## code to prepare `HK_delay_data` dataset goes here
max_delay_confirm <- 30
max_date <- as.Date("2020-08-01")
HK_linelist_data_url <- "https://api.data.gov.hk/v2/filter?q=%7B%22resource%22%3A%22http%3A%2F%2Fwww.chp.gov.hk%2Ffiles%2Fmisc%2Fenhanced_sur_covid_19_eng.csv%22%2C%22section%22%3A1%2C%22format%22%3A%22csv%22%7D"
HK_linelist_data <- try(readr::read_csv(HK_linelist_data_url,
col_types = list(
`Case no.` = readr::col_double(),
`Report date` = readr::col_character(),
`Date of onset` = readr::col_character(),
Gender = readr::col_character(),
Age = readr::col_double(),
`Name of hospital admitted` = readr::col_character(),
`Hospitalised/Discharged/Deceased` = readr::col_character(),
`HK/Non-HK resident` = readr::col_character(),
`Case classification*` = readr::col_character(),
`Confirmed/probable` = readr::col_character()
)
))
if ("try-error" %in% class(HK_linelist_data)) {
stop(stringr::str_c("Couldn't read Hong Kong linelist data at ", HK_linelist_data_url))
}
HK_delay_data <- HK_linelist_data %>%
dplyr::filter(.data$`Confirmed/probable` == "Confirmed") %>%
# only keep confirmed cases
dplyr::transmute(
event_date = as.Date(.data$`Date of onset`, format = "%d/%m/%Y"), # rename/reformat columns
report_date = as.Date(.data$`Report date`, format = "%d/%m/%Y")
) %>%
dplyr::filter(!is.na(.data$event_date), !is.na(.data$report_date)) %>%
dplyr::filter(.data$event_date < max_date) %>%
dplyr::mutate(report_delay = as.integer(.data$report_date - .data$event_date)) %>%
# extract reporting delays
dplyr::mutate(report_delay = if_else(!between(.data$report_delay, 0, max_delay_confirm), # curate negative or too large reporting delays
as.integer(NA),
.data$report_delay
)) %>%
dplyr::filter(!is.na(.data$report_delay)) %>%
# remove NA values
dplyr::select(-.data$report_date) %>%
# rearrange dataset
dplyr::arrange(.data$event_date)
usethis::use_data(HK_delay_data, overwrite = TRUE, compress = "xz", version = 2)
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