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Craig Williams
2019-04-11 12:17:23 +01:00
parent 94b7fcb0e6
commit 7752b73bc6
2 changed files with 158 additions and 158 deletions
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224
app.R
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@@ -42,7 +42,7 @@ legends <- c('Pressures',
addResourcePath("js", "./www/js")
ui<-dashboardPage(
dashboardHeader(title = "JNCC MESO online",
dashboardHeader(title = "JNCC MESO online",
tags$li(
id = "dropdownHelp",
class = "dropdown",
@@ -98,8 +98,8 @@ ui<-dashboardPage(
selectInput("modelSelect", "Select MESO model", choices=c(""), selected=NULL, multiple=FALSE),
downloadButton("download", "", icon=icon("download")),
uiOutput("pressureList")
#selectInput("layerSelect", "Select Transition",
# choices=transitions,
#selectInput("layerSelect", "Select Transition",
# choices=transitions,
# selected=NULL, multiple=FALSE)
)
),
@@ -108,7 +108,7 @@ ui<-dashboardPage(
tabItem(tabName = "1", h2('Impact Distribution'),
fluidRow(
column(
width=6,
width=6,
h4('Effect on bio-assemblage')
),
column(
@@ -164,7 +164,7 @@ ui<-dashboardPage(
# 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:"),
@@ -179,32 +179,32 @@ ui<-dashboardPage(
server <- function(input, output, session) {
#SERVER Constants
print('Loading data')
#set_key("AIzaSyAw8_btgGN1drf8qhCxNcotP6r11qEXA_M")
dataStorage <- 'data/'
models<-NULL
pressures <- NULL
.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,
p_es = NULL
)
setPressures <- function(newPressures) {
pressures <<- newPressures
}
.resistanceScores <- c(
ins= -0.01,
hr = -0.2,
@@ -214,15 +214,15 @@ server <- function(input, output, session) {
ssgr = 0,
pressSD = 0.5
)
.selections <- reactiveValues(
model=1,
bbnImpact=1,
model=1,
bbnImpact=1,
bbnNames=FALSE,
bbnEdges=FALSE,
pressStatus=NULL
)
getImpact <- function(v) {
print(v)
if ((v == "INS") || (v == "IV")) return(.resistanceScores[1])
@@ -232,20 +232,20 @@ server <- function(input, output, session) {
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])))
tmp <- parser$parseSheet(paste0(dataStorage, fileList[idx]))
print(tmp)
tmp$edges$values <- sapply(tmp$edges$impact, getImpact)
if (!is.null(tmp)) {
modelList[[cnt]] <- tmp
models <<- c(models, substr(fileList[idx], 1, (nchar(fileList[idx])-5)))
@@ -257,36 +257,36 @@ server <- function(input, output, session) {
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({
#if (layer==1) layerStr='ba' else if (layer==2) layerStr='op' else if (layer==3) layerStr='es'
#layerRange <- which(startsWith(thisModel$nodes$code, layerStr))
#nodeCodes <-thisModel$nodes$code[layerRange]
#nodeNames <- thisModel$nodes$name[layerRange]
thisModel <- modelList[[.selections$model]]
modelList[[.selections$model]]$edges$values <<- sapply(thisModel$edges$impact, getImpact)
modelList[[.selections$model]]$nodes$growth <<- .resistanceScores['ssgr']
modelList[[.selections$model]]$nodes$confidence <<- .resistanceScores['pressSD']
thisModel <- modelList[[.selections$model]]
MEANPOS=1
MEANNEG=0
expr <- "list("
for (p in 1:nrow(pressStatus)) {
if (pressStatus$status[p] == 'On') {
@@ -294,12 +294,12 @@ server <- function(input, output, session) {
} else {
threshold = MEANNEG
}
expr <- paste0(expr, "\"", pressStatus$code[p], "\"=", threshold, ", ")
}
expr <-substr(expr, 1, nchar(expr)-2)
expr<-paste0(expr, ')')
thisNet <- parser$buildGraph(thisModel, desc=list(inputCode='p', outputCodes=c('ba', 'op', 'es')))
sampleDists <- cpdist(
@@ -311,16 +311,16 @@ server <- function(input, output, session) {
debug=TRUE
)
})
print(sampleDists)
#displayCols <- match(nodeCodes, colnames(sampleDists))
sampleDists <- sampleDists[,match(thisModel$nodes$code, colnames(sampleDists))]
means <- apply(sampleDists, 2, mean)
stdDev <- apply(sampleDists, 2, sd)
print(paste('Building likelihoods from model, sample dists', length(thisModel$nodes$name), length(sampleDists)))
return(data.frame(
name = thisModel$nodes$name,
code = thisModel$nodes$code,
@@ -337,12 +337,12 @@ server <- function(input, output, session) {
stringsAsFactors=FALSE
))
}
observeEvent(input$modelSelect, {
.selections$model <<- match(input$modelSelect, models)
})
observeEvent(reactiveValuesToList(input), {
isolate(myList <- reactiveValuesToList(input))
matches <- match(pressures$code, names(myList))
@@ -350,9 +350,9 @@ server <- function(input, output, session) {
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$p_ba <<- calcLikelihood(1, newStatus)
@@ -362,42 +362,42 @@ server <- function(input, output, session) {
#write.xlsx(.likelihoods$p_es, 'tmp.xlsx')
.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$model]]$nodes)) {
pressCodes <- which(startsWith(modelList[[.selections$model]]$nodes$code, 'p'))
pressures <- data.frame(code = modelList[[.selections$model]]$nodes$code[pressCodes],
pressures <- data.frame(code = modelList[[.selections$model]]$nodes$code[pressCodes],
name = modelList[[.selections$model]]$nodes$name[pressCodes], stringsAsFactors=FALSE)
setPressures(pressures)
btnList <- apply(pressures, 1, makeRadioButtons)
}
})
observeEvent(input$bbnImpactSelect, {
#filter nodes and edges to
#filter nodes and edges to
.selections$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$bbnDisplayEdges, {
.selections$bbnEdges <- input$bbnDisplayEdges
})
observeEvent(input$layer1Slider, {
showModal(
modalDialog({
@@ -410,8 +410,8 @@ server <- function(input, output, session) {
sliderInput("ssgr", "Steady state growth rate", -0.1, 0.1,.resistanceScores[6], step=0.01),
sliderInput("l1PressSD", "Pressure Std Dev", 0.1, 1.0, .resistanceScores[7], step=0.01)
)
},
title='Layer 1 controls',
},
title='Layer 1 controls',
footer=tagList(
modalButton("Cancel"),
actionButton("modalOK", "OK")
@@ -419,7 +419,7 @@ server <- function(input, output, session) {
size='s')
)
})
observeEvent(input$modalOK, {
print('Modal ok pressed')
@@ -430,7 +430,7 @@ server <- function(input, output, session) {
.resistanceScores['ins'] <<- -input$l1VL
.resistanceScores['ssgr'] <<- input$ssgr
.resistanceScores['pressSD'] <<- input$l1PressSD
print('Running calc')
#.likelihoods$p_ba <<- calcLikelihood(1, .selections$pressStatus)
#.likelihoods$ba_os <<- calcLikelihood(2, .selections$pressStatus)
@@ -439,31 +439,31 @@ server <- function(input, output, session) {
removeModal()
})
output$nodeTable <- DT::renderDataTable(
modelList[[.selections$model]]$nodes,
selection = 'single',options = list(searching = TRUE, pageLength = 10, editable=TRUE),server = TRUE, escape = FALSE,rownames= TRUE
)
output$edgeTable <- DT::renderDataTable(
modelList[[.selections$model]]$edges,
selection = 'single',options = list(searching = TRUE, pageLength = 10, editable=TRUE),server = TRUE, escape = FALSE,rownames= 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$nodes
if (.selections$bbnEdges) {labels <- sapply(model$edges$values, getLabel)} else {labels <- rep("", nrow(model$edges))}
edges <- data.frame(
id = rownames(model$edges),
from=match(model$edges$input, nodes$code),
@@ -472,13 +472,13 @@ server <- function(input, output, session) {
label=labels,
arrows="to",
stringsAsFactors=FALSE
)
)
if (.selections$bbnNames) {labels <- nodes$name} else {labels <- nodes$code}
nodeSpacing <- ifelse(.selections$bbnNames, 600, 150)
palette <- brewer.pal(length(legends), "RdYlGn")
nodes <- data.frame(
id = rownames(nodes),
label = labels,
@@ -487,81 +487,81 @@ server <- function(input, output, session) {
color = palette[as.integer(nodes$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
#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:length(legends),
label = legends,
color = palette,
stringsAsFactors = FALSE
)
visNetwork(nodeNet, edgeNet, width = "100%", main='Bayesian Belief Network', submain=input$modelSelect) %>%
visNetwork(nodeNet, edgeNet, width = "100%", main='Bayesian Belief Network', submain=input$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$modelSelect, '.xlsx')
}
genPlot <- function(boxPlot, title) {
if (nrow(boxPlot)>0) {
palette <- brewer.pal(length(legends), "RdYlGn")
#print(palette)
colours <- palette[as.integer(boxPlot$Group)]
#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 = colours, 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$code, code)
@@ -571,22 +571,22 @@ server <- function(input, output, session) {
genPlot(thisPlot, title)
}
}
output$layer1 <- renderPlotly({
prepPlot("ba", "Bio-Assemblage")
})
output$layer2 <- renderPlotly({
prepPlot("op", "Output Processes")
})
output$layer3 <- renderPlotly({
prepPlot("es", "Ecosystem Services")
})
export <- function(model) {
#Get the network graph
l1 <- orca(prepPlot("ba", "Bio-Assemblage"), 'tmp/layer1.png')
l2 <- orca(prepPlot("op", "Output Processes"),'tmp/layer2.png')
@@ -594,27 +594,27 @@ server <- function(input, output, session) {
#Save pressure list, confidence levels, node and edge tables in xlsx
l <- list(
pressures = .selections$pressStatus,
pressures = .selections$pressStatus,
nodes = model$nodes,
edges = model$edges,
settings = as.data.frame(cbind(names(.resistanceScores), .resistanceScores), stringsAsFactors=FALSE)
)
xl <- write.xlsx(l, 'tmp/dataset.xlsx')
print('saving xlsx file export tmp/dataset.xlsx')
zipFile <- zipr(paste0('tmp/MESO-', format(Sys.time(), "%m%d_%H%M"), '.zip'), c('tmp/layer1.png', 'tmp/layer2.png', 'tmp/layer3.png', 'tmp/dataset.xlsx'))
print(paste('zip file complete', zipFile))
return(zipFile)
}
output$linkBackgroundData <- downloadHandler(
filename = getModelName(),
content = function(file) {
@@ -622,7 +622,7 @@ server <- function(input, output, session) {
},
contentType = "application/xlsx"
)
output$download <-downloadHandler(
filename = paste0('MESO-', format(Sys.time(), "%m%d_%H%M"), '.zip'),
content = function(file) {
@@ -632,7 +632,7 @@ server <- function(input, output, session) {
contentType = "application/zip"
)
}
shinyApp(ui, server)