Get International Linelist Data

This function downloads the latest non-Hubei linelist. It uses memoise to cache the results locally. To clear the cache and redownload the data use reset_cache. The cache will be located in the directory at which the function is run. As this linelist is experiencing a high user demand it may not always be available. To account for this we keep a cache in the NCoVUtils GitHub repo which this function will fall back to with a warning if the source cannot be downloaded.

get_international_linelist(
  countries = NULL,
  cities = NULL,
  provinces = NULL,
  clean = TRUE
)

Arguments

countries	Character vector identifying the countries to extract data for.
cities	Character vector identifying the cities to extract data for.
provinces	Character vector identifying the provinces to extract data for.
clean	Logical, defaults to TRUE. Should the data returned be cleaned for use.

Value

A linelist of case data

Author

Sam Abbott sam.abbott@lshtm.ac.uk

Examples

get_international_linelist
#> function (countries = NULL, cities = NULL, provinces = NULL, 
#>     clean = TRUE) 
#> {
#>     country <- NULL
#>     city <- NULL
#>     travel_history_location <- NULL
#>     travel_history_dates <- NULL
#>     date_confirmation <- NULL
#>     date_onset_symptoms <- NULL
#>     date_confirm <- NULL
#>     date_onset <- NULL
#>     report_delay <- NULL
#>     import_status <- NULL
#>     message("Downloading linelist data")
#>     ch <- memoise::cache_filesystem(".cache")
#>     url <- "https://raw.github.com/beoutbreakprepared/nCoV2019/master/latest_data/latestdata.tar.gz"
#>     mem_read <- memoise::memoise(readr::read_csv, cache = ch)
#>     linelist <- suppressWarnings(suppressMessages(try(R.utils::withTimeout(mem_read(url) %>% 
#>         tibble::as_tibble(), timeout = 15, onTimeout = "error"), 
#>         silent = TRUE)))
#>     if (any(class(linelist) %in% "try-error")) {
#>         warning("Could not access linelist source. Using the NCoVUtils cache, this may not be up to date.\n    See the git history to confirm last cache date.")
#>         url <- "https://raw.githubusercontent.com/epiforecasts/NCoVUtils/master/data-raw/linelist.csv"
#>         linelist <- suppressWarnings(suppressMessages(mem_read(url) %>% 
#>             tibble::as_tibble()))
#>     }
#>     if (!is.null(countries)) {
#>         linelist <- linelist %>% dplyr::filter(country %in% countries)
#>     }
#>     if (!is.null(cities)) {
#>         linelist <- linelist %>% dplyr::filter(city %in% cities)
#>     }
#>     if (!is.null(provinces)) {
#>         linelist <- linelist %>% dplyr::filter(province %in% 
#>             provinces)
#>     }
#>     if (clean) {
#>         linelist <- linelist %>% dplyr::mutate(travel_history_location = ifelse(travel_history_location %in% 
#>             "", NA, travel_history_location), travel_history_dates = ifelse(travel_history_dates %in% 
#>             "", NA, travel_history_dates)) %>% dplyr::mutate(import_status = dplyr::if_else(!is.na(travel_history_location) | 
#>             !is.na(travel_history_dates), "imported", "local"), 
#>             date_confirm = lubridate::dmy(date_confirmation), 
#>             date_onset = lubridate::dmy(date_onset_symptoms), 
#>             report_delay = as.integer(as.Date(date_confirm) - 
#>                 as.Date(date_onset))) %>% dplyr::select(date_onset, 
#>             date_confirm, report_delay, import_status, country) %>% 
#>             tidyr::drop_na(date_confirm)
#>     }
#>     return(linelist)
#> }
#> <bytecode: 0x7ff8ec571fe8>
#> <environment: namespace:NCoVUtils>