750 lines
24 KiB
R
750 lines
24 KiB
R
modules::import(shiny)
|
|
modules::import(shinydashboard)
|
|
modules::import(shinydashboardPlus)
|
|
modules::import(shinycssloaders)
|
|
modules::import(shinyjs)
|
|
|
|
modules::import(bnlearn)
|
|
modules::import(visNetwork)
|
|
modules::import(RColorBrewer)
|
|
modules::import(plotly)
|
|
modules::import(openxlsx)
|
|
modules::import(zip)
|
|
modules::import(DT)
|
|
|
|
parser <- modules::use("Parses.R")
|
|
|
|
|
|
addResourcePath("js", "./www/js")
|
|
|
|
|
|
layers <- c("Pressures to Bio-Assemblages", "Bio-Assemblages to Output Processes", "Output Processes to Ecosystem services")
|
|
transitions <- c("Pressures to Bio-Assemblages", "Pressures to Output Processes", "Pressures to Ecosystem services")
|
|
impacts <- c("Very High", ">= High", ">= Medium", ">= Low", "All")
|
|
thresholds <- c(0.97, 0.9, 0.45, 0.17, 0)
|
|
impLabels <- c("Very High", "High", "Medium", "Low", "Very Low")
|
|
|
|
|
|
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 tab"
|
|
)
|
|
)
|
|
),
|
|
tags$li(
|
|
tags$div(
|
|
style = "margin-left: auto; margin-right: auto; width: 90%;",
|
|
tags$a(
|
|
href = "Report.pdf",
|
|
target = "_BLANK",
|
|
"Open Final Report in tab"
|
|
)
|
|
)
|
|
)
|
|
)
|
|
)
|
|
),
|
|
dashboardSidebar(
|
|
sidebarMenu(id = "tabs",
|
|
menuItem("Introduction", tabName = "1", icon = icon("arrow-down")),
|
|
menuItem("Pressure Test", tabName = "2", icon = icon("arrow-down")),
|
|
menuItem("Bayesian Network", tabName = "3", icon = icon("atom")),
|
|
#menuItem("Habitats", tabName = "3", icon = icon("atlas")),
|
|
#menuItem("Ingestion", tabName = "3", icon = icon("utensils")),
|
|
selectInput("modelSelect", "Select MESO model", choices = c(""), selected = NULL, multiple = FALSE),
|
|
downloadButton("download", "", icon = icon("download")),
|
|
uiOutput("pressureList")
|
|
)
|
|
),
|
|
dashboardBody(
|
|
tabItems(
|
|
tabItem(
|
|
tabName = "1", h2("Introduction"),
|
|
tags$p(
|
|
style = "font-size: 12pt",
|
|
"This website is provided for the Joint Nature Conservation Committee (JNCC) and is provided by",
|
|
tags$a(href = "https://avsdev.uk", "AVS Developments", target = "_BLANK"),
|
|
", working under contract to ",
|
|
tags$a(href = "https://www.mba.ac.uk", "the Marine Biology Association.", target = "_BLANK")
|
|
),
|
|
tags$p(
|
|
style = "font-size: 12pt",
|
|
"This website provides a Proof of Concept visualisation tool to assist in understanding the probabilitic impact that
|
|
Anthropogenic Pressures (i.e. human activities) has on the habitats of sub-littoral areas of the United Kingdom."
|
|
),
|
|
tags$p(
|
|
style = "font-size: 12pt",
|
|
"The tool provides a mapping using a Continuous Gaussian Bayesian Belief Network from the
|
|
Anthropogenic Pressures through the biotopes and to the output processes and ultimately the
|
|
Ecosystem services, to which the habitat supports."
|
|
),
|
|
tags$p(
|
|
style = "font-size: 12pt",
|
|
"By selecting combinations of pressures on the left hand side bar, the impact on biotopes and functions of the
|
|
habitat can be estimated on the graphs shown on the Pressure test page.
|
|
The Bayesian Network page shows the structure of the Bayesian Network itself. "
|
|
),
|
|
tags$p(
|
|
style = "font-size: 12pt",
|
|
"Five substrate types have been modelled (coarse sediment, mixed sediment, mud, rock and sand)."
|
|
),
|
|
tags$p(
|
|
style = "font-size: 12pt",
|
|
"Impact of pressures are as defined in ",
|
|
tags$a(href = "https://www.marlin.ac.uk/sensitivity/sensitivity_rationale",
|
|
"the Marine Evidence based Sensitivity Assessment (MarESA).", target = "_BLANK")
|
|
),
|
|
tags$p(
|
|
style = "margin-top: 150px; font-size: 12pt",
|
|
"Further information on the rationalale and supporting information can be found in the Studiy's Final Report
|
|
available as a download from the Help pages selectable from the Question Mark logo on the
|
|
top right hand side of the website."
|
|
),
|
|
tags$p(
|
|
style = "margin-top: 150px; font-size: 10pt",
|
|
"GDPR Notice: This website only uses cookies to provide core functionality. No personal data cookies are used."
|
|
),
|
|
tags$p(
|
|
style = "font-size: 10pt",
|
|
"Copyright Notice: All images, logos and sources are property and copyright of their respected owners"
|
|
)
|
|
),
|
|
tabItem(tabName = "2", h2("Impact Distribution"),
|
|
fluidRow(
|
|
column(
|
|
width = 6,
|
|
h4("Effect on bio-assemblage")
|
|
),
|
|
column(
|
|
width = 1,
|
|
actionButton("layer1Slider", "1", icon = icon("sliders-h"))
|
|
),
|
|
column(
|
|
width = 5,
|
|
strong("Customise sensitivity weightings")
|
|
)
|
|
),
|
|
plotlyOutput("layer1", height = "270px") %>% withSpinner(),
|
|
h4("Effect on Output Processes"),
|
|
plotlyOutput("layer2", height = "270px") %>% withSpinner(),
|
|
h4("Effect on Ecosystem services"),
|
|
plotlyOutput("layer3", height = "270px") %>% withSpinner()
|
|
),
|
|
tabItem(tabName = "3",h2("Bayesian Network"),
|
|
fluidPage(
|
|
p("Graphical output of the Bayesian Network. Note: The graph will only draw if pressures are applied!"),
|
|
fluidRow(
|
|
column(
|
|
width = 4,
|
|
checkboxInput("bbnDisplayNames", "Display Node names", value = FALSE)
|
|
),
|
|
column(
|
|
width = 4,
|
|
checkboxInput("bbnDisplayEdges", "Display edge status", value = FALSE)
|
|
),
|
|
column(
|
|
width = 4,
|
|
selectInput("bbnImpactSelect", "Impact Threshold", choices = impacts, selected = "All")
|
|
)
|
|
),
|
|
fluidRow(
|
|
visNetworkOutput("bbnGraphPlot", width = "100%", height = "1000px")
|
|
),
|
|
fluidRow(
|
|
column(
|
|
width = 6,
|
|
h4("Ecoservice nodes"),
|
|
DT::dataTableOutput("nodeTable")
|
|
),
|
|
column(
|
|
width = 6,
|
|
h4("Ecoservice influences"),
|
|
DT::dataTableOutput("edgeTable")
|
|
)
|
|
)
|
|
)
|
|
)
|
|
)
|
|
)
|
|
)
|
|
|
|
server <- function(input, output, session) {
|
|
#SERVER Constants
|
|
|
|
print("Loading data")
|
|
|
|
dataStorage <- "data/"
|
|
|
|
palette <- c("firebrick", "coral", "rosybrown", "tan", "salmon", "olivedrab", "seagreen", "aquamarine", "darkcyan", "dodgerblue", "steelblue", "royalblue")
|
|
|
|
|
|
models <- NULL
|
|
pressures <- NULL
|
|
|
|
.loadStatus <- reactiveValues(
|
|
valid = c(p = FALSE, ba = FALSE, op = FALSE, es = FALSE),
|
|
msgs = NULL
|
|
)
|
|
|
|
.likelihoods <- reactiveValues(
|
|
p_es = NULL
|
|
)
|
|
|
|
setPressures <- function(newPressures) {
|
|
pressures <<- newPressures
|
|
}
|
|
|
|
|
|
.resistanceScores <- c(
|
|
ins = -0.01,
|
|
hr = -0.2,
|
|
mr = -0.75,
|
|
lr = -0.95,
|
|
nr = -0.99,
|
|
ssgr = 0,
|
|
pressSD = 1.0
|
|
)
|
|
|
|
.selections <- reactiveValues(
|
|
model = 1,
|
|
#runOnce = FALSE,
|
|
bbnImpact = 1,
|
|
bbnNames = FALSE,
|
|
bbnEdges = FALSE,
|
|
pressStatus = NULL
|
|
)
|
|
|
|
getImpact <- function(v) {
|
|
if ((v == "INS") || (v == "IV")) return(.resistanceScores[1])
|
|
if ((v == "HR") || (v == "III")) return(.resistanceScores[2])
|
|
if ((v == "MR") || (v == "II")) return(.resistanceScores[3])
|
|
if ((v == "LR") || (v == "I")) return(.resistanceScores[4])
|
|
if (v == "NR") return(.resistanceScores[5])
|
|
as.numeric(v)
|
|
}
|
|
|
|
getAvailableModels <- function() {
|
|
fileList <- list.files(dataStorage, pattern = ".xlsx")
|
|
|
|
modelList <- list()
|
|
cnt <- 1
|
|
|
|
for (idx in 1:length(fileList)) {
|
|
print(paste("attempting to load", paste0(dataStorage, fileList[idx])))
|
|
|
|
wb <- parser$parseSheet(paste0(dataStorage, fileList[idx]))
|
|
#print(tmp)
|
|
wb$p_es$edges$values <- sapply(wb$p_es$edges$impact, getImpact)
|
|
|
|
if (!is.null(wb)) {
|
|
modelList[[cnt]] <- wb
|
|
models <<- c(models, substr(fileList[idx], 1, (nchar(fileList[idx])-5)))
|
|
print(paste("Model file successfully loaded", fileList[idx]))
|
|
#save(tmp, file = "tmp.RData")
|
|
cnt <- cnt+1
|
|
}
|
|
}
|
|
updateSelectInput(session, "modelSelect", choices = models)
|
|
return(modelList)
|
|
}
|
|
|
|
#parse on load sheets in the input sheet folder - replace with R Data
|
|
modelList <- getAvailableModels()
|
|
|
|
|
|
calcLikelihood <- function(layer, pressStatus) {
|
|
|
|
isolate({
|
|
|
|
modelList[[.selections$model]]$p_es$edges$values <<- sapply(modelList[[.selections$model]]$p_es$edges$impact, getImpact)
|
|
modelList[[.selections$model]]$p_es$nodes$growth <<- .resistanceScores["ssgr"]
|
|
modelList[[.selections$model]]$p_es$nodes$confidence <<- .resistanceScores["pressSD"]
|
|
|
|
thisModel <- modelList[[.selections$model]]
|
|
|
|
|
|
MEANPOS <- 1
|
|
MEANNEG <- 0
|
|
|
|
expr <- "list("
|
|
for (p in 1:nrow(pressStatus)) {
|
|
if (pressStatus$status[p] == "On") {
|
|
threshold <- MEANPOS
|
|
} else {
|
|
threshold <- MEANNEG
|
|
}
|
|
|
|
expr <- paste0(expr, "\"", pressStatus$code[p], "\" = ", threshold, ", ")
|
|
}
|
|
expr <- substr(expr, 1, nchar(expr)-2)
|
|
expr <- paste0(expr, ")")
|
|
|
|
thisNet <- parser$buildGraph(thisModel$p_es, desc = list(inputCode = "p", outputCodes = c("ba", "op", "es")))
|
|
|
|
sampleDists <- cpdist(
|
|
fitted = thisNet$cfit,
|
|
nodes = bnlearn::nodes(thisNet$cfit),
|
|
evidence = eval(parse(text = expr)),
|
|
method = "lw",
|
|
n = 10000,
|
|
debug = FALSE
|
|
)
|
|
})
|
|
|
|
#print(sampleDists)
|
|
|
|
#displayCols <- match(nodeCodes, colnames(sampleDists))
|
|
sampleDists <- sampleDists[,match(thisModel$p_es$nodes$code, colnames(sampleDists))]
|
|
means <- apply(sampleDists, 2, mean)
|
|
stdDev <- apply(sampleDists, 2, sd)
|
|
|
|
print(paste("Building likelihoods from model, sample dists", length(thisModel$p_es$nodes$name), length(sampleDists)))
|
|
|
|
return(data.frame(
|
|
name = thisModel$p_es$nodes$name,
|
|
code = thisModel$p_es$nodes$code,
|
|
layer = thisModel$p_es$nodes$layer,
|
|
range = c(
|
|
apply(sampleDists, 2, min),
|
|
means - 2*stdDev,
|
|
means - stdDev,
|
|
means,
|
|
means + stdDev,
|
|
means + 2*stdDev,
|
|
apply(sampleDists, 2, max)
|
|
),
|
|
stringsAsFactors = FALSE
|
|
))
|
|
}
|
|
|
|
|
|
observeEvent(input$modelSelect, {
|
|
.selections$model <<- match(input$modelSelect, models)
|
|
#.selections$runOnce <<- TRUE
|
|
})
|
|
|
|
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$pressStatus)) { #} || .selections$runOnce) {
|
|
#.selections$runOnce = FALSE
|
|
print("Running calc")
|
|
.likelihoods$p_es <<- calcLikelihood(0, newStatus)
|
|
.selections$pressStatus <<- newStatus
|
|
}
|
|
|
|
}
|
|
})
|
|
|
|
makeRadioButtons <- function(row) {
|
|
radioButtons(row["code"], row["name"], choices = c("Off", "On"), selected = "Off", inline = TRUE)
|
|
}
|
|
|
|
output$pressureList <- renderUI({
|
|
#isolate({
|
|
if (!is.null(modelList[[.selections$model]]$p_es$nodes)) {
|
|
pressCodes <- which(startsWith(modelList[[.selections$model]]$p_es$nodes$code, "p"))
|
|
|
|
#if (is.null(.selections$pressStatus)) status <- rep("Off", length(pressCodes)) else status <- .selections$pressStatus$status
|
|
pressures <- data.frame(
|
|
code = modelList[[.selections$model]]$p_es$nodes$code[pressCodes],
|
|
name = modelList[[.selections$model]]$p_es$nodes$name[pressCodes],
|
|
#status = status,
|
|
stringsAsFactors = FALSE
|
|
)
|
|
print(pressures)
|
|
#This assumes all pressures are the same...
|
|
|
|
setPressures(pressures)
|
|
btnList <- apply(pressures, 1, makeRadioButtons)
|
|
}
|
|
})
|
|
|
|
observeEvent(input$bbnImpactSelect, {
|
|
#filter nodes and edges to
|
|
.selections$bbnImpact <- thresholds[match(input$bbnImpactSelect, impacts)]
|
|
})
|
|
|
|
observeEvent(input$bbnDisplayNames, {
|
|
.selections$bbnNames <- input$bbnDisplayNames
|
|
})
|
|
|
|
observeEvent(input$bbnDisplayEdges, {
|
|
.selections$bbnEdges <- input$bbnDisplayEdges
|
|
|
|
})
|
|
|
|
|
|
observeEvent(input$layer1Slider, {
|
|
showModal(
|
|
modalDialog({
|
|
tagList(
|
|
sliderInput("l1VL", "Insensitive", 0.01, 0.2, abs(.resistanceScores[1]), step = 0.01),
|
|
sliderInput("l1L", "Low Sensitivity/High resistance", 0.15, 0.5, abs(.resistanceScores[2]), step = 0.01),
|
|
sliderInput("l1M", "Medium Sensitivity/Med resistance", 0.5, 0.75, abs(.resistanceScores[3]), step = 0.01),
|
|
sliderInput("l1H", "High Sensitivity/Low resistance", 0.75, 1.0, abs(.resistanceScores[4]), step = 0.01),
|
|
sliderInput("l1VH", "Very High Sensitivity/No resistance", 0.9, 1.0, abs(.resistanceScores[5]), step = 0.01),
|
|
sliderInput("ssgr", "Zero intercept", -0.1, 0.1,.resistanceScores[6], step = 0.01),
|
|
sliderInput("l1PressSD", "Std Dev", 0.1, 1.0, .resistanceScores[7], step = 0.01)
|
|
)
|
|
},
|
|
title = "Layer 1 controls",
|
|
footer = tagList(
|
|
modalButton("Cancel"),
|
|
actionButton("modalOK", "OK")
|
|
),
|
|
size = "s")
|
|
)
|
|
})
|
|
|
|
observeEvent(input$modalOK, {
|
|
print("Modal ok pressed")
|
|
|
|
.resistanceScores["nr"] <<- -input$l1VH
|
|
.resistanceScores["lr"] <<- -input$l1H
|
|
.resistanceScores["mr"] <<- -input$l1M
|
|
.resistanceScores["hr"] <<- -input$l1L
|
|
.resistanceScores["ins"] <<- -input$l1VL
|
|
.resistanceScores["ssgr"] <<- input$ssgr
|
|
.resistanceScores["pressSD"] <<- input$l1PressSD
|
|
|
|
print("Running calc")
|
|
.likelihoods$p_es <<- calcLikelihood(0, .selections$pressStatus)
|
|
removeModal()
|
|
|
|
})
|
|
|
|
|
|
output$nodeTable <- DT::renderDataTable(
|
|
modelList[[.selections$model]]$p_es$nodes,
|
|
selection = "single",
|
|
server = TRUE,
|
|
escape = FALSE,
|
|
rownames = TRUE,
|
|
options = list(searching = TRUE, pageLength = 10, editable = TRUE)
|
|
)
|
|
|
|
output$edgeTable <- DT::renderDataTable(
|
|
modelList[[.selections$model]]$p_es$edges,
|
|
selection = "single",
|
|
server = TRUE,
|
|
escape = FALSE,
|
|
rownames = TRUE,
|
|
options = list(searching = TRUE, pageLength = 10, editable = TRUE)
|
|
)
|
|
|
|
getLabel <- function(value) {
|
|
sign <- ifelse(value < 0, "-", "+")
|
|
idx <- min(which((abs(value) >= thresholds) == TRUE))
|
|
return(paste0(sign, impLabels[idx]))
|
|
}
|
|
|
|
makeBbnGraph <- function(model) {
|
|
nodes <- model$p_es$nodes
|
|
|
|
if (.selections$bbnEdges) {
|
|
labels <- sapply(model$p_es$edges$values, getLabel)
|
|
} else {
|
|
labels <- rep("", nrow(model$p_es$edges))
|
|
}
|
|
|
|
edges <- data.frame(
|
|
id = rownames(model$p_es$edges),
|
|
from = match(model$p_es$edges$input, nodes$code),
|
|
to = match(model$p_es$edges$output, nodes$code),
|
|
values = model$p_es$edges$values,
|
|
label = labels,
|
|
arrows = "to",
|
|
stringsAsFactors = FALSE
|
|
)
|
|
if (.selections$bbnNames) {
|
|
labels <- nodes$name
|
|
} else {
|
|
labels <- nodes$code
|
|
}
|
|
|
|
nodeSpacing <- ifelse(.selections$bbnNames, 600, 150)
|
|
|
|
|
|
nodes <- data.frame(
|
|
id = rownames(nodes),
|
|
label = labels,
|
|
level = nodes$layer,
|
|
group = nodes$layer,
|
|
color = palette[as.integer(nodes$layer)],
|
|
code = nodes$code,
|
|
stringsAsFactors = FALSE
|
|
)
|
|
|
|
edges <- edges[(abs(edges$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$layer3 <- renderPlotly({
|
|
prepPlot("es", "Ecosystem Services")
|
|
})
|
|
|
|
|
|
isAbsolutePath = function( path ){
|
|
if( path == "~" )
|
|
return(TRUE);
|
|
if( grepl("^~/", path) )
|
|
return(TRUE);
|
|
if( grepl("^.:(/|\\\\)", path) )
|
|
return(TRUE);
|
|
if( grepl("^(/|\\\\)", path) )
|
|
return(TRUE);
|
|
return(FALSE);
|
|
}
|
|
|
|
exportOrca <- function (p, file = "plot.png", format = tools::file_ext(file),
|
|
scale = NULL, width = NULL, height = NULL,
|
|
verbose = FALSE, debug = FALSE, safe = FALSE)
|
|
{
|
|
if (Sys.which("orca") == "") {
|
|
stop("The orca command-line utility is required to use the `orca()` function.\n\n",
|
|
"Follow the installation instructions here -- https://github.com/plotly/orca#installation",
|
|
call. = FALSE)
|
|
}
|
|
|
|
b <- plotly_build(p)
|
|
|
|
plotlyjs <- b$dependencies[sapply(b$dependencies, function(d) { d$name == "plotly-main" })][[1]]
|
|
|
|
if (!isAbsolutePath(plotlyjs$src$file)) {
|
|
plotlyjs_file <- NULL
|
|
for(n in 1:length(.Library.site)) {
|
|
if (!is.null(plotlyjs_file)) {
|
|
next
|
|
}
|
|
f <- paste0(.Library.site[[n]], "/plotly/", plotlyjs$src$file, "/", plotlyjs$script)
|
|
if (file.exists(f)) {
|
|
plotlyjs_file <- f
|
|
}
|
|
}
|
|
} else {
|
|
plotlyjs_file <- file.path(plotlyjs$src$file, plotlyjs$script)
|
|
}
|
|
|
|
args <- c(
|
|
"graph", paste0("'", jsonlite::toJSON(
|
|
b$x[c("data", "layout")],
|
|
digits = 50,
|
|
auto_unbox = TRUE,
|
|
force = TRUE,
|
|
null = "null",
|
|
na = "null"
|
|
), "'"),
|
|
"-o", file,
|
|
"--format", format,
|
|
"--plotlyjs", plotlyjs_file
|
|
)
|
|
if (debug)
|
|
args <- c(args, "--debug")
|
|
if (verbose)
|
|
args <- c(args, "--verbose")
|
|
if (safe)
|
|
args <- c(args, "--safe-mode")
|
|
if (!is.null(scale))
|
|
args <- c(args, "--scale", scale)
|
|
if (!is.null(width))
|
|
args <- c(args, "--width", width)
|
|
if (!is.null(height))
|
|
args <- c(args, "--height", height)
|
|
|
|
invisible(system2("orca", args))
|
|
}
|
|
|
|
output$linkBackgroundData <- downloadHandler(
|
|
filename = getModelName(),
|
|
content = function(file) {
|
|
file.copy(getModelName(), file)
|
|
},
|
|
contentType = "application/xlsx"
|
|
)
|
|
|
|
output$download <- downloadHandler(
|
|
filename = function() { paste0("MESO-", format(Sys.time(), "%m%d_%H%M"), ".zip") },
|
|
content = function(file) {
|
|
showModal(
|
|
modalDialog(
|
|
fluidRow(
|
|
column(width = 12) %>% withSpinner(type = 5, proxy.height = "200px")
|
|
),
|
|
footer=div()
|
|
)
|
|
)
|
|
|
|
oldDir <- getwd()
|
|
|
|
tmp <- tempfile("")
|
|
dir.create(tmp)
|
|
setwd(tmp)
|
|
|
|
#Get the network graph
|
|
l1 <- exportOrca(prepPlot("ba", "Bio-Assemblage"), "layer1.png")
|
|
l2 <- exportOrca(prepPlot("op", "Output Processes"),"layer2.png")
|
|
l3 <- exportOrca(prepPlot("es", "Ecosystem Services"),"layer3.png")
|
|
|
|
#Save pressure list, confidence levels, node and edge tables in xlsx
|
|
l <- list(
|
|
pressures = .selections$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)
|