getAvailableModels()
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calcLikelihood <- function(layer, pressStatus) {
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# isolate({
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# if (layer==1) layerStr='ba' else if (layer==2) layerStr='op' else layerStr ='es'
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# nodeList <- modelList[[.selections$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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#
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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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if (layer==1) layerStr='ba' else if (layer==2) layerStr='op' else if (layer==3) layerStr='es'
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layerRange <- which(startsWith(modelList[[.selections$model]][[layer]]$nodes$code, layerStr))
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nodeCodes <- modelList[[.selections$model]][[layer]]$nodes$code[layerRange]
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nodeNames <- modelList[[.selections$model]][[layer]]$nodes$name[layerRange]
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MEANPOS=1
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MEANNEG=0
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# expr <- "("
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# for (p in 1:nrow(pressStatus)) {
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# if (pressStatus$status[p] == 'On') {
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# threshold = MEANPOS
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# } else {
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# threshold = MEANNEG
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# }
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#
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# expr <- paste0(expr, "(\"", pressStatus$code[p], "\">=", threshold, ") & ")
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# }
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# expr <-substr(expr, 1, nchar(expr)-2)
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# expr<-paste0(expr, ')')
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#
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# print(expr)
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expr <- "list("
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for (p in 1:nrow(pressStatus)) {
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if (pressStatus$status[p] == 'On') {
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threshold = MEANPOS
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} else {
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threshold = MEANNEG
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}
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expr <- paste0(expr, "\"", pressStatus$code[p], "\"=", threshold, ", ")
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}
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expr <-substr(expr, 1, nchar(expr)-2)
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expr<-paste0(expr, ')')
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print(expr)
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#txtStringWkg = "((\"p1\">=0.5) & (\"p10\">=0.5) & (\"p2\">=0.5))"
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print(bnlearn::nodes(modelList[[.selections$model]][[layer]]$cfit))
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sampleDists <- cpdist(
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fitted = modelList[[.selections$model]][[layer]]$cfit,
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nodes = bnlearn::nodes(modelList[[.selections$model]][[layer]]$cfit),
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#evidence = eval(parse(text = expr)),
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evidence = eval(parse(text = expr)),
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method = "lw",
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n = 10000,
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debug=TRUE
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)
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})
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#print (sum(res[, 1] * attr(res, "weights")) / sum(attr(res, "weights")))
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print("Sample dists")
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print(sampleDists)
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print("Weights")
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print(unique(attr(sampleDists, "weights")))
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displayCols <- match(nodeCodes, colnames(sampleDists))
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sampleDists <- sampleDists[,displayCols]
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means <- apply(sampleDists, 2, mean)
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stdDev <- apply(sampleDists, 2, sd)
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print(modelList[[.selections$model]][[layer]]$nodes$name)
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return(data.frame(
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nodeNames = nodeNames,
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range = c(
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apply(sampleDists, 2, min),
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means - 2*stdDev,
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means - stdDev,
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means,
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means + stdDev,
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means + 2*stdDev,
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apply(sampleDists, 2, max)
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),
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stringsAsFactors=FALSE
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))
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}
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renderStatus <- function(layer) {
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isolate({
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if (.loadStatus$valid[layer]) return('check-square') else return('times-circle')
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})
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}
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output$status <- renderUI({
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tagList(
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fluidRow(
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column(width=3, h4('Pressures')),
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column(width=3, h4('Bio-assemblages')),
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column(width=3, h4('Output processes')),
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column(width=3, h4('Eco-system services'))
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),
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fluidRow(
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column(width=3, icon(renderStatus(1))),
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column(width=3, icon(renderStatus(2))),
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column(width=3, icon(renderStatus(3))),
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column(width=3, icon(renderStatus(4)))
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)#,
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#fluidRow(
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# verbatimTextOutput("msgBoard", .loadStatus$msg, placeholder=TRUE)
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#)
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)
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})
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observeEvent(input$loadAB, {
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#TO DO get spreadsheet
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#copy validated sheet into the data folder and either add or replace the sheet in the RData file
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#reload the RData file
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print('Load button pressed')
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})
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observeEvent(input$modelSelect, {
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.selections$model <<- match(input$modelSelect, models)
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})
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observeEvent(input$layerSelect, {
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.selections$layer <<- match(input$layerSelect, transitions)
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})
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observeEvent(input$calcAB, {
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#get the status of action buttons
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isolate(myList <- reactiveValuesToList(input))
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matches <- match(pressures$code, names(myList))
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status <-NULL
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for (n in 1:length(matches)) status[n] = myList[[matches[n]]]
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pressStatus <- data.frame(code=pressures$code, status=status, stringsAsFactors = FALSE)
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.likelihoods$p_ba <<- calcLikelihood(1, pressStatus)
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.likelihoods$ba_os <<- calcLikelihood(2, pressStatus)
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.likelihoods$os_es <<- calcLikelihood(3, pressStatus)
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})
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output$map <- renderGoogle_map({
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google_map(location = c(55, 0), zoom = 7)
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})
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makeRadioButtons <- function(row) {
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radioButtons(row['code'], row['name'], choices=c('Off', 'On'), selected='Off', inline=TRUE)
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}
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output$pressureList <- renderUI({
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#isolate({
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if (!is.null(modelList[[.selections$model]][[1]]$nodes)) {
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pressCodes <- which(startsWith(modelList[[.selections$model]][[1]]$nodes$code, 'p'))
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pressures <- data.frame(code = modelList[[.selections$model]][[1]]$nodes$code[pressCodes],
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name = modelList[[.selections$model]][[1]]$nodes$name[pressCodes], stringsAsFactors=FALSE)
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setPressures(pressures)
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btnList <- apply(pressures, 1, makeRadioButtons)
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}
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})
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output$nodeTable <- DT::renderDataTable(
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modelList[[.selections$model]][[.selections$layer]]$nodes,
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selection = 'single',options = list(searching = TRUE, pageLength = 10, editable=TRUE),server = TRUE, escape = FALSE,rownames= TRUE
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)
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output$edgeTable <- DT::renderDataTable(
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modelList[[.selections$model]][[.selections$layer]]$edges,
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selection = 'single',options = list(searching = TRUE, pageLength = 10, editable=TRUE),server = TRUE, escape = FALSE,rownames= TRUE
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)
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output$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$p_ba, y = ~range, color = ~nodeNames, type = "box") %>%
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layout(xaxis = list(zerolinewidth=2)) #%>%
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#withSpinner()
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})
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output$layer2 <- renderPlotly({
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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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layout(xaxis = list(zerolinewidth=2)) #%>%
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#withSpinner()
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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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layout(xaxis = list(zerolinewidth=2)) #%>%
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#withSpinner()
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}
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})
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}
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shinyApp(ui, server) |