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app.R
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#required libraries
source("utils/libraries.R")
#Change directory depending on where I am:
if ( getwd()!="/srv/shiny-server/sentinel_hrmntr/Sentinel")
{
setwd('/media/herimanitra/DONNEES/IPM_sentinelle/sentinel_hrmntr 291115/Sentinel')
} else {
}
# on my laptop or at office
############################################## server ######################
source("utils/facies_class.R")
server<-function(input, output,session) {
source("import_data.R")
#reactive computation of detection algorithms
#(centile,MinSan,C-sum,TDR+/fiever==>case of Malaria)
#depending on different parameters:
preprocessing = observeEvent(input$diseases,{
if ( !(paste0(input$diseases,Sys.Date(),".csv") %in% list.files(paste0(getwd(),"/temp"))) )
{
##############selection of disease data depending on user input choice##
source("utils/diseases_control.R")
mydata=select_disease(disease=input$diseases)
####################################data preprocessing############
cat("keep only sites that already have historical values...\n")
include_index= match(include,names(mydata)) #introduce dplyr
mydata=mydata[,include,with=F]
cat('reshape select data...')
mydata=as.data.table(gather(mydata,key=sites,value=occurence,-c(code,deb_sem)))
cat('DONE\n')
source("utils/create_facies.R")
mydata=create_facies(mydata)
cat('Extract weeks and years from PaluConf...')
mydata[,weeks:=as.numeric(substr(code,6,8))]
mydata[,years:=as.numeric(substr(code,1,4))]
cat('DONE\n')
cat("convert site to character...")
mydata[,sites:=as.character(sites)]
setkeyv(mydata,c('sites','weeks','years','code'))
cat("DONE\n")
#some sort of cache of preprocessed data to speed up things:
cat("writing ",input$diseases," to a temporary file\n")
write.table(mydata,paste0("temp/",input$diseases,Sys.Date(),".csv"),sep=";",row.names = F)
}
#return(NULL)
})
percentile_algorithm = reactive({
#some sort of cache of preprocessed data to speed up things:
cat("reading ",input$diseases," from a temporary file\n")
mydata=fread(paste0("temp/",input$diseases,Sys.Date(),".csv"))
cat('Calculation of percentile and proportion of sites in alert begin...\n')
source("algorithms/percentile.R")
mylist= calculate_percentile(data=mydata,
week_length=input$comet_map,
percentile_value=input$Centile_map,
disease=input$diseases
)
return(mylist)
})
minsan_algorithm = reactive({
#some sort of cache of preprocessed data to speed up things:
cat("reading ",input$diseases," from a temporary file\n")
mydata=fread(paste0("temp/",input$diseases,Sys.Date(),".csv"))
#####################################Doublement du nb de cas (MinSan algo) ###################
cat('Calculation of minsan and proportion of sites in alert begin...\n')
source("algorithms/minsan.R")
mylist=calculate_minsan(data=mydata,slope_minsan=input$slope_minsan,
minsan_weekrange=input$minsan_weekrange,
minsan_consecutive_week=input$minsan_consecutive_week,
byvar="code")
return (mylist)
})
csum_algorithm = reactive({
cat("reading ",input$diseases," from a temporary file\n")
mydata=fread(paste0("temp/",input$diseases,Sys.Date(),".csv"))
######################################Cumulative sum (cumsum algo) ##########################
cat('Calculation of csum and proportion of sites in alert begin...')
source("algorithms/csum.R")
cat('DONE\n')
mylist= calculate_csum(data=mydata,
Csum_year_map=input$Csum_year_map,
Sd_csum_map=input$Sd_csum_map,
week_Csum_map=input$week_Csum_map
,byvar="code")
return (mylist)
##############################Output all results in a list: ##################################
})
tdrplus_fever_ind = reactive({
##############################TDR + / Fever Indicator algorithm###################
if ( !(paste0("PaluConf_SyndF",".rda") %in% list.files(paste0(getwd(),"/temp"))) ) {
source("utils/preprocess_PaluConf_SyndF.R",local = T)
} else {
load(file=paste0("temp/PaluConf_SyndF",".rda"))
}
cat('looking for an alert when Malaria cases among fever cases exceed:',
input$exp_map,'% or malaria cases among consultation number...',
input$expC_map,'...')
PaluConf_SyndF[,alert_status:=ifelse(100*sum(malaria_cases,na.rm = T)/sum(nb_fievre,na.rm = T)>as.numeric(input$exp_map) &
100*sum(malaria_cases,na.rm = T)/sum(nb_consultation,na.rm = T)>as.numeric(input$expC_map),
"alert","normal"),by="sites"]
PaluConf_SyndF[,alert_status_hist:=ifelse(100*(malaria_cases)/(nb_fievre)>as.numeric(input$exp_map) &
100*(malaria_cases)/(nb_consultation)>as.numeric(input$expC_map),
"alert","normal")]
cat('DONE\n')
###proportion of sites in alert (weekly for all, and weekly by facies)##########
cat('calculate weekly proportion of sites in alert using tdr+/fever algorithm...')
Nbsite_beyond=PaluConf_SyndF[ alert_status_hist=="alert",
length(unique(sites)),by="code"]
setnames(Nbsite_beyond,"V1","eff_beyond")
Nbsite_beyond=merge(Nbsite_beyond,unique(PaluConf_SyndF[,list(code,deb_sem)],by=NULL),
by.x="code",by.y="code",all.x=T)
Nbsite_withdata=PaluConf_SyndF[is.na(alert_status_hist)==F,length(unique(sites)),by="code"]
setnames(Nbsite_withdata,"V1","eff_total")
propsite_alerte_fever=merge(x=Nbsite_withdata,y=Nbsite_beyond,
by.x="code",by.y="code",all.x=T)
propsite_alerte_fever[,prop:=ifelse(is.na(eff_beyond/eff_total)==T,0.0,
eff_beyond/eff_total)]
cat('DONE\n')
source("utils/create_facies.R")
PaluConf_SyndF=create_facies(data=PaluConf_SyndF)
####################new method to handle facies###################################
list_facies= c("East","South","High_land","Fringe","excepted_East","excepted_High_land")
datalist_facies=list()
for ( f in list_facies)
{
cat('calculate weekly proportion of sites in alert using RDT+/fever for ',f,'...')
Nbsite_beyond=PaluConf_SyndF[ alert_status_hist=="alert" & get(f)==1,
length(unique(sites)),by=c("code",f)]
setnames(Nbsite_beyond,"V1","eff_beyond")
Nbsite_beyond=merge(Nbsite_beyond,unique(PaluConf_SyndF[,list(code,deb_sem)],by=NULL),
by.x=c("code"),by.y=c("code"),all.x=T)
Nbsite_withdata=PaluConf_SyndF[is.na(alert_status_hist)==F & get(f)==1,
length(unique(sites)),by=c("code",f)]
setnames(Nbsite_withdata,"V1","eff_total")
myfacies=merge(x=Nbsite_withdata,
y=Nbsite_beyond,
by.x=c("code",f),
by.y=c("code",f),all.x=T )
myfacies[,prop:=ifelse(is.na(eff_beyond/eff_total)==T,0.0,
eff_beyond/eff_total)]
rm(Nbsite_beyond);rm(Nbsite_withdata);gc()
cat('DONE\n')
datalist_facies[[f]]=myfacies
#append to a single and unique dataframe:
if ( f==list_facies[1])
{
propsite_alerte_fever_byfacies= datalist_facies[[f]]
propsite_alerte_fever_byfacies[,f:=NULL,with=F]
propsite_alerte_fever_byfacies[,eff_total:=NULL]
propsite_alerte_fever_byfacies[,eff_beyond:=NULL]
propsite_alerte_fever_byfacies[,facies:=f]
} else {
tmp= datalist_facies[[f]]
tmp[,f:=NULL,with=F]
tmp[,eff_total:=NULL]
tmp[,eff_beyond:=NULL]
tmp[,facies:=f]
propsite_alerte_fever_byfacies=rbind(propsite_alerte_fever_byfacies,tmp)
rm(tmp);gc()
}
cat("DONE\n")
}
cat('calculate radius for per site for percentile algorithm alert...')
setkey(PaluConf_SyndF,alert_status_hist)
PaluConf_SyndF[alert_status=="alert", myradius:=15.0]
PaluConf_SyndF[alert_status_hist=="normal", sum_occurence_week:=sum(malaria_cases,na.rm=T),by="code"]
PaluConf_SyndF[alert_status=="normal", myradius:=15.0*malaria_cases/sum_occurence_week,by="sites,code"]
PaluConf_SyndF[alert_status_hist %in% NA, myradius:=5.0]
cat('DONE\n')
PaluConf_SyndF[,deb_sem:=as.Date(deb_sem,origin="1970-01-01")]
max_deb_sem= max(PaluConf_SyndF$deb_sem)
max_code=paste0(year(max_deb_sem),"_",isoweek(max_deb_sem))
if (max_code==paste0(year(Sys.Date()),"_",isoweek(Sys.Date())) ) {
#if max_date == current week then exclude this current week
#from calculation of alert , otherwise include
mycode=paste0(year(Sys.Date()-7),"_",isoweek(Sys.Date()-7))
tdrplus_ind_currentweek=PaluConf_SyndF[code==mycode,]
} else {
tdrplus_ind_currentweek=PaluConf_SyndF[code==max_code,]
}
return(list(mydata=PaluConf_SyndF,
tdrplus_ind_currentweek =tdrplus_ind_currentweek,
propsite_alerte_fever=propsite_alerte_fever,
propsite_alerte_fever_byfacies=propsite_alerte_fever_byfacies))
})
#display proportion of sites in alert with these HFI
output$propsite_alerte = renderPlotly({
source("utils/create_facies.R")
#TODO: move this computation to initialize_data.R
cat("loading and transforming HF Indicators...")
hfi=data.table(gather(hfi,key=sites,
value=myvalue,-c(code,deb_sem,type_val)))
hfi= create_facies(hfi)
cat("DONE\n")
cat("dispatch data from HFI...")
setkey(hfi,type_val)
caid = hfi[type_val=="CAID"];setnames(caid,old="myvalue",new="caid_value")
pmm = hfi[type_val=="PRECIPITATION"];setnames(pmm,old="myvalue",new="pmm_value")
lst = hfi[type_val=="LST_DAY"];setnames(lst,old="myvalue",new="temperature")
ndvi = hfi[type_val=="NDVI"];setnames(ndvi,old="myvalue",new="ndvi_value")
cat("DONE\n")
source("utils/introducing_caid.R",local = T) #==>now in caid
source("utils/introducing_mild.R",local = T)
source("utils/introducing_pmm.R",local = T) #==>now in hfi
source("utils/introducing_lst.R",local = T) #==>now in hfi
source("utils/introducing_ndvi.R",local = T) #==>now in hfi
source("utils/if_percentile_viz.R",local = T)
source("utils/if_minsan_viz.R",local = T)
source("utils/if_csum_viz.R",local = T)
source("utils/if_tdrfever_viz.R",local = T)
setkey(myprop,"code");setkey(caid,"code")
cat('merging caid data with proportion of sites in alert...')
myprop=merge(myprop,unique(caid[,list(code,caid_value)],by=NULL),
by.x=c("code"),
by.y=c("code"), all.x=T )
cat('DONE\n')
setkey(myprop,"code");setkey(mild,"code")
cat('merging mild data with proportion of sites in alert...')
myprop=merge(myprop,unique(mild[,list(code,mild_value)],by=NULL),
by.x=c("code"),
by.y=c("code"), all.x=T )
cat('DONE\n')
cat('merging ndvi data with proportion of sites in alert...')
setkey(myprop,"code");setkey(ndvi,"code")
myprop=merge(myprop,unique(ndvi[,list(code,ndvi_value)],by=NULL),
by.x=c("code"),
by.y=c("code"), all.x=T )
cat('DONE\n')
cat('nrow of myprop after merge with ndvi are:',nrow(myprop),"\n")
cat('merging temperature data with proportion of sites in alert...')
setkey(myprop,"code");setkey(lst,"code")
myprop=merge(myprop,unique(lst[,list(code,temperature)],by=NULL),
by.x=c("code"),
by.y=c("code"), all.x=T )
cat('DONE\n')
cat('nrow of myprop after merge with lst are:',nrow(myprop),"\n")
setkey(myprop,"code");setkey(pmm,code)
cat('merging rainFall data with proportion of sites in alert...')
myprop=merge(myprop,unique(pmm[,list(code,pmm_value)],by=NULL),
by.x=c("code"),
by.y=c("code"), all.x=T )
cat('DONE\n')
#using plotly:
#line width (epaisseur de la ligne):
line_width=1.0
p <- plot_ly(myprop, x = deb_sem,
y = 100*prop,name=input$diseases,
line = list(width=line_width,color = "rgb(255, 0, 0)") )
p = p %>% layout( legend=list(x = 0.5, y = 0),
paper_bgcolor="rgb(213, 226, 233)",
plot_bgcolor = "rgb(213, 226, 233)",
title="%sites in alert")
#these are only make sense when Malaria (not for other diseases)
if ( input$diseases=="Malaria")
{
p = p %>% add_trace(x = deb_sem, y = caid_value, name = "IRS",
type="bar",
color= "IRS",
colors="#dfb678",
visible='legendonly')
p = p %>% add_trace(x = deb_sem, y = mild_value, name = "LLIN",
color="LLIN",
opacity=0.5,
colors="#132B43",
type="bar",
visible='legendonly')
}
p = p %>% add_trace(x = deb_sem, y = 100*ndvi_value, name = "NDVI",
line = list(width=line_width,color="#11d938"),visible='legendonly')
p = p %>% add_trace(x = deb_sem, y = pmm_value, name = "Rainfall(mm)",
line = list(width=line_width,color="#307ff0"),
visible='legendonly')
p = p %>% add_trace(x = deb_sem, y = temperature, name = "Temp.",
line = list(width=line_width,color = "#ff8d00"),
visible='legendonly')
p = p %>% layout(
legend = list(x = 0, y = 100),
xaxis =list(title="Weeks"),
yaxis =list(title="Values")
)
p
})
#display sites in alert for the current week into the map (02 map choices currently)
output$madagascar_map <- renderLeaflet({
if (input$diseases=="Malaria")
{
if (input$Algorithmes_eval1=="Percentile" )
{
cat("display alert status into the map using percentile algorithm...\n")
#setkey for fast merging
setkey(sentinel_latlong,sites)
setkey(percentile_algorithm()$percentile_alerte_currentweek,sites)
sentinel_latlong=merge(sentinel_latlong,percentile_algorithm()$percentile_alerte_currentweek
,by.x="sites",by.y = "sites",all.x=T)
}
if (input$Algorithmes_eval1=="MinSan" )
{
cat("display alert status into the map using MinSan algorithm...\n")
setkey(sentinel_latlong,sites);
setkey(minsan_algorithm()$minsan_alerte_currentweek,sites)
sentinel_latlong=merge(sentinel_latlong,minsan_algorithm()$minsan_alerte_currentweek
,by.x="sites",by.y = "sites",all.x=T)
}
if (input$Algorithmes_eval1=="Csum" )
{
cat("display alert status into the map using Csum algorithm...\n")
setkey(sentinel_latlong,sites);
setkey(csum_algorithm()$csum_alerte,sites)
sentinel_latlong=merge(sentinel_latlong,
csum_algorithm()$csum_alerte_currentweek
,by.x="sites",by.y = "sites",all.x=T)
}
if (input$Algorithmes_eval1=="Ind" )
{
setkey(sentinel_latlong,sites);
setkey(tdrplus_fever_ind()$tdrplus_ind_currentweek,sites)
sentinel_latlong=merge(sentinel_latlong,
tdrplus_fever_ind()$tdrplus_ind_currentweek
,by.x="sites",by.y = "sites",all.x=T)
cat("display alert status into the map using a simple indicator...\n")
}
} else {
if (input$Algorithmes_eval2=="Percentile" )
{
cat("display alert status into the map using percentile algorithm...\n")
#setkey for fast merging
setkey(sentinel_latlong,sites)
setkey(percentile_algorithm()$percentile_alerte_currentweek,sites)
sentinel_latlong=merge(sentinel_latlong,percentile_algorithm()$percentile_alerte_currentweek
,by.x="sites",by.y = "sites",all.x=T)
}
if (input$Algorithmes_eval2=="MinSan" )
{
cat("display alert status into the map using MinSan algorithm...\n")
setkey(sentinel_latlong,sites);
setkey(minsan_algorithm()$minsan_alerte_currentweek,sites)
sentinel_latlong=merge(sentinel_latlong,minsan_algorithm()$minsan_alerte_currentweek
,by.x="sites",by.y = "sites",all.x=T)
}
if (input$Algorithmes_eval2=="Csum" )
{
cat("display alert status into the map using Csum algorithm...\n")
setkey(sentinel_latlong,sites);
setkey(csum_algorithm()$csum_alerte,sites)
sentinel_latlong=merge(sentinel_latlong,csum_algorithm()$csum_alerte_currentweek
,by.x="sites",by.y = "sites",all.x=T)
}
}
#feeding leaflet with our data:
mada_map=leaflet()
#exclude Haute Terre Centrale (High_land) from the map
mada_map=leaflet(sentinel_latlong[!(sites%in% High_land)])
mada_map=mada_map %>% setView(lng = 47.051532 ,
lat =-19.503781 , zoom = 5)
mada_map=mada_map %>% addTiles(urlTemplate="http://server.arcgisonline.com/ArcGIS/rest/services/World_Street_Map/MapServer/tile/{z}/{y}/{x}")
#change color to red when alert is triggered:
#navy
pal <- colorFactor(c("red", "darkgreen"), domain = c("normal", "alert"))
mada_map=mada_map %>% addCircleMarkers(lng = ~Long,
lat = ~Lat,
weight = 1,
radius = ~myradius,
color = ~pal(alert_status),
fillOpacity = 0.7,
popup = ~name)
mada_map
})
# output$madagascar_map2 <- renderPlot({
# if (input$diseases=="Malaria" )
# {
# if (input$Algorithmes_eval1=="Percentile" )
# {
# cat("display alert status into the map using percentile algorithm...\n")
# #setkey for fast merging
# setkey(sentinel_latlong,sites)
# setkey(percentile_algorithm()$percentile_alerte_currentweek,sites)
# sentinel_latlong=merge(sentinel_latlong,percentile_algorithm()$percentile_alerte_currentweek
# ,by.x="sites",by.y = "sites",all.x=T)
#
# }
# if (input$Algorithmes_eval1=="MinSan" )
# {
# cat("display alert status into the map using MinSan algorithm...\n")
# setkey(sentinel_latlong,sites);
# setkey(minsan_algorithm()$minsan_alerte_currentweek,sites)
# sentinel_latlong=merge(sentinel_latlong,minsan_algorithm()$minsan_alerte_currentweek
# ,by.x="sites",by.y = "sites",all.x=T)
# }
# if (input$Algorithmes_eval1=="Csum" )
# {
# cat("display alert status into the map using Csum algorithm...\n")
# setkey(sentinel_latlong,sites);
# setkey(csum_algorithm()$csum_alerte,sites)
# sentinel_latlong=merge(sentinel_latlong,csum_algorithm()$csum_alerte_currentweek
# ,by.x="sites",by.y = "sites",all.x=T)
# }
# if (input$Algorithmes_eval1=="Ind" )
# {
# setkey(sentinel_latlong,sites);
# setkey(tdrplus_fever_ind()$tdrplus_ind_currentweek,sites)
# sentinel_latlong=merge(sentinel_latlong,tdrplus_fever_ind()$tdrplus_ind_currentweek
# ,by.x="sites",by.y = "sites",all.x=T)
# cat("display alert status into the map using a simple indicator...\n")
# }
# } else {
# if (input$Algorithmes_eval2=="Percentile" )
# {
# cat("display alert status into the map using percentile algorithm...\n")
# #setkey for fast merging
# setkey(sentinel_latlong,sites)
# setkey(percentile_algorithm()$percentile_alerte_currentweek,sites)
# sentinel_latlong=merge(sentinel_latlong,percentile_algorithm()$percentile_alerte_currentweek
# ,by.x="sites",by.y = "sites",all.x=T)
#
# }
# if (input$Algorithmes_eval2=="MinSan" )
# {
# cat("display alert status into the map using MinSan algorithm...\n")
# setkey(sentinel_latlong,sites);
# setkey(minsan_algorithm()$minsan_alerte_currentweek,sites)
# sentinel_latlong=merge(sentinel_latlong,
# minsan_algorithm()$minsan_alerte_currentweek
# ,by.x="sites",by.y = "sites",all.x=T)
# }
# if (input$Algorithmes_eval2=="Csum" )
# {
# cat("display alert status into the map using Csum algorithm...\n")
# setkey(sentinel_latlong,sites);
# setkey(csum_algorithm()$csum_alerte,sites)
# sentinel_latlong=merge(sentinel_latlong,
# csum_algorithm()$csum_alerte_currentweek
# ,by.x="sites",by.y = "sites",all.x=T)
# }
#
# }
# #exclude Haute Terre Centrale (High_land) from the map
# sentinel_latlong=sentinel_latlong[!(sites %in% High_land)]
# #latest verification fo radius and alert status (handle NA's)
# sentinel_latlong[is.na(alert_status)==T,alert_status:="No data"]
# sentinel_latlong[is.na(myradius)==T,myradius:=5.0]
#
# #load the map
# madagascar_layer=readRDS("madagascar.rds")
# madagascar_map2=ggmap(madagascar_layer,base_layer = ggplot(data = sentinel_latlong,aes(x=Long,y=Lat)))
# if ( length(unique(sentinel_latlong$alert_status))!=1) {
#
# madagascar_map2 = madagascar_map2 + geom_point(data = sentinel_latlong,
# alpha=0.8,
# aes(color=alert_status,
# size=myradius
# ),show.legend = T)
# madagascar_map2 = madagascar_map2 + scale_colour_manual(values=c("#f05249", "#808284", "#69c39a"))
# } else {
# madagascar_map2 = madagascar_map2 + geom_point(data = sentinel_latlong,
# alpha=0.8,
# aes(color=alert_status,
# size=myradius),
# color="blue")
# }
#
# madagascar_map2 = madagascar_map2 + scale_size_continuous(range=range(sentinel_latlong$myradius))
# return(madagascar_map2)
#
# },height = 640)
#Leaflet or ggmap:
mymapchoice = reactive({
return(input$mapchoice)
})
#click event handler for leaflet:
selected_site_leaflet=eventReactive(input$madagascar_map_marker_click,{
event= input$madagascar_map_marker_click
return(sentinel_latlong[Long==event$lng & Lat==event$lat,get("sites")])
},ignoreNULL=F)
#click event handler (news since shiny 0.13)
selected_site=eventReactive(input$madagascar_map2_brush, {
Z <- brushedPoints(sentinel_latlong,
input$madagascar_map2_brush,
xvar="Long",yvar="Lat",allRows = TRUE)
return(sentinel_latlong[Z$selected_,get("sites")])
})
clicked_site=eventReactive(input$madagascar_map2_click, {
Z <- nearPoints(sentinel_latlong,
input$madagascar_map2_click,
xvar="Long",yvar="Lat",allRows = TRUE)
return(sentinel_latlong[Z$selected_,get("sites")])
})
#render mean ILI
output$propili = renderPlotly({
############################### new way to handle ILI ##################
sites34= include[-c(1:2)]
tdr_eff=as.data.table(gather(ili,key=sites,value=Synd_g,-c(code,deb_sem)))
################################
propili_2015 = tdr_eff[sites %in% sites34 & year(as.Date(deb_sem,origin="1970-01-01"))>2014]
stat_ili= tdr_eff[sites %in% sites34 & year(as.Date(deb_sem,origin="1970-01-01")) <2015]
Consultations=as.data.table(gather(Consultations,
key=sites,value=NxConsltTotal,
-c(code,deb_sem)))
propili_2015=merge(propili_2015,Consultations,
by.x=c("code","sites","deb_sem"),
by.y=c("code","sites","deb_sem"),all.x=T)
propili_2015[,prop := 100*sum(Synd_g,na.rm = T)/sum(NxConsltTotal,na.rm=T) ,by="deb_sem"]
propili_2015= unique(propili_2015[,list(deb_sem,prop)],by=NULL)
#create a key to merge later:
propili_2015[,weekOfday:=week(as.Date(deb_sem,origin="1970-01-01"))]
gc()
cat("calculating mean and max for historical ILI data...")
stat_ili[,weekOfday:=week(as.Date(deb_sem,origin="1970-01-01"))]
stat_ili[,mymax:=max(Synd_g,na.rm = T),by="weekOfday"]
stat_ili[,mymean:=mean(Synd_g,na.rm = T),by="weekOfday"]
stat_ili= unique(stat_ili[,list(weekOfday,mymax,mymean)],by=NULL)
#create a key to merge later
cat("DONE\n")
gc()
#merge:
line_width=1.0
propili_2015= merge(propili_2015,stat_ili,
by.x="weekOfday",by.y="weekOfday", all.x=T)
setorder(propili_2015,-deb_sem)
#handle date format:
propili_2015[,deb_sem:=as.character((as.Date(deb_sem,origin="1970-01-01")))]
#
p <- plot_ly(propili_2015,
x =deb_sem,
y = prop,name="prop.",
line = list(width=line_width))
p = p %>% layout(title="prop of ILI among consultants",
paper_bgcolor="rgb(213, 226, 233)",plot_bgcolor = "rgb(213, 226, 233)",
font=list(size=11),
xaxis = list(title = "Date(Weeks)"),
legend = list(x = 0,
y = 10)
)
p = p %>% add_trace(x=deb_sem,line = list(width=line_width),
y = mymean, name = "Mean<2015",visible='legendonly')
p = p %>% add_trace(x=deb_sem,line = list(width=line_width),
y = mymax, name = "Max<2015",visible='legendonly')
p
})
#render Syndrome Grippal, Syndrom Dengue-Like
output$ili_graph = renderPlotly({
#select dataset based on user's selection:
if (input$sites!="All") {
ili=ili[,c("code","deb_sem",input$sites),with=F]
arbosusp=arbosusp[,c("code","deb_sem",input$sites),with=F]
}
#
ili=as.data.table(gather(ili,key=sites,value=Synd_g,-c(code,deb_sem)))
arbosusp=as.data.table(gather(arbosusp,key=sites,value=ArboSusp,-c(code,deb_sem)))
ili=merge(ili,arbosusp,
by.x=c("code","sites","deb_sem"),
by.y=c("code","sites","deb_sem"),all.x=T)
#aggregate cases per week:
ili[,Synd_g:=sum(Synd_g,na.rm = T),by="code"]
ili[,ArboSusp:=sum(ArboSusp,na.rm = T),by="code"]
#data processing:
ili=unique(ili,by=NULL)
#34 sites we need:
sites34= include[-c(1:2)]
#filter rows: sites %in% sites34 & 2009
myili=ili[ year(as.Date(deb_sem ,origin="1970-01-01"))>=2016]
#handle date format:
myili[,deb_sem:=as.character((as.Date(deb_sem,origin="1970-01-01")))]
p <- plot_ly(myili, x = deb_sem, type="bar",y = Synd_g,name="ILI",
marker=list(color = "rgb(0,0,102)"))
p = p %>% add_trace(x = deb_sem, type="bar",y = ArboSusp, name = "Dengue-Like",
marker=list(color = "rgb(204,0,0)"))
#position legend at top of the graph
p= p %>% layout( paper_bgcolor="rgb(213, 226, 233)",plot_bgcolor = "rgb(213, 226, 233)",
title="ILI & Dengue-LIKE (34 sites)",
xaxis = list(title = "Date(weeks)"),
yaxis = list(title = "#Cases"),
legend = list(x = 0, y = 40)
)
p
})
#render weekly diseases cases for a clicked site
output$weekly_disease_cases_persite = renderPlotly({
mydata=percentile_algorithm()$mydata
#recupere date d'alerte percentile (seuleument pour cet algo) pour chaque site
#cree une nouvelle variable = valeur de cette alerte
if ( length(input$Algorithmes_eval1)>0 )
{
if (input$Algorithmes_eval1=="Percentile")
{
mydata[,myalerte:=0]
mydata[,myalerte:=ifelse(alert_status=="alert",occurence,NA)]
}
}
if ( length(input$Algorithmes_eval2)>0 )
{
if ( input$Algorithmes_eval2=="Percentile")
{
mydata[,myalerte:=0]
mydata[,myalerte:=ifelse(alert_status=="alert",occurence,NA)]
}
}
setkey(mydata,sites)
cat("reshape HFI to extract rainFall...")
hfi=data.table(gather(hfi,key=sites,
value=myvalue,-c(code,deb_sem,type_val)))
setkeyv(hfi,c("type_val","sites"))
cat("DONE\n")
if ( mymapchoice()=="other" )
{
sites_list=unique(c(selected_site(),clicked_site()))
#if the user has clicked on the map:
mydata=mydata[sites %in% sites_list ,
list(occurence=sum(occurence,na.rm=T)),by="code"]
# RainFall ou Precipitation:
cat("merge rainfall or precipitation with mydata for chosen site(s)...")
pmm = hfi[type_val=="PRECIPITATION" & sites %in% sites_list];
setnames(pmm,old="myvalue",new="rainFall")
# merge with mydata
mydata=merge(mydata,pmm,by.x=c("code"),by.y=c("code"),all.x=T)
#
cat("DONE\n")
} else {
mydata=mydata[sites %in% selected_site_leaflet(),]
# RainFall ou Precipitation:
cat("merge rainfall or precipitation with mydata for chosen site(s)...")
pmm = hfi[type_val=="PRECIPITATION" & sites %in% selected_site_leaflet()];
setnames(pmm,old="myvalue",new="rainFall")
# merge with mydata
pmm[,deb_sem:=NULL]
mydata=merge(mydata,pmm,by.x=c("code"),by.y=c("code"),all.x=T)
#
cat("DONE\n")
}
#handle case where no site hasn't yet been selected !
if ( length(selected_site_leaflet())==0 )
{
source("utils/introducing_mild.R",local = T)
mild_tmp=mild
} else {
cat('selection of LLIN corresponding to: ',selected_site_leaflet() )
mild_tmp=mild[,c("deb_sem",selected_site_leaflet()),with=F]
mild_tmp[,code:=paste0(year(as.Date(deb_sem)),"_",isoweek(as.Date(deb_sem)))]
setnames(mild_tmp,old=selected_site_leaflet(),new="mild_value")
#for graphical display purpose only(to get a bar chart that cover all vertical):
max_val= max(mydata$occurence,na.rm = T)
mild_tmp[,mild_value:=ifelse(is.na(mild_value)==T,NA,max_val)]
cat(' DONE\n')
}
#
mydata=merge(mydata,mild_tmp[,list(code,mild_value)],
by.x=c("code"),by.y=c("code"),all.x=T)
#reorder time series
mydata[,deb_sem:=as.Date(deb_sem)]
mydata=mydata[order(deb_sem),]
setnames(mydata,"deb_sem","Semaine")
#handle title programmatically (depends on user choice of disease, site)
mytitle=paste0("Weekly ",input$diseases," cases number in ",sentinel_latlong[sites==selected_site_leaflet(),get("name")])
#
line_width=1
p=plot_ly(data=mydata,
y=occurence,
x=Semaine,name=paste0(input$diseases),
line = list(width=line_width,color = "rgb(255, 0, 0)") #red for disease
)
#position legend at top of the graph
#90th percentile as horizontal line:
p = p %>% layout(title=mytitle,
paper_bgcolor="rgb(213, 226, 233)",plot_bgcolor = "rgb(213, 226, 233)",
font=list(size=9),
legend = list(x = 0, y =10 ),
xaxis =list(title="Weeks"),
yaxis =list(title="#Cases")
)
if ( length(input$Algorithmes_eval1)>0 | length(input$Algorithmes_eval2)>0)
{
if (input$Algorithmes_eval1=="Percentile" )
{
p = p %>% add_trace(x=Semaine,y=myalerte,line=list(color="rgb(165,41,157)"),
name="percentile alerte")
}
}
p = p %>% add_trace(x = Semaine,
y = rainFall/10,
line = list(width=line_width,color = "#84a6df"),
name = "Rainfall/10",visible='legendonly')
p = p %>% add_trace(x = Semaine, y = mild_value, name = "LLIN",
color="LLIN",
opacity=0.5,
colors="#132B43",
type="bar",
visible='legendonly')
p
})
#Health heatmap plot of percentile rank (value) that reacts to chosen disease:
output$heatmap_perc_rank = renderD3heatmap({
cat("load and read data...")
disease_chosen = paste0("percentile_rank/",input$diseases,"_rank.csv")
X= fread(disease_chosen)
cat("\n")
cat("merge with sentinel latlong...")
X=merge(X,sentinel_latlong[,list(sites,name)],
by.x="sites",by.y="sites")
cat("\n")
cat("spreading data...")
X= as.data.frame(spread(unique(X[,list(name,deb_sem,perc_rank)],by=NULL),
deb_sem,perc_rank))
row.names(X)=X$name
cat("\n")
cat("define custom color palette...")
palette <- c("#d3f1ff", "#afd9ff", "#0077dd","#ac0000")
breaks <- c(0, 50, 70,80, 90)
require(scales)
colorFunc <- col_bin(palette, bins = rescale(breaks))
cat("\n")
cat("define custom label for weeks...")
mylabCol = ifelse ( months(as.Date(colnames(X[,-1]),origin="1970-01-01") ) %in% c("janoary","janvier","january"),
colnames(X[,-1]),"")
cat("\n")
d3heatmap(X[,-1],
dendrogram = "none",scale = "none",
colors = colorFunc,
labCol=mylabCol,
xaxis_font_size="9px")
})
#Health heatmap plot of alert status that reacts to algorithms ,
#chosen sites and diseases
output$heatmap_alert_status = renderD3heatmap({
#some sort of cache of preprocessed data to speed up things:
cat("reading ",input$diseases," from a temporary file\n")
mydata=fread(paste0("temp/",input$diseases,Sys.Date(),".csv"))
if ( input$diseases=="Malaria" )
{
#depending on the algorithm chosen, display heatmap:
if ( input$Algorithmes_eval1 == 'Percentile' ) {
source("algorithms/percentile.R")
X=calculate_percentile(data=mydata,
week_length=input$comet_map,
percentile_value=input$Centile_map,
disease=input$diseases)$propsite_alerte_percentile
}
if ( input$Algorithmes_eval1 == 'Csum' ) {
source("algorithms/csum.R")
X=calculate_csum(data=mydata,
Csum_year_map=input$Csum_year_map,
Sd_csum_map=input$Sd_csum_map,
week_choice=ifelse(Sys.Date()-as.Date(paste0(year(Sys.Date()),"-01-01"))<8
,1,week(Sys.Date())),
week_Csum_map=input$week_Csum_map,
year_choice=year(Sys.Date()),byvar="code")$propsite_alerte_csum
}
if ( input$Algorithmes_eval1 == 'MinSan' ) {
source("algorithms/minsan.R")
X=calculate_minsan(data=mydata,slope_minsan=input$slope_minsan,
year_choice=year(Sys.Date()),
week_choice=ifelse(Sys.Date()-as.Date(paste0(year(Sys.Date()),"-01-01"))<8
,1,week(Sys.Date())),
minsan_weekrange=input$minsan_weekrange,
minsan_consecutive_week=input$minsan_consecutive_week,byvar="code")$propsite_alerte_minsan
}
if ( input$Algorithmes_eval1 == 'Ind') {
X= tdrplus_fever_ind()$mydata
}
} else {
if ( input$Algorithmes_eval2 == 'Percentile' ) {
source("algorithms/percentile.R")
X=calculate_percentile(data=mydata,
week_length=input$comet_map,
percentile_value=input$Centile_map,
disease=input$diseases)$propsite_alerte_percentile
}
if ( input$Algorithmes_eval2 == 'Csum' ) {
source("algorithms/csum.R")
X=calculate_csum(data=mydata,
Csum_year_map=input$Csum_year_map,
Sd_csum_map=input$Sd_csum_map,
week_choice=ifelse(Sys.Date()-as.Date(paste0(year(Sys.Date()),"-01-01"))<8
,1,week(Sys.Date())),
week_Csum_map=input$week_Csum_map,
year_choice=year(Sys.Date()),byvar="code")$propsite_alerte_csum
}
if ( input$Algorithmes_eval2 == 'MinSan' ) {
source("algorithms/minsan.R")
X=calculate_minsan(data=mydata,slope_minsan=input$slope_minsan,
year_choice=year(Sys.Date()),
week_choice=ifelse(Sys.Date()-as.Date(paste0(year(Sys.Date()),"-01-01"))<8
,1,week(Sys.Date())),
minsan_weekrange=input$minsan_weekrange,
minsan_consecutive_week=input$minsan_consecutive_week,byvar="code")$propsite_alerte_minsan
}
}
#selection by facies:
if (input$Cluster_algo !="Total") {
cat("you choose:",input$Cluster_algo,"\n")
setkeyv(X,input$Cluster_algo)
X=X[get(input$Cluster_algo)==1,]
}
cat('recode alert status...')
setkey(X,alert_status)
X[is.na(alert_status)==T,alert_status2:=0]
X[alert_status=="normal",alert_status2:=1]
X[alert_status=="alert",alert_status2:=2]
cat('DONE\n')
cat('spreading data...')
X[,years:=year(as.Date(deb_sem,origin="1970-01-01"))]
X=merge(X,sentinel_latlong[,list(sites,name)],by.x="sites",by.y="sites",all.x=T)
X=X[years %in% (2016-as.numeric(input$nbyear)):2016,]
#as.data.frame otherwise It won't work
myz= as.data.frame(spread(unique(X[,list(name,deb_sem,alert_status2)],by=NULL),
deb_sem,alert_status2))
cat("DONE\n")
cat("define custom label for weeks...")
colval= colnames(myz[,-1])
mylabCol_tobedisplayed = colval[seq(1,length(colval),4)]
mylabCol = ifelse (colval %in% mylabCol_tobedisplayed, mylabCol_tobedisplayed,"")
cat("\n")
#print(head(myz)); print(tail(myz))
# require(heatmaply)
# heatmaply(myz[,names(myz)[-1]],colors=c("grey","darkgreen","red"),
# column_text_angle = 90,dendrogram = "none") %>% layout(margin = list(l = 130, b = 40))
row.names(myz) <- myz$name
d3heatmap(myz[,-1],
dendrogram = "none",
scale = "none",
xaxis_font_size="9px",
labCol=mylabCol,
color=c("grey","darkgreen","red"))
})
#Bubble chart to display past alert:
output$mybubble = renderPlotly({
#some sort of cache of preprocessed data to speed up things:
cat("reading ",input$diseases," from a temporary file\n")
mydata=fread(paste0("temp/",input$diseases,Sys.Date(),".csv"))
cat('Calculation of percentile and proportion of sites in alert begin...')
source("algorithms/percentile.R")
X=calculate_percentile(data=mydata,
week_length=input$comet_map,
percentile_value=input$Centile_map,
disease=input$diseases)$mydata
cat("DONE\n")
#order time series ascending (2007==>now)
setorder(X,sites,deb_sem)
if (input$Cluster_algo=="Total")
{
X=X[as.Date(deb_sem,origin = "1970-01-01")>=as.Date("2016-01-01",origin = "1970-01-01") & alert_status=="alert" ,]
} else {
X=X[as.Date(deb_sem,origin = "1970-01-01")>=as.Date("2016-01-01",origin = "1970-01-01") & alert_status=="alert" & get(input$Cluster_algo)==1,]
}
X=merge(X,sentinel_latlong,by.x="sites",by.y="sites",all.x=T)
p=plot_ly(X, y = occurence, x=deb_sem,color=name )
p = p %>% layout( paper_bgcolor="rgb(213, 226, 233)",
plot_bgcolor = "rgb(213, 226, 233)",
#legend=list(orientation="h"),
xaxis =list(title="Weeks"),
yaxis =list(title="#Cases"))
p
})
#download report handler (for Malaria and Diarrhea):
output$downloadReport <- downloadHandler(
filename <- function() {
paste("output", "zip", sep=".")
},
content = function(file) {
file.copy('report/report.zip', file)
},
contentType = "application/zip"
)
#Forecasting of # cases of Malaria:
#in the future should take anykind of diseases:
output$forecast_plot = renderPlotly({
mymodel=input$mymodel
source("forecast/prepare_data_forecast.R",local = T)
X=prepare_load(mymodel=mymodel)
source("forecast/forecasting_functions.R",local = T)
#########################################################################################################
if ( input$mymodel=="HLT" & input$forecast_type=="retrospective")
{
direction="retrospective"
load(file = "forecast/holt_retrospective.rda")
L_preds= length(preds)
L=length(X$occurence)
X[,mymonth:=paste0(mymonth,"/",substr(myyear,3,4))]
################# plotting begins ##################################
line_width=1.5
cat ("MAE of holt retrospective:",mae(X$occurence[1:L_preds],preds),"\n")
p = plot_ly(X, x=mymonth, y = occurence,
name="Monthly cases of Malaria",
mode = 'lines+markers',
line = list(width=line_width,color = "rgb(250,67,69)"))
p = p %>% layout(legend = list(x = 0, y = 350),
paper_bgcolor="rgb(213, 226, 233)",plot_bgcolor = "rgb(213, 226, 233)",
title="Actual serie (Farafanga & Mananjary) vs forecasts",
xaxis =list(title="",dtick=3, tickangle=90),
yaxis =list(title="#Cases"))