library(tidyverse)
library(reshape2)
library(tidyr)
library(MMWRweek)
#2018
country_iso='BEN'
file_name=paste(country_iso,'UNICEF_2018.csv',sep='_')
data=read.csv(file_name,fileEncoding = 'UTF-8-BOM',na.strings=c("","NA"))

data=data[,!colnames(data)%in%c('Row.Labels')]
new_data=
data%>%
  unite(Location,
       who_region,
       Country,
       ISO_A1,
       ISO_A2,
       ISO_A3,
       na.rm=T,
       sep='::')%>%
  melt(id.vars="Location")%>%
  arrange(Location)%>%
  mutate(year=2018,
         epiweek=parse_number(as.character(variable),
                              locale=locale(grouping_mark=". ", decimal_mark=",")),
         TL=MMWRweek2Date(MMWRyear = year,
                          MMWRweek = epiweek,
                          MMWRday = 2),
         TR=TL+6,
         Primary=T,
         Phantom=F,
         Report="2018_weekly_csv_report")%>%
  rename(sCh=value)%>%
  select(Location,TL,TR,sCh,Primary,Phantom,Report)%>%
  subset(is.na(sCh)==F)

  write.csv(new_data,paste0('OC_',file_name),na='',row.names = F)

  #2019 sCh
  file_name=paste(country_iso,'UNICEF_2019.csv',sep='_')
  sCh_file_name=paste(country_iso,'UNICEF_2019_sCh.csv',sep='_')
  sCh=read.csv(sCh_file_name,fileEncoding = 'UTF-8-BOM',na.strings=c("","NA"))
  
  sCh=sCh[,!colnames(sCh)%in%c('Row.Labels')]
  new_sCh=
    sCh%>%
    unite(Location,
          who_region,
          Country,
          ISO_A1,
          ISO_A2,
          ISO_A3,
          na.rm=T,
          sep='::')%>%
    melt(id.vars="Location")%>%
    arrange(Location)%>%
    mutate(year=2019,
           epiweek=parse_number(as.character(variable),
                                locale=locale(grouping_mark=". ", decimal_mark=",")),
           TL=MMWRweek2Date(MMWRyear = year,
                            MMWRweek = epiweek,
                            MMWRday = 2),
           TR=TL+6,
           Primary=T,
           Phantom=F,
           Report="2019_weekly_csv_report")%>%
    rename(sCh=value)%>%
    select(Location,TL,TR,sCh,Primary,Phantom,Report)  
  
#2019 deaths
  death_file_name=paste(country_iso,'UNICEF_2019_deaths.csv',sep='_')
  deaths=read.csv(death_file_name,fileEncoding = 'UTF-8-BOM',na.strings=c("","NA"))
  
  deaths=deaths[,!colnames(deaths)%in%c('Row.Labels')]
  new_deaths=
    deaths%>%
    unite(Location,
          who_region,
          Country,
          ISO_A1,
          ISO_A2,
          ISO_A3,
          na.rm=T,
          sep='::')%>%
    melt(id.vars="Location")%>%
    arrange(Location)%>%
    mutate(year=2019,
           epiweek=parse_number(as.character(variable),
                                locale=locale(grouping_mark=". ", decimal_mark=",")),
           TL=MMWRweek2Date(MMWRyear = year,
                            MMWRweek = epiweek,
                            MMWRday = 2),
           TR=TL+6,
           Primary=T,
           Phantom=F,
           Report="2019_weekly_csv_report")%>%
    rename(deaths=value)%>%
    select(Location,TL,TR,deaths,Primary,Phantom,Report)
  
  
  new_data_2019=full_join(new_deaths,new_sCh,by=c('Location','TL','TR',"Primary","Phantom","Report"))%>%
    subset(!(is.na(new_data_2019$deaths)&is.na(new_data_2019$sCh)))

  write.csv(new_data_2019,paste0('OC_',file_name),na='',row.names = F)