R/LoadZimbabwe.R

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

#' LoadZimbabwe
#'
#' @description Reads in subnational data for Zimbabwe to calculate most recent estimate of per capita active COVID-19 cases. Use with LoadData() is recommended.
#'
#' @note
#' Sourced from the COVID-19 Data Repository by African Surveyors Connect \url{https://github.com/African-Surveyors-Connect/Zimbabwe-COVID-19-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
#' Zimbabwe <- LoadZimbabwe()
#' @seealso [LoadData()]
#' @export
LoadZimbabwe <- function() {
  # COVID-19 Data Repository by African Surveyors Connect https://github.com/African-Surveyors-Connect/Zimbabwe-COVID-19-Data/
  # Dashboard: https://surveyor-jr.maps.arcgis.com/apps/dashboards/8ef907d2658c44c6a143819aa7979b20

  geomZimbabwe <- pop_zimbabwe <- NULL
  # note could be improved by calling data from ESRI, provincial timeseries feature Id: 20703dd3a24f45f08ea37034285d3492
  utils::data("geomZimbabwe", envir = environment())
  utils::data("pop_zimbabwe", envir = environment())
  geomZimbabwe <- sf::st_as_sf(geomZimbabwe)

  Data <- utils::read.csv("https://raw.githubusercontent.com/African-Surveyors-Connect/Zimbabwe-COVID-19-Data/master/time_series_data/daily_provincial_records.csv")

  Dates <- as.character(as.Date(Data$reportDate))
  DateReport <- max(Dates)
  DATERANGE <- which(as.Date(Dates) > (as.Date(DateReport) - 14))

  CaseDiff <- c()
  CaseDiff[1] <- (10 / 14) * sum(Data$case_newHarare[DATERANGE])
  CaseDiff[2] <- (10 / 14) * sum(Data$case_newManicaland[DATERANGE])
  CaseDiff[3] <- (10 / 14) * sum(Data$case_newMashCentral[DATERANGE])
  CaseDiff[4] <- (10 / 14) * sum(Data$case_newMashEast[DATERANGE])
  CaseDiff[5] <- (10 / 14) * sum(Data$case_newMashWest[DATERANGE])
  CaseDiff[6] <- (10 / 14) * sum(Data$case_newMidlands[DATERANGE])
  CaseDiff[7] <- (10 / 14) * sum(Data$case_newMasvingo[DATERANGE])
  CaseDiff[8] <- (10 / 14) * sum(Data$case_newMatNorth[DATERANGE])
  CaseDiff[9] <- (10 / 14) * sum(Data$case_newMatSouth[DATERANGE])
  CaseDiff[10] <- (10 / 14) * sum(Data$case_newBulawayo[DATERANGE])

  Province <- c("Harare", "Manicaland", "Mashonaland Central", "Mashonaland East", "Mashonaland West", "Midlands", "Masvingo", "Matabeleland North", "Matabeleland South", "Bulawayo")

  # Pop = read.csv("https://github.com/zimgeospatial/census/raw/master/province_population.csv")
  # write.csv(Pop,"countries/data/Zimbabwe_pop.csv",row.names = FALSE)
  zimbabwedf <- data.frame(Province, DateReport, CaseDiff)


  # geometry
  # geomZimbabwe = st_read("https://github.com/zimgeospatial/admin_boundaries/raw/master/admin_level1_provinces.geojson") %>%
  # st_cast("MULTIPOLYGON") #NEED TO CAST TO MULTIPOLYGON
  # st_write(geomZimbabwe,"countries/data/geom/geomZimbabwe.geojson")

  ZimbabweMap <- dplyr::inner_join(geomZimbabwe, zimbabwedf, by = c("micro_name" = "Province"))
  ZimbabweMap$micro_code <- as.numeric(ZimbabweMap$micro_code)
  ZimbabweMap <- dplyr::inner_join(ZimbabweMap, pop_zimbabwe, by = c("micro_code" = "provincepc"))
  ZimbabweMap$Country <- ZimbabweMap$country_name
  ZimbabweMap$RegionName <- paste(ZimbabweMap$micro_name, ZimbabweMap$Country, sep = ", ")
  ZimbabweMap$pInf <- ZimbabweMap$CaseDiff / ZimbabweMap$pop_2012
  Zimbabwe_DATA <- subset(ZimbabweMap, select = c("DateReport", "geoid", "RegionName", "Country", "pInf", "geometry"))
  return(Zimbabwe_DATA)
}