553 lines
17 KiB
R
553 lines
17 KiB
R
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(bnlearn)
|
|
modules::import(visNetwork)
|
|
|
|
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, 0.7, 0.17, 0)
|
|
impLabels <- c('Very High', 'High', 'Medium', 'Low', 'Very Low')
|
|
|
|
addResourcePath("js", "./www/js")
|
|
|
|
ui<-dashboardPage(
|
|
dashboardHeader(title = "JNCC MESO online",
|
|
|
|
tags$li(
|
|
id = "dropdownHelp",
|
|
class = "dropdown",
|
|
tags$head(
|
|
tags$script(
|
|
paste0(
|
|
"$(document).ready(function(){",
|
|
" $('#dropdownHelp')",
|
|
" .find('ul')",
|
|
" .click(function(e) { e.stopPropagation(); });",
|
|
"});"
|
|
)
|
|
)
|
|
),
|
|
tags$a(
|
|
href = "javascript:void(0);",
|
|
class = "dropdown-toggle",
|
|
`data-toggle` = "dropdown",
|
|
icon("question")
|
|
),
|
|
tags$ul(
|
|
class = "dropdown-menu",
|
|
style = "left: auto; right: 0; min-width: 200px",
|
|
tags$li(
|
|
tags$div(
|
|
style = "margin-left: auto; margin-right: auto; width: 90%;",
|
|
tags$a(
|
|
href = "Manual.pdf",
|
|
target = "_BLANK",
|
|
"Open user guide in a new tab"
|
|
)
|
|
)
|
|
),
|
|
tags$li(
|
|
tags$div(
|
|
style = "margin-left: auto; margin-right: auto; width: 90%;",
|
|
downloadLink(
|
|
"linkBackgroundData",
|
|
"Download excel sheets"
|
|
)
|
|
)
|
|
)
|
|
)
|
|
)
|
|
),
|
|
dashboardSidebar(
|
|
sidebarMenu(id = "tabs",
|
|
menuItem("Pressure Test", tabName = "1", icon = icon("arrow-down")),
|
|
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),
|
|
uiOutput("pressureList")
|
|
#selectInput("layerSelect", "Select Transition",
|
|
# choices=transitions,
|
|
# selected=NULL, multiple=FALSE)
|
|
)
|
|
),
|
|
dashboardBody(
|
|
tabItems(
|
|
tabItem(tabName = "1", h2('Impact Distribution'),
|
|
fluidRow(
|
|
column(
|
|
width=6,
|
|
h4('Effect on bio-assemblage')
|
|
),
|
|
column(
|
|
width=6,
|
|
actionButton("layer1Slider", "1", icon=icon("sliders-h"))
|
|
)
|
|
),
|
|
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: large networks may never stabilise!'),
|
|
fluidRow(
|
|
column(
|
|
width=4,
|
|
checkboxInput("bbnDisplayNames", "Display Node names", value=FALSE)
|
|
),
|
|
column(
|
|
width=4,
|
|
selectInput("bbnImpactSelect", "Impact Threshold", choices=impacts, selected='All')
|
|
)
|
|
),
|
|
fluidRow(
|
|
visNetworkOutput("bbnGraphPlot")
|
|
),
|
|
fluidRow(
|
|
column(
|
|
width=6,
|
|
h4('Ecoservice nodes'),
|
|
DT::dataTableOutput('nodeTable')
|
|
),
|
|
column(
|
|
width=6,
|
|
h4('Ecoservice influences'),
|
|
DT::dataTableOutput('edgeTable')
|
|
)
|
|
)
|
|
)
|
|
),
|
|
#tabItem(tabName = "3",h4("Habitats"),
|
|
# fluidPage(
|
|
# google_mapOutput(outputId = "map", width = "100%", height = "750px")
|
|
# )
|
|
#),
|
|
tabItem(tabName = "3",h4("Ingestion"),
|
|
fluidPage(
|
|
p("Select a spreadsheet from your network for input into the JNCC Bayesian Network Analyser:"),
|
|
fileInput("fileSelect", "Choose Excel Spreadsheet File (xlsx format)", multiple = FALSE, accept = "xlsx"),
|
|
fluidRow(renderUI('status')),
|
|
actionButton('loadAB', 'Load') # icon='upload')
|
|
)
|
|
)
|
|
)
|
|
)
|
|
)
|
|
|
|
server <- function(input, output, session) {
|
|
#SERVER Constants
|
|
|
|
print('Loading data')
|
|
|
|
#set_key("AIzaSyAw8_btgGN1drf8qhCxNcotP6r11qEXA_M")
|
|
dataStorage <- 'data/'
|
|
|
|
models<-NULL
|
|
pressures <- NULL
|
|
|
|
|
|
#disable(input$loadAb)
|
|
|
|
.loadStatus <- reactiveValues(
|
|
valid = c(p=FALSE, ba=FALSE, op=FALSE, es=FALSE),
|
|
msgs = NULL
|
|
)
|
|
|
|
.likelihoods <-reactiveValues(
|
|
p_ba = NULL,
|
|
ba_os = NULL,
|
|
os_es = NULL
|
|
)
|
|
|
|
setPressures <- function(newPressures) {
|
|
pressures <<- newPressures
|
|
}
|
|
|
|
validateSheets <- function() {
|
|
req(inputs$selectFile)
|
|
|
|
##TO DO - run parser on it and output the errors to
|
|
}
|
|
|
|
getAvailableModels <- function() {
|
|
fileList <- list.files(dataStorage, pattern='.xlsx')
|
|
|
|
print(fileList)
|
|
modelList <- list()
|
|
cnt<-1
|
|
|
|
for (idx in 1:length(fileList)) {
|
|
print(paste('attempting to load', paste0(dataStorage, fileList[idx])))
|
|
|
|
tmp <- parser$parseSheet(paste0(dataStorage, fileList[idx]))
|
|
|
|
if (!is.null(tmp)) {
|
|
modelList[[cnt]] <- tmp
|
|
models <<- c(models, substr(fileList[idx], 1, (nchar(fileList[idx])-5)))
|
|
print(paste('Model file successfully loaded', fileList[idx]))
|
|
cnt=cnt+1
|
|
|
|
}
|
|
}
|
|
|
|
updateSelectInput(session, "modelSelect", choices=models)
|
|
|
|
return(modelList)
|
|
}
|
|
|
|
|
|
.selections <- reactiveValues(
|
|
model=1,
|
|
layer=1,
|
|
bbnImpact=1,
|
|
bbnNames=FALSE,
|
|
pressStatus=NULL
|
|
)
|
|
|
|
#parse on load sheets in the input sheet folder - replace with R Data
|
|
modelList <- getAvailableModels()
|
|
|
|
calcLikelihood <- function(layer, pressStatus, confLevels) {
|
|
|
|
isolate({
|
|
|
|
if (layer==1) layerStr='ba' else if (layer==2) layerStr='op' else if (layer==3) layerStr='es'
|
|
|
|
layerRange <- which(startsWith(modelList[[.selections$model]][[3]]$nodes$code, layerStr))
|
|
|
|
nodeCodes <- modelList[[.selections$model]][[layer]]$nodes$code[layerRange]
|
|
nodeNames <- modelList[[.selections$model]][[layer]]$nodes$name[layerRange]
|
|
|
|
MEANPOS=1
|
|
MEANNEG=0
|
|
|
|
expr <- "list("
|
|
for (p in 1:nrow(pressStatus)) {
|
|
if (pressStatus$status[p] == 'On') {
|
|
threshold = MEANPOS
|
|
} else {
|
|
threshold = MEANNEG
|
|
}
|
|
|
|
expr <- paste0(expr, "\"", pressStatus$code[p], "\"=", threshold, ", ")
|
|
}
|
|
expr <-substr(expr, 1, nchar(expr)-2)
|
|
expr<-paste0(expr, ')')
|
|
|
|
|
|
#build the graph
|
|
#parser$buildGraph(p_es, desc=list(inputCode='p', outputCodes=c('ba', 'op', 'es')))
|
|
|
|
sampleDists <- cpdist(
|
|
fitted = modelList[[.selections$model]][[layer]]$cfit,
|
|
nodes = bnlearn::nodes(modelList[[.selections$model]][[layer]]$cfit),
|
|
evidence = eval(parse(text = expr)),
|
|
method = "lw",
|
|
n = 10000,
|
|
debug=TRUE
|
|
)
|
|
})
|
|
|
|
displayCols <- match(nodeCodes, colnames(sampleDists))
|
|
sampleDists <- sampleDists[,displayCols]
|
|
means <- apply(sampleDists, 2, mean)
|
|
stdDev <- apply(sampleDists, 2, sd)
|
|
|
|
return(data.frame(
|
|
nodeNames = nodeNames,
|
|
range = c(
|
|
apply(sampleDists, 2, min),
|
|
means - 2*stdDev,
|
|
means - stdDev,
|
|
means,
|
|
means + stdDev,
|
|
means + 2*stdDev,
|
|
apply(sampleDists, 2, max)
|
|
),
|
|
stringsAsFactors=FALSE
|
|
))
|
|
}
|
|
|
|
renderStatus <- function(layer) {
|
|
isolate({
|
|
if (.loadStatus$valid[layer]) return('check-square') else return('times-circle')
|
|
})
|
|
}
|
|
|
|
output$status <- renderUI({
|
|
|
|
tagList(
|
|
fluidRow(
|
|
column(width=3, h4('Pressures')),
|
|
column(width=3, h4('Bio-assemblages')),
|
|
column(width=3, h4('Output processes')),
|
|
column(width=3, h4('Ecosystem services'))
|
|
),
|
|
fluidRow(
|
|
column(width=3, icon(renderStatus(1))),
|
|
column(width=3, icon(renderStatus(2))),
|
|
column(width=3, icon(renderStatus(3))),
|
|
column(width=3, icon(renderStatus(4)))
|
|
)
|
|
)
|
|
})
|
|
|
|
|
|
observeEvent(input$modelSelect, {
|
|
.selections$model <<- match(input$modelSelect, models)
|
|
})
|
|
|
|
#observeEvent(input$layerSelect, {
|
|
# .selections$layer <<- match(input$layerSelect, transitions)
|
|
#})
|
|
|
|
observeEvent(reactiveValuesToList(input), {
|
|
isolate(myList <- reactiveValuesToList(input))
|
|
matches <- match(pressures$code, names(myList))
|
|
|
|
if (length(matches)>0) {
|
|
status <-NULL
|
|
for (n in 1:length(matches)) status[n] = myList[[matches[n]]]
|
|
|
|
newStatus <- data.frame(code=pressures$code, status=status, stringsAsFactors = FALSE)
|
|
|
|
if (!identical(newStatus, .selections$pressStatus)) {
|
|
print('Running calc')
|
|
.likelihoods$p_ba <<- calcLikelihood(1, newStatus)
|
|
.likelihoods$ba_os <<- calcLikelihood(2, newStatus)
|
|
.likelihoods$os_es <<- calcLikelihood(3, newStatus)
|
|
|
|
.selections$pressStatus <<- newStatus
|
|
}
|
|
|
|
}
|
|
sliderControls <- c("l1VH", "l1H", "l1M", "l1L", "l1VL", "l1Conf")
|
|
matches <- match(sliderControls, names(myList))
|
|
if (length(matches)>0) {
|
|
print(matches)
|
|
}
|
|
})
|
|
|
|
#output$map <- renderGoogle_map({
|
|
# google_map(location = c(55, 0), zoom = 7)
|
|
#})
|
|
|
|
makeRadioButtons <- function(row) {
|
|
radioButtons(row['code'], row['name'], choices=c('Off', 'On'), selected='Off', inline=TRUE)
|
|
}
|
|
|
|
output$linkBackgroundData <- downloadHandler(
|
|
filename = "JNCC MESO.xlsx",
|
|
content = function(file) {
|
|
file.copy("JNCC MESO.xlsx", file)
|
|
},
|
|
contentType = "application/xlsx"
|
|
)
|
|
|
|
|
|
output$pressureList <- renderUI({
|
|
#isolate({
|
|
if (!is.null(modelList[[.selections$model]][[1]]$nodes)) {
|
|
pressCodes <- which(startsWith(modelList[[.selections$model]][[1]]$nodes$code, 'p'))
|
|
pressures <- data.frame(code = modelList[[.selections$model]][[1]]$nodes$code[pressCodes],
|
|
name = modelList[[.selections$model]][[1]]$nodes$name[pressCodes], stringsAsFactors=FALSE)
|
|
setPressures(pressures)
|
|
btnList <- apply(pressures, 1, makeRadioButtons)
|
|
}
|
|
})
|
|
|
|
observeEvent(input$bbnImpactSelect, {
|
|
#filter nodes and edges to
|
|
.selections$bbnImpact <- thresholds[match(input$bbnImpactSelect, impacts)]
|
|
print(paste("Setting bbn impact", .selections$bbnImpact))
|
|
})
|
|
|
|
observeEvent(input$bbnDisplayNames, {
|
|
.selections$bbnNames <- input$bbnDisplayNames
|
|
print(.selections$bbnNames)
|
|
})
|
|
|
|
observeEvent(input$layer1Slider, {
|
|
showModal(
|
|
modalDialog({
|
|
tagList(
|
|
sliderInput("l1VH", "Very High Sensitivity", 0.9, 1.0, 0.99, step=0.01),
|
|
sliderInput("l1H", "High Sensitivity", 0.75, 1.0, 0.95, step=0.01),
|
|
sliderInput("l1M", "Medium Sensitivity", 0.5, 0.75, 0.95, step=0.01),
|
|
sliderInput("l1L", "Low Sensitivity", 0.15, 0.5, 0.2, step=0.01),
|
|
sliderInput("l1VL", "Very Low Sensitivity", 0.01, 0.2, 0.15, step=0.01),
|
|
sliderInput("pressStdDev", "Pressure SD", 0.1, 1, 0.5, step=0.1),
|
|
sliderInput("baStdDev", "Bio-Assemblage SD", 0.1, 1, 0.5, step=0.1)
|
|
)
|
|
}, title='Layer 1 controls', size='s')
|
|
)
|
|
})
|
|
|
|
|
|
|
|
|
|
|
|
output$nodeTable <- DT::renderDataTable(
|
|
|
|
modelList[[.selections$model]][[.selections$layer]]$nodes,
|
|
selection = 'single',options = list(searching = TRUE, pageLength = 10, editable=TRUE),server = TRUE, escape = FALSE,rownames= TRUE
|
|
)
|
|
|
|
output$edgeTable <- DT::renderDataTable(
|
|
|
|
modelList[[.selections$model]][[.selections$layer]]$edges,
|
|
selection = 'single',options = list(searching = TRUE, pageLength = 10, editable=TRUE),server = TRUE, escape = FALSE,rownames= TRUE
|
|
)
|
|
|
|
getLabel <- function(impact) {
|
|
sign <- ifelse(impact<0, "-", "+")
|
|
idx <- min(which((abs(impact)>=thresholds)==TRUE))
|
|
return(paste0(sign, impLabels[idx]))
|
|
}
|
|
|
|
getLevels <- function(code) {
|
|
if (startsWith(code, 'p')) return(1)
|
|
else if (startsWith(code, 'ba')) return(2)
|
|
else if (startsWith(code, 'op')) return(3)
|
|
else if (startsWith(code, 'es')) return(4)
|
|
else return(5)
|
|
}
|
|
|
|
|
|
output$bbnGraphPlot <- renderVisNetwork({
|
|
#graphviz.plot(modelList[[.selections$model]][[.selections$layer]]$net)
|
|
|
|
nodes <- modelList[[.selections$model]][[.selections$layer]]$nodes
|
|
|
|
edges <- data.frame(
|
|
id = rownames(modelList[[.selections$model]][[.selections$layer]]$edges),
|
|
from=match(modelList[[.selections$model]][[.selections$layer]]$edges$input, nodes$name),
|
|
to=match(modelList[[.selections$model]][[.selections$layer]]$edges$output, nodes$name),
|
|
impact=modelList[[.selections$model]][[.selections$layer]]$edges$impact,
|
|
label=sapply(modelList[[.selections$model]][[.selections$layer]]$edges$impact, getLabel),
|
|
arrows="to",
|
|
stringsAsFactors=FALSE
|
|
)
|
|
if (.selections$bbnNames) {labels <- nodes$name} else {labels <- nodes$code}
|
|
|
|
nodeSpacing <- ifelse(.selections$bbnNames, 600, 150)
|
|
|
|
nodes <- data.frame(
|
|
id = rownames(nodes),
|
|
label = labels,
|
|
level = sapply(nodes$code, getLevels),
|
|
code = nodes$code,
|
|
stringsAsFactors=FALSE
|
|
)
|
|
|
|
|
|
|
|
edges <- edges[(abs(edges$impact)>=.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
|
|
|
|
visNetwork(nodeNet, edgeNet, width = "100%") %>%
|
|
visHierarchicalLayout(nodeSpacing=nodeSpacing) %>%
|
|
visOptions(highlightNearest = TRUE) %>%
|
|
#visPhysics(hierarchicalRepulsion = nodeSpacing) %>%
|
|
visInteraction(navigationButtons = TRUE, dragNodes = TRUE, dragView = TRUE, zoomView = TRUE)
|
|
})
|
|
|
|
observe({
|
|
visNetworkProxy("bbnGraphPlot") %>%
|
|
visStabilize(iterations=10)
|
|
})
|
|
|
|
|
|
output$layer1 <- renderPlotly({
|
|
if (length(.likelihoods$p_ba)>0) {
|
|
|
|
.likelihoods$p_ba$nodeNames <- factor(.likelihoods$p_ba$nodeNames, levels = unique(.likelihoods$p_ba$nodeNames))
|
|
|
|
xform <- list(categoryorder = "array",
|
|
categoryarray = .likelihoods$p_ba$nodeNames,
|
|
zerolinewidth=10)
|
|
|
|
plot_ly(.likelihoods$p_ba, y = ~range, color = ~nodeNames, type = "box") %>%
|
|
layout(xaxis = xform)
|
|
|
|
}
|
|
})
|
|
|
|
output$layer2 <- renderPlotly({
|
|
if (length(.likelihoods$ba_os)>0) {
|
|
|
|
.likelihoods$ba_os$nodeNames <- factor(.likelihoods$ba_os$nodeNames, levels = unique(.likelihoods$ba_os$nodeNames))
|
|
|
|
xform <- list(categoryorder = "array",
|
|
categoryarray = .likelihoods$ba_os$nodeNames,
|
|
zerolinewidth=5)
|
|
|
|
|
|
plot_ly(.likelihoods$ba_os, y = ~range, color = ~nodeNames, type = "box") %>%
|
|
layout(xaxis = xform)
|
|
|
|
}
|
|
})
|
|
|
|
output$layer3 <- renderPlotly({
|
|
|
|
if (length(.likelihoods$os_es)>0) {
|
|
|
|
.likelihoods$os_es$nodeNames <- factor(.likelihoods$os_es$nodeNames, levels = unique(.likelihoods$os_es$nodeNames))
|
|
|
|
xform <- list(categoryorder = "array",
|
|
categoryarray = .likelihoods$os_es$nodeNames,
|
|
zerolinewidth=5)
|
|
|
|
|
|
plot_ly(.likelihoods$os_es, y = ~range, color = ~nodeNames, type = "box") %>%
|
|
layout(xaxis = xform)
|
|
|
|
}
|
|
})
|
|
}
|
|
|
|
shinyApp(ui, server) |