R/get_uk_nhs_region_cases.R

In epiforecasts/NCoVUtils: Data Extraction Tools for the Covid-19 Outbreak

#' Get UK daily cases by NHS region
#'
#'
#' @description Get UK regional cases.
#' @return A dataframe of case counts in English NHS regions
#' @export
#' @importFrom dplyr mutate select filter full_join arrange group_by ungroup n lag
#' @importFrom readr read_csv
#' @importFrom tidyr gather drop_na
#' @importFrom stringr str_squish
#' @examples
#' get_uk_nhs_region_cases
#'
#' \dontrun{
#' uk_shp <- readRDS("uk_shp.rds")
#' cases <- NCoVUtils::get_uk_regional_cases()
#' cases <- cases[1:23,]
#' uk_shp %>%
#' dplyr::full_join(cases, by = "region") %>%
#'  ggplot2::ggplot(ggplot2::aes(fill = cases)) + ggplot2::geom_sf()
#' }
get_uk_nhs_region_cases <- function() {

  warning("The soource data for this function is no longer updated. Try get_uk_regional_cases for similar data")
  # Path to csv
  path <- "https://raw.githubusercontent.com/emmadoughty/Daily_COVID-19/8a0ea87f40edf746519afdb4d1e7ed95c816df1e/Data/COVID19_cum.csv"

  cases <- readr::read_csv(file = path)


  locations <- c("London","South East","South West","East of England","Midlands",
                 "North East and Yorkshire", "North West", "Ayrshire and Arran",
                 "Borders", "Dumfries and Galloway",
                 "Fife", "Forth Valley", "Grampian", "Greater Glasgow and Clyde",
                 "Highland", "Lanarkshire", "Lothian", "Shetland", "Tayside",
                 "Wales", "Northern Ireland", "Orkney", "Western Isles")

  # Filter to only NHS regions
  cases_nhs <- cases %>%
    dplyr::mutate(date = stringr::str_replace_all(Date,pattern = "\\.",replacement = "/")) %>%
    dplyr::select(-Date) %>%
    tidyr::gather("UTLA","confirm",-date) %>%
    tidyr::drop_na(date, confirm) %>%
    dplyr::mutate(UTLA = stringr::str_replace_all(UTLA, "_", " "),
                  confirm = confirm %>%
                    stringr::str_squish() %>%
                    as.numeric) %>%
    dplyr::filter(UTLA %in% c("London","South East","South West","East of England","Midlands",
                              "North East and Yorkshire", "North West", "Ayrshire and Arran",
                              "Borders", "Dumfries and Gallow",
                              "Fife", "Forth Valley", "Grampian", "Greater Glasgow and Clyde",
                              "Highlands", "Lanarkshire", "Lothian", "Shetland", "Tayside",
                              "Wales", "Northern Ireland")) %>%
    dplyr::mutate(UTLA = dplyr::recode(UTLA,"Dumfries and Gallow" = "Dumfries and Galloway",
                                       "Highlands" = "Highland"),
                  date = lubridate::dmy(date)) %>%
    tidyr::complete(UTLA = locations,
                    date = seq(min(date), max(date), by = "day")) %>%
    dplyr::mutate(confirm = tidyr::replace_na(confirm, 0))



  # Reformat to daily cases
  cases_nhs <- cases_nhs %>%
    dplyr::select(region = UTLA,
                  total_cases = confirm,
                  date) %>%
    # dplyr::mutate(date = as.Date(date, format = "%d/%m/%Y")) %>%
    dplyr::arrange(date) %>%
    dplyr::group_by(region) %>%
    dplyr::mutate(
      index = 1:dplyr::n(),
      cases = total_cases - ifelse(index == 1, 0, dplyr::lag(total_cases))) %>%
    dplyr::ungroup() %>%
    dplyr::select(-index, -total_cases) %>%
    ## Adjust negative cases by setting to 0
    dplyr::mutate(cases = ifelse(cases < 0 , 0, cases))


  return(cases_nhs)
}