R/LoadJapan.R

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

#' getDataJapan
#'
#' @param i prefecture index.
#'
#' @return COVID-19 data for Japan for this prefecture index. Used in LoadJapan().
#' @keywords internal
getDataJapan <- function(dataSet, i) {
  info <- unlist(dataSet[i])
  fields <- names(unlist(dataSet[i])) # get the column names
  daily <- info[startsWith(fields, "dailyConfirmedCount")] # find any column start with dailyConfirmedCount
  lenOfdaily <- length(daily) # get the index for the latest date
  past_id <- lenOfdaily - 14 # get the index for 14 days ago
  summation <- sum(as.numeric(daily[past_id:lenOfdaily])) ## get the total cases for 14 days
  difference <- round(summation * 10 / 14) # remodified
  # get the name for this prefecture
  len <- length(unlist(unlist(dataSet[i])))
  name <- as.character(unlist(as.data.frame(unlist(dataSet[i])[len])))
  temp <- c(name, difference)
  return(temp)
}

#' LoadJapan
#'
#' @description Reads in subnational data for Japan to calculate most recent estimate of per capita active COVID-19 cases. Use with LoadData() is recommended.
#'
#' @note
#' Data from covid19japan.com, based on national and prefectural government reports: \url{https://github.com/reustle/covid19japan-data/}.
#'
#' @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
#' Japan <- LoadJapan()
#' @seealso [LoadData()]
#' @export
LoadJapan <- function() {
  # Data from covid19japan.com, based on national and prefectural government reports: https://github.com/reustle/covid19japan-data/
  pop_japan <- geomJapan <- NULL
  utils::data("geomJapan", envir = environment())
  utils::data("pop_japan", envir = environment())
  geomJapan <- sf::st_as_sf(geomJapan)

  dataJapan <- jsonlite::read_json("https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/summary/latest.json", encoding = "UTF-8")
  # get updated date:
  dateJapan <- as.Date(dataJapan$updated)
  ## PREFECTURE = COUNTY
  # in data, there are 4 layers
  # take the prefecture layer to get necessary data
  dataSet <- dataJapan$prefectures
  # List the name of prefectures:
  vec <- data.frame(Prefecture = as.character(), Difference = as.numeric())

  # get data table
  for (i in 1:49) {
    dataVec <- getDataJapan(dataSet = dataSet, i)
    vec <- rbind(vec, dataVec)
  }
  names(vec) <- c("Prefecture", "Difference")

  ## Geojson file
  # geomJapan <- st_read('https://raw.githubusercontent.com/deldersveld/topojson/master/countries/japan/jp-prefectures.json')
  # geomJapan <- geomJapan[c('NAME_1','geometry')]
  # geomJapan[geomJapan$NAME_1 == 'Naoasaki',1] <- "Nagasaki"
  # geomJapan = st_set_crs(geomJapan,4326)
  # data("geomJapan")

  ## Population
  japandf <- dplyr::inner_join(vec, pop_japan, by = "Prefecture")
  japandf$Difference <- as.numeric(japandf$Difference)

  JapanMap <- dplyr::inner_join(geomJapan, japandf, by = c("micro_name" = "Prefecture"))
  JapanMap$RegionName <- paste(JapanMap$micro_name, JapanMap$country_name, sep = ", ")
  JapanMap$Country <- JapanMap$country_name
  JapanMap$DateReport <- as.character(dateJapan)
  JapanMap$pInf <- JapanMap$Difference / JapanMap$Population

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