691 lines
22 KiB
R
691 lines
22 KiB
R
modules::import(shiny)
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modules::import(shinydashboard)
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modules::import(shinydashboardPlus)
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modules::import(shinycssloaders)
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modules::import(shinyjs)
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modules::import(bnlearn)
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modules::import(visNetwork)
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modules::import(RColorBrewer)
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modules::import(plotly)
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modules::import(openxlsx)
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modules::import(zip)
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modules::import(DT)
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parser <- modules::use("Parses.R")
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addResourcePath("js", "./www/js")
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layers <- c("Pressures to Bio-Assemblages", "Bio-Assemblages to Output Processes", "Output Processes to Ecosystem services")
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transitions <- c("Pressures to Bio-Assemblages", "Pressures to Output Processes", "Pressures to Ecosystem services")
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impacts <- c("Very High", ">= High", ">= Medium", ">= Low", "All")
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thresholds <- c(0.97, 0.9, 0.45, 0.17, 0)
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impLabels <- c("Very High", "High", "Medium", "Low", "Very Low")
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ui <- dashboardPage(
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dashboardHeader(title = "JNCC MESO online",
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tags$li(
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id = "dropdownHelp",
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class = "dropdown",
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tags$head(
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tags$script(
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paste0(
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"$(document).ready(function(){",
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" $('#dropdownHelp')",
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" .find('ul')",
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" .click(function(e) { e.stopPropagation(); });",
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"});"
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)
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)
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),
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tags$a(
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href = "javascript:void(0);",
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class = "dropdown-toggle",
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`data-toggle` = "dropdown",
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icon("question")
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),
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tags$ul(
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class = "dropdown-menu",
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style = "left: auto; right: 0; min-width: 200px",
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tags$li(
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tags$div(
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style = "margin-left: auto; margin-right: auto; width: 90%;",
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tags$a(
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href = "Manual.pdf",
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target = "_BLANK",
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"Open user guide in tab"
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)
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)
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),
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tags$li(
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tags$div(
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style = "margin-left: auto; margin-right: auto; width: 90%;",
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tags$a(
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href = "Report.pdf",
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target = "_BLANK",
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"Open Final Report in tab"
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)
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)
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)
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)
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)
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),
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dashboardSidebar(
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sidebarMenu(id = "tabs",
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menuItem("Introduction", tabName = "1", icon = icon("arrow-down")),
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menuItem("Pressure Test", tabName = "2", icon = icon("arrow-down")),
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menuItem("Bayesian Network", tabName = "3", icon = icon("atom")),
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#menuItem("Habitats", tabName = "3", icon = icon("atlas")),
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#menuItem("Ingestion", tabName = "3", icon = icon("utensils")),
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selectInput("modelSelect", "Select MESO model", choices = c(""), selected = NULL, multiple = FALSE),
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downloadButton("download", "", icon = icon("download")),
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uiOutput("pressureList")
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)
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),
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dashboardBody(
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tabItems(
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tabItem(
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tabName = "1", h2("Introduction"),
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tags$p(
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style = "font-size: 12pt",
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"This website is provided for the Joint Nature Conservation Committee (JNCC) and is provided by",
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tags$a(href = "https://avsdev.uk", "AVS Developments", target = "_BLANK"),
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", working under contract to ",
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tags$a(href = "https://www.mba.ac.uk", "the Marine Biology Association.", target = "_BLANK")
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),
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tags$p(
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style = "font-size: 12pt",
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"This website provides a Proof of Concept visualisation tool to assist in understanding the probabilitic impact that
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Anthropogenic Pressures (i.e. human activities) has on the habitats of sub-littoral areas of the United Kingdom."
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),
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tags$p(
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style = "font-size: 12pt",
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"The tool provides a mapping using a Continuous Gaussian Bayesian Belief Network from the
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Anthropogenic Pressures through the biotopes and to the output processes and ultimately the
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Ecosystem services, to which the habitat supports."
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),
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tags$p(
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style = "font-size: 12pt",
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"By selecting combinations of pressures on the left hand side bar, the impact on biotopes and functions of the
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habitat can be estimated on the graphs shown on the Pressure test page.
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The Bayesian Network page shows the structure of the Bayesian Network itself. "
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),
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tags$p(
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style = "font-size: 12pt",
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"Five substrate types have been modelled (coarse sediment, mixed sediment, mud, rock and sand)."
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),
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tags$p(
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style = "font-size: 12pt",
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"Impact of pressures are as defined in ",
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tags$a(href = "https://www.marlin.ac.uk/sensitivity/sensitivity_rationale",
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"the Marine Evidence based Sensitivity Assessment (MarESA).", target = "_BLANK")
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),
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tags$p(
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style = "margin-top: 150px; font-size: 12pt",
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"Further information on the rationalale and supporting information can be found in the Studiy's Final Report
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available as a download from the Help pages selectable from the Question Mark logo on the
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top right hand side of the website."
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),
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tags$p(
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style = "margin-top: 150px; font-size: 10pt",
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"GDPR Notice: This website only uses cookies to provide core functionality. No personal data cookies are used."
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),
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tags$p(
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style = "font-size: 10pt",
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"Copyright Notice: All images, logos and sources are property and copyright of their respected owners"
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)
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),
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tabItem(tabName = "2", h2("Impact Distribution"),
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fluidRow(
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column(
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width = 6,
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h4("Effect on bio-assemblage")
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),
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column(
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width = 1,
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actionButton("layer1Slider", "1", icon = icon("sliders-h"))
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),
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column(
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width = 5,
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strong("Customise sensitivity weightings")
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)
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),
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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 Ecosystem services"),
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plotlyOutput("layer3", height = "270px") %>% withSpinner()
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),
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tabItem(tabName = "3",h2("Bayesian Network"),
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fluidPage(
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p("Graphical output of the Bayesian Network. Note: The graph will only draw if pressures are applied!"),
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fluidRow(
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column(
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width = 4,
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checkboxInput("bbnDisplayNames", "Display Node names", value = FALSE)
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),
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column(
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width = 4,
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checkboxInput("bbnDisplayEdges", "Display edge status", value = FALSE)
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),
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column(
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width = 4,
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selectInput("bbnImpactSelect", "Impact Threshold", choices = impacts, selected = "All")
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)
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),
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fluidRow(
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visNetworkOutput("bbnGraphPlot", width = "100%", height = "1000px")
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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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)
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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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dataStorage <- "data/"
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palette <- c("firebrick", "coral", "rosybrown", "tan", "salmon", "olivedrab", "seagreen", "aquamarine", "darkcyan", "dodgerblue", "steelblue", "royalblue")
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models <- NULL
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pressures <- NULL
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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_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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.resistanceScores <- c(
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ins = -0.01,
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hr = -0.2,
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mr = -0.75,
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lr = -0.95,
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nr = -0.99,
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ssgr = 0,
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pressSD = 1.0
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)
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.selections <- reactiveValues(
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model = 1,
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#runOnce = FALSE,
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bbnImpact = 1,
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bbnNames = FALSE,
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bbnEdges = FALSE,
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pressStatus = NULL
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)
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getImpact <- function(v) {
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if ((v == "INS") || (v == "IV")) return(.resistanceScores[1])
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if ((v == "HR") || (v == "III")) return(.resistanceScores[2])
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if ((v == "MR") || (v == "II")) return(.resistanceScores[3])
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if ((v == "LR") || (v == "I")) return(.resistanceScores[4])
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if (v == "NR") return(.resistanceScores[5])
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as.numeric(v)
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}
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getAvailableModels <- function() {
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fileList <- list.files(dataStorage, pattern = ".xlsx")
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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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wb <- parser$parseSheet(paste0(dataStorage, fileList[idx]))
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#print(tmp)
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wb$p_es$edges$values <- sapply(wb$p_es$edges$impact, getImpact)
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if (!is.null(wb)) {
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modelList[[cnt]] <- wb
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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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#save(tmp, file = "tmp.RData")
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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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#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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modelList[[.selections$model]]$p_es$edges$values <<- sapply(modelList[[.selections$model]]$p_es$edges$impact, getImpact)
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modelList[[.selections$model]]$p_es$nodes$growth <<- .resistanceScores["ssgr"]
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modelList[[.selections$model]]$p_es$nodes$confidence <<- .resistanceScores["pressSD"]
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thisModel <- modelList[[.selections$model]]
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MEANPOS <- 1
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MEANNEG <- 0
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expr <- "list("
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for (p in 1:nrow(pressStatus)) {
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if (pressStatus$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$code[p], "\" = ", threshold, ", ")
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}
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expr <- substr(expr, 1, nchar(expr)-2)
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expr <- paste0(expr, ")")
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thisNet <- parser$buildGraph(thisModel$p_es, desc = list(inputCode = "p", outputCodes = c("ba", "op", "es")))
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sampleDists <- cpdist(
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fitted = thisNet$cfit,
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nodes = bnlearn::nodes(thisNet$cfit),
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evidence = eval(parse(text = expr)),
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method = "lw",
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n = 10000,
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debug = FALSE
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)
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})
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#print(sampleDists)
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#displayCols <- match(nodeCodes, colnames(sampleDists))
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sampleDists <- sampleDists[,match(thisModel$p_es$nodes$code, colnames(sampleDists))]
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means <- apply(sampleDists, 2, mean)
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stdDev <- apply(sampleDists, 2, sd)
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print(paste("Building likelihoods from model, sample dists", length(thisModel$p_es$nodes$name), length(sampleDists)))
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return(data.frame(
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name = thisModel$p_es$nodes$name,
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code = thisModel$p_es$nodes$code,
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layer = thisModel$p_es$nodes$layer,
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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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observeEvent(input$modelSelect, {
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.selections$model <<- match(input$modelSelect, models)
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#.selections$runOnce <<- TRUE
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})
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observeEvent(reactiveValuesToList(input), {
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isolate(myList <- reactiveValuesToList(input))
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matches <- match(pressures$code, names(myList))
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if (length(matches) > 0) {
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status <- NULL
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for (n in 1:length(matches)) {
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status[n] <- myList[[matches[n]]]
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}
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newStatus <- data.frame(code = pressures$code, status = status, stringsAsFactors = FALSE)
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if (!identical(newStatus, .selections$pressStatus)) { #} || .selections$runOnce) {
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#.selections$runOnce = FALSE
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print("Running calc")
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.likelihoods$p_es <<- calcLikelihood(0, newStatus)
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.selections$pressStatus <<- newStatus
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}
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}
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})
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makeRadioButtons <- function(row) {
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radioButtons(row["code"], row["name"], choices = c("Off", "On"), selected = "Off", inline = TRUE)
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}
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output$pressureList <- renderUI({
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#isolate({
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if (!is.null(modelList[[.selections$model]]$p_es$nodes)) {
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pressCodes <- which(startsWith(modelList[[.selections$model]]$p_es$nodes$code, "p"))
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#if (is.null(.selections$pressStatus)) status <- rep("Off", length(pressCodes)) else status <- .selections$pressStatus$status
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pressures <- data.frame(
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code = modelList[[.selections$model]]$p_es$nodes$code[pressCodes],
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name = modelList[[.selections$model]]$p_es$nodes$name[pressCodes],
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#status = status,
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stringsAsFactors = FALSE
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)
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print(pressures)
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#This assumes all pressures are the same...
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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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observeEvent(input$bbnImpactSelect, {
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#filter nodes and edges to
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.selections$bbnImpact <- thresholds[match(input$bbnImpactSelect, impacts)]
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})
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observeEvent(input$bbnDisplayNames, {
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.selections$bbnNames <- input$bbnDisplayNames
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})
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observeEvent(input$bbnDisplayEdges, {
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.selections$bbnEdges <- input$bbnDisplayEdges
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})
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observeEvent(input$layer1Slider, {
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showModal(
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modalDialog({
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tagList(
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sliderInput("l1VL", "Insensitive", 0.01, 0.2, abs(.resistanceScores[1]), step = 0.01),
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sliderInput("l1L", "Low Sensitivity/High resistance", 0.15, 0.5, abs(.resistanceScores[2]), step = 0.01),
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sliderInput("l1M", "Medium Sensitivity/Med resistance", 0.5, 0.75, abs(.resistanceScores[3]), step = 0.01),
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sliderInput("l1H", "High Sensitivity/Low resistance", 0.75, 1.0, abs(.resistanceScores[4]), step = 0.01),
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sliderInput("l1VH", "Very High Sensitivity/No resistance", 0.9, 1.0, abs(.resistanceScores[5]), step = 0.01),
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sliderInput("ssgr", "Zero intercept", -0.1, 0.1,.resistanceScores[6], step = 0.01),
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sliderInput("l1PressSD", "Std Dev", 0.1, 1.0, .resistanceScores[7], step = 0.01)
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)
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},
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title = "Layer 1 controls",
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footer = tagList(
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modalButton("Cancel"),
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actionButton("modalOK", "OK")
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),
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size = "s")
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)
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})
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observeEvent(input$modalOK, {
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print("Modal ok pressed")
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.resistanceScores["nr"] <<- -input$l1VH
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.resistanceScores["lr"] <<- -input$l1H
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.resistanceScores["mr"] <<- -input$l1M
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.resistanceScores["hr"] <<- -input$l1L
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.resistanceScores["ins"] <<- -input$l1VL
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.resistanceScores["ssgr"] <<- input$ssgr
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.resistanceScores["pressSD"] <<- input$l1PressSD
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print("Running calc")
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.likelihoods$p_es <<- calcLikelihood(0, .selections$pressStatus)
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removeModal()
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})
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output$nodeTable <- DT::renderDataTable(
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modelList[[.selections$model]]$p_es$nodes,
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selection = "single",
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server = TRUE,
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escape = FALSE,
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rownames = TRUE,
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options = list(searching = TRUE, pageLength = 10, editable = TRUE)
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)
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output$edgeTable <- DT::renderDataTable(
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modelList[[.selections$model]]$p_es$edges,
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selection = "single",
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server = TRUE,
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escape = FALSE,
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rownames = TRUE,
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options = list(searching = TRUE, pageLength = 10, editable = TRUE)
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)
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getLabel <- function(value) {
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sign <- ifelse(value < 0, "-", "+")
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idx <- min(which((abs(value) >= thresholds) == TRUE))
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return(paste0(sign, impLabels[idx]))
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}
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makeBbnGraph <- function(model) {
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nodes <- model$p_es$nodes
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if (.selections$bbnEdges) {
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labels <- sapply(model$p_es$edges$values, getLabel)
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} else {
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labels <- rep("", nrow(model$p_es$edges))
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}
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edges <- data.frame(
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id = rownames(model$p_es$edges),
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from = match(model$p_es$edges$input, nodes$code),
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to = match(model$p_es$edges$output, nodes$code),
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values = model$p_es$edges$values,
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label = labels,
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arrows = "to",
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stringsAsFactors = FALSE
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)
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if (.selections$bbnNames) {
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labels <- nodes$name
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} else {
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labels <- nodes$code
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}
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nodeSpacing <- ifelse(.selections$bbnNames, 600, 150)
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nodes <- data.frame(
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id = rownames(nodes),
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label = labels,
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level = nodes$layer,
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group = nodes$layer,
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color = palette[as.integer(nodes$layer)],
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code = nodes$code,
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stringsAsFactors = FALSE
|
|
)
|
|
|
|
edges <- edges[(abs(edges$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
|
|
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:nrow(model$legend),
|
|
label = model$legend,
|
|
color = palette,
|
|
stringsAsFactors = FALSE
|
|
)
|
|
|
|
print(legendDF)
|
|
|
|
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, paletteLength) {
|
|
if (nrow(boxPlot) > 0) {
|
|
|
|
#print(paste('Palette length', paletteLength))
|
|
|
|
#palette <- brewer.pal(paletteLength, "Set3")
|
|
|
|
#palette <- c("red", "sienna3", "plum2", "rosybrown4", "sandybrown", "yellow", "seashell3", "palegreen", "springgreen4", "steelblue", "azure")
|
|
|
|
names(palette) <- 1:length(palette)
|
|
|
|
#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 = as.character(boxPlot$Group), colors = palette, type = "box") %>%
|
|
layout(xaxis = xform, showlegend = FALSE, title = title)
|
|
|
|
}
|
|
}
|
|
|
|
prepPlot <- function(code = "ba", name = "Bio-Assemblage") {
|
|
if (!is.null(.likelihoods$p_es)) {
|
|
inScope <- startsWith(.likelihoods$p_es$code, code)
|
|
thisPlot <- .likelihoods$p_es[inScope, c(1,3,4)]
|
|
colnames(thisPlot) <- c(name, "Group", "Range")
|
|
title <- paste(input$modelSelect, name, "Box Plot")
|
|
paletteLength <- nrow(modelList[[.selections$model]]$legend)
|
|
print(paste('prep plot palette', paletteLength))
|
|
genPlot(thisPlot, title, paletteLength)
|
|
}
|
|
}
|
|
|
|
output$layer1 <- renderPlotly({
|
|
prepPlot("ba", "Bio-Assemblage")
|
|
})
|
|
|
|
output$layer2 <- renderPlotly({
|
|
prepPlot("op", "Output Processes")
|
|
})
|
|
|
|
output$layer3 <- renderPlotly({
|
|
prepPlot("es", "Ecosystem Services")
|
|
})
|
|
|
|
|
|
export <- function(model, fPath) {
|
|
|
|
|
|
return(zipFile)
|
|
}
|
|
|
|
|
|
output$linkBackgroundData <- downloadHandler(
|
|
filename = getModelName(),
|
|
content = function(file) {
|
|
file.copy(getModelName(), file)
|
|
},
|
|
contentType = "application/xlsx"
|
|
)
|
|
|
|
output$download <- downloadHandler(
|
|
filename = function() {paste0("MESO-", format(Sys.time(), "%m%d_%H%M"), ".zip")},
|
|
content = function(file) {
|
|
showModal(
|
|
modalDialog(
|
|
fluidRow(
|
|
column(width = 12) %>% withSpinner(type = 5, proxy.height = "200px")
|
|
),
|
|
footer=div()
|
|
)
|
|
)
|
|
|
|
oldDir <- getwd()
|
|
|
|
tmp <- tempfile("")
|
|
|
|
print(tmp)
|
|
dir.create(tmp)
|
|
|
|
setwd(tmp)
|
|
|
|
#Get the network graph
|
|
l1 <- orca(prepPlot("ba", "Bio-Assemblage"), "layer1.png")
|
|
l2 <- orca(prepPlot("op", "Output Processes"),"layer2.png")
|
|
l3 <- orca(prepPlot("es", "Ecosystem Services"),"layer3.png")
|
|
|
|
|
|
#Save pressure list, confidence levels, node and edge tables in xlsx
|
|
l <- list(
|
|
pressures = .selections$pressStatus,
|
|
nodes = modelList[[.selections$model]]$p_es$nodes,
|
|
edges = modelList[[.selections$model]]$p_es$edges,
|
|
settings = as.data.frame(cbind(names(.resistanceScores), .resistanceScores), stringsAsFactors = FALSE)
|
|
)
|
|
xl <- write.xlsx(l, "dataset.xlsx")
|
|
|
|
zipFile <- zipr(file, c("layer1.png", "layer2.png", "layer3.png", "dataset.xlsx"))
|
|
|
|
print(paste("zip file complete", zipFile))
|
|
|
|
setwd(oldDir)
|
|
unlink(tmp)
|
|
|
|
removeModal()
|
|
},
|
|
contentType = "application/zip"
|
|
)
|
|
|
|
|
|
}
|
|
|
|
shinyApp(ui, server)
|