tests/testthat/test_accelerate_hep.R
In gpw13/billionaiRe: Calculate the WHO Triple Billions
testthat::test_that("espar returns appropriate values", {
df <- tidyr::expand_grid(
iso3 = c("AFG", "BGD", "PAK", "BRN", "CHE", "POL", "SWE", "VUT"),
year = 2018:2020,
ind = billion_ind_codes("hep")[stringr::str_detect(billion_ind_codes("hep"), "espar")]
) %>%
dplyr::mutate(
value = c(
35, 13, 20, 20, 0, 60, 60, 60, 80, 20, 40, 40, 20, 60, 80, 80, 80, 40, 27, 20, 20, 40, 40, 60, 20, 40, 20, 20, 20, 20, 0, 20, 43, 33, 20, 40, 40, 80, 100, 60, 80, 20, 47, 60, 20, 60, 80, 80,
80, 40, 33, 20, 40, 40, 53, 40, 40, 80, 20, 30, 40, 20, 20, 20, 47, 33, 20, 40, 40, 90, 80, 100, 80, 20, 60, 60, 60, 60, 70, 60, 80, 40, 80, 80, 80, 80, 53, 40, 40, 80, 20, 30, 20, 40, 20, 20,
58, 60, 60, 40, 80, 80, 100, 60, 80, 40, 73, 100, 40, 80, 80, 80, 80, 40, 47, 40, 60, 40, 60, 40, 60, 80, 60, 60, 60, 60, 40, 40, 67, 80, 80, 60, 100, 90, 100, 80, 80, 40, 73, 100, 40, 80, 80, 80,
80, 40, 53, 40, 60, 60, 73, 60, 80, 80, 80, 60, 60, 60, 60, 60, 70, 80, 80, 60, 100, 90, 100, 80, 80, 60, 73, 100, 40, 80, 80, 80, 80, 40, 53, 40, 60, 60, 73, 60, 80, 80, 80, 80, 80, 80, 60, 60,
51, 27, 20, 20, 40, 80, 60, 100, 60, 40, 60, 60, 40, 80, 60, 60, 60, 60, 47, 40, 40, 60, 33, 40, 20, 40, 20, 40, 40, 40, 40, 100, 49, 27, 20, 20, 40, 50, 60, 40, 60, 40, 60, 60, 40, 80, 60, 60,
60, 60, 47, 40, 40, 60, 33, 40, 20, 40, 20, 40, 40, 40, 40, 100, 52, 33, 20, 40, 40, 50, 60, 40, 60, 40, 60, 60, 40, 80, 60, 60, 60, 60, 60, 40, 60, 80, 47, 40, 40, 60, 20, 40, 40, 40, 40, 100,
rep(NA_integer_, 96),
rep(NA_integer_, 32), 95, 93, 100, 100, 80, 100, 100, 100, 100, 80, 100, 100, 100, 100, 90, 100, 80, 80, 100, 100, 100, 100, 87, 100, 100, 60, 100, 100, 100, 100, 100, 100, rep(NA_integer_, 32),
rep(NA_integer_, 32), 66, 80, 100, 60, 80, 100, 100, 100, 100, 40, 67, 100, 80, 20, 0, 0, 0, 60, 80, 80, 80, 80, 27, 0, 80, 0, 80, 40, 80, 0, 80, 100, 50, 0, 0, 0, 0, 100, 100, 100, 100, 0, 87, 100, 80, 80, 80, 80,
80, 0, 33, 40, 40, 20, 33, 0, 0, 100, 0, 60, 80, 40, 60, 100,
92, 100, 100, 100, 100, 100, 100, 100, 100, 80, 100, 100, 100, 100, 100, 100, 100, 80, 80, 80, 80, 80, 93, 80, 100, 100, 80, 100, 100, 100, 80, 100, 92, 100, 100, 100, 100, 100, 100, 100, 100, 80, 100, 100, 100, 100, 100, 100,
100, 80, 80, 80, 80, 80, 93, 80, 100, 100, 80, 100, 100, 100, 80, 100, 91, 100, 100, 100, 100, 100, 100, 100, 100, 80, 93, 100, 80, 100, 100, 100, 100, 80, 80, 80, 80, 80, 93, 80, 100, 100, 80, 100, 100, 100, 80, 100,
34, 27, 20, 40, 20, 30, 40, 20, 20, 20, 27, 20, 20, 40, 80, 100, 60, 40, 53, 20, 60, 80, 27, 20, 20, 40, 60, 20, 20, 20, 20, 20,
rep(NA_integer_, 32),
55, 47, 60, 40, 40, 30, 40, 20, 20, 80, 100, 100, 100, 100, 90, 100, 80, 40, 73, 40, 100, 80, 40, 40, 40, 40, 80, 40, 40, 40, 40, 40
),
type = "reported",
scenario = "default",
source = NA_character_
)
df_accelerated <- accelerate_espar(df,
start_year = 2020,
baseline_year = 2018,
keep_better_values = TRUE)
df_accelerated_2025 <- df_accelerated %>%
dplyr::filter(scenario == "acceleration", year == 2025) %>%
dplyr::arrange(iso3, year, ind) %>%
dplyr::pull(value)
testthat::expect_equal(df_accelerated_2025, c(49.28205128, 65.89743590, 34.07692308, 100.00000, 100.00000, 100.00000, 100.00000, 40.0))
df_add_indicator <- add_scenario_indicator(df,
indicator = "espar",
scenario_function = "accelerate",
baseline_year = 2018,
bau_scenario = "default"
)
df_add_indicator_2025 <- df_add_indicator %>%
dplyr::filter(scenario == "acceleration", year == 2025) %>%
dplyr::arrange(iso3, year, ind) %>%
dplyr::pull(value)
testthat::expect_equal(df_add_indicator_2025, df_accelerated_2025)
df_add_scenario <- add_scenario(df,
scenario_function = "accelerate",
baseline_year = 2018,
bau_scenario = "default"
)
df_add_scenario_2025 <- df_add_scenario %>%
dplyr::filter(scenario == "acceleration", year == 2025) %>%
dplyr::arrange(iso3, year, ind) %>%
dplyr::pull(value)
testthat::expect_equal(df_add_scenario_2025, df_accelerated_2025)
})
basic_hep_test <- function(ind) {
testthat::test_that(paste0(ind, " returns appropriate values"), {
df <- tibble::tibble(
value = 60:80,
year = 2010:2030,
ind = ind,
iso3 = "testalia",
scenario = "default",
source = NA_character_,
type = dplyr::if_else(year > 2021, "projected", "reported")
)
df_add_indicator <- add_scenario_indicator(df,
indicator = ind,
scenario_function = "accelerate",
baseline_year = 2018,
bau_scenario = "default",
start_scenario_last_default = FALSE,
expend_bau = FALSE
)
df_add_indicator_2025 <- df_add_indicator %>%
dplyr::filter(scenario == "acceleration", year == 2025) %>%
dplyr::pull(value)
testthat::expect_equal(df_add_indicator_2025, 75)
df_2018 <- df %>%
dplyr::filter(year <= 2018)
df_add_indicator <- add_scenario_indicator(df_2018,
indicator = ind,
scenario_function = "accelerate",
baseline_year = 2018,
bau_scenario = "default",
start_scenario_last_default = FALSE,
expend_bau = FALSE
)
df_add_indicator_2025 <- df_add_indicator %>%
dplyr::filter(scenario == "acceleration", year == 2025) %>%
dplyr::pull(value)
testthat::expect_equal(df_add_indicator_2025, 68)
})
}
purrr::walk(c("respond", "notify", "detect", "detect_respond"), basic_hep_test)
test_data <- load_misc_data("test_data/test_data/test_data_2022-03-06T09-30-41.parquet")
testthat::test_that("accelerate_cholera_campaign returns accurate results:", {
hep_test_df <- test_data %>%
make_default_scenario(billion = "hep") %>%
dplyr::filter(ind %in% billion_ind_codes("hep")[stringr::str_detect(billion_ind_codes("hep"), "cholera_campaign")],
scenario == "default") %>%
dplyr::mutate(source = NA_character_)
calculated_test_data <- add_scenario(hep_test_df,
"accelerate",
bau_scenario = "default",
expend_bau = FALSE)
testthat::expect_equal(nrow(calculated_test_data), 238)
num_bgd_2025 <- calculated_test_data %>%
dplyr::filter(
year == 2025, scenario == "acceleration",
iso3 == "BGD", ind == "cholera_campaign_num"
) %>%
dplyr::pull(value)
testthat::expect_equal(num_bgd_2025, 5833333.33)
num_tza_2025 <- calculated_test_data %>%
dplyr::filter(
year == 2025, scenario == "acceleration",
iso3 == "TZA", ind == "cholera_campaign_num"
) %>%
dplyr::pull(value)
testthat::expect_equal(round(num_tza_2025, 1), round(542710.42 * 0.7177101, 1))
})
testthat::test_that("accelerate_measles_routine returns accurate results:", {
df <- tibble::tibble(
value = 60:80,
year = 2010:2030,
ind = "measles_routine",
iso3 = "testalia",
scenario = "default",
source = NA_character_,
type = "reported"
)
aroc_2025 <- scenario_aroc(df, percent_change = 20, aroc_type = "percent_change", baseline_year = 2013) %>%
dplyr::filter(year == 2025, scenario != "default") %>%
dplyr::pull(value)
df_add_scenario <- add_scenario(df,
"accelerate",
start_scenario_last_default = FALSE,
bau_scenario = "default")
df_add_scenario_2025 <- df_add_scenario %>%
dplyr::filter(year == 2025, scenario == "acceleration") %>%
dplyr::pull(value)
testthat::expect_equal(df_add_scenario_2025, aroc_2025)
df_add_scenario_indicator <- add_scenario_indicator(df,
"accelerate",
"measles_routine",
bau_scenario = "default",
start_scenario_last_default = FALSE)
df_add_scenario_indicator_2025 <- df_add_scenario_indicator %>%
dplyr::filter(year == 2025, scenario == "acceleration") %>%
dplyr::pull(value)
testthat::expect_equal(df_add_scenario_2025, aroc_2025)
# Try with default stopping in 2021.
df_2021 <- df %>%
dplyr::mutate(scenario = dplyr::case_when(
year > 2021 ~ "historical",
TRUE ~ "default"),
type = dplyr::case_when(
year > 2012 ~ "projected",
TRUE ~ "reported"
))
df_add_scenario_indicator_2021 <- add_scenario_indicator(df_2021,
"accelerate",
"measles_routine",
bau_scenario = "default")
df_add_scenario_indicator_2021_2025 <- df_add_scenario_indicator_2021 %>%
dplyr::filter(year == 2025, scenario == "acceleration") %>%
dplyr::pull(value)
testthat::expect_equal(df_add_scenario_indicator_2021_2025, 75.53902, tolerance = 5)
})
testthat::test_that("accelerate_meningitis_campaign returns accurate results:", {
hep_test_df <- test_data %>%
make_default_scenario(billion = "hep") %>%
dplyr::filter(ind %in% billion_ind_codes("hep")[stringr::str_detect(billion_ind_codes("hep"), "meningitis_campaign")],
scenario == "default") %>%
dplyr::mutate(source = NA_character_)
calculated_test_data <- add_scenario(hep_test_df,
"accelerate",
bau_scenario = "default",
start_scenario_last_default = FALSE,
make_default = FALSE,
expend_bau = FALSE)
testthat::expect_equal(nrow(calculated_test_data), 6)
num_BDI_2018 <- calculated_test_data %>%
dplyr::filter(
year == 2018, scenario == "acceleration",
iso3 == "BDI", ind == "meningitis_campaign_num"
) %>%
dplyr::pull(value)
testthat::expect_equal(num_BDI_2018, 7968553)
})
testthat::test_that("accelerate_meningitis_routine returns accurate results:", {
df <- tibble::tibble(
value = c(60:80),
year = 2010:2030,
ind = "meningitis_routine",
iso3 = "testalia",
scenario = c(rep("default", 12), rep("historical", 9)),
type = c(rep("reported", 12), rep("projected", 9)),
source = NA_character_
)
fixed_target_2025 <- df %>%
dplyr::filter(scenario == "default") %>%
scenario_fixed_target(target_value = 90, target_year = 2030) %>%
dplyr::filter(year == 2025, scenario != "default") %>%
dplyr::pull(value)
df_add_scenario <- add_scenario(df, "accelerate",
bau_scenario = "historical",
start_scenario_last_default = FALSE)
df_add_scenario_2025 <- df_add_scenario %>%
dplyr::filter(year == 2025, scenario == "acceleration") %>%
dplyr::pull(value)
testthat::expect_equal(df_add_scenario_2025, fixed_target_2025)
test_data_df <- test_data %>%
dplyr::filter(scenario != "default") %>%
make_default_scenario(billion = "hep") %>%
dplyr::filter(scenario == "default") %>%
dplyr::mutate(source = NA_character_)
df_add_scenario_indicator <- add_scenario_indicator(test_data_df,
"accelerate",
"meningitis_routine",
bau_scenario = "default",
start_scenario_last_default = FALSE,
make_default = FALSE,
expend_bau = FALSE)
df_add_scenario_indicator_2025 <- df_add_scenario_indicator %>%
dplyr::filter(year == 2025, scenario == "acceleration") %>%
dplyr::pull(value)
testthat::expect_equal(df_add_scenario_indicator_2025, c(90/(2030-2018) *(2025-2018), 90/(2030-2018) *(2025-2018)))
df_with_zero <- df %>%
dplyr::mutate(value = dplyr::case_when(
year == 2018 ~ 0L,
TRUE ~ value
))
df_add_scenario_indicator <- add_scenario_indicator(df_with_zero,
"accelerate",
"meningitis_routine",
bau_scenario = "historical",
start_scenario_last_default = FALSE)
df_add_scenario_indicator_2025 <- df_add_scenario_indicator %>%
dplyr::filter(year == 2025, scenario == "acceleration") %>%
dplyr::pull(value)
testthat::expect_equal(df_add_scenario_indicator_2025, 90/(2030-2018) *(2025-2018))
fixed_target_2021_2025 <- df %>%
dplyr::filter(scenario == "default") %>%
scenario_fixed_target(target_value = 90, target_year = 2030, start_year = 2021) %>%
dplyr::filter(year == 2025, scenario != "default") %>%
dplyr::pull(value)
df_add_scenario <- add_scenario(df, "accelerate",
bau_scenario = "historical",
start_scenario_last_default = TRUE)
df_add_scenario_2025 <- df_add_scenario %>%
dplyr::filter(year == 2025, scenario == "acceleration") %>%
dplyr::pull(value)
testthat::expect_equal(fixed_target_2021_2025, df_add_scenario_2025)
testthat::expect_equal(df_add_scenario_2025, 71+((90-71)/(2030-2021) *(2025-2021)))
})
testthat::test_that("accelerate_polio_routine returns accurate results:", {
df <- tibble::tibble(
value = 60:80,
year = 2010:2030,
ind = "polio_routine",
iso3 = "testalia",
scenario = "default",
source = NA_character_,
type = "reported"
)
aroc_2025 <- scenario_aroc(df, percent_change = 20,
aroc_type = "percent_change",
baseline_year = 2015) %>%
dplyr::filter(year == 2025, scenario != "default") %>%
dplyr::pull(value)
df_add_scenario <- add_scenario(df,
"accelerate",
bau_scenario = "default",
start_scenario_last_default = FALSE)
df_add_scenario_2025 <- df_add_scenario %>%
dplyr::filter(year == 2025, scenario == "acceleration") %>%
dplyr::pull(value)
testthat::expect_equal(df_add_scenario_2025, aroc_2025)
df_add_scenario_indicator <- add_scenario_indicator(df,
"accelerate",
"polio_routine",
bau_scenario = "default",
start_scenario_last_default = FALSE)
df_add_scenario_indicator_2025 <- df_add_scenario_indicator %>%
dplyr::filter(year == 2025, scenario == "acceleration") %>%
dplyr::pull(value)
testthat::expect_equal(df_add_scenario_2025, aroc_2025)
df_2021 <- df %>%
dplyr::mutate(scenario = c(rep("default", 12), rep("historical", 9)),
type = c(rep("reported", 12), rep("projected", 9))
)
df_add_scenario_indicator_2021 <- add_scenario_indicator(df_2021,
"accelerate",
"polio_routine",
bau_scenario = "default",
start_scenario_last_default = TRUE)
df_add_scenario_indicator_2021_2025 <- df_add_scenario_indicator_2021 %>%
dplyr::filter(year == 2025, scenario == "acceleration") %>%
dplyr::pull(value)
aroc_2021_2025 <- scenario_aroc(df, percent_change = 20,
aroc_type = "percent_change",
start_year = 2021,
baseline_year = 2015) %>%
dplyr::filter(year == 2025, scenario != "default") %>%
dplyr::pull(value)
testthat::expect_equal(df_add_scenario_indicator_2021_2025, aroc_2021_2025)
})
testthat::test_that("accelerate_yellow_fever_campaigns returns accurate results:", {
hep_test_df <- load_misc_data("scenarios/yellow_fever_campaign/test_data_campaign_yellow_fever.parquet") %>%
dplyr::mutate(source = NA_character_)
calculated_test_data <- add_scenario(hep_test_df, "accelerate", bau_scenario = "default")
num_UGA_2024 <- calculated_test_data %>%
dplyr::filter(
year == 2024, scenario == "acceleration",
iso3 == "UGA", ind == "yellow_fever_campaign_denom"
) %>%
dplyr::pull(value)
num_UGA_2024_planned <- load_misc_data("scenarios/yellow_fever_campaign/yellow_fever_campaign_planned.csv") %>%
dplyr::filter(iso3 == "UGA") %>%
dplyr::pull("2024_campaign_targeted_population")
testthat::expect_equal(num_UGA_2024, num_UGA_2024_planned)
})
testthat::test_that("accelerate_yellow_fever_routine returns accurate results:", {
df <- tibble::tibble(
value = 60:80,
year = 2010:2030,
ind = "yellow_fever_routine",
iso3 = "testalia",
scenario = "default",
source = NA_character_,
type = "reported"
)
aroc_2025 <- scenario_aroc(df, percent_change = 20,
aroc_type = "percent_change",
baseline_year = 2015) %>%
dplyr::filter(year == 2025, scenario != "default") %>%
dplyr::pull(value)
df_add_scenario <- add_scenario(df,
"accelerate",
bau_scenario = "default",
start_scenario_last_default = FALSE)
df_add_scenario_2025 <- df_add_scenario %>%
dplyr::filter(year == 2025, scenario == "acceleration") %>%
dplyr::pull(value)
testthat::expect_equal(df_add_scenario_2025, aroc_2025)
df_add_scenario_indicator <- add_scenario_indicator(df,
"accelerate",
"yellow_fever_routine",
bau_scenario = "default",
start_scenario_last_default = FALSE)
df_add_scenario_indicator_2025 <- df_add_scenario_indicator %>%
dplyr::filter(year == 2025, scenario == "acceleration") %>%
dplyr::pull(value)
testthat::expect_equal(df_add_scenario_2025, aroc_2025)
aroc2021_2025 <- scenario_aroc(df, percent_change = 20,
aroc_type = "percent_change",
baseline_year = 2015,
start_year = 2021) %>%
dplyr::filter(year == 2025, scenario != "default") %>%
dplyr::pull(value)
df <- df %>%
dplyr::mutate(
scenario = dplyr::case_when(
year > 2021 ~ "historical",
TRUE ~ "default"
)
)
df_add_scenario_indicator_2021 <- add_scenario_indicator(df,
"accelerate",
"yellow_fever_routine",
bau_scenario = "historical",
start_scenario_last_default = TRUE)
df_add_scenario_indicator_2021_2025 <- df_add_scenario_indicator_2021 %>%
dplyr::filter(year == 2025, scenario == "acceleration") %>%
dplyr::pull(value)
testthat::expect_equal(df_add_scenario_indicator_2021_2025, aroc2021_2025)
})
testthat::test_that("acceleration can be run on all hep indicators:", {
hep_test_df <- test_data %>%
dplyr::filter(scenario != "default") %>%
make_default_scenario(billion = "hep") %>%
dplyr::filter(ind %in% billion_ind_codes("hep") &
!.data[["ind"]] %in% billion_ind_codes("hep")[stringr::str_detect(billion_ind_codes("hep"), "espar")],
scenario == "default") %>%
dplyr::mutate(source = NA_character_)
testthat::expect_error(add_scenario(hep_test_df, "accelerate", bau_scenario = "default", make_default = FALSE, expend_bau = FALSE), NA)
df_espar <- tidyr::expand_grid(
iso3 = c("AFG", "BGD", "PAK", "BRN", "CHE", "POL", "SWE", "VUT"),
year = 2018:2020,
ind = billion_ind_codes("hep")[stringr::str_detect(billion_ind_codes("hep"), "espar")]
) %>%
dplyr::mutate(
value = c(
35, 13, 20, 20, 0, 60, 60, 60, 80, 20, 40, 40, 20, 60, 80, 80, 80, 40, 27, 20, 20, 40, 40, 60, 20, 40, 20, 20, 20, 20, 0, 20, 43, 33, 20, 40, 40, 80, 100, 60, 80, 20, 47, 60, 20, 60, 80, 80,
80, 40, 33, 20, 40, 40, 53, 40, 40, 80, 20, 30, 40, 20, 20, 20, 47, 33, 20, 40, 40, 90, 80, 100, 80, 20, 60, 60, 60, 60, 70, 60, 80, 40, 80, 80, 80, 80, 53, 40, 40, 80, 20, 30, 20, 40, 20, 20,
58, 60, 60, 40, 80, 80, 100, 60, 80, 40, 73, 100, 40, 80, 80, 80, 80, 40, 47, 40, 60, 40, 60, 40, 60, 80, 60, 60, 60, 60, 40, 40, 67, 80, 80, 60, 100, 90, 100, 80, 80, 40, 73, 100, 40, 80, 80, 80,
80, 40, 53, 40, 60, 60, 73, 60, 80, 80, 80, 60, 60, 60, 60, 60, 70, 80, 80, 60, 100, 90, 100, 80, 80, 60, 73, 100, 40, 80, 80, 80, 80, 40, 53, 40, 60, 60, 73, 60, 80, 80, 80, 80, 80, 80, 60, 60,
51, 27, 20, 20, 40, 80, 60, 100, 60, 40, 60, 60, 40, 80, 60, 60, 60, 60, 47, 40, 40, 60, 33, 40, 20, 40, 20, 40, 40, 40, 40, 100, 49, 27, 20, 20, 40, 50, 60, 40, 60, 40, 60, 60, 40, 80, 60, 60,
60, 60, 47, 40, 40, 60, 33, 40, 20, 40, 20, 40, 40, 40, 40, 100, 52, 33, 20, 40, 40, 50, 60, 40, 60, 40, 60, 60, 40, 80, 60, 60, 60, 60, 60, 40, 60, 80, 47, 40, 40, 60, 20, 40, 40, 40, 40, 100,
rep(NA_integer_, 96),
rep(NA_integer_, 32), 95, 93, 100, 100, 80, 100, 100, 100, 100, 80, 100, 100, 100, 100, 90, 100, 80, 80, 100, 100, 100, 100, 87, 100, 100, 60, 100, 100, 100, 100, 100, 100, rep(NA_integer_, 32),
rep(NA_integer_, 32), 66, 80, 100, 60, 80, 100, 100, 100, 100, 40, 67, 100, 80, 20, 0, 0, 0, 60, 80, 80, 80, 80, 27, 0, 80, 0, 80, 40, 80, 0, 80, 100, 50, 0, 0, 0, 0, 100, 100, 100, 100, 0, 87, 100, 80, 80, 80, 80,
80, 0, 33, 40, 40, 20, 33, 0, 0, 100, 0, 60, 80, 40, 60, 100,
92, 100, 100, 100, 100, 100, 100, 100, 100, 80, 100, 100, 100, 100, 100, 100, 100, 80, 80, 80, 80, 80, 93, 80, 100, 100, 80, 100, 100, 100, 80, 100, 92, 100, 100, 100, 100, 100, 100, 100, 100, 80, 100, 100, 100, 100, 100, 100,
100, 80, 80, 80, 80, 80, 93, 80, 100, 100, 80, 100, 100, 100, 80, 100, 91, 100, 100, 100, 100, 100, 100, 100, 100, 80, 93, 100, 80, 100, 100, 100, 100, 80, 80, 80, 80, 80, 93, 80, 100, 100, 80, 100, 100, 100, 80, 100,
34, 27, 20, 40, 20, 30, 40, 20, 20, 20, 27, 20, 20, 40, 80, 100, 60, 40, 53, 20, 60, 80, 27, 20, 20, 40, 60, 20, 20, 20, 20, 20,
rep(NA_integer_, 32),
55, 47, 60, 40, 40, 30, 40, 20, 20, 80, 100, 100, 100, 100, 90, 100, 80, 40, 73, 40, 100, 80, 40, 40, 40, 40, 80, 40, 40, 40, 40, 40
),
type = "reported",
scenario = "default",
source = NA_character_
)
testthat::expect_error(add_scenario(df_espar,
scenario_function = "accelerate",
bau_scenario = "default",
make_default = FALSE,
expend_bau = FALSE),
NA)
})
gpw13/billionaiRe documentation built on Sept. 27, 2024, 10:05 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
testthat::test_that("espar returns appropriate values", {
df <- tidyr::expand_grid(
iso3 = c("AFG", "BGD", "PAK", "BRN", "CHE", "POL", "SWE", "VUT"),
year = 2018:2020,
ind = billion_ind_codes("hep")[stringr::str_detect(billion_ind_codes("hep"), "espar")]
) %>%
dplyr::mutate(
value = c(
35, 13, 20, 20, 0, 60, 60, 60, 80, 20, 40, 40, 20, 60, 80, 80, 80, 40, 27, 20, 20, 40, 40, 60, 20, 40, 20, 20, 20, 20, 0, 20, 43, 33, 20, 40, 40, 80, 100, 60, 80, 20, 47, 60, 20, 60, 80, 80,
80, 40, 33, 20, 40, 40, 53, 40, 40, 80, 20, 30, 40, 20, 20, 20, 47, 33, 20, 40, 40, 90, 80, 100, 80, 20, 60, 60, 60, 60, 70, 60, 80, 40, 80, 80, 80, 80, 53, 40, 40, 80, 20, 30, 20, 40, 20, 20,
58, 60, 60, 40, 80, 80, 100, 60, 80, 40, 73, 100, 40, 80, 80, 80, 80, 40, 47, 40, 60, 40, 60, 40, 60, 80, 60, 60, 60, 60, 40, 40, 67, 80, 80, 60, 100, 90, 100, 80, 80, 40, 73, 100, 40, 80, 80, 80,
80, 40, 53, 40, 60, 60, 73, 60, 80, 80, 80, 60, 60, 60, 60, 60, 70, 80, 80, 60, 100, 90, 100, 80, 80, 60, 73, 100, 40, 80, 80, 80, 80, 40, 53, 40, 60, 60, 73, 60, 80, 80, 80, 80, 80, 80, 60, 60,
51, 27, 20, 20, 40, 80, 60, 100, 60, 40, 60, 60, 40, 80, 60, 60, 60, 60, 47, 40, 40, 60, 33, 40, 20, 40, 20, 40, 40, 40, 40, 100, 49, 27, 20, 20, 40, 50, 60, 40, 60, 40, 60, 60, 40, 80, 60, 60,
60, 60, 47, 40, 40, 60, 33, 40, 20, 40, 20, 40, 40, 40, 40, 100, 52, 33, 20, 40, 40, 50, 60, 40, 60, 40, 60, 60, 40, 80, 60, 60, 60, 60, 60, 40, 60, 80, 47, 40, 40, 60, 20, 40, 40, 40, 40, 100,
rep(NA_integer_, 96),
rep(NA_integer_, 32), 95, 93, 100, 100, 80, 100, 100, 100, 100, 80, 100, 100, 100, 100, 90, 100, 80, 80, 100, 100, 100, 100, 87, 100, 100, 60, 100, 100, 100, 100, 100, 100, rep(NA_integer_, 32),
rep(NA_integer_, 32), 66, 80, 100, 60, 80, 100, 100, 100, 100, 40, 67, 100, 80, 20, 0, 0, 0, 60, 80, 80, 80, 80, 27, 0, 80, 0, 80, 40, 80, 0, 80, 100, 50, 0, 0, 0, 0, 100, 100, 100, 100, 0, 87, 100, 80, 80, 80, 80,
80, 0, 33, 40, 40, 20, 33, 0, 0, 100, 0, 60, 80, 40, 60, 100,
92, 100, 100, 100, 100, 100, 100, 100, 100, 80, 100, 100, 100, 100, 100, 100, 100, 80, 80, 80, 80, 80, 93, 80, 100, 100, 80, 100, 100, 100, 80, 100, 92, 100, 100, 100, 100, 100, 100, 100, 100, 80, 100, 100, 100, 100, 100, 100,
100, 80, 80, 80, 80, 80, 93, 80, 100, 100, 80, 100, 100, 100, 80, 100, 91, 100, 100, 100, 100, 100, 100, 100, 100, 80, 93, 100, 80, 100, 100, 100, 100, 80, 80, 80, 80, 80, 93, 80, 100, 100, 80, 100, 100, 100, 80, 100,
34, 27, 20, 40, 20, 30, 40, 20, 20, 20, 27, 20, 20, 40, 80, 100, 60, 40, 53, 20, 60, 80, 27, 20, 20, 40, 60, 20, 20, 20, 20, 20,
rep(NA_integer_, 32),
55, 47, 60, 40, 40, 30, 40, 20, 20, 80, 100, 100, 100, 100, 90, 100, 80, 40, 73, 40, 100, 80, 40, 40, 40, 40, 80, 40, 40, 40, 40, 40
),
type = "reported",
scenario = "default",
source = NA_character_
)
df_accelerated <- accelerate_espar(df,
start_year = 2020,
baseline_year = 2018,
keep_better_values = TRUE)
df_accelerated_2025 <- df_accelerated %>%
dplyr::filter(scenario == "acceleration", year == 2025) %>%
dplyr::arrange(iso3, year, ind) %>%
dplyr::pull(value)
testthat::expect_equal(df_accelerated_2025, c(49.28205128, 65.89743590, 34.07692308, 100.00000, 100.00000, 100.00000, 100.00000, 40.0))
df_add_indicator <- add_scenario_indicator(df,
indicator = "espar",
scenario_function = "accelerate",
baseline_year = 2018,
bau_scenario = "default"
)
df_add_indicator_2025 <- df_add_indicator %>%
dplyr::filter(scenario == "acceleration", year == 2025) %>%
dplyr::arrange(iso3, year, ind) %>%
dplyr::pull(value)
testthat::expect_equal(df_add_indicator_2025, df_accelerated_2025)
df_add_scenario <- add_scenario(df,
scenario_function = "accelerate",
baseline_year = 2018,
bau_scenario = "default"
)
df_add_scenario_2025 <- df_add_scenario %>%
dplyr::filter(scenario == "acceleration", year == 2025) %>%
dplyr::arrange(iso3, year, ind) %>%
dplyr::pull(value)
testthat::expect_equal(df_add_scenario_2025, df_accelerated_2025)
})
basic_hep_test <- function(ind) {
testthat::test_that(paste0(ind, " returns appropriate values"), {
df <- tibble::tibble(
value = 60:80,
year = 2010:2030,
ind = ind,
iso3 = "testalia",
scenario = "default",
source = NA_character_,
type = dplyr::if_else(year > 2021, "projected", "reported")
)
df_add_indicator <- add_scenario_indicator(df,
indicator = ind,
scenario_function = "accelerate",
baseline_year = 2018,
bau_scenario = "default",
start_scenario_last_default = FALSE,
expend_bau = FALSE
)
df_add_indicator_2025 <- df_add_indicator %>%
dplyr::filter(scenario == "acceleration", year == 2025) %>%
dplyr::pull(value)
testthat::expect_equal(df_add_indicator_2025, 75)
df_2018 <- df %>%
dplyr::filter(year <= 2018)
df_add_indicator <- add_scenario_indicator(df_2018,
indicator = ind,
scenario_function = "accelerate",
baseline_year = 2018,
bau_scenario = "default",
start_scenario_last_default = FALSE,
expend_bau = FALSE
)
df_add_indicator_2025 <- df_add_indicator %>%
dplyr::filter(scenario == "acceleration", year == 2025) %>%
dplyr::pull(value)
testthat::expect_equal(df_add_indicator_2025, 68)
})
}
purrr::walk(c("respond", "notify", "detect", "detect_respond"), basic_hep_test)
test_data <- load_misc_data("test_data/test_data/test_data_2022-03-06T09-30-41.parquet")
testthat::test_that("accelerate_cholera_campaign returns accurate results:", {
hep_test_df <- test_data %>%
make_default_scenario(billion = "hep") %>%
dplyr::filter(ind %in% billion_ind_codes("hep")[stringr::str_detect(billion_ind_codes("hep"), "cholera_campaign")],
scenario == "default") %>%
dplyr::mutate(source = NA_character_)
calculated_test_data <- add_scenario(hep_test_df,
"accelerate",
bau_scenario = "default",
expend_bau = FALSE)
testthat::expect_equal(nrow(calculated_test_data), 238)
num_bgd_2025 <- calculated_test_data %>%
dplyr::filter(
year == 2025, scenario == "acceleration",
iso3 == "BGD", ind == "cholera_campaign_num"
) %>%
dplyr::pull(value)
testthat::expect_equal(num_bgd_2025, 5833333.33)
num_tza_2025 <- calculated_test_data %>%
dplyr::filter(
year == 2025, scenario == "acceleration",
iso3 == "TZA", ind == "cholera_campaign_num"
) %>%
dplyr::pull(value)
testthat::expect_equal(round(num_tza_2025, 1), round(542710.42 * 0.7177101, 1))
})
testthat::test_that("accelerate_measles_routine returns accurate results:", {
df <- tibble::tibble(
value = 60:80,
year = 2010:2030,
ind = "measles_routine",
iso3 = "testalia",
scenario = "default",
source = NA_character_,
type = "reported"
)
aroc_2025 <- scenario_aroc(df, percent_change = 20, aroc_type = "percent_change", baseline_year = 2013) %>%
dplyr::filter(year == 2025, scenario != "default") %>%
dplyr::pull(value)
df_add_scenario <- add_scenario(df,
"accelerate",
start_scenario_last_default = FALSE,
bau_scenario = "default")
df_add_scenario_2025 <- df_add_scenario %>%
dplyr::filter(year == 2025, scenario == "acceleration") %>%
dplyr::pull(value)
testthat::expect_equal(df_add_scenario_2025, aroc_2025)
df_add_scenario_indicator <- add_scenario_indicator(df,
"accelerate",
"measles_routine",
bau_scenario = "default",
start_scenario_last_default = FALSE)
df_add_scenario_indicator_2025 <- df_add_scenario_indicator %>%
dplyr::filter(year == 2025, scenario == "acceleration") %>%
dplyr::pull(value)
testthat::expect_equal(df_add_scenario_2025, aroc_2025)
# Try with default stopping in 2021.
df_2021 <- df %>%
dplyr::mutate(scenario = dplyr::case_when(
year > 2021 ~ "historical",
TRUE ~ "default"),
type = dplyr::case_when(
year > 2012 ~ "projected",
TRUE ~ "reported"
))
df_add_scenario_indicator_2021 <- add_scenario_indicator(df_2021,
"accelerate",
"measles_routine",
bau_scenario = "default")
df_add_scenario_indicator_2021_2025 <- df_add_scenario_indicator_2021 %>%
dplyr::filter(year == 2025, scenario == "acceleration") %>%
dplyr::pull(value)
testthat::expect_equal(df_add_scenario_indicator_2021_2025, 75.53902, tolerance = 5)
})
testthat::test_that("accelerate_meningitis_campaign returns accurate results:", {
hep_test_df <- test_data %>%
make_default_scenario(billion = "hep") %>%
dplyr::filter(ind %in% billion_ind_codes("hep")[stringr::str_detect(billion_ind_codes("hep"), "meningitis_campaign")],
scenario == "default") %>%
dplyr::mutate(source = NA_character_)
calculated_test_data <- add_scenario(hep_test_df,
"accelerate",
bau_scenario = "default",
start_scenario_last_default = FALSE,
make_default = FALSE,
expend_bau = FALSE)
testthat::expect_equal(nrow(calculated_test_data), 6)
num_BDI_2018 <- calculated_test_data %>%
dplyr::filter(
year == 2018, scenario == "acceleration",
iso3 == "BDI", ind == "meningitis_campaign_num"
) %>%
dplyr::pull(value)
testthat::expect_equal(num_BDI_2018, 7968553)
})
testthat::test_that("accelerate_meningitis_routine returns accurate results:", {
df <- tibble::tibble(
value = c(60:80),
year = 2010:2030,
ind = "meningitis_routine",
iso3 = "testalia",
scenario = c(rep("default", 12), rep("historical", 9)),
type = c(rep("reported", 12), rep("projected", 9)),
source = NA_character_
)
fixed_target_2025 <- df %>%
dplyr::filter(scenario == "default") %>%
scenario_fixed_target(target_value = 90, target_year = 2030) %>%
dplyr::filter(year == 2025, scenario != "default") %>%
dplyr::pull(value)
df_add_scenario <- add_scenario(df, "accelerate",
bau_scenario = "historical",
start_scenario_last_default = FALSE)
df_add_scenario_2025 <- df_add_scenario %>%
dplyr::filter(year == 2025, scenario == "acceleration") %>%
dplyr::pull(value)
testthat::expect_equal(df_add_scenario_2025, fixed_target_2025)
test_data_df <- test_data %>%
dplyr::filter(scenario != "default") %>%
make_default_scenario(billion = "hep") %>%
dplyr::filter(scenario == "default") %>%
dplyr::mutate(source = NA_character_)
df_add_scenario_indicator <- add_scenario_indicator(test_data_df,
"accelerate",
"meningitis_routine",
bau_scenario = "default",
start_scenario_last_default = FALSE,
make_default = FALSE,
expend_bau = FALSE)
df_add_scenario_indicator_2025 <- df_add_scenario_indicator %>%
dplyr::filter(year == 2025, scenario == "acceleration") %>%
dplyr::pull(value)
testthat::expect_equal(df_add_scenario_indicator_2025, c(90/(2030-2018) *(2025-2018), 90/(2030-2018) *(2025-2018)))
df_with_zero <- df %>%
dplyr::mutate(value = dplyr::case_when(
year == 2018 ~ 0L,
TRUE ~ value
))
df_add_scenario_indicator <- add_scenario_indicator(df_with_zero,
"accelerate",
"meningitis_routine",
bau_scenario = "historical",
start_scenario_last_default = FALSE)
df_add_scenario_indicator_2025 <- df_add_scenario_indicator %>%
dplyr::filter(year == 2025, scenario == "acceleration") %>%
dplyr::pull(value)
testthat::expect_equal(df_add_scenario_indicator_2025, 90/(2030-2018) *(2025-2018))
fixed_target_2021_2025 <- df %>%
dplyr::filter(scenario == "default") %>%
scenario_fixed_target(target_value = 90, target_year = 2030, start_year = 2021) %>%
dplyr::filter(year == 2025, scenario != "default") %>%
dplyr::pull(value)
df_add_scenario <- add_scenario(df, "accelerate",
bau_scenario = "historical",
start_scenario_last_default = TRUE)
df_add_scenario_2025 <- df_add_scenario %>%
dplyr::filter(year == 2025, scenario == "acceleration") %>%
dplyr::pull(value)
testthat::expect_equal(fixed_target_2021_2025, df_add_scenario_2025)
testthat::expect_equal(df_add_scenario_2025, 71+((90-71)/(2030-2021) *(2025-2021)))
})
testthat::test_that("accelerate_polio_routine returns accurate results:", {
df <- tibble::tibble(
value = 60:80,
year = 2010:2030,
ind = "polio_routine",
iso3 = "testalia",
scenario = "default",
source = NA_character_,
type = "reported"
)
aroc_2025 <- scenario_aroc(df, percent_change = 20,
aroc_type = "percent_change",
baseline_year = 2015) %>%
dplyr::filter(year == 2025, scenario != "default") %>%
dplyr::pull(value)
df_add_scenario <- add_scenario(df,
"accelerate",
bau_scenario = "default",
start_scenario_last_default = FALSE)
df_add_scenario_2025 <- df_add_scenario %>%
dplyr::filter(year == 2025, scenario == "acceleration") %>%
dplyr::pull(value)
testthat::expect_equal(df_add_scenario_2025, aroc_2025)
df_add_scenario_indicator <- add_scenario_indicator(df,
"accelerate",
"polio_routine",
bau_scenario = "default",
start_scenario_last_default = FALSE)
df_add_scenario_indicator_2025 <- df_add_scenario_indicator %>%
dplyr::filter(year == 2025, scenario == "acceleration") %>%
dplyr::pull(value)
testthat::expect_equal(df_add_scenario_2025, aroc_2025)
df_2021 <- df %>%
dplyr::mutate(scenario = c(rep("default", 12), rep("historical", 9)),
type = c(rep("reported", 12), rep("projected", 9))
)
df_add_scenario_indicator_2021 <- add_scenario_indicator(df_2021,
"accelerate",
"polio_routine",
bau_scenario = "default",
start_scenario_last_default = TRUE)
df_add_scenario_indicator_2021_2025 <- df_add_scenario_indicator_2021 %>%
dplyr::filter(year == 2025, scenario == "acceleration") %>%
dplyr::pull(value)
aroc_2021_2025 <- scenario_aroc(df, percent_change = 20,
aroc_type = "percent_change",
start_year = 2021,
baseline_year = 2015) %>%
dplyr::filter(year == 2025, scenario != "default") %>%
dplyr::pull(value)
testthat::expect_equal(df_add_scenario_indicator_2021_2025, aroc_2021_2025)
})
testthat::test_that("accelerate_yellow_fever_campaigns returns accurate results:", {
hep_test_df <- load_misc_data("scenarios/yellow_fever_campaign/test_data_campaign_yellow_fever.parquet") %>%
dplyr::mutate(source = NA_character_)
calculated_test_data <- add_scenario(hep_test_df, "accelerate", bau_scenario = "default")
num_UGA_2024 <- calculated_test_data %>%
dplyr::filter(
year == 2024, scenario == "acceleration",
iso3 == "UGA", ind == "yellow_fever_campaign_denom"
) %>%
dplyr::pull(value)
num_UGA_2024_planned <- load_misc_data("scenarios/yellow_fever_campaign/yellow_fever_campaign_planned.csv") %>%
dplyr::filter(iso3 == "UGA") %>%
dplyr::pull("2024_campaign_targeted_population")
testthat::expect_equal(num_UGA_2024, num_UGA_2024_planned)
})
testthat::test_that("accelerate_yellow_fever_routine returns accurate results:", {
df <- tibble::tibble(
value = 60:80,
year = 2010:2030,
ind = "yellow_fever_routine",
iso3 = "testalia",
scenario = "default",
source = NA_character_,
type = "reported"
)
aroc_2025 <- scenario_aroc(df, percent_change = 20,
aroc_type = "percent_change",
baseline_year = 2015) %>%
dplyr::filter(year == 2025, scenario != "default") %>%
dplyr::pull(value)
df_add_scenario <- add_scenario(df,
"accelerate",
bau_scenario = "default",
start_scenario_last_default = FALSE)
df_add_scenario_2025 <- df_add_scenario %>%
dplyr::filter(year == 2025, scenario == "acceleration") %>%
dplyr::pull(value)
testthat::expect_equal(df_add_scenario_2025, aroc_2025)
df_add_scenario_indicator <- add_scenario_indicator(df,
"accelerate",
"yellow_fever_routine",
bau_scenario = "default",
start_scenario_last_default = FALSE)
df_add_scenario_indicator_2025 <- df_add_scenario_indicator %>%
dplyr::filter(year == 2025, scenario == "acceleration") %>%
dplyr::pull(value)
testthat::expect_equal(df_add_scenario_2025, aroc_2025)
aroc2021_2025 <- scenario_aroc(df, percent_change = 20,
aroc_type = "percent_change",
baseline_year = 2015,
start_year = 2021) %>%
dplyr::filter(year == 2025, scenario != "default") %>%
dplyr::pull(value)
df <- df %>%
dplyr::mutate(
scenario = dplyr::case_when(
year > 2021 ~ "historical",
TRUE ~ "default"
)
)
df_add_scenario_indicator_2021 <- add_scenario_indicator(df,
"accelerate",
"yellow_fever_routine",
bau_scenario = "historical",
start_scenario_last_default = TRUE)
df_add_scenario_indicator_2021_2025 <- df_add_scenario_indicator_2021 %>%
dplyr::filter(year == 2025, scenario == "acceleration") %>%
dplyr::pull(value)
testthat::expect_equal(df_add_scenario_indicator_2021_2025, aroc2021_2025)
})
testthat::test_that("acceleration can be run on all hep indicators:", {
hep_test_df <- test_data %>%
dplyr::filter(scenario != "default") %>%
make_default_scenario(billion = "hep") %>%
dplyr::filter(ind %in% billion_ind_codes("hep") &
!.data[["ind"]] %in% billion_ind_codes("hep")[stringr::str_detect(billion_ind_codes("hep"), "espar")],
scenario == "default") %>%
dplyr::mutate(source = NA_character_)
testthat::expect_error(add_scenario(hep_test_df, "accelerate", bau_scenario = "default", make_default = FALSE, expend_bau = FALSE), NA)
df_espar <- tidyr::expand_grid(
iso3 = c("AFG", "BGD", "PAK", "BRN", "CHE", "POL", "SWE", "VUT"),
year = 2018:2020,
ind = billion_ind_codes("hep")[stringr::str_detect(billion_ind_codes("hep"), "espar")]
) %>%
dplyr::mutate(
value = c(
35, 13, 20, 20, 0, 60, 60, 60, 80, 20, 40, 40, 20, 60, 80, 80, 80, 40, 27, 20, 20, 40, 40, 60, 20, 40, 20, 20, 20, 20, 0, 20, 43, 33, 20, 40, 40, 80, 100, 60, 80, 20, 47, 60, 20, 60, 80, 80,
80, 40, 33, 20, 40, 40, 53, 40, 40, 80, 20, 30, 40, 20, 20, 20, 47, 33, 20, 40, 40, 90, 80, 100, 80, 20, 60, 60, 60, 60, 70, 60, 80, 40, 80, 80, 80, 80, 53, 40, 40, 80, 20, 30, 20, 40, 20, 20,
58, 60, 60, 40, 80, 80, 100, 60, 80, 40, 73, 100, 40, 80, 80, 80, 80, 40, 47, 40, 60, 40, 60, 40, 60, 80, 60, 60, 60, 60, 40, 40, 67, 80, 80, 60, 100, 90, 100, 80, 80, 40, 73, 100, 40, 80, 80, 80,
80, 40, 53, 40, 60, 60, 73, 60, 80, 80, 80, 60, 60, 60, 60, 60, 70, 80, 80, 60, 100, 90, 100, 80, 80, 60, 73, 100, 40, 80, 80, 80, 80, 40, 53, 40, 60, 60, 73, 60, 80, 80, 80, 80, 80, 80, 60, 60,
51, 27, 20, 20, 40, 80, 60, 100, 60, 40, 60, 60, 40, 80, 60, 60, 60, 60, 47, 40, 40, 60, 33, 40, 20, 40, 20, 40, 40, 40, 40, 100, 49, 27, 20, 20, 40, 50, 60, 40, 60, 40, 60, 60, 40, 80, 60, 60,
60, 60, 47, 40, 40, 60, 33, 40, 20, 40, 20, 40, 40, 40, 40, 100, 52, 33, 20, 40, 40, 50, 60, 40, 60, 40, 60, 60, 40, 80, 60, 60, 60, 60, 60, 40, 60, 80, 47, 40, 40, 60, 20, 40, 40, 40, 40, 100,
rep(NA_integer_, 96),
rep(NA_integer_, 32), 95, 93, 100, 100, 80, 100, 100, 100, 100, 80, 100, 100, 100, 100, 90, 100, 80, 80, 100, 100, 100, 100, 87, 100, 100, 60, 100, 100, 100, 100, 100, 100, rep(NA_integer_, 32),
rep(NA_integer_, 32), 66, 80, 100, 60, 80, 100, 100, 100, 100, 40, 67, 100, 80, 20, 0, 0, 0, 60, 80, 80, 80, 80, 27, 0, 80, 0, 80, 40, 80, 0, 80, 100, 50, 0, 0, 0, 0, 100, 100, 100, 100, 0, 87, 100, 80, 80, 80, 80,
80, 0, 33, 40, 40, 20, 33, 0, 0, 100, 0, 60, 80, 40, 60, 100,
92, 100, 100, 100, 100, 100, 100, 100, 100, 80, 100, 100, 100, 100, 100, 100, 100, 80, 80, 80, 80, 80, 93, 80, 100, 100, 80, 100, 100, 100, 80, 100, 92, 100, 100, 100, 100, 100, 100, 100, 100, 80, 100, 100, 100, 100, 100, 100,
100, 80, 80, 80, 80, 80, 93, 80, 100, 100, 80, 100, 100, 100, 80, 100, 91, 100, 100, 100, 100, 100, 100, 100, 100, 80, 93, 100, 80, 100, 100, 100, 100, 80, 80, 80, 80, 80, 93, 80, 100, 100, 80, 100, 100, 100, 80, 100,
34, 27, 20, 40, 20, 30, 40, 20, 20, 20, 27, 20, 20, 40, 80, 100, 60, 40, 53, 20, 60, 80, 27, 20, 20, 40, 60, 20, 20, 20, 20, 20,
rep(NA_integer_, 32),
55, 47, 60, 40, 40, 30, 40, 20, 20, 80, 100, 100, 100, 100, 90, 100, 80, 40, 73, 40, 100, 80, 40, 40, 40, 40, 80, 40, 40, 40, 40, 40
),
type = "reported",
scenario = "default",
source = NA_character_
)
testthat::expect_error(add_scenario(df_espar,
scenario_function = "accelerate",
bau_scenario = "default",
make_default = FALSE,
expend_bau = FALSE),
NA)
})
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.