R/LoadTaiwan.R

In sjbeckett/subregionalcovid19: Processing and mapping COVID-19 case data at subnational scales

#' LoadTaiwan
#'
#' @description Reads in subnational data for Taiwan to calculate most recent estimate of per capita active COVID-19 cases. Use with LoadData() is recommended.
#'
#' @note
#' Data sourced from Taiwan Centers for Disease Control \url{https://data.cdc.gov.tw/en/dataset/aagsdctable-day-19cov}.
#'
#' @return A simple feature returning the date of most recent data (DateReport), a unique region code (geoid), the region name (RegionName) and country name (Country), the number of active cases per capita (pInf) and the regions geometry (geometry).
#'
#' @examples
#' \dontrun{
#' Taiwan <- LoadTaiwan()
#' }
#' @seealso [LoadData()]
#' @export
LoadTaiwan <- function() {
  # sourced from Taiwan Centers for Disease Control https://data.cdc.gov.tw/en/dataset/aagsdctable-day-19cov

  pop_taiwan <- geomTaiwan <- NULL
  utils::data("geomTaiwan", envir = environment())
  utils::data("pop_taiwan", envir = environment())
  geomTaiwan <- sf::st_as_sf(geomTaiwan)

  # x<- read.csv("https://od.cdc.gov.tw/eic/Day_Confirmation_Age_County_Gender_19CoV.csv",encoding = "UTF-8")
  x <- vroom::vroom("https://od.cdc.gov.tw/eic/Day_Confirmation_Age_County_Gender_19CoV.csv", show_col_types = FALSE, progress = FALSE)
  names(x) <- c("Disease Name", "Date_Confirmation", "County_living", "Town_living", "Sex", "Imported", "Age_Group", "Number_of_confirmed_cases")

  regions <- unique(x$County_living)
  DateReport <- c()
  CaseDifference <- c()
  for (aa in 1:length(regions)) {
    subsetdata <- x[which(x$County_living == regions[aa]), ]
    Lastdate <- max(as.Date(subsetdata$Date_Confirmation))
    DateReport[aa] <- as.character(Lastdate)
    caseindex <- which(as.Date(subsetdata$Date_Confirmation) > (Lastdate - 14))
    CaseDifference[aa] <- (10 / 14) * sum(subsetdata$Number_of_confirmed_cases[caseindex])
  }
  caseTable <- data.frame(regions, DateReport, CaseDifference)

  # population
  # Pop <- vrvroom("countries/data/TaiwanPop.csv", .name_repair = make.names)
  TWdf <- dplyr::inner_join(caseTable, pop_taiwan, by = c("regions" = "Chinese.name"))


  # geomTaiwan <- st_read("countries/data/geom/geomTaiwan.geojson")

  # integrate datasets
  MapTaiwan <- dplyr::inner_join(geomTaiwan, TWdf, by = c("micro_name" = "regions"))

  MapTaiwan$RegionName <- paste(paste(MapTaiwan$micro_name, MapTaiwan$English.name, sep = "/"), MapTaiwan$country_name, sep = ", ")
  MapTaiwan$Country <- MapTaiwan$country_name
  MapTaiwan$pInf <- MapTaiwan$CaseDifference / MapTaiwan$Population.2020

  TAIWAN_DATA <- subset(MapTaiwan, select = c("DateReport", "geoid", "RegionName", "Country", "pInf", "geometry"))
  return(TAIWAN_DATA)
}