context("so2 99 percentile")
# Load data and create artificial exceedances
so1 <- readRDS("so2_daily1.rds")
so2 <- readRDS("so2_daily2.rds") %>%
dplyr::mutate(max_24h = replace(max_24h, ems_id == "0500886" &
date > as.Date("2013-04-20") &
date < as.Date("2013-07-30"),
NA),
max_24h = replace(max_24h, ems_id == "0500886" &
date > as.Date("2013-06-20") &
date < as.Date("2013-07-01"),
get_std("so2_3yr") + 1),
max_24h = replace(max_24h, ems_id == "0500886" &
date > as.Date("2015-06-20") &
date < as.Date("2015-07-01"),
get_std("so2_3yr") + 1))
test_that("Runs with silently", {
expect_silent(r1 <- so2_yearly_99(so1))
expect_silent(r2 <- so2_yearly_99(so2, by = c("ems_id", "site")))
saveRDS(r1, "so2_yearly99_1.rds")
saveRDS(r2, "so2_yearly99_2.rds")
})
ret1 <- readRDS("so2_yearly99_1.rds")
ret2 <- readRDS("so2_yearly99_2.rds")
test_that("has correct classes", {
for(r in list(ret1, ret2)){
expect_is(r, "data.frame")
expect_is(r$year, "numeric")
expect_is(r$valid_in_year, "numeric")
expect_is(r$quarter_1, "numeric")
expect_is(r$quarter_2, "numeric")
expect_is(r$quarter_3, "numeric")
expect_is(r$quarter_4, "numeric")
expect_is(r$ann_99_percentile, "numeric")
expect_is(r$exceed, "logical")
expect_is(r$flag_daily_incomplete, "logical")
expect_is(r$flag_yearly_incomplete, "logical")
}
})
test_that("has correct dimensions", {
nrows <- length(unique(format(so1$date, "%Y")))
expect_equal(dim(ret1), c(nrows, 11))
nrows <- dplyr::mutate(so2, year = format(date, "%Y")) %>%
dplyr::summarize(n = length(unique(year))) %>%
dplyr::pull(n) %>%
sum(.)
expect_equal(dim(ret2), c(nrows, 13))
})
test_that("has correct data", {
for(r in list(ret1, ret2[ret2$ems_id == "E231866",])){
expect_equivalent(r$valid_in_year[1:3], c(0.951, 0.989, 0.992), tolerance = 0.001)
expect_equivalent(r$quarter_1[1:3], c(0.889, 1, 0.989), tolerance = 0.001)
expect_equivalent(r$quarter_2[1:3], c(1, 1, 1), tolerance = 0.001)
expect_equivalent(r$quarter_3[1:3], c(0.913, 0.978, 1), tolerance = 0.001)
expect_equivalent(r$quarter_4[1:3], c(1, 0.978, 0.978), tolerance = 0.001)
expect_equivalent(r$ann_99_percentile[1:3], c(14.8, 19.4, 17.1), tolerance = 0.001)
}
})
test_that("performs data completeness accurately", {
for(r in list(ret1, ret2)){
expect_true(all(!r$valid_year[(r$quarter_1 < 0.6 |
r$quarter_2 < 0.6|
r$quarter_3 < 0.6|
r$quarter_4 < 0.6)]))
expect_true(all(r$valid_year[!(r$quarter_1 < 0.6 |
r$quarter_2 < 0.6|
r$quarter_3 < 0.6|
r$quarter_4 < 0.6)]))
expect_true(all(is.na(r$ann_99_percentile[!r$valid_year & !r$exceed])))
expect_true(all(!is.na(r$ann_99_percentile[!r$valid_year & r$exceed])))
expect_true(all(r$flag_yearly_incomplete[!r$valid_year & r$exceed]))
}
expect_true(all(!ret1$exceed))
expect_true(any(ret2$exceed))
})
test_that("can exclude data rows", {
high_dates <- dplyr::filter(so2, max_24h > get_std("so2_3yr")) %>%
dplyr::select(ems_id, site, date)
expect_silent(ret3 <- so2_yearly_99(so2, by = c("ems_id", "site"),
exclude_df = high_dates,
exclude_df_dt = c("date"),
management = TRUE,
quiet = TRUE))
expect_equivalent(dplyr::select(ret2, "ems_id", "site", "year",
"valid_in_year", "quarter_1",
"quarter_2", "quarter_3", "quarter_4",
"valid_year",
"flag_daily_incomplete"),
dplyr::select(ret3, "ems_id", "site", "year",
"valid_in_year", "quarter_1",
"quarter_2", "quarter_3", "quarter_4",
"valid_year",
"flag_daily_incomplete"))
expect_false(all(ret2$ann_99_percentile == ret3$ann_99_percentile, na.rm = TRUE))
expect_true(all(ret2$ann_99_percentile >= ret3$ann_99_percentile, na.rm = TRUE))
expect_is(ret3$excluded, "logical")
expect_equal(ret3$excluded, c(TRUE, TRUE, TRUE, FALSE, FALSE, FALSE))
expect_equivalent(ret3$ann_99_percentile, c(NA, NA, 1.80, 14.8, 19.4, 17.1))
expect_false(all(ret2$exceed == ret3$exceed))
})
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