R/LoadCanada.R

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

#' LoadCanada
#'
#' @description Reads in subnational data for Canada to calculate most recent estimate of per capita active COVID-19 cases. Use with LoadData() is recommended.
#'
#' @note
#' Data aggregated by the COVID-19 Canada Open Data Working Group from local health resources at \url{https://github.com/ccodwg/Covid19Canada}.
#'
#' @references
#' Berry I, Soucy J-PR, Tuite A, Fisman D. Open access epidemiologic data and an interactive dashboard to monitor the COVID-19 outbreak in Canada. CMAJ. 2020 Apr 14;192(15):E420. doi: \url{https://doi.org/10.1503/cmaj.75262}.
#'
#' @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
#' Canada <- LoadCanada()
#' @seealso [LoadData()]
#' @export
LoadCanada <- function() {
  # Load geometry and population data
  pop_canada <- NULL
  utils::data("geomCanada", "pop_canada", envir = environment())
  geomCanada <- sf::st_as_sf(geomCanada)

  # Data aggregated by the COVID-19 Canada Open Data Working Group https://github.com/ccodwg/Covid19Canada
  # Berry I, Soucy J-PR, Tuite A, Fisman D. Open access epidemiologic data and an interactive dashboard to monitor the COVID-19 outbreak in Canada. CMAJ. 2020 Apr 14;192(15):E420. doi:  https://doi.org/10.1503/cmaj.75262.

  # Berry, I., O’Neill, M., Sturrock, S. L., Wright, J. E., Acharya, K., Brankston, G., Harish, V., Kornas, K., Maani, N., Naganathan, T., Obress, L., Rossi, T., Simmons, A. E., Van Camp, M., Xie, X., Tuite, A. R., Greer, A. L., Fisman, D. N., & Soucy, J.-P. R. (2021). A sub-national real-time epidemiological and vaccination database for the COVID-19 pandemic in Canada. Scientific Data, 8(1). doi: https://doi.org/10.1038/s41597-021-00955-2

  CANADADATA <- vroom::vroom("https://raw.githubusercontent.com/ccodwg/CovidTimelineCanada/main/data/hr/cases_hr.csv", show_col_types = FALSE, progress = FALSE)
  pop_canada <- vroom::vroom("https://raw.githubusercontent.com/ccodwg/CovidTimelineCanada/main/geo/hr.csv",show_col_types = FALSE, progress = FALSE) # contains pop and health region ID codes

  # get dates
  DATES <- as.Date(CANADADATA$date)


  # Assemble the data required to calculate risk scores and match health region ID's
  CANADARISK <- c()
  C_hr <- unique(CANADADATA$sub_region_1) # list of unique health regions
  C_hr <- C_hr[-which(C_hr == 9999)] # remove entries not assigned a health region


  for (aa in 1:length(C_hr)) { # loop over health regions
    subsetdata <- CANADADATA[which(CANADADATA$sub_region_1 == C_hr[aa]), ]
    MD <- max(subsetdata$date)
    LD <- MD - 14
    MC <- subsetdata$value[which(subsetdata$date == MD)]
    LC <- subsetdata$value[which(subsetdata$date == LD)]

    CANADARISK$DateReport[aa] <- as.character(MD)
    CANADARISK$HR_UID[aa] <- C_hr[aa]
    CANADARISK$CaseDiff[aa] <- (MC - LC) / 14 * 10
  }

  CANDT <- data.frame(CANADARISK)

  # integrate datasets

  CanJoin <- dplyr::inner_join(CANDT, pop_canada, by = c("HR_UID" = "hruid"))

  CanJoin$HR_UID <- as.character(CanJoin$HR_UID)

  CanMap <- dplyr::inner_join(geomCanada, CanJoin, by = c("micro_code" = "HR_UID"))


  CanMap$pInf <- CanMap$CaseDiff / CanMap$pop
  CanMap$Country <- "Canada"
  CanMap$RegionName <- paste(CanMap$name_ccodwg, CanMap$macro_name, "Canada", sep = ", ")


  CANADA_DATA <- subset(CanMap, select = c("DateReport", "geoid", "RegionName", "Country", "pInf", "geometry"))

  return(CANADA_DATA)
}