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app.R
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app.R
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library(shiny)
library(magrittr)
# library(raster)
library(sp)
library(reshape2)
library(ggplot2)
library(plyr)
library(ggmap)
library(leaflet)
library(shinythemes)
library(maptools) ## For wrld_simpl
library(rgdal)
library(sf)
library(terra)
# global
source('./getBug.R')
source('./develop.fun.R')
################## INTIALISE DATA #######################
myLabelFormat = function(...,dates=FALSE){
if(dates){
function(type = "numeric", cuts){
format(as.Date(cuts, origin="1970-01-01"), '%b-%d')
}
}else{
labelFormat(...)
}
}
Tmin <- rast('data/mu_Tmin_for_DOY_ag10.tif')
Tmax <- rast('data/mu_Tmax_for_DOY_ag10.tif')
crs(Tmin) <- as.character(CRS("+init=epsg:4326"))
crs(Tmax) <- as.character(CRS("+init=epsg:4326"))
curYear <- format(Sys.time(), '%Y')
bug.files<- list.files('bugs/')
bugs<-sapply(X = bug.files, FUN = strsplit, '[.]')
bugs<-unlist(bugs)[(1:length(bugs))*2-1]
bugs<-bugs[order(bugs)]
bugList<-list()
#build list of all bugs with data
# test
for (bug in bugs){
insect<-getBug(bug)
bugList[insect$name]<-bug
}
# r <- Tmax[[1]]
# projection(r)<-'+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0'
## Example SpatialPolygonsDataFrame
data(wrld_simpl)
SPDF <- subset(wrld_simpl, NAME=="Australia") %>%
sf::st_as_sf() %>%
sf::st_set_crs(CRS("+init=epsg:4326"))
## crop and mask
r1 <- terra::mask(Tmax[[1]], terra::vect(SPDF))
# function for check long lat
# xy_in_aus = function(long,lat){
# xy = data.frame(long = long, lat = lat)
# coordinates(xy) <- ~ long + lat
# proj4string(xy) <- proj4string(SPDF)
# nrow(SPDF[xy,]) != 0 # 0 when no overlap
# }
xy_in_aus <- function(long, lat){
data.frame(x = long, y = lat) %>%
sf::st_as_sf(coords = 1:2, crs = 4326) %>%
sf::st_intersection(sf::st_geometry(SPDF)) %>%
nrow() != 0
}
#UI
ui <-
shinyUI(navbarPage("DARABUG2",selected = 'Local', theme = shinytheme("superhero"),
#################### LOCAL UI ####################
tabPanel("Local",
fluidRow(
column(width = 4,offset =0, "",
textInput("simname", value = 'My simulation 1',
label = h4("1. Input simulation name for plotting"),
placeholder = "e.g. My simulation 1", width="100%"),
selectInput("species", label = h4("2. Species observed:"),
choices = bugList,
selected = bugList[[1]],
width = '100%'),
dateInput('startDate', label = h4("3. Date observed:"),
value = paste0(curYear,'-6-1'),
min = paste0(curYear,'-1-1'),
max = paste0(curYear,'-12-31'),
format = "dd-MM", startview = "month", weekstart = 0,
language = "en", width = '100%'),
uiOutput("startStage"),
h4('5. Choose location, year and number of generations'),
span(textOutput("checklatlong"), style="color:GoldenRod"),
column(7,
leafletOutput("smap", height = 310)
),
# h4('6. Choose year of climate data (or years to average)'),
column(5,
selectInput("yearstart", label = h4("Start year"),
choices = 1950:(as.numeric(curYear)-1),
selected = as.numeric(curYear)-1),
selectInput("yearfinish", label = h4("End year"),
choices = 1950:(as.numeric(curYear)-1),
selected = as.numeric(curYear)-1),
selectInput("gens",
label = h4("Number of generations to simulate"),
choices = 1:5,
selected = 1),
),
# textOutput("checklatlong"), style="color:red"
# HTML('<br/>'),
# HTML('<br/>'),
h4('6. Run simulation'),
actionButton("update", "Run"),
HTML('<br/>'),
h4('7. Run another simulation or reset plot'),
actionButton("reset", "Reset"),
HTML('<br/>'),
h4('8. Download data as table'),
downloadButton('downloadData.csv', 'Download'),
HTML('<br/>')
),
column(8,
# fluidRow(
div(
style = "position:relative",
plotOutput("phenology",
hover = hoverOpts("plot_hover", delay = 100, delayType = "debounce")),
uiOutput("hover_info")
)
# )
)
)
),
################# REGIONAL UI ########################
tabPanel("Regional", id = 'regional',includeCSS("styles.css"),
div(class="outer",includeCSS("styles.css"),
tags$style(type = "text/css", ".outer {color:black; position: fixed; top: 41px; left: 0; right: 0; bottom: 0; overflow: hidden; padding: 0}"),
leafletOutput("map2", width = "100%", height = "100%"),
absolutePanel(top = 100, right = 10, draggable=TRUE,
h4(tags$b('Regional Prediction')),
selectInput("species2", label = h5("1. Species observed:"),
choices = bugList,
selected = bugList[[1]],
width = '100%'),
uiOutput("startStage2"),
dateInput('startDate2', label = h5("3. Date observed:"),
value = paste0(curYear,'-6-1'),
min = paste0(curYear,'-1-1'),
max = paste0(curYear,'-12-31'),
format = "dd-MM", startview = "month", weekstart = 0,
language = "en", width = '100%'),
# actionButton('reload', 'Reload'),
h5('5. Run simulation'),
actionButton("update2", "Run", width = '100%'),
uiOutput("endStage2"),
sliderInput('dateRange2',label = h5('7. Restrict legend range:'),
value = as.Date(c(paste0(curYear,'-1-1'), paste0(curYear,'-12-31'))),
min = as.Date(paste0(curYear,'-1-1')),max = as.Date(paste0(curYear,'-12-31'))),
HTML('<br/>')
))
),
################ ABOUT UI #######################
tabPanel("About",
fluidRow(column(5,'',
h1('DARAGRUB'),
h2('Description'),
h4('Effective management of insect pests in crops requires an understanding of the rate at which insects develop. For example, it might be important to know how long a damaging stage of a pest may persist in the crop, or when eggs might hatch. The DARAGRUB program provides a convenient and readily available means of predicting development times using different insect models. Gridded climatic data of daily temperatures is used in these models to generate estimates of the dates of occurrence for each stage throughout the whole life-cycle of an insect.'),
h2('Climate data'),
h4('The Australian gridded climatic data used to estimate developmental times is derived from 15-year averages of the max and min temperatures at each day of the year. A daily temperature profile is calculated using a simple trigonometric function, with an amplitude spanning the max and min daily temperatures over a period of 24 hours.'),
h2('Insect data'),
h4('The rate of growth and development of insects and other invertebrates is strongly influenced by temperature. The temperature dependence of development varies between species, thus each species has a unique temperature response. Indeed, even within a species the temperature dependence may vary between different stages. This is accounted for in the model by assigning unique developmental functions to each stage of each insect. This functional response is derived from empirical data.'),
h4('The temperature - growth rate relationship for each of the pest species modelled in this platform can be viewed opposite. The species-specific variables and rate functions for each were derived from published records, and can be varied in consultation with Dr James Maino. Similarly, new insect models for different pests can be added to this platform at any time, when based on published empirical data.'),
h4("This program was developed using many sources of data and with intellectual input from international researchers and representatives of QDAF, SARDI, DPIRD, cesar, and NSW DPI. Development of the tool was supported through funding from GRDC. Notably, the framework builds on past international and domestic efforts to predict the phenology of crop pests such as Agriculture Victoria's original DARABUG program and UC IPM (ipm.ucanr.edu). Individuals who provided input were Garry McDonald, Melina Miles, Julia Severi, Dusty Severtson and Jessica Lye. The tool was developed by James Maino, who can be contacted for any maintenance or technical requirements (info@cesaraustralia.com).
"),
HTML('<br/>'),
h5("McDonald G (1990) 'DARUBUG: a computer program for simulating development rates of insect pests in Victorian agriculture.' (Technical Report - Department of Agriculture and Rural Affairs (Melbourne) Melbourne)")
),
column(5,'',
selectInput("species3", label = h4("Select species:"),
choices = bugList,
selected = bugList[[1]]),
plotOutput('tempresponse'),
HTML('<br/>'),
htmlOutput('source')
)
)
)
)
)
# SERVER
server <- function(input, output, session){
######################### LOCAL PLOT ###################################
output$startStage <- renderUI({
insect<-getBug(input$species)
stageList<-lapply(1:length(names(insect$dev.funs)), FUN = function(x) x)
names(stageList)<-names(insect$dev.funs)
selectInput("startStage", label = h4("4. Life stage observed:"),
choices = stageList,
selected = 2, width = '100%')
})
values <- reactiveValues()
values$df <- NULL
values$count <- 1
values$raster <- r1
# values$raster[]<-NA
values$regionalSim <- NULL
# values$setpoints <- data.frame(start = NULL, species = NULL)
observe({
if(input$reset>0){
isolate({
values$df <- NULL
values$count <-1
})
}
})
# set default values for click
input_coords <- reactiveValues()
input_coords$long <- 140.0
input_coords$lat <- -35.0
# update the click
observe({
if(!is.null(input$smap_click)){
input_coords$long <- round(input$smap_click$lng, 2)
input_coords$lat <- round(input$smap_click$lat, 2)
}
})
# add the small map
output$smap <- renderLeaflet({
leaflet() %>%
setView(lng = 135.51, lat = -25.98, zoom = 3) %>%
addTiles()
})
# show coordinates with click
output$checklatlong <- renderText({
# update the text if click on the map
if(xy_in_aus(input_coords$long, input_coords$lat)){
paste0("Selected coordinates: longitude ", input_coords$long, " latitude ", input_coords$lat)
} else{
"Selected coordinates is not in Australia"
}
})
newEntry <- observe({
# browser()
if(input$update>=0 &
isolate(xy_in_aus(input_coords$long, input_coords$lat) & (input$yearstart <= input$yearfinish))){
withProgress(message = "LOADING. PLEASE WAIT...", value = 0, { # create progress bar
isolate({
startDay<-as.numeric(format(input$startDate,'%j'))
# get temp from silo
params = list(
lat=paste(input_coords$lat),
lon=paste(input_coords$long),
start=sprintf("%s0101", input$yearstart),
finish=sprintf("%s1231",input$yearfinish),
format="csv",
comment="RXN",
username="john.doe@xyz.com.au",
password="silo"
)
res <- httr::GET("https://www.longpaddock.qld.gov.au/cgi-bin/silo/DataDrillDataset.php", query=params)
# browser()
silodata <- readr::read_csv(httr::content(res, as="text"))
silodata$jday = format(silodata$`YYYY-MM-DD`, "%j")
silodata = silodata[silodata$jday != "366", ]
TMAX = aggregate(silodata$max_temp, list(silodata$jday), FUN = mean, na.rm=T)$x
TMIN = aggregate(silodata$min_temp, list(silodata$jday), FUN = mean, na.rm=T)$x
# get temp from aggregated AWAP layer
# TMAX <- extract(Tmax, matrix(c(input_coords$long, input_coords$lat), ncol = 2))
# TMIN <- extract(Tmin, matrix(c(input_coords$long, input_coords$lat), ncol = 2))
startStage <- ifelse(is.null(input$startStage),2,as.numeric(input$startStage))
insect <- getBug(input$species)
# browser()
data <- develop(TMAX, TMIN, startDay, startStage, insect, gens=as.numeric(input$gens))
})
})
isolate({
df<-as.data.frame(data[1,,])
df$stage<-names(insect$dev.funs)
df$life<-insect$life
df$location<-input$simname
df$long = input_coords$long
df$lat = input_coords$lat
df$generation = ceiling(1:nrow(df) / length(insect$dev.funs))
df$species<-paste0(stringr::str_pad( isolate(values$count), 2, pad='0'), '. ',insect$name, '\n',input$simname)
mdf <- melt(df, measure.vars = c("Time_start", "Time_end"))
mdf$value <- as.Date(paste0(input$yearfinish,'-01-01')) + mdf$value
values$df <-rbind(values$df ,mdf)
# values$setpoints <-rbind(values$setpoints,
# data.frame(start=as.Date(paste0(curYear,'-1-1'))+startDay-1,
# species=df$species[1]
# )
# )
values$count<-values$count+1
})
}
})
output$hover_info <- renderUI({
# browser()
input$plot_hover
data<-isolate(values$df)
if(length(data)>0){
hover <- input$plot_hover
point <- nearPoints(data, coordinfo = hover, threshold = 10, maxpoints = 1, addDist = TRUE)
if (nrow(point) == 0) return(NULL)
# calculate point position INSIDE the image as percent of total dimensions
# from left (horizontal) and from top (vertical)
left_pct <- (hover$x - hover$domain$left) / (hover$domain$right - hover$domain$left)
top_pct <- (hover$domain$top - hover$y) / (hover$domain$top - hover$domain$bottom)
# calculate distance from left and bottom side of the picture in pixels
left_px <- hover$range$left + left_pct * (hover$range$right - hover$range$left)
top_px <- hover$range$top + top_pct * (hover$range$bottom - hover$range$top)
# create style property fot tooltip
# background color is set so tooltip is a bit transparent
# z-index is set so we are sure are tooltip will be on top
style <- paste0("position:absolute; z-index:100; color:black; background-color: rgba(245, 245, 245, 0.85); ",
"left:", left_px + 2, "px; top:", top_px + 2, "px;")
# actual tooltip created as wellPanel
wellPanel(
style = style,
p(HTML(paste0("<b> Stage: </b>", point$stage, "<br/>",
"<b> ",point$variable,": </b>", format(point$value,'%d-%b'), "<br/>",
"<b> Duration (d): </b>", round(point$Stage_duration,1), "<br/>"
# "<b> Distance from left: </b>", left_px, "<b>, from top: </b>", top_px
)))
)
}else{return(NULL)}
})
output$phenology<- renderPlot({
mytheme<-theme_bw()+
theme(text = element_text(size=20,family='Nirmala UI', color = 'white'),
axis.text.x = element_text(color = 'white',angle=45, vjust=1, hjust=1),
axis.text.y = element_text(color = 'white'),
legend.title=element_blank(),
legend.key = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
legend.background = element_blank(),
axis.line.x = element_line(color="white"),
plot.background = element_blank()
)
if(input$update>=0){
if(isolate(length(values$df))){
if(TRUE){
data<-values$df
data$life<-factor(data$life)
if('adult'%in%levels(data$life))
data$life<-factor(data$life, levels = c(levels(factor(data$life))[-1],levels(factor(data$life))[1]) ,ordered = TRUE)
# setPoints = list()
# for(i in 1:nrow(values$setpoints)){
# x<-isolate(as.Date(values$setpoints[i,1]))
# y<-isolate(values$setpoints[i,2])
# setPoints[[i]]<-geom_point(aes(x=x,y=y),
# shape = 124, size = 10,colour = 'red')
# }
weekspan<-as.numeric( max(data$value)-min(data$value))/7
p <- ggplot(data)+
geom_line(aes(value, species, colour = life,
group=paste(life, generation, species)),size = 6) +
geom_point(aes(value, species), colour = 'black', size=6)+
ylab(NULL) +
xlab(NULL) +
# setPoints+
geom_vline(xintercept = isolate(as.numeric(input$startDate)))+
geom_text(aes(x=isolate(input$startDate), label="date observed", y=data$species[1]), colour=rgb(0.5,0.5,0.5), vjust = 2.2,hjust = .33)+
scale_x_date(limits = c(min(data$value), max(data$value)),
date_breaks = paste(ifelse(weekspan>20,4,1),"weeks"),date_minor_breaks = '1 week',
date_labels = "%d %b" ) +
mytheme
# browser()
# p<-ggplot(data ,aes(value, Stage_duration, colour = life))+
# geom_point(colour = 'black')
return(p)
}
}
}else{return(NULL)}
},bg="transparent", width = 1000, height = 500)
output$downloadData.csv <- downloadHandler(
filename = function() { paste('data_darabug.csv', sep='') },
content = function(file) {
df<-values$df
dfw<-dcast(df,formula = species + location +long + lat + generation +
life + stage + Stage_duration ~variable)
dfw$Time_end<-as.Date(dfw$Time_end,origin = '1970-1-1')
dfw$Time_start<-as.Date(dfw$Time_start,origin = '1970-1-1')
dfw = dfw[order(dfw$species, dfw$Time_start), ]
write.csv(dfw, file, row.names = FALSE)
}
)
################################### REGIONAL PLOT #################################
output$startStage2 <- renderUI({
insect<-getBug(input$species2)
stageList<-lapply(1:length(names(insect$dev.funs)), FUN = function(x) x)
names(stageList)<-names(insect$dev.funs)
selectInput("startStage2", label = h5("2. Life stage observed:"),
choices = stageList,
selected = 1, width = '100%')
})
output$endStage2 <- renderUI({
insect<-getBug(input$species2)
stageList<-lapply(1:length(names(insect$dev.funs)), FUN = function(x) x)
names(stageList)<-names(insect$dev.funs)
selectInput("endStage2", label = h5("6. Life stage to predict:"),
choices = stageList,
selected = length(stageList), width = '100%')
})
# A reactive expression that returns a cropped raster based on screen bounds
# in bounds right now
rasterBounds <- reactive({
input$stage2
if (is.null(input$map2_bounds))
return(isolate(values$raster))
bounds <- input$map2_bounds
latRng <- range(bounds$north, bounds$south)
lngRng <- range(bounds$east, bounds$west)
tryCatch(terra::crop(values$raster, terra::ext(c(lngRng, latRng))),
error = function(x) values$raster)
})
pal <- reactive( {
# browser()
# if
# tryCatch(colorNumeric(c("#EE0000","#B2DFEE","#007f00"), values(rasterBounds()),
# na.color = "transparent"),
# error = colorNumeric(c("#EE0000","#B2DFEE","#007f00"), 1,
# na.color = "transparent"))
colorNumeric(c("#EE0000","#B2DFEE","#007f00"), values(rasterBounds()),
na.color = "transparent")
})
output$map2 <- renderLeaflet({
leaflet() %>%
setView(lng = 135.51, lat = -25.98, zoom = 4) %>%
addTiles(
# urlTemplate = "//{s}.tiles.mapbox.com/v3/jcheng.map-5ebohr46/{z}/{x}/{y}.png",
# attribution = 'Maps by <a href="http://www.mapbox.com/">Mapbox</a>'
# options = providerTileOptions( minZoom = 4)
)
# %>% addRasterImage(rasterBounds(), opacity=0.5, layerId = 'rasimg')
})
observe({
if(input$update2>0&!all(is.na(values(rasterBounds())))){
# browser()
leafletProxy('map2')%>%
removeTiles(layerId="rasimg") %>%
addRasterImage(raster::raster(rasterBounds()), opacity=0.5, layerId = 'rasimg',colors = pal()) %>%
clearControls() %>%
addLegend(pal = pal(), values = values(rasterBounds()),
title = paste0(names(getBug(input$species2)$dev.funs)[as.integer(input$endStage2)],
' monitoring \n date'), position = 'bottomleft',
labFormat = myLabelFormat(dates=TRUE))
}else{
# if no data clear raster image
leafletProxy('map2')%>%
removeImage(layerId="rasimg")%>%
clearControls()
}
})
TMAX <- Tmax+0.000001 # save to memory with +0.000001
TMIN <- Tmin+0.000001
observe({
if(input$update2 > 0) {
withProgress(message = "LOADING. PLEASE WAIT...", { # create progress bar
isolate({
startDay<-as.numeric(format(input$startDate2,'%j'))
startStage<-rep(as.numeric(input$startStage2), ncell(TMAX[[1]]))
endStage<-as.numeric(input$endStage2)
insect<-getBug(input$species2)
# load('data.Rdata') # bypass computation during debugging
data <- develop(TMAX,TMIN, startDay, startStage, insect)
values$regionalSim <- data
# browser()
values$raster[] <- data[,endStage,'Time_end']
values$raster <- terra::mask(values$raster, terra::vect(SPDF))
})
})
}
})
observe({
input$endStage2
input$dateRange2
# browser()
if(!is.null(values$regionalSim)){
# browser()
isolate({
endStage<-as.numeric(input$endStage2)
values$raster[] <- values$regionalSim[,endStage,'Time_end']
# browser()
minDate<-as.numeric(format(input$dateRange2[1],'%j'))
maxDate<-as.numeric(format(input$dateRange2[2],'%j'))
values$raster[which(values$raster[] < minDate)] <- NA
values$raster[which(values$raster[] > maxDate)] <- NA
values$raster <- terra::mask(values$raster, terra::vect(SPDF))
})
}
})
########################## INSECT PLOT PAGE ###########################
# include about here
output$tempresponse<- renderPlot(bg="transparent",{
temps<- seq(0,50,length = 1000)
insect<-getBug(input$species3)
df <- data.frame(temp = temps)
stages<-names(insect$dev.funs)
for (stage in stages){
df[,stage]<-insect$dev.funs[[stage]](temps)
}
dl<-reshape2::melt(df, id = 'temp', variable.name = 'stage',
value.name = 'dev')
p<-ggplot() + geom_line(data = dl, aes(x = temp, y = dev, linetype = stage, color = stage)) +
xlab('Temperature (C)')+
ylab('Development rate (1/d)')+ theme(text = element_text(size=20, colour = 'white'), #family='Nirmala UI',
axis.text = element_text(size=20, colour = 'white'),
legend.title=element_blank(),
legend.key = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
legend.background = element_blank(),
axis.line.x = element_line(color="white"),
axis.line.y = element_line(color="white"),
plot.background = element_blank(),
axis.ticks = element_line(color = "white")
)
return(p)
})
output$source<-reactive({
insect<-getBug(input$species3)
return(HTML(paste('Source:<br>', insect$source)))
})
} # server(...
# output$vals <- renderPrint({
# hover <- input$plot_hover
# y <- nearPoints(values$df, input$plot_hover)[,c('stage','variable','value','Stage_duration')]
# # y <- nearPoints(data(), input$plot_hover)["wt"]
# y<-subset(y, variable == 'Time_start')
# req(nrow(y) != 0)
# # y is a data frame and you can freely edit content of the tooltip
# # with "paste" function
#
# print(sprintf('%s stage begins on %s \nlasting %1.1f days','egg', format(as.Date('2016-1-1'),'%d-%b'), 2.33))
# })
shinyApp(ui = ui, server = server)