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app.R
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library(shiny)
library(shinydashboard)
library(DT)
library(xts)
library(stringr)
library(ISOweek)
library(dygraphs)
library(googlesheets)
library(mosaic)
library(ROAuth)
library(streamR)
library(tidyverse)
library(leaflet)
library(tm)
library(wordcloud)
library(RColorBrewer)
library(maps)
library(rnoaa)
library(ggplot2)
library(zoo)
library(gtrendsR)
# gs_auth(token="googlesheets_token.rds")
# sheets <- gs_ls()
# datGS <- sheets$sheet_title
# datGS <- gsub('.{18}$', '', datGS[1:length(datGS)])
# UI ----------------------------------------------------------------------
ui <- dashboardPage(
dashboardHeader(
title="EWARS v.0.2a"
),
dashboardSidebar(
sidebarMenu(menuItem("Prospective EWS", tabName="retro", icon=icon("line-chart")),
menuItem("Datasets", tabName = "dataset", icon = icon("database")),
menuItem("Help", tabName="help", icon=icon("info"))
),
div(style="padding-left: 15px; padding-top: 40px; padding-right: 15px; ",
p(class="small", "TDR | World Health Organization", tags$br(),
"")
)
),
dashboardBody(
tabItems(
tabItem(tabName="retro",
#h2("Prediction"),
fluidRow(tags$head(includeScript("google-analytics.js")),
## left
box(width=3, title="## parameters",
# tags$b("area:"), textOutput("par_area"),tags$br(),
# tags$b("year:"), textOutput("par_year"),tags$br(),
# tags$b("population:"), textOutput("par_population"),tags$br(),
tags$b("alarm indicator 1:"), textOutput("par_alarm_1"),tags$br(),
tags$b("alarm indicator 2:"), textOutput("par_alarm_2"),tags$br(),
tags$b("Z outbreak:"), textOutput("par_Z_outbreak"),tags$br(),
tags$b("prediction distance:"), textOutput("par_pred_distance"),tags$br(),
tags$b("outbreak window size:"), textOutput("par_out_window_size"),tags$br(),
tags$b("alarm window size:"), textOutput("par_alarm_window_size"),tags$br(),
tags$b("outbreak threshold:"), textOutput("par_out_threshold"),tags$br(),
tags$b("alarm threshold:"), textOutput("par_alarm_threshold"),tags$br(),
tags$b("outbreak week length:"), textOutput("par_out_week_length"),tags$br()),
## right
box(width=9,
tabsetPanel(
tabPanel(title="surveillance workbook",
tags$br(),
fluidRow(
column(width=2,
selectInput('country_code', 'country code',
c("CO","XX"),
selected="XX")),
column(width=3,
textInput("ID_area", "district/ municipality code", "X")),
column(width=3,
textInput("psswd", "password", "password"))),
tags$hr(),
textOutput("area"),
checkboxInput("tblWB_reload", "RE-load data", value=FALSE),
tags$br(),tags$br(),
DT::dataTableOutput('tblWB')
),
tabPanel("input data",
tags$br(),
fluidRow(
column(width=3,
selectInput('gs_year', 'year',
c(2011:2020),selected=2015),
selectInput('gs_EpiWeek', 'epidemiological week',
c(2:53),selected=53),
actionButton("go1", label="send"),
tags$br(),tags$br()),
column(width=3,
tags$br(),tags$br(),
numericInput("gs_population","population", value=0),
numericInput("gs_cases","hospitalized cases", value=0)),
column(width=2,
tags$br(),tags$br(),
numericInput("gs_alarm1","alarm indicator 1", value=0),
numericInput("gs_alarm2","alarm indicator 2", value=0)))),
tabPanel("outbreak",
tags$br(),
#dygraphOutput("graph_outbreak"),
plotOutput("graph_outbreak2"),
tags$br(),tags$br()),
tabPanel("probability",
tags$br(),
#dygraphOutput("graph_probability"),
plotOutput("graph_probability2"),
tags$br(),tags$br()),
tabPanel("alarm plus outbreak",
tags$br(),
#dygraphOutput("graph_alarm"),
plotOutput("graph_alarm2"),
tags$br(),tags$br()),
tabPanel("response",
tags$br(),
#dygraphOutput("graph_response"),
plotOutput("graph_response2"),
tags$br(),tags$br())
))
)),
tabItem(tabName = "dataset",
#h2("Available Datasets"),
fluidRow(tabBox(width = 12,
tabPanel(title = "weather",
tags$h4("study area"),
fluidRow(
column(width=3,
numericInput("area_lat", label="latitude", value = 3.139)),
column(width=3,
numericInput("area_lon", label="longitude", value = 101.6869)),
column(width=3,
numericInput("area_rad", label="radius (in km)", value = 100))),
actionButton("go_wStatL", label = "click to search weather stations..."),
tags$br(),tags$br(),tags$hr(),
leafletOutput("map_wStat"),
tags$hr(),
tags$h4("observation period"),
textInput("wStat_min", label="the earliest date (yyyy-mm-dd)", value = "2017-01-01"),
textInput("wStat_max", label="the latest date (yyyy-mm-dd)", value = "2017-12-31"),
actionButton("go_wStatT", label = "click to proceed..."),
tags$br(),tags$br(),tags$hr(),
plotOutput("Wplot"),
tags$hr(),
tags$h4("select the observation station"),
textInput("WstatID", label="weather station ID"),
actionButton("go_wStatS", label = "click to select..."),
tags$br(),tags$br(),tags$hr(),
DT::dataTableOutput('tblwStatS'),
tags$hr()),
# tabPanel(title = "Twitter",
# tags$b("the last 15 minutes data retrieve from the Twitter public streaming API"),
# fluidRow(
# valueBoxOutput("n_tweets", width = 2),
# valueBoxOutput("n_users", width = 2),
# valueBoxOutput("n_usersLoc", width = 6)
# ),
# fluidRow(
# ## viewer
# box(width = 12, title = "",
# #verbatimTextOutput("value_form"),
# leafletOutput("mymap"))),
# tags$hr()),
# tabPanel(title = "Wikipedia",
# tags$b("Pageviews Analysis"),
# tags$br(),tags$br(),tags$br(),
# dygraphOutput("graph_wiki")
# ),
# tabPanel(title = "Google search-term",
# textInput("GST", label="search-term", value="dengue, zika, chikungunya"),
# plotOutput("graph_GST")),
tabPanel(title = "++"))
)),
tabItem(tabName="help",
#h2("Help"),
fluidRow(## left
box(width=3, #title="about us",
tags$p("TDR, the Special Programme for Research and Training in Tropical Diseases,
is a global programme of scientific collaboration that helps facilitate,
support and influence efforts to combat diseases of poverty.
It is hosted at the World Health Organization (WHO),
and is sponsored by the United Nations Children’s Fund (UNICEF),
the United Nations Development Programme (UNDP), the World Bank and WHO."),
tags$br(),tags$br(),tags$br(),tags$br()),
## right
box(width=9,
tabsetPanel(
# tabPanel("glossary",
# tags$br(),tags$br(),tags$br()),
# tabPanel("operational guide",
# tags$br(),tags$br(),tags$br()),
# tabPanel("references",
# tags$br(),tags$br(),tags$br()),
tabPanel("++ early release ++",
tags$br(),tags$br(),
tags$div(
HTML(paste(tags$strong("Early Warning and Response System for Dengue Outbreaks: Operational Guide using the web-based Dashboard"),
tags$a(href="http://globalminers8973.cloudapp.net/EWARS-R_dashboard.pdf", target="_blank", tags$br()," [download the pdf file here]"),
sep = ""))
),
tags$br(),tags$br(),
htmlOutput("video"),
tags$br(),tags$br(),tags$br())
)
)
))
)
))
# SERVER ------------------------------------------------------------------
server <- function(input, output) {
gs_country_code <- reactive({input$country_code})
gs_ID_area <- reactive({input$ID_area})
gs_psswd <- reactive({input$psswd})
tblWB_reload <- reactive({input$tblWB_reload})
gs_year <- reactive({input$gs_year})
gs_EpiWeek <- reactive({input$gs_EpiWeek})
gs_cases <- reactive({input$gs_cases})
gs_population <- reactive({input$gs_population})
gs_alarm1 <- reactive({input$gs_alarm1})
gs_alarm2 <- reactive({input$gs_alarm2})
output$area <- renderText({
paste("you have selected surveillance workbook for country code: ",gs_country_code(),
", and district/ municipality code: ",gs_ID_area(),sep='')
})
datLoad <- reactive({
validate(
need(nchar(gs_psswd()) == 8, "the password should be 8 digit")
)
gs_token <- paste("data/GST_",gs_country_code(),".rds",sep="")
gs_auth(token=gs_token)
wb_name <- paste("Surveillance workbook_R",gs_ID_area(),".xlsxyour_",
gs_psswd(),"_",gs_country_code(),gs_country_code(),sep='')
wb_list <- gs_ls()[1]
validate(
need(wb_name %in% wb_list$sheet_title == TRUE, "you specified an invalid value for the [area code] AND/OR the [password]")
)
if(tblWB_reload()==FALSE){
dat_gsheet <- gs_title(wb_name)
datLoad <- gs_read(dat_gsheet)
datLoad <- datLoad[c(11:62),c(1:12)]
names(datLoad) <- datLoad[1,]
datLoad <- datLoad[-1,]
}
if(tblWB_reload()==TRUE){
dat_gsheet <- gs_title(wb_name)
datLoad <- gs_read(dat_gsheet)
datLoad <- datLoad[c(11:62),c(1:12)]
names(datLoad) <- datLoad[1,]
datLoad <- datLoad[-1,]
}
return(datLoad)
})
datPar <- reactive({
gs_token <- paste("data/GST_",gs_country_code(),".rds",sep="")
gs_auth(token=gs_token)
wb_name <- paste("Surveillance workbook_R",gs_ID_area(),".xlsxyour_",
gs_psswd(),"_",gs_country_code(),gs_country_code(),sep='')
wb_list <- gs_ls()[1]
validate(
need(wb_name %in% wb_list$sheet_title == TRUE, "")
)
dat_gsheet <- gs_title(wb_name)
datPar <- gs_read(dat_gsheet)
datPar <- datPar[c(1:8),c(1:2)]
return(datPar)
})
output$tblWB <- DT::renderDataTable({
datINPUT <- datLoad()
datINPUT <- datINPUT[,c(2:5,7:9,11,12)]
datINPUT[] <- lapply(datINPUT, function(x) as.numeric(as.character(x)))
colnames(datINPUT)[1] <- "epi_week"
colnames(datINPUT)[2] <- "out_indic"
colnames(datINPUT)[3] <- "end_channel"
colnames(datINPUT)[4] <- "out_prob"
colnames(datINPUT)[5] <- "out_period"
colnames(datINPUT)[6] <- "alarm_sign"
colnames(datINPUT)[7] <- "cases"
colnames(datINPUT)[8] <- "alarm1"
colnames(datINPUT)[9] <- "alarm2"
datINPUT$out_indic <- round(datINPUT$out_indic,7)
datINPUT$end_channel <- round(datINPUT$end_channel,7)
datINPUT$out_prob <- round(datINPUT$out_prob,7)
datINPUT$alarm1 <- round(datINPUT$alarm1,2)
datINPUT$alarm2 <- round(datINPUT$alarm2,2)
datINPUT$out_indic <- NULL
datINPUT$out_period <- NULL
datINPUT$alarm_sign <- NULL
datatable(datINPUT, rownames=FALSE)
})
# output$par_area <- renderText({
# datPar <- datPar()
# x <- paste(as.character(names(dat_par)[2]))
# paste(x)
# })
# output$par_year <- renderText({
# datPar <- data()
# paste(as.character(unique(datPar$Year)))
# })
# output$par_population <- renderText({
# datPar <- data()
# paste(as.character(unique(datPar$population)))
# })
output$par_alarm_1 <- renderText({
datPar <- datLoad()
colnames(datPar)[(ncol(datPar)-1)]
})
output$par_alarm_2 <- renderText({
datPar <- datLoad()
colnames(datPar)[(ncol(datPar))]
})
output$par_Z_outbreak <- renderText({
datPar <- datPar()
paste(as.character(datPar[2,2]))
})
output$par_pred_distance <- renderText({
datPar <- datPar()
paste(as.character(datPar[3,2]))
})
output$par_out_window_size <- renderText({
datPar <- datPar()
paste(as.character(datPar[4,2]))
})
output$par_alarm_window_size <- renderText({
datPar <- datPar()
paste(as.character(datPar[5,2]))
})
output$par_out_threshold <- renderText({
datPar <- datPar()
paste(as.character(datPar[6,2]))
})
output$par_alarm_threshold <- renderText({
datPar <- datPar()
paste(as.character(datPar[7,2]))
})
output$par_out_week_length <- renderText({
datPar <- datPar()
paste(as.character(datPar[8,2]))
})
observeEvent(input$go1, {
gs_token <- paste("data/GST_",gs_country_code(),".rds",sep="")
gs_auth(token=gs_token)
wb_name <- paste("Surveillance workbook_R",gs_ID_area(),".xlsxyour_",
gs_psswd(),"_",gs_country_code(),gs_country_code(),sep='')
dat_gsheet <- gs_title(wb_name)
i <- 11+as.numeric(as.character(gs_EpiWeek()))
# cases
gs_edit_cells(dat_gsheet, ws=1, input=gs_cases(), anchor=paste("I",i,sep=""), trim=FALSE)
# population
gs_edit_cells(dat_gsheet, ws=1, input=gs_population(), anchor=paste("J",i,sep=""), trim=FALSE)
# alarm1
gs_edit_cells(dat_gsheet, ws=1, input=gs_alarm1(), anchor=paste("K",i,sep=""), trim=FALSE)
# alarm2
gs_edit_cells(dat_gsheet, ws=1, input=gs_alarm2(), anchor=paste("L",i,sep=""), trim=FALSE)
})
datWB <- reactive({
datWB <- datLoad()
year_num <- gs_year()
datWB$Year <- rep(year_num,nrow(datWB))
datWB[] <- lapply(datWB, function(x) as.numeric(as.character(x)))
datWB$WoY <- paste("0",datWB$Week,sep="")
datWB$WoY <- str_sub(datWB$WoY,start=-2)
datWB$WoY <- paste(datWB$Year,"-W",datWB$WoY,"-7",sep="")
datWB$WoYdate <- ISOweek2date(datWB$WoY)
datWB$WoY <- ISOweek(datWB$WoYdate)
##
names(datWB)[3] <- "outbreak_indicator"
names(datWB)[4] <- "endemic_channel"
names(datWB)[5] <- "outbreak_probability"
names(datWB)[6] <- "alarm_threshold"
names(datWB)[7] <- "outbreak_period"
names(datWB)[8] <- "alarm_signal"
##
shift<-function(x,shift_by){
stopifnot(is.numeric(shift_by))
stopifnot(is.numeric(x))
if (length(shift_by)>1)
return(sapply(shift_by,shift, x=x))
out<-NULL
abs_shift_by=abs(shift_by)
if (shift_by > 0 )
out<-c(tail(x,-abs_shift_by),rep(NA,abs_shift_by))
else if (shift_by < 0 )
out<-c(rep(NA,abs_shift_by), head(x,-abs_shift_by))
else
out<-x
out
}
##
datWB$resA <- NA
datWB <-
mutate(datWB,
resA = derivedFactor(
"1" = (outbreak_probability>alarm_threshold),
.method = "first",
.default = 0
))
datWB$resA <- as.numeric(as.character(datWB$resA))
datWB$resA_L1 <-shift(datWB$resA,-1)
datWB$resA_L2 <-shift(datWB$resA,-2)
datWB$resA[is.na(datWB$resA)] <- 0
datWB$resA_L1[is.na(datWB$resA_L1)] <- 0
datWB$resA_L2[is.na(datWB$resA_L2)] <- 0
##
datWB$resB <- NA
datWB <-
mutate(datWB,
resB = derivedFactor(
"1" = (outbreak_indicator>endemic_channel),
.method = "first",
.default = 0
))
datWB$resB <- as.numeric(as.character(datWB$resB))
datWB$resB_L1 <-shift(datWB$resB,-1)
datWB$resB_L2 <-shift(datWB$resB,-2)
datWB$resB[is.na(datWB$resB)] <- 0
datWB$resB_L1[is.na(datWB$resB_L1)] <- 0
datWB$resB_L2[is.na(datWB$resB_L2)] <- 0
## trigger
datWB$res_no <- datWB$resA
datWB$res_no2w <- datWB$resA + datWB$resA_L1
datWB$res_no3w <- datWB$resA + datWB$resA_L1 + datWB$resA_L2
datWB$res_late3w <- datWB$resB + datWB$resB_L1 + datWB$resB_L2
## trigger
datWB$trigger <- NA
datWB <-
mutate(datWB,
trigger = derivedFactor(
"4" = (res_late3w==3),
"3" = (res_no==1&res_no3w==3),
"2" = (res_no==1&res_no2w==2),
"1" = (res_no==1&res_no3w<3&res_no2w<2),
.method = "first",
.default = NA
))
datWB$trigger[is.na(datWB$weekly_hospitalised_cases)] <- NA
datWB$trigger <- as.numeric(as.character(datWB$trigger))
datWB <- datWB[,c(1:14,25)]
return(datWB)
})
# output$graph_outbreak <- renderDygraph({
#
# datPro <- datWB()
#
# datProOut <- select(datPro, WoYdate,outbreak_indicator,endemic_channel)
# datProOut_xts <- xts(datProOut[,-1], order.by=datProOut$WoYdate)
#
# dygraph(datProOut_xts) %>%
# dySeries("outbreak_indicator", label="confirmed cases", color="red") %>%
# dySeries("endemic_channel", label="endemic channel", fillGraph=TRUE, color="grey") %>%
# dyLegend(width=700) %>%
# dyOptions(drawGrid=F)
# })
output$graph_outbreak2 <- renderPlot({
dat <- datWB()
dat <- select(dat, Week,endemic_channel,outbreak_indicator)
line_x <- c(1,dat$Week,53)
line_y <- c(0,dat$endemic_channel,0)
plot(1, type="n", xlab="epidemiological week", ylab="", axes=F, xlim=c(2,52), ylim=c(0,0.8))
axis(1, at=c(2:52),labels=c(2:52))
axis(2, at=c(0,0.1,0.2,0.3,0.4,0.5),labels=c(0,0.1,0.2,0.3,0.4,0.5))
polygon(line_x, line_y, col='grey75', border='grey75')
points(dat$Week, dat$outbreak_indicator, type="o", col="red", lwd=2)
legend("topleft", legend=c("confirmed cases", "endemic channel"),
col=c("red", "grey75"), lty=1:1, cex=1.0,
box.lty=0)
})
# output$graph_probability <- renderDygraph({
#
# datPro <- datWB()
#
# datProProb <- select(datPro, WoYdate,outbreak_probability,alarm_threshold)
# datProProb$Outbreak.probability[is.na(datPro$weekly_hospitalised_cases)] <- NA
#
# datProProb_xts <- xts(datProProb[,-1], order.by=datProProb$WoYdate)
#
# dygraph(datProProb_xts) %>%
# dySeries("outbreak_probability", label="outbreak probability", color="darkgreen") %>%
# dySeries("alarm_threshold", label="alarm threshold", color="blue",
# strokeWidth=2, strokePattern="dashed") %>%
# dyLegend(width=700) %>%
# dyOptions(drawGrid=F)
# })
output$graph_probability2 <- renderPlot({
dat <- datWB()
dat <- select(dat, Week,outbreak_probability,alarm_threshold,weekly_hospitalised_cases)
dat$outbreak_probability[is.na(dat$weekly_hospitalised_cases)] <- NA
plot(1, type="n", xlab="epidemiological week", ylab="", axes=F, xlim=c(2,52), ylim=c(0,0.8))
axis(1, at=c(2:52),labels=c(2:52))
axis(2, at=c(0,0.1,0.2,0.3,0.4,0.5),labels=c(0,0.1,0.2,0.3,0.4,0.5))
points(dat$Week, dat$alarm_threshold, type="l", col="darkgreen", lwd=2, lty=2)
points(dat$Week, dat$outbreak_probability, type="o", col="darkblue", lwd=2)
legend("topleft", legend=c("alarm threshold", "outbreak probability"),
col=c("darkgreen", "darkblue"), lty=1:1, cex=1.0,
box.lty=0)
})
# output$graph_alarm <- renderDygraph({
#
# datPro <- datWB()
#
# datProALARM <- select(datPro, WoYdate,outbreak_indicator,endemic_channel,outbreak_probability,alarm_threshold)
# datProALARM$outbreak_indicator[is.na(datPro$weekly_hospitalised_cases)] <- NA
# datProALARM$endemic_channel[is.na(datPro$weekly_hospitalised_cases)] <- NA
# datProALARM$outbreak_probability[is.na(datPro$weekly_hospitalised_cases)] <- NA
#
# datProALARM_xts <- xts(datProALARM[,-1], order.by=datProALARM$WoYdate)
#
# dygraph(datProALARM_xts) %>%
# dySeries("outbreak_indicator", label="confirmed cases", color="red", axis='y2') %>%
# dySeries("endemic_channel", label="endemic channel", fillGraph=TRUE, color="grey", axis='y2') %>%
# dySeries("outbreak_probability", label="outbreak probability", color="darkgreen") %>%
# dySeries("alarm_threshold", label="alarm threshold", color="blue",
# strokeWidth=2, strokePattern="dashed") %>%
# dyLegend(width=700) %>%
# dyOptions(drawGrid=F)
# })
output$graph_alarm2 <- renderPlot({
dat <- datWB()
dat <- select(dat, Week,endemic_channel,outbreak_indicator,outbreak_probability,alarm_threshold,weekly_hospitalised_cases)
dat$outbreak_probability[is.na(dat$weekly_hospitalised_cases)] <- NA
line_x <- c(1,dat$Week,53)
line_y <- c(0,dat$endemic_channel,0)
plot(1, type="n", xlab="epidemiological week", ylab="", axes=F, xlim=c(2,52), ylim=c(0,0.8))
axis(1, at=c(2:52),labels=c(2:52))
axis(2, at=c(0,0.1,0.2,0.3,0.4,0.5),labels=c(0,0.1,0.2,0.3,0.4,0.5))
polygon(line_x, line_y, col='grey75', border='grey75')
points(dat$Week, dat$outbreak_indicator, type="o", col="red", lwd=2)
points(dat$Week, dat$alarm_threshold, type="l", col="darkgreen", lwd=2, lty=2)
points(dat$Week, dat$outbreak_probability, type="o", col="darkblue", lwd=2)
legend("topleft", legend=c("confirmed cases", "endemic channel", "alarm threshold", "outbreak probability"),
col=c("red", "grey75", "darkgreen", "darkblue"), lty=1:1, cex=1.0,
box.lty=0)
})
# output$graph_response <- renderDygraph({
#
# datPro <- datWB()
#
# datTRIGGER <- select(datPro, WoYdate,trigger)
# datTRIGGER$trigger[is.na(datPro$weekly_hospitalised_cases)] <- NA
# datTRIGGER_xts <- xts(datTRIGGER[,-1], order.by=datTRIGGER$WoYdate)
#
# #library(dygraphs)
#
# dygraph(datTRIGGER_xts) %>%
# dyAxis("y", valueRange=c(0.5,4.5)) %>%
# dySeries("trigger", label="response", color="red", stepPlot=TRUE) %>%
# #dySeries("weekly_hospitalised_cases", label="cases", color="darkblue") %>%
# dyLegend(width=700) %>%
# dyOptions(drawGrid=F, drawPoints=TRUE, pointSize=4)
# })
output$graph_response2 <- renderPlot({
dat <- datWB()
dat <- select(dat, Week,trigger)
# plot(1, type="n", xlab="epidemiological week", ylab="", axes=F, xlim=c(2,52), ylim=c(0,6))
# axis(1, at=c(2:52),labels=c(2:52))
# #axis(2, at=c(1:4),labels=c(1:4))
#
# abline(h=c(1:4), col=c("darkgreen","darkorange","red","brown"), lty=1)
# text(1,1, "no response", col="darkgreen", adj=c(0,-1))
# text(1,2, "initial response", col="darkorange", adj=c(0,-1))
# text(1,3, "early response", col="red", adj=c(0,-1))
# text(1,4, "late/emergency response", col="brown", adj=c(0,-1))
#
# points(dat$Week, dat$trigger, type="h", lwd=2)
#plot(1, type="n", xlab="epidemiological week", ylab="", axes=F, xlim=c(2,52), ylim=c(0,6))
plot(1, type="n", xlab="epidemiological week", ylab="", axes=F, xlim=c(-15,52), ylim=c(0,6))
axis(1, at=c(2:52),labels=c(2:52))
abline(h=c(1:4), col=c("darkgreen","darkorange","red","brown"), lty=1)
text(-15,1, "no response", col="darkgreen", adj=c(0,-1))
text(-15,2, "initial response", col="darkorange", adj=c(0,-1))
text(-15,3, "early response", col="red", adj=c(0,-1))
text(-15,4, "late/emergency response", col="brown", adj=c(0,-1))
points(dat$Week, dat$trigger, type="p", lwd=2)
})
dat_wStatL <- eventReactive(input$go_wStatL, {
load("datStation.RData")
lat_lon_df <- data.frame(id="study_area", name="study_area",
latitude=input$area_lat, longitude=input$area_lon) #-7.797068 #110.370529
# Get all stations within 150 kilometers
meteo_stat <- meteo_nearby_stations(lat_lon_df = lat_lon_df, station_data = station_data,
radius = input$area_rad, var = c("PRCP","TMIN","TMAX"))
meteo_stat <- meteo_stat$study_area
meteo_stat <- rbind(lat_lon_df,meteo_stat[,c(1:4)])
return(meteo_stat)
})
output$map_wStat <- renderLeaflet({
datMap <- dat_wStatL()
lat_lon_df <- datMap[1,]
meteo_stat <- datMap[-1,]
leaflet() %>% addTiles() %>%
addCircleMarkers(data=lat_lon_df, ~longitude, ~latitude, radius = 5, color = "red", label = ~as.character(id)) %>%
addCircleMarkers(data=meteo_stat, ~longitude, ~latitude, radius = 5, color = "blue", popup = ~as.character(id), label = ~as.character(name))
})
dat_wStatT <- eventReactive(input$go_wStatT, {
datMap <- dat_wStatL()
meteo_stat <- datMap[-1,]
meteo_dat <- meteo_pull_monitors(meteo_stat$id,
date_min = input$wStat_min,
date_max = input$wStat_max) %>%
rename(day = date, location = id)
return(meteo_dat)
})
dat_wStatS <- eventReactive(input$go_wStatS, {
meteo_datS <- meteo_pull_monitors(input$WstatID,
date_min = input$wStat_min,
date_max = input$wStat_max) %>%
rename(day = date, location = id)
meteo_datS <- select(meteo_datS, day,prcp,tavg) %>% mutate(tavg=(tavg/10),
prcp=(prcp/10))
#meteo_datS$YM <- as.yearmon(meteo_datS$day)
meteo_datS$ISOweek <- ISOweek(meteo_datS$day)
meteo_datS <- select(meteo_datS, ISOweek,prcp,tavg) %>% group_by(ISOweek) %>%
summarise(temp_avg=round(mean(tavg),2),
precipitation_sum=(sum(prcp)))
meteo_datS$ISOweek <- as.character(meteo_datS$ISOweek)
return(meteo_datS)
})
output$Wplot <- renderPlot({
meteo_dat <- dat_wStatT()
meteo_plot <- meteo_dat %>%
select(-tmax, -tmin) %>%
tidyr::gather(parameter, value, tavg:prcp)
ggplot(meteo_plot) +
geom_line(aes(x = day, y = value, col = location)) +
facet_grid(parameter ~ ., scales = "free_y") + theme_bw()
})
##
output$tblwStatS <- DT::renderDataTable({
tblwStatS <- dat_wStatS()
datatable(tblwStatS, options = list(pageLength=53))
})
datTweets <- reactive({
download.file("http://globalminers8973.cloudapp.net/gl_twts.csv.gz", "data/gl_twts.csv.gz")
datTweets <- read_csv("data/gl_twts.csv.gz")
return(datTweets)
})
datTweetsGeo <- reactive({
datTweetsGeo <- datTweets()
datTweetsGeo <- drop_na(datTweetsGeo, c(lon,lat))
return(datTweetsGeo)
})
output$mymap <- renderLeaflet({
datTweetsGeo <- datTweetsGeo()
leaflet() %>% addTiles() %>%
addCircleMarkers(data=datTweetsGeo, ~lon, ~lat, radius=1, color="red", label=~as.character(location))
})
output$n_tweets <- renderValueBox({
datTweets <- datTweets()
valueBox("Tweets", nrow(datTweets), icon = icon("twitter"), color = "purple" )
})
output$n_users <- renderValueBox({
datTweets <- datTweets()
valueBox("Users", length((unique(datTweets$user_id_str))), icon = icon("user"), color = "yellow" )
})
output$n_usersLoc <- renderValueBox({
datTweets <- datTweetsGeo()
valueBox("Users with location data", length((unique(datTweets$user_id_str))), icon = icon("user"), color = "green" )
})
datGST <- reactive({
validate(
need(gs_country_code() != "XX", "the country code is not valid")
)
datGST <- gtrends(c("dengue","zika","chikungunya"), geo=c(gs_country_code()))
return(datGST)
})
output$graph_GST <- renderPlot({
datGST <- datGST()
plot(datGST)
})
output$graph_wiki <- renderDygraph({
wikiDen <- read.csv(url("http://globalminers8973.cloudapp.net/pageviews.csv"))
wikiDen$Date <- as.Date(wikiDen$Date, format="%Y-%m-%d")
wikiDen_xts <- xts(wikiDen[,-1], order.by=wikiDen$Date)
dygraph(wikiDen_xts) %>%
dySeries("Dengue.fever", label="en.wikipedia.org/wiki/Dengue_fever", color="red") %>%
dySeries("Dengue.virus", label="en.wikipedia.org/wiki/Dengue_virus", color="blue") %>%
dyLegend(width=700) %>%
dyOptions(drawGrid=F, drawPoints=TRUE) %>%
dyRangeSelector()
})
output$video <- renderUI({
my_vid <- tags$iframe(src="https://www.youtube.com/embed/japeI6ul9uk", height=409.5, width=728, frameborder="0")
print(my_vid)
my_vid
})
}
# APP ---------------------------------------------------------------------
shinyApp(ui, server)