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R/DiseasystoreGoogleCovid19.R
In diseasystore: Feature Stores for the 'diseasy' Framework
Defines functions
google_covid_19_metric
#' `FeatureHandler` factory for Google COVID-19 epidemic metrics
#'
#' @description
#' This function implements a `?FeatureHandler` factory for Google COVID-19 epidemic metrics.
#' This factory is used when defining the `DiseasystoreGoogleCovid19` feature store.
#' @param google_pattern (`character`)\cr
#' A regexp pattern that matches Google's naming of the metric in "by-age.csv.gz".
#' @param out_name (`character`)\cr
#' A the name to store the metric in our our feature store.
#' @return
#' A new instance of `?FeatureHandler` [R6][R6::R6Class] class corresponding to the epidemic metric.
#' @importFrom rlang .data
#' @noRd
google_covid_19_metric <- function(google_pattern, out_name) { # nocov start
FeatureHandler$new(
compute = function(start_date, end_date, slice_ts, source_conn, ...) {
checkmate::assert_character(source_conn, len = 1)
# Load and parse
data <- source_conn_path(source_conn, "by-age.csv") |>
readr::read_csv(n_max = diseasyoption("n_max", "DiseasystoreGoogleCovid19", .default = Inf),
show_col_types = FALSE) |>
dplyr::mutate("date" = as.Date(.data$date)) |>
dplyr::filter(.data$date >= as.Date("2020-01-01"),
{{ start_date }} <= .data$date, .data$date <= {{ end_date }}) |>
dplyr::select("location_key", "date", tidyselect::starts_with(glue::glue("new_{google_pattern}"))) |>
tidyr::pivot_longer(!c("location_key", "date"),
names_to = c("tmp", "key_age_bin"), names_sep = "_age_",
values_to = out_name, values_transform = as.numeric) |>
dplyr::mutate("valid_from" = .data$date, "valid_until" = as.Date(.data$date + lubridate::days(1))) |>
dplyr::select(tidyselect::all_of(c("location_key", "key_age_bin", out_name, "valid_from", "valid_until"))) |>
dplyr::rename("key_location" = "location_key")
return(data)
},
key_join = key_join_sum
)
} # nocov end
#' @title feature store handler of Google Health COVID-19 Open Data features
#'
#' @description
#' This `DiseasystoreGoogleCovid19` [R6][R6::R6Class] brings support for using the Google
#' Health COVID-19 Open Data repository.
#' See the vignette("diseasystore-google-covid-19") for details on how to configure the feature store.
#' @examplesIf requireNamespace("RSQLite", quietly = TRUE)
#' ds <- DiseasystoreGoogleCovid19$new(
#' source_conn = ".",
#' target_conn = DBI::dbConnect(RSQLite::SQLite())
#' )
#'
#* ds$available_features
#'
#' rm(ds)
#' @return
#' A new instance of the `DiseasystoreGoogleCovid19` [R6][R6::R6Class] class.
#' @export
#' @importFrom R6 R6Class
DiseasystoreGoogleCovid19 <- R6::R6Class( # nolint: object_name_linter.
classname = "DiseasystoreGoogleCovid19",
inherit = DiseasystoreBase,
private = list(
.ds_map = list(
"n_population" = "google_covid_19_population",
"age_group" = "google_covid_19_age_group",
"country_id" = "google_covid_19_index",
"country" = "google_covid_19_index",
"region_id" = "google_covid_19_index",
"region" = "google_covid_19_index",
"subregion_id" = "google_covid_19_index",
"subregion" = "google_covid_19_index",
"n_hospital" = "google_covid_19_hospital",
"n_deaths" = "google_covid_19_deaths",
"n_positive" = "google_covid_19_positive",
"n_icu" = "google_covid_19_icu",
"n_ventilator" = "google_covid_19_ventilator",
"min_temperature" = "google_covid_19_min_temperature",
"max_temperature" = "google_covid_19_max_temperature"
),
.observables_regex = r"{^n_(?=\w)|(?<=\w)_temperature$}",
.label = "Google COVID-19",
.min_start_date = as.Date("2020-01-01"),
.max_end_date = as.Date("2022-09-15"), # Data source is no longer actively updated
google_covid_19_population = FeatureHandler$new(
compute = function(start_date, end_date, slice_ts, source_conn, ...) {
checkmate::assert_character(source_conn, len = 1)
# Load and parse
out <- source_conn_path(source_conn, "demographics.csv") |>
readr::read_csv(n_max = diseasyoption("n_max", "DiseasystoreGoogleCovid19", .default = Inf),
show_col_types = FALSE) |>
dplyr::select("location_key", tidyselect::starts_with("population_age_")) |>
tidyr::pivot_longer(!"location_key",
names_to = c("tmp", "age_group"),
names_sep = "_age_",
values_to = "n_population") |>
dplyr::mutate(age_group_lower = stringr::str_extract(.data$age_group, r"{^\d*}"),
age_group_upper = stringr::str_extract(.data$age_group, r"{\d*$}"),
age_group_sep = dplyr::if_else(.data$age_group_upper == "", "+", "-")) |>
tidyr::unite("age_group", "age_group_lower", "age_group_sep", "age_group_upper", sep = "", na.rm = TRUE) |>
dplyr::select("key_location" = "location_key", "age_group", "n_population") |>
dplyr::mutate(valid_from = as.Date("2020-01-01"), valid_until = as.Date(NA))
return(out)
},
key_join = key_join_sum
),
google_covid_19_index = FeatureHandler$new(
compute = function(start_date, end_date, slice_ts, source_conn, ...) {
checkmate::assert_character(source_conn, len = 1)
# Load and parse
out <- source_conn_path(source_conn, "index.csv") |>
readr::read_csv(n_max = diseasyoption("n_max", "DiseasystoreGoogleCovid19", .default = Inf),
show_col_types = FALSE) |>
dplyr::transmute("key_location" = .data$location_key,
"country_id" = .data$country_code,
"country" = .data$country_name,
.data$country_code,
.data$subregion1_code,
"region" = .data$subregion1_name,
"subregion_id" = .data$location_key,
"subregion" = .data$subregion2_name,
.data$aggregation_level) |>
tidyr::unite("region_id", "country_code", "subregion1_code", na.rm = TRUE) |>
dplyr::mutate(
"region_id" = dplyr::if_else(.data$country_id == .data$region_id, NA, .data$region_id),
"subregion_id" = dplyr::if_else(.data$key_location != .data$subregion_id, NA, .data$subregion_id),
"valid_from" = as.Date("2020-01-01"),
"valid_until" = as.Date(NA)
)
return(out)
},
key_join = key_join_sum
),
google_covid_19_hospital = google_covid_19_metric("hospitalized_patients", "n_hospital"),
google_covid_19_deaths = google_covid_19_metric("deceased", "n_deaths"),
google_covid_19_positive = google_covid_19_metric("confirmed", "n_positive"),
google_covid_19_icu = google_covid_19_metric("intensive_care_patients", "n_icu"),
google_covid_19_ventilator = google_covid_19_metric("ventilator_patients", "n_ventilator"),
google_covid_19_age_group = FeatureHandler$new(
compute = function(start_date, end_date, slice_ts, source_conn, ...) {
checkmate::assert_character(source_conn, len = 1)
# Load and parse
out <- source_conn_path(source_conn, "by-age.csv") |>
readr::read_csv(n_max = diseasyoption("n_max", "DiseasystoreGoogleCovid19", .default = Inf),
show_col_types = FALSE)
# We need a map between age_bin and age_group
age_bin_map <- out |>
dplyr::group_by(.data$location_key) |>
dplyr::select("location_key", tidyselect::starts_with("age_bin")) |>
dplyr::distinct() |>
dplyr::left_join(dplyr::select(out, "location_key", "date", tidyselect::starts_with("age_bin")),
by = colnames(dplyr::select(out, "location_key", tidyselect::starts_with("age_bin"))),
multiple = "first")
# Some regions changes age stratification. Discard for now
age_bin_map <- age_bin_map |>
dplyr::select("location_key") |>
dplyr::filter(dplyr::n() == 1) |>
dplyr::inner_join(age_bin_map, by = "location_key")
# With these filtered, we map age_bins to age_groups
age_bin_map <- age_bin_map |>
dplyr::select("location_key", "date", tidyselect::everything()) |>
tidyr::pivot_longer(!c("location_key", "date"),
names_to = c("tmp", "age_bin"),
names_sep = "_bin_",
values_to = "age_group") |>
dplyr::mutate(age_group_lower = as.numeric(stringr::str_extract(.data$age_group, r"{^\d*}"))) |>
dplyr::group_modify(~ {
na_bins <- is.na(.x$age_group_lower)
data.frame(age_bin = .x$age_bin[!na_bins],
age_group = age_labels(.x$age_group_lower[!na_bins]))
})
# And finally copy to the DB
out <- age_bin_map |>
dplyr::rename("key_age_bin" = "age_bin", "key_location" = "location_key") |>
dplyr::mutate("valid_from" = as.Date("2020-01-01"), "valid_until" = as.Date(NA)) |>
dplyr::ungroup()
return(out)
},
key_join = key_join_sum
),
google_covid_19_min_temperature = FeatureHandler$new(
compute = function(start_date, end_date, slice_ts, source_conn, ...) {
checkmate::assert_character(source_conn, len = 1)
# Load and parse
out <- source_conn_path(source_conn, "weather.csv") |>
readr::read_csv(n_max = diseasyoption("n_max", "DiseasystoreGoogleCovid19", .default = Inf),
show_col_types = FALSE) |>
dplyr::mutate("date" = as.Date(.data$date)) |>
dplyr::filter({{ start_date }} <= .data$date, .data$date <= {{ end_date }}) |>
dplyr::select("key_location" = "location_key",
"date",
"min_temperature" = "minimum_temperature_celsius") |>
dplyr::mutate("valid_from" = .data$date, "valid_until" = as.Date(.data$date + lubridate::days(1)))
return(out)
},
key_join = key_join_min
),
google_covid_19_max_temperature = FeatureHandler$new(
compute = function(start_date, end_date, slice_ts, source_conn, ...) {
checkmate::assert_character(source_conn, len = 1)
# Load and parse
out <- source_conn_path(source_conn, "weather.csv") |>
readr::read_csv(n_max = diseasyoption("n_max", "DiseasystoreGoogleCovid19", .default = Inf),
show_col_types = FALSE) |>
dplyr::mutate("date" = as.Date(.data$date)) |>
dplyr::filter({{ start_date }} <= .data$date, .data$date <= {{ end_date }}) |>
dplyr::select("key_location" = "location_key",
"date",
"max_temperature" = "maximum_temperature_celsius") |>
dplyr::mutate("valid_from" = .data$date, "valid_until" = as.Date(.data$date + lubridate::days(1)))
return(out)
},
key_join = key_join_max
),
key_join_filter = function(.data, stratification_features,
start_date = private %.% start_date,
end_date = private %.% end_date) {
# The Google data contains surplus data depending on the stratification.
# I.e. some individuals are counted more than once.
# Eg. once at the country level, and then again at the region level etc.
# We need to filter at the appropriate stratification level when these doubly counted
# features are requested.
# Manually perform filtering
if (is.null(stratification_features) ||
(!is.null(stratification_features) &&
purrr::none(stratification_features,
~ . %in% c("country_id", "country",
"region_id", "region",
"subregion_id", "subregion")))) {
# If no spatial stratification is requested, use the largest available per country
filter_level <- self$get_feature("country_id", start_date, end_date) |>
dplyr::group_by(.data$country_id) |>
dplyr::slice_min(.data$aggregation_level) |>
dplyr::ungroup() |>
dplyr::select("key_location")
return(dplyr::inner_join(.data, filter_level, by = "key_location", copy = TRUE))
} else if (purrr::some(stratification_features, ~ . %in% c("country_id", "country"))) {
return(.data |> dplyr::filter(.data$key_location == .data$country_id))
} else if (purrr::some(stratification_features, ~ . %in% c("region_id", "region"))) {
return(.data |> dplyr::filter(.data$key_location == .data$region_id))
} else if (purrr::some(stratification_features, ~ . %in% c("subregion_id", "subregion"))) {
return(.data |> dplyr::filter(.data$key_location == .data$subregion_id))
} else {
stop("Edge case detected in $key_join_filter() (DiseasyStoreGoogleCovid19)", call. = FALSE)
}
}
)
)
# Set default options for the package related to the Google COVID-19 store
rlang::on_load({
options("diseasystore.DiseasystoreGoogleCovid19.remote_conn" = "https://storage.googleapis.com/covid19-open-data/v3/")
options("diseasystore.DiseasystoreGoogleCovid19.source_conn" = "https://storage.googleapis.com/covid19-open-data/v3/")
options("diseasystore.DiseasystoreGoogleCovid19.target_conn" = "")
options("diseasystore.DiseasystoreGoogleCovid19.target_schema" = "")
})
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Defines functions google_covid_19_metric
#' `FeatureHandler` factory for Google COVID-19 epidemic metrics
#'
#' @description
#' This function implements a `?FeatureHandler` factory for Google COVID-19 epidemic metrics.
#' This factory is used when defining the `DiseasystoreGoogleCovid19` feature store.
#' @param google_pattern (`character`)\cr
#' A regexp pattern that matches Google's naming of the metric in "by-age.csv.gz".
#' @param out_name (`character`)\cr
#' A the name to store the metric in our our feature store.
#' @return
#' A new instance of `?FeatureHandler` [R6][R6::R6Class] class corresponding to the epidemic metric.
#' @importFrom rlang .data
#' @noRd
google_covid_19_metric <- function(google_pattern, out_name) { # nocov start
FeatureHandler$new(
compute = function(start_date, end_date, slice_ts, source_conn, ...) {
checkmate::assert_character(source_conn, len = 1)
# Load and parse
data <- source_conn_path(source_conn, "by-age.csv") |>
readr::read_csv(n_max = diseasyoption("n_max", "DiseasystoreGoogleCovid19", .default = Inf),
show_col_types = FALSE) |>
dplyr::mutate("date" = as.Date(.data$date)) |>
dplyr::filter(.data$date >= as.Date("2020-01-01"),
{{ start_date }} <= .data$date, .data$date <= {{ end_date }}) |>
dplyr::select("location_key", "date", tidyselect::starts_with(glue::glue("new_{google_pattern}"))) |>
tidyr::pivot_longer(!c("location_key", "date"),
names_to = c("tmp", "key_age_bin"), names_sep = "_age_",
values_to = out_name, values_transform = as.numeric) |>
dplyr::mutate("valid_from" = .data$date, "valid_until" = as.Date(.data$date + lubridate::days(1))) |>
dplyr::select(tidyselect::all_of(c("location_key", "key_age_bin", out_name, "valid_from", "valid_until"))) |>
dplyr::rename("key_location" = "location_key")
return(data)
},
key_join = key_join_sum
)
} # nocov end
#' @title feature store handler of Google Health COVID-19 Open Data features
#'
#' @description
#' This `DiseasystoreGoogleCovid19` [R6][R6::R6Class] brings support for using the Google
#' Health COVID-19 Open Data repository.
#' See the vignette("diseasystore-google-covid-19") for details on how to configure the feature store.
#' @examplesIf requireNamespace("RSQLite", quietly = TRUE)
#' ds <- DiseasystoreGoogleCovid19$new(
#' source_conn = ".",
#' target_conn = DBI::dbConnect(RSQLite::SQLite())
#' )
#'
#* ds$available_features
#'
#' rm(ds)
#' @return
#' A new instance of the `DiseasystoreGoogleCovid19` [R6][R6::R6Class] class.
#' @export
#' @importFrom R6 R6Class
DiseasystoreGoogleCovid19 <- R6::R6Class( # nolint: object_name_linter.
classname = "DiseasystoreGoogleCovid19",
inherit = DiseasystoreBase,
private = list(
.ds_map = list(
"n_population" = "google_covid_19_population",
"age_group" = "google_covid_19_age_group",
"country_id" = "google_covid_19_index",
"country" = "google_covid_19_index",
"region_id" = "google_covid_19_index",
"region" = "google_covid_19_index",
"subregion_id" = "google_covid_19_index",
"subregion" = "google_covid_19_index",
"n_hospital" = "google_covid_19_hospital",
"n_deaths" = "google_covid_19_deaths",
"n_positive" = "google_covid_19_positive",
"n_icu" = "google_covid_19_icu",
"n_ventilator" = "google_covid_19_ventilator",
"min_temperature" = "google_covid_19_min_temperature",
"max_temperature" = "google_covid_19_max_temperature"
),
.observables_regex = r"{^n_(?=\w)|(?<=\w)_temperature$}",
.label = "Google COVID-19",
.min_start_date = as.Date("2020-01-01"),
.max_end_date = as.Date("2022-09-15"), # Data source is no longer actively updated
google_covid_19_population = FeatureHandler$new(
compute = function(start_date, end_date, slice_ts, source_conn, ...) {
checkmate::assert_character(source_conn, len = 1)
# Load and parse
out <- source_conn_path(source_conn, "demographics.csv") |>
readr::read_csv(n_max = diseasyoption("n_max", "DiseasystoreGoogleCovid19", .default = Inf),
show_col_types = FALSE) |>
dplyr::select("location_key", tidyselect::starts_with("population_age_")) |>
tidyr::pivot_longer(!"location_key",
names_to = c("tmp", "age_group"),
names_sep = "_age_",
values_to = "n_population") |>
dplyr::mutate(age_group_lower = stringr::str_extract(.data$age_group, r"{^\d*}"),
age_group_upper = stringr::str_extract(.data$age_group, r"{\d*$}"),
age_group_sep = dplyr::if_else(.data$age_group_upper == "", "+", "-")) |>
tidyr::unite("age_group", "age_group_lower", "age_group_sep", "age_group_upper", sep = "", na.rm = TRUE) |>
dplyr::select("key_location" = "location_key", "age_group", "n_population") |>
dplyr::mutate(valid_from = as.Date("2020-01-01"), valid_until = as.Date(NA))
return(out)
},
key_join = key_join_sum
),
google_covid_19_index = FeatureHandler$new(
compute = function(start_date, end_date, slice_ts, source_conn, ...) {
checkmate::assert_character(source_conn, len = 1)
# Load and parse
out <- source_conn_path(source_conn, "index.csv") |>
readr::read_csv(n_max = diseasyoption("n_max", "DiseasystoreGoogleCovid19", .default = Inf),
show_col_types = FALSE) |>
dplyr::transmute("key_location" = .data$location_key,
"country_id" = .data$country_code,
"country" = .data$country_name,
.data$country_code,
.data$subregion1_code,
"region" = .data$subregion1_name,
"subregion_id" = .data$location_key,
"subregion" = .data$subregion2_name,
.data$aggregation_level) |>
tidyr::unite("region_id", "country_code", "subregion1_code", na.rm = TRUE) |>
dplyr::mutate(
"region_id" = dplyr::if_else(.data$country_id == .data$region_id, NA, .data$region_id),
"subregion_id" = dplyr::if_else(.data$key_location != .data$subregion_id, NA, .data$subregion_id),
"valid_from" = as.Date("2020-01-01"),
"valid_until" = as.Date(NA)
)
return(out)
},
key_join = key_join_sum
),
google_covid_19_hospital = google_covid_19_metric("hospitalized_patients", "n_hospital"),
google_covid_19_deaths = google_covid_19_metric("deceased", "n_deaths"),
google_covid_19_positive = google_covid_19_metric("confirmed", "n_positive"),
google_covid_19_icu = google_covid_19_metric("intensive_care_patients", "n_icu"),
google_covid_19_ventilator = google_covid_19_metric("ventilator_patients", "n_ventilator"),
google_covid_19_age_group = FeatureHandler$new(
compute = function(start_date, end_date, slice_ts, source_conn, ...) {
checkmate::assert_character(source_conn, len = 1)
# Load and parse
out <- source_conn_path(source_conn, "by-age.csv") |>
readr::read_csv(n_max = diseasyoption("n_max", "DiseasystoreGoogleCovid19", .default = Inf),
show_col_types = FALSE)
# We need a map between age_bin and age_group
age_bin_map <- out |>
dplyr::group_by(.data$location_key) |>
dplyr::select("location_key", tidyselect::starts_with("age_bin")) |>
dplyr::distinct() |>
dplyr::left_join(dplyr::select(out, "location_key", "date", tidyselect::starts_with("age_bin")),
by = colnames(dplyr::select(out, "location_key", tidyselect::starts_with("age_bin"))),
multiple = "first")
# Some regions changes age stratification. Discard for now
age_bin_map <- age_bin_map |>
dplyr::select("location_key") |>
dplyr::filter(dplyr::n() == 1) |>
dplyr::inner_join(age_bin_map, by = "location_key")
# With these filtered, we map age_bins to age_groups
age_bin_map <- age_bin_map |>
dplyr::select("location_key", "date", tidyselect::everything()) |>
tidyr::pivot_longer(!c("location_key", "date"),
names_to = c("tmp", "age_bin"),
names_sep = "_bin_",
values_to = "age_group") |>
dplyr::mutate(age_group_lower = as.numeric(stringr::str_extract(.data$age_group, r"{^\d*}"))) |>
dplyr::group_modify(~ {
na_bins <- is.na(.x$age_group_lower)
data.frame(age_bin = .x$age_bin[!na_bins],
age_group = age_labels(.x$age_group_lower[!na_bins]))
})
# And finally copy to the DB
out <- age_bin_map |>
dplyr::rename("key_age_bin" = "age_bin", "key_location" = "location_key") |>
dplyr::mutate("valid_from" = as.Date("2020-01-01"), "valid_until" = as.Date(NA)) |>
dplyr::ungroup()
return(out)
},
key_join = key_join_sum
),
google_covid_19_min_temperature = FeatureHandler$new(
compute = function(start_date, end_date, slice_ts, source_conn, ...) {
checkmate::assert_character(source_conn, len = 1)
# Load and parse
out <- source_conn_path(source_conn, "weather.csv") |>
readr::read_csv(n_max = diseasyoption("n_max", "DiseasystoreGoogleCovid19", .default = Inf),
show_col_types = FALSE) |>
dplyr::mutate("date" = as.Date(.data$date)) |>
dplyr::filter({{ start_date }} <= .data$date, .data$date <= {{ end_date }}) |>
dplyr::select("key_location" = "location_key",
"date",
"min_temperature" = "minimum_temperature_celsius") |>
dplyr::mutate("valid_from" = .data$date, "valid_until" = as.Date(.data$date + lubridate::days(1)))
return(out)
},
key_join = key_join_min
),
google_covid_19_max_temperature = FeatureHandler$new(
compute = function(start_date, end_date, slice_ts, source_conn, ...) {
checkmate::assert_character(source_conn, len = 1)
# Load and parse
out <- source_conn_path(source_conn, "weather.csv") |>
readr::read_csv(n_max = diseasyoption("n_max", "DiseasystoreGoogleCovid19", .default = Inf),
show_col_types = FALSE) |>
dplyr::mutate("date" = as.Date(.data$date)) |>
dplyr::filter({{ start_date }} <= .data$date, .data$date <= {{ end_date }}) |>
dplyr::select("key_location" = "location_key",
"date",
"max_temperature" = "maximum_temperature_celsius") |>
dplyr::mutate("valid_from" = .data$date, "valid_until" = as.Date(.data$date + lubridate::days(1)))
return(out)
},
key_join = key_join_max
),
key_join_filter = function(.data, stratification_features,
start_date = private %.% start_date,
end_date = private %.% end_date) {
# The Google data contains surplus data depending on the stratification.
# I.e. some individuals are counted more than once.
# Eg. once at the country level, and then again at the region level etc.
# We need to filter at the appropriate stratification level when these doubly counted
# features are requested.
# Manually perform filtering
if (is.null(stratification_features) ||
(!is.null(stratification_features) &&
purrr::none(stratification_features,
~ . %in% c("country_id", "country",
"region_id", "region",
"subregion_id", "subregion")))) {
# If no spatial stratification is requested, use the largest available per country
filter_level <- self$get_feature("country_id", start_date, end_date) |>
dplyr::group_by(.data$country_id) |>
dplyr::slice_min(.data$aggregation_level) |>
dplyr::ungroup() |>
dplyr::select("key_location")
return(dplyr::inner_join(.data, filter_level, by = "key_location", copy = TRUE))
} else if (purrr::some(stratification_features, ~ . %in% c("country_id", "country"))) {
return(.data |> dplyr::filter(.data$key_location == .data$country_id))
} else if (purrr::some(stratification_features, ~ . %in% c("region_id", "region"))) {
return(.data |> dplyr::filter(.data$key_location == .data$region_id))
} else if (purrr::some(stratification_features, ~ . %in% c("subregion_id", "subregion"))) {
return(.data |> dplyr::filter(.data$key_location == .data$subregion_id))
} else {
stop("Edge case detected in $key_join_filter() (DiseasyStoreGoogleCovid19)", call. = FALSE)
}
}
)
)
# Set default options for the package related to the Google COVID-19 store
rlang::on_load({
options("diseasystore.DiseasystoreGoogleCovid19.remote_conn" = "https://storage.googleapis.com/covid19-open-data/v3/")
options("diseasystore.DiseasystoreGoogleCovid19.source_conn" = "https://storage.googleapis.com/covid19-open-data/v3/")
options("diseasystore.DiseasystoreGoogleCovid19.target_conn" = "")
options("diseasystore.DiseasystoreGoogleCovid19.target_schema" = "")
})
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