Nothing
tests/testthat/test-summariseMissingData.R
In OmopSketch: Characterise Tables of an OMOP Common Data Model Instance
test_that("summariseMissingData() works", {
skip_on_cran()
# Load mock database ----
cdm <- cdmEunomia()
# Check all tables work ----
expect_true(inherits(summariseMissingData(cdm, "drug_exposure"), "summarised_result"))
expect_no_error(y <- summariseMissingData(cdm, "observation_period"))
checkResultType(y, "summarise_missing_data")
expect_no_error(x <- summariseMissingData(cdm, "visit_occurrence"))
expect_no_error(summariseMissingData(cdm, "condition_occurrence"))
expect_no_error(summariseMissingData(cdm, "drug_exposure"))
expect_no_error(summariseMissingData(cdm, "procedure_occurrence", interval = "years"))
expect_warning(summariseMissingData(cdm, "device_exposure"))
expect_no_error(z <- summariseMissingData(cdm, "measurement"))
expect_no_error(s <- summariseMissingData(cdm, "observation"))
expect_warning(de <- summariseMissingData(cdm, "death"))
checkResultType(de, "summarise_missing_data")
expect_warning(p <- summariseMissingData(cdm, "person", ageGroup = list(c(0, 50))))
expect_true(omopgenerics::settings(p)$strata == "")
expect_no_error(all <- summariseMissingData(cdm, c("observation_period", "visit_occurrence", "measurement")))
expect_equal(all, dplyr::bind_rows(y, x, z))
expect_equal(summariseMissingData(cdm, "observation"), summariseMissingData(cdm, "observation", col = colnames(cdm[["observation"]])))
x <- summariseMissingData(cdm, "procedure_occurrence", col = "procedure_date")
expect_equal(summariseMissingData(cdm, c("procedure_occurrence", "observation"), col = "procedure_date"), dplyr::bind_rows(x, s))
y <- summariseMissingData(cdm, "observation", col = "observation_date")
expect_equal(summariseMissingData(cdm, c("procedure_occurrence", "observation"), col = c("procedure_date", "observation_date")), dplyr::bind_rows(x, y))
# Check inputs ----
expect_true(summariseMissingData(cdm, "procedure_occurrence", col = "person_id") |>
dplyr::select(estimate_value) |>
dplyr::mutate(estimate_value = as.numeric(estimate_value)) |>
dplyr::summarise(sum = sum(estimate_value)) |>
dplyr::pull() == 0)
expect_true(summariseMissingData(cdm, "procedure_occurrence", col = "person_id", sex = TRUE, ageGroup = list(c(0, 50), c(51, Inf))) |>
dplyr::distinct(.data$strata_level) |>
dplyr::tally() |>
dplyr::pull() == 9)
expect_true(summariseMissingData(cdm, "procedure_occurrence", col = "person_id", ageGroup = list(c(0, 50))) |>
dplyr::distinct(.data$strata_level) |>
dplyr::tally() |>
dplyr::pull() == 3)
cdm$procedure_occurrence <- cdm$procedure_occurrence |>
dplyr::mutate(procedure_concept_id = NA_integer_) |>
dplyr::compute(name = "procedure_occurrence", temporary = FALSE)
expect_warning(summariseMissingData(cdm, "procedure_occurrence", col = "procedure_concept_id", ageGroup = list(c(0, 50))))
expect_no_error(summariseMissingData(cdm, "procedure_occurrence", col = "procedure_concept_id", ageGroup = list(c(0, 50)), sample = 100))
})
test_that("dateRange argument works", {
skip_on_cran()
# Load mock database ----
cdm <- cdmEunomia()
expect_no_error(summariseMissingData(cdm, "condition_occurrence", dateRange = as.Date(c("2012-01-01", "2018-01-01"))))
expect_message(x <- summariseMissingData(cdm, "drug_exposure", dateRange = as.Date(c("2012-01-01", "2025-01-01"))))
observationRange <- cdm$observation_period |>
dplyr::summarise(
minobs = min(.data$observation_period_start_date, na.rm = TRUE),
maxobs = max(.data$observation_period_end_date, na.rm = TRUE)
)
expect_no_error(y <- summariseMissingData(cdm, "drug_exposure", dateRange = as.Date(c("2012-01-01", observationRange |> dplyr::pull("maxobs")))))
expect_equal(x, y, ignore_attr = TRUE)
expect_false(settings(x)$study_period_end == settings(y)$study_period_end)
expect_error(summariseMissingData(cdm, "drug_exposure", dateRange = as.Date(c("2015-01-01", "2014-01-01"))))
expect_warning(z <- summariseMissingData(cdm, "drug_exposure", dateRange = as.Date(c("2020-01-01", "2021-01-01"))))
expect_equal(z, omopgenerics::emptySummarisedResult(), ignore_attr = TRUE)
expect_equal(summariseMissingData(cdm, "drug_exposure", dateRange = as.Date(c("2012-01-01", NA))), y, ignore_attr = TRUE)
checkResultType(z, "summarise_missing_data")
expect_equal(colnames(settings(z)), colnames(settings(x)))
PatientProfiles::mockDisconnect(cdm = cdm)
})
test_that("tableMissingData() works", {
skip_on_cran()
# Load mock database ----
cdm <- cdmEunomia()
# Check that works ----
expect_no_error(x <- tableMissingData(summariseMissingData(cdm, "condition_occurrence")))
expect_true(inherits(x, "gt_tbl"))
expect_no_error(y <- tableMissingData(summariseMissingData(cdm, c(
"observation_period",
"measurement"
))))
expect_true(inherits(y, "gt_tbl"))
expect_warning(t <- summariseMissingData(cdm, "death"))
expect_warning(inherits(tableMissingData(t), "gt_tbl"))
PatientProfiles::mockDisconnect(cdm = cdm)
})
test_that("col not present in table", {
skip_on_cran()
# Load mock database ----
# Load mock database
cdm <- omopgenerics::cdmFromTables(
tables = list(
person = dplyr::tibble(
person_id = as.integer(1:4),
gender_concept_id = c(8507L, 8532L, 8532L, 8507L),
year_of_birth = 2010L,
month_of_birth = 1L,
day_of_birth = 1L,
race_concept_id = 0L,
ethnicity_concept_id = 0L
),
observation_period = dplyr::tibble(
observation_period_id = as.integer(1:8),
person_id = c(1, 1, 1, 2, 2, 3, 3, 4) |> as.integer(),
observation_period_start_date = as.Date(c(
"2020-03-01", "2020-03-25", "2020-04-25", "2020-08-10", "2020-03-10",
"2020-03-01", "2020-04-10", "2020-03-10"
)),
observation_period_end_date = as.Date(c(
"2020-03-20", "2020-03-30", "2020-08-15", "2020-12-31", "2020-03-27",
"2020-03-09", "2020-05-08", "2020-12-10"
)),
period_type_concept_id = 0L
)
),
cdmName = "mock data"
)
cdm <- CDMConnector::copyCdmTo(
con = connection(), cdm = cdm, schema = schema()
)
expect_no_error(expect_message(summariseMissingData(cdm, "person", col = NULL)))
PatientProfiles::mockDisconnect(cdm = cdm)
})
test_that("no tables created", {
skip_on_cran()
# Load mock database ----
cdm <- cdmEunomia()
startNames <- CDMConnector::listSourceTables(cdm)
results <- summariseMissingData(
cdm = cdm,
omopTableName = c("drug_exposure", "condition_occurrence"),
interval = "years",
sex = TRUE,
ageGroup = list(
c(0, 17),
c(18, 65),
c(66, 100)
),
dateRange = as.Date(c("2012-01-01", "2018-01-01")),
sample = 100
)
endNames <- CDMConnector::listSourceTables(cdm)
expect_true(length(setdiff(endNames, startNames)) == 0)
PatientProfiles::mockDisconnect(cdm = cdm)
})
test_that("interval argument works", {
skip_on_cran()
# Load mock database ----
cdm <- cdmEunomia()
expect_no_error(y <- summariseMissingData(
cdm = cdm,
omopTableName = "drug_exposure",
interval = "years"
))
expect_no_error(o <- summariseMissingData(
omopTableName = "drug_exposure",
cdm = cdm,
interval = "overall"
))
expect_no_error(q <- summariseMissingData(
omopTableName = "drug_exposure",
cdm = cdm,
interval = "quarters"
))
expect_no_error(m <- summariseMissingData(
omopTableName = "drug_exposure",
cdm = cdm,
interval = "months"
))
m_quarters <- m |>
omopgenerics::splitAdditional() |>
omopgenerics::pivotEstimates() |>
dplyr::filter(time_interval != "overall") |>
dplyr::mutate(
start_date = as.Date(sub(" to .*", "", time_interval)),
quarter_start = lubridate::quarter(start_date, type = "date_first"),
quarter_end = lubridate::quarter(start_date, type = "date_last"),
quarter = paste(quarter_start, "to", quarter_end)
) |>
dplyr::select(!c("time_interval", "start_date", "quarter_start", "quarter_end")) |>
dplyr::group_by(quarter, variable_name) |>
dplyr::summarise(na_count = sum(na_count), .groups = "drop") |>
dplyr::rename("time_interval" = quarter) |>
dplyr::arrange(time_interval)
q_quarters <- q |>
omopgenerics::splitAdditional() |>
omopgenerics::pivotEstimates() |>
dplyr::filter(time_interval != "overall") |>
dplyr::select(time_interval, variable_name, na_count) |>
dplyr::arrange(time_interval)
expect_equal(m_quarters |>
sortTibble(), q_quarters |> sortTibble())
m_year <- m |>
omopgenerics::splitAdditional() |>
dplyr::filter(time_interval != "overall") |>
dplyr::mutate(
# Extract the start date
start_date = clock::date_parse(stringr::str_extract(time_interval, "^\\d{4}-\\d{2}-\\d{2}")),
# Convert start_date to a year-month-day object and extract the year
year = clock::get_year(clock::as_year_month_day(start_date))
) |>
omopgenerics::pivotEstimates() |>
dplyr::group_by(year, variable_name) |>
dplyr::summarise(
na_count = sum(na_count),
.groups = "drop"
) |>
dplyr::arrange(year)
y_year <- y |>
omopgenerics::splitAdditional() |>
dplyr::filter(time_interval != "overall") |>
dplyr::mutate(
# Extract the start date
start_date = clock::date_parse(stringr::str_extract(time_interval, "^\\d{4}-\\d{2}-\\d{2}")),
# Convert start_date to a year-month-day object and extract the year
year = clock::get_year(clock::as_year_month_day(start_date))
) |>
omopgenerics::pivotEstimates() |>
dplyr::select(year, variable_name, na_count) |>
dplyr::arrange(year)
expect_equal(m_year |> sortTibble(), y_year |> sortTibble())
o <- o |>
omopgenerics::splitAdditional() |>
omopgenerics::pivotEstimates() |>
dplyr::select(variable_name, na_count)
expect_equal(y_year |> dplyr::group_by(variable_name) |> dplyr::summarise(na_count = sum(na_count), .groups = "drop") |> sortTibble(), o |> sortTibble())
q_year <- q |>
omopgenerics::splitAdditional() |>
dplyr::filter(time_interval != "overall") |>
dplyr::mutate(
# Extract the start date
start_date = clock::date_parse(stringr::str_extract(time_interval, "^\\d{4}-\\d{2}-\\d{2}")),
# Convert start_date to a year-month-day object and extract the year
year = clock::get_year(clock::as_year_month_day(start_date))
) |>
omopgenerics::pivotEstimates() |>
dplyr::group_by(year, variable_name) |>
dplyr::summarise(
na_count = sum(na_count),
.groups = "drop"
) |>
dplyr::arrange(year)
expect_equal(q_year |> sortTibble(), y_year |> sortTibble())
expect_no_error(x <- summariseMissingData(cdm, "drug_exposure", sex = TRUE, interval = "years"))
expect_true(x |> dplyr::distinct(.data$additional_level) |> dplyr::tally() > 1)
PatientProfiles::mockDisconnect(cdm = cdm)
})
test_that("summariseMissingData() works", {
skip_on_cran()
# Load mock database ----
cdm <- cdmEunomia()
# Check all tables work ----
expect_true(inherits(summariseMissingData(cdm, "drug_exposure"), "summarised_result"))
expect_no_error(y <- summariseMissingData(cdm, "observation_period"))
checkResultType(y, "summarise_missing_data")
expect_no_error(x <- summariseMissingData(cdm, "visit_occurrence"))
expect_no_error(summariseMissingData(cdm, "condition_occurrence"))
expect_no_error(summariseMissingData(cdm, "drug_exposure"))
expect_no_error(summariseMissingData(cdm, "procedure_occurrence", interval = "years"))
expect_warning(summariseMissingData(cdm, "device_exposure"))
expect_no_error(z <- summariseMissingData(cdm, "measurement"))
expect_no_error(s <- summariseMissingData(cdm, "observation"))
expect_warning(de <- summariseMissingData(cdm, "death"))
checkResultType(de, "summarise_missing_data")
expect_warning(p <- summariseMissingData(cdm, "person", ageGroup = list(c(0, 50))))
expect_true(omopgenerics::settings(p)$strata == "")
expect_no_error(all <- summariseMissingData(cdm, c("observation_period", "visit_occurrence", "measurement")))
expect_equal(all, dplyr::bind_rows(y, x, z))
expect_equal(summariseMissingData(cdm, "observation"), summariseMissingData(cdm, "observation", col = colnames(cdm[["observation"]])))
x <- summariseMissingData(cdm, "procedure_occurrence", col = "procedure_date")
expect_equal(summariseMissingData(cdm, c("procedure_occurrence", "observation"), col = "procedure_date"), dplyr::bind_rows(x, s))
y <- summariseMissingData(cdm, "observation", col = "observation_date")
expect_equal(summariseMissingData(cdm, c("procedure_occurrence", "observation"), col = c("procedure_date", "observation_date")), dplyr::bind_rows(x, y))
# Check inputs ----
expect_true(summariseMissingData(cdm, "procedure_occurrence", col = "person_id") |>
dplyr::select(estimate_value) |>
dplyr::mutate(estimate_value = as.numeric(estimate_value)) |>
dplyr::summarise(sum = sum(estimate_value)) |>
dplyr::pull() == 0)
expect_true(summariseMissingData(cdm, "procedure_occurrence", col = "person_id", sex = TRUE, ageGroup = list(c(0, 50), c(51, Inf))) |>
dplyr::distinct(.data$strata_level) |>
dplyr::tally() |>
dplyr::pull() == 9)
expect_true(summariseMissingData(cdm, "procedure_occurrence", col = "person_id", ageGroup = list(c(0, 50))) |>
dplyr::distinct(.data$strata_level) |>
dplyr::tally() |>
dplyr::pull() == 3)
cdm$procedure_occurrence <- cdm$procedure_occurrence |>
dplyr::mutate(procedure_concept_id = NA_integer_) |>
dplyr::compute(name = "procedure_occurrence", temporary = FALSE)
expect_warning(summariseMissingData(cdm, "procedure_occurrence", col = "procedure_concept_id", ageGroup = list(c(0, 50))))
expect_no_error(summariseMissingData(cdm, "procedure_occurrence", col = "procedure_concept_id", ageGroup = list(c(0, 50)), sample = 100))
})
test_that("dateRange argument works", {
skip_on_cran()
# Load mock database ----
cdm <- cdmEunomia()
expect_no_error(summariseMissingData(cdm, "condition_occurrence", dateRange = as.Date(c("2012-01-01", "2018-01-01"))))
expect_message(x <- summariseMissingData(cdm, "drug_exposure", dateRange = as.Date(c("2012-01-01", "2025-01-01"))))
observationRange <- cdm$observation_period |>
dplyr::summarise(
minobs = min(.data$observation_period_start_date, na.rm = TRUE),
maxobs = max(.data$observation_period_end_date, na.rm = TRUE)
)
expect_no_error(y <- summariseMissingData(cdm, "drug_exposure", dateRange = as.Date(c("2012-01-01", observationRange |> dplyr::pull("maxobs")))))
expect_equal(x, y, ignore_attr = TRUE)
expect_false(settings(x)$study_period_end == settings(y)$study_period_end)
expect_error(summariseMissingData(cdm, "drug_exposure", dateRange = as.Date(c("2015-01-01", "2014-01-01"))))
expect_warning(z <- summariseMissingData(cdm, "drug_exposure", dateRange = as.Date(c("2020-01-01", "2021-01-01"))))
expect_equal(z, omopgenerics::emptySummarisedResult(), ignore_attr = TRUE)
expect_equal(summariseMissingData(cdm, "drug_exposure", dateRange = as.Date(c("2012-01-01", NA))), y, ignore_attr = TRUE)
checkResultType(z, "summarise_missing_data")
expect_equal(colnames(settings(z)), colnames(settings(x)))
PatientProfiles::mockDisconnect(cdm = cdm)
})
test_that("tableMissingData() works", {
skip_on_cran()
# Load mock database ----
cdm <- cdmEunomia()
# Check that works ----
expect_no_error(x <- tableMissingData(summariseMissingData(cdm, "condition_occurrence")))
expect_true(inherits(x, "gt_tbl"))
expect_no_error(y <- tableMissingData(summariseMissingData(cdm, c(
"observation_period",
"measurement"
))))
expect_true(inherits(y, "gt_tbl"))
expect_warning(t <- summariseMissingData(cdm, "death"))
expect_warning(inherits(tableMissingData(t), "gt_tbl"))
PatientProfiles::mockDisconnect(cdm = cdm)
})
test_that("col not present in table", {
skip_on_cran()
# Load mock database ----
# Load mock database
cdm <- omopgenerics::cdmFromTables(
tables = list(
person = dplyr::tibble(
person_id = as.integer(1:4),
gender_concept_id = c(8507L, 8532L, 8532L, 8507L),
year_of_birth = 2010L,
month_of_birth = 1L,
day_of_birth = 1L,
race_concept_id = 0L,
ethnicity_concept_id = 0L
),
observation_period = dplyr::tibble(
observation_period_id = as.integer(1:8),
person_id = c(1, 1, 1, 2, 2, 3, 3, 4) |> as.integer(),
observation_period_start_date = as.Date(c(
"2020-03-01", "2020-03-25", "2020-04-25", "2020-08-10", "2020-03-10",
"2020-03-01", "2020-04-10", "2020-03-10"
)),
observation_period_end_date = as.Date(c(
"2020-03-20", "2020-03-30", "2020-08-15", "2020-12-31", "2020-03-27",
"2020-03-09", "2020-05-08", "2020-12-10"
)),
period_type_concept_id = 0L
)
),
cdmName = "mock data"
)
cdm <- CDMConnector::copyCdmTo(
con = connection(), cdm = cdm, schema = schema()
)
expect_no_error(expect_message(summariseMissingData(cdm, "person", col = NULL)))
PatientProfiles::mockDisconnect(cdm = cdm)
})
test_that("no tables created", {
skip_on_cran()
# Load mock database ----
cdm <- cdmEunomia()
startNames <- CDMConnector::listSourceTables(cdm)
results <- summariseMissingData(
cdm = cdm,
omopTableName = c("drug_exposure", "condition_occurrence"),
interval = "years",
sex = TRUE,
ageGroup = list(
c(0, 17),
c(18, 65),
c(66, 100)
),
dateRange = as.Date(c("2012-01-01", "2018-01-01")),
sample = 100
)
endNames <- CDMConnector::listSourceTables(cdm)
expect_true(length(setdiff(endNames, startNames)) == 0)
PatientProfiles::mockDisconnect(cdm = cdm)
})
test_that("zero count argument works", {
skip_on_cran()
# Load mock database ----
cdm <- cdmEunomia()
cdm$person <- cdm$person |>
dplyr::mutate(person_id = dplyr::if_else(.data$person_id == 6, 0, .data$person_id))
expect_equal(summariseMissingData(cdm, omopTableName = "person") |>
dplyr::filter(variable_name == "person_id" & estimate_name == "zero_count") |>
dplyr::pull(.data$estimate_value) |> as.integer(), 1L)
columns <- cdm$drug_exposure |> colnames()
columns_zero <- columns[grepl("_id$", columns)]
expect_equal(summariseMissingData(cdm, "drug_exposure") |>
dplyr::filter(estimate_name == "zero_count") |>
dplyr::distinct(variable_name) |>
dplyr::pull() |> sort(), columns_zero |> sort())
PatientProfiles::mockDisconnect(cdm = cdm)
})
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test_that("summariseMissingData() works", {
skip_on_cran()
# Load mock database ----
cdm <- cdmEunomia()
# Check all tables work ----
expect_true(inherits(summariseMissingData(cdm, "drug_exposure"), "summarised_result"))
expect_no_error(y <- summariseMissingData(cdm, "observation_period"))
checkResultType(y, "summarise_missing_data")
expect_no_error(x <- summariseMissingData(cdm, "visit_occurrence"))
expect_no_error(summariseMissingData(cdm, "condition_occurrence"))
expect_no_error(summariseMissingData(cdm, "drug_exposure"))
expect_no_error(summariseMissingData(cdm, "procedure_occurrence", interval = "years"))
expect_warning(summariseMissingData(cdm, "device_exposure"))
expect_no_error(z <- summariseMissingData(cdm, "measurement"))
expect_no_error(s <- summariseMissingData(cdm, "observation"))
expect_warning(de <- summariseMissingData(cdm, "death"))
checkResultType(de, "summarise_missing_data")
expect_warning(p <- summariseMissingData(cdm, "person", ageGroup = list(c(0, 50))))
expect_true(omopgenerics::settings(p)$strata == "")
expect_no_error(all <- summariseMissingData(cdm, c("observation_period", "visit_occurrence", "measurement")))
expect_equal(all, dplyr::bind_rows(y, x, z))
expect_equal(summariseMissingData(cdm, "observation"), summariseMissingData(cdm, "observation", col = colnames(cdm[["observation"]])))
x <- summariseMissingData(cdm, "procedure_occurrence", col = "procedure_date")
expect_equal(summariseMissingData(cdm, c("procedure_occurrence", "observation"), col = "procedure_date"), dplyr::bind_rows(x, s))
y <- summariseMissingData(cdm, "observation", col = "observation_date")
expect_equal(summariseMissingData(cdm, c("procedure_occurrence", "observation"), col = c("procedure_date", "observation_date")), dplyr::bind_rows(x, y))
# Check inputs ----
expect_true(summariseMissingData(cdm, "procedure_occurrence", col = "person_id") |>
dplyr::select(estimate_value) |>
dplyr::mutate(estimate_value = as.numeric(estimate_value)) |>
dplyr::summarise(sum = sum(estimate_value)) |>
dplyr::pull() == 0)
expect_true(summariseMissingData(cdm, "procedure_occurrence", col = "person_id", sex = TRUE, ageGroup = list(c(0, 50), c(51, Inf))) |>
dplyr::distinct(.data$strata_level) |>
dplyr::tally() |>
dplyr::pull() == 9)
expect_true(summariseMissingData(cdm, "procedure_occurrence", col = "person_id", ageGroup = list(c(0, 50))) |>
dplyr::distinct(.data$strata_level) |>
dplyr::tally() |>
dplyr::pull() == 3)
cdm$procedure_occurrence <- cdm$procedure_occurrence |>
dplyr::mutate(procedure_concept_id = NA_integer_) |>
dplyr::compute(name = "procedure_occurrence", temporary = FALSE)
expect_warning(summariseMissingData(cdm, "procedure_occurrence", col = "procedure_concept_id", ageGroup = list(c(0, 50))))
expect_no_error(summariseMissingData(cdm, "procedure_occurrence", col = "procedure_concept_id", ageGroup = list(c(0, 50)), sample = 100))
})
test_that("dateRange argument works", {
skip_on_cran()
# Load mock database ----
cdm <- cdmEunomia()
expect_no_error(summariseMissingData(cdm, "condition_occurrence", dateRange = as.Date(c("2012-01-01", "2018-01-01"))))
expect_message(x <- summariseMissingData(cdm, "drug_exposure", dateRange = as.Date(c("2012-01-01", "2025-01-01"))))
observationRange <- cdm$observation_period |>
dplyr::summarise(
minobs = min(.data$observation_period_start_date, na.rm = TRUE),
maxobs = max(.data$observation_period_end_date, na.rm = TRUE)
)
expect_no_error(y <- summariseMissingData(cdm, "drug_exposure", dateRange = as.Date(c("2012-01-01", observationRange |> dplyr::pull("maxobs")))))
expect_equal(x, y, ignore_attr = TRUE)
expect_false(settings(x)$study_period_end == settings(y)$study_period_end)
expect_error(summariseMissingData(cdm, "drug_exposure", dateRange = as.Date(c("2015-01-01", "2014-01-01"))))
expect_warning(z <- summariseMissingData(cdm, "drug_exposure", dateRange = as.Date(c("2020-01-01", "2021-01-01"))))
expect_equal(z, omopgenerics::emptySummarisedResult(), ignore_attr = TRUE)
expect_equal(summariseMissingData(cdm, "drug_exposure", dateRange = as.Date(c("2012-01-01", NA))), y, ignore_attr = TRUE)
checkResultType(z, "summarise_missing_data")
expect_equal(colnames(settings(z)), colnames(settings(x)))
PatientProfiles::mockDisconnect(cdm = cdm)
})
test_that("tableMissingData() works", {
skip_on_cran()
# Load mock database ----
cdm <- cdmEunomia()
# Check that works ----
expect_no_error(x <- tableMissingData(summariseMissingData(cdm, "condition_occurrence")))
expect_true(inherits(x, "gt_tbl"))
expect_no_error(y <- tableMissingData(summariseMissingData(cdm, c(
"observation_period",
"measurement"
))))
expect_true(inherits(y, "gt_tbl"))
expect_warning(t <- summariseMissingData(cdm, "death"))
expect_warning(inherits(tableMissingData(t), "gt_tbl"))
PatientProfiles::mockDisconnect(cdm = cdm)
})
test_that("col not present in table", {
skip_on_cran()
# Load mock database ----
# Load mock database
cdm <- omopgenerics::cdmFromTables(
tables = list(
person = dplyr::tibble(
person_id = as.integer(1:4),
gender_concept_id = c(8507L, 8532L, 8532L, 8507L),
year_of_birth = 2010L,
month_of_birth = 1L,
day_of_birth = 1L,
race_concept_id = 0L,
ethnicity_concept_id = 0L
),
observation_period = dplyr::tibble(
observation_period_id = as.integer(1:8),
person_id = c(1, 1, 1, 2, 2, 3, 3, 4) |> as.integer(),
observation_period_start_date = as.Date(c(
"2020-03-01", "2020-03-25", "2020-04-25", "2020-08-10", "2020-03-10",
"2020-03-01", "2020-04-10", "2020-03-10"
)),
observation_period_end_date = as.Date(c(
"2020-03-20", "2020-03-30", "2020-08-15", "2020-12-31", "2020-03-27",
"2020-03-09", "2020-05-08", "2020-12-10"
)),
period_type_concept_id = 0L
)
),
cdmName = "mock data"
)
cdm <- CDMConnector::copyCdmTo(
con = connection(), cdm = cdm, schema = schema()
)
expect_no_error(expect_message(summariseMissingData(cdm, "person", col = NULL)))
PatientProfiles::mockDisconnect(cdm = cdm)
})
test_that("no tables created", {
skip_on_cran()
# Load mock database ----
cdm <- cdmEunomia()
startNames <- CDMConnector::listSourceTables(cdm)
results <- summariseMissingData(
cdm = cdm,
omopTableName = c("drug_exposure", "condition_occurrence"),
interval = "years",
sex = TRUE,
ageGroup = list(
c(0, 17),
c(18, 65),
c(66, 100)
),
dateRange = as.Date(c("2012-01-01", "2018-01-01")),
sample = 100
)
endNames <- CDMConnector::listSourceTables(cdm)
expect_true(length(setdiff(endNames, startNames)) == 0)
PatientProfiles::mockDisconnect(cdm = cdm)
})
test_that("interval argument works", {
skip_on_cran()
# Load mock database ----
cdm <- cdmEunomia()
expect_no_error(y <- summariseMissingData(
cdm = cdm,
omopTableName = "drug_exposure",
interval = "years"
))
expect_no_error(o <- summariseMissingData(
omopTableName = "drug_exposure",
cdm = cdm,
interval = "overall"
))
expect_no_error(q <- summariseMissingData(
omopTableName = "drug_exposure",
cdm = cdm,
interval = "quarters"
))
expect_no_error(m <- summariseMissingData(
omopTableName = "drug_exposure",
cdm = cdm,
interval = "months"
))
m_quarters <- m |>
omopgenerics::splitAdditional() |>
omopgenerics::pivotEstimates() |>
dplyr::filter(time_interval != "overall") |>
dplyr::mutate(
start_date = as.Date(sub(" to .*", "", time_interval)),
quarter_start = lubridate::quarter(start_date, type = "date_first"),
quarter_end = lubridate::quarter(start_date, type = "date_last"),
quarter = paste(quarter_start, "to", quarter_end)
) |>
dplyr::select(!c("time_interval", "start_date", "quarter_start", "quarter_end")) |>
dplyr::group_by(quarter, variable_name) |>
dplyr::summarise(na_count = sum(na_count), .groups = "drop") |>
dplyr::rename("time_interval" = quarter) |>
dplyr::arrange(time_interval)
q_quarters <- q |>
omopgenerics::splitAdditional() |>
omopgenerics::pivotEstimates() |>
dplyr::filter(time_interval != "overall") |>
dplyr::select(time_interval, variable_name, na_count) |>
dplyr::arrange(time_interval)
expect_equal(m_quarters |>
sortTibble(), q_quarters |> sortTibble())
m_year <- m |>
omopgenerics::splitAdditional() |>
dplyr::filter(time_interval != "overall") |>
dplyr::mutate(
# Extract the start date
start_date = clock::date_parse(stringr::str_extract(time_interval, "^\\d{4}-\\d{2}-\\d{2}")),
# Convert start_date to a year-month-day object and extract the year
year = clock::get_year(clock::as_year_month_day(start_date))
) |>
omopgenerics::pivotEstimates() |>
dplyr::group_by(year, variable_name) |>
dplyr::summarise(
na_count = sum(na_count),
.groups = "drop"
) |>
dplyr::arrange(year)
y_year <- y |>
omopgenerics::splitAdditional() |>
dplyr::filter(time_interval != "overall") |>
dplyr::mutate(
# Extract the start date
start_date = clock::date_parse(stringr::str_extract(time_interval, "^\\d{4}-\\d{2}-\\d{2}")),
# Convert start_date to a year-month-day object and extract the year
year = clock::get_year(clock::as_year_month_day(start_date))
) |>
omopgenerics::pivotEstimates() |>
dplyr::select(year, variable_name, na_count) |>
dplyr::arrange(year)
expect_equal(m_year |> sortTibble(), y_year |> sortTibble())
o <- o |>
omopgenerics::splitAdditional() |>
omopgenerics::pivotEstimates() |>
dplyr::select(variable_name, na_count)
expect_equal(y_year |> dplyr::group_by(variable_name) |> dplyr::summarise(na_count = sum(na_count), .groups = "drop") |> sortTibble(), o |> sortTibble())
q_year <- q |>
omopgenerics::splitAdditional() |>
dplyr::filter(time_interval != "overall") |>
dplyr::mutate(
# Extract the start date
start_date = clock::date_parse(stringr::str_extract(time_interval, "^\\d{4}-\\d{2}-\\d{2}")),
# Convert start_date to a year-month-day object and extract the year
year = clock::get_year(clock::as_year_month_day(start_date))
) |>
omopgenerics::pivotEstimates() |>
dplyr::group_by(year, variable_name) |>
dplyr::summarise(
na_count = sum(na_count),
.groups = "drop"
) |>
dplyr::arrange(year)
expect_equal(q_year |> sortTibble(), y_year |> sortTibble())
expect_no_error(x <- summariseMissingData(cdm, "drug_exposure", sex = TRUE, interval = "years"))
expect_true(x |> dplyr::distinct(.data$additional_level) |> dplyr::tally() > 1)
PatientProfiles::mockDisconnect(cdm = cdm)
})
test_that("summariseMissingData() works", {
skip_on_cran()
# Load mock database ----
cdm <- cdmEunomia()
# Check all tables work ----
expect_true(inherits(summariseMissingData(cdm, "drug_exposure"), "summarised_result"))
expect_no_error(y <- summariseMissingData(cdm, "observation_period"))
checkResultType(y, "summarise_missing_data")
expect_no_error(x <- summariseMissingData(cdm, "visit_occurrence"))
expect_no_error(summariseMissingData(cdm, "condition_occurrence"))
expect_no_error(summariseMissingData(cdm, "drug_exposure"))
expect_no_error(summariseMissingData(cdm, "procedure_occurrence", interval = "years"))
expect_warning(summariseMissingData(cdm, "device_exposure"))
expect_no_error(z <- summariseMissingData(cdm, "measurement"))
expect_no_error(s <- summariseMissingData(cdm, "observation"))
expect_warning(de <- summariseMissingData(cdm, "death"))
checkResultType(de, "summarise_missing_data")
expect_warning(p <- summariseMissingData(cdm, "person", ageGroup = list(c(0, 50))))
expect_true(omopgenerics::settings(p)$strata == "")
expect_no_error(all <- summariseMissingData(cdm, c("observation_period", "visit_occurrence", "measurement")))
expect_equal(all, dplyr::bind_rows(y, x, z))
expect_equal(summariseMissingData(cdm, "observation"), summariseMissingData(cdm, "observation", col = colnames(cdm[["observation"]])))
x <- summariseMissingData(cdm, "procedure_occurrence", col = "procedure_date")
expect_equal(summariseMissingData(cdm, c("procedure_occurrence", "observation"), col = "procedure_date"), dplyr::bind_rows(x, s))
y <- summariseMissingData(cdm, "observation", col = "observation_date")
expect_equal(summariseMissingData(cdm, c("procedure_occurrence", "observation"), col = c("procedure_date", "observation_date")), dplyr::bind_rows(x, y))
# Check inputs ----
expect_true(summariseMissingData(cdm, "procedure_occurrence", col = "person_id") |>
dplyr::select(estimate_value) |>
dplyr::mutate(estimate_value = as.numeric(estimate_value)) |>
dplyr::summarise(sum = sum(estimate_value)) |>
dplyr::pull() == 0)
expect_true(summariseMissingData(cdm, "procedure_occurrence", col = "person_id", sex = TRUE, ageGroup = list(c(0, 50), c(51, Inf))) |>
dplyr::distinct(.data$strata_level) |>
dplyr::tally() |>
dplyr::pull() == 9)
expect_true(summariseMissingData(cdm, "procedure_occurrence", col = "person_id", ageGroup = list(c(0, 50))) |>
dplyr::distinct(.data$strata_level) |>
dplyr::tally() |>
dplyr::pull() == 3)
cdm$procedure_occurrence <- cdm$procedure_occurrence |>
dplyr::mutate(procedure_concept_id = NA_integer_) |>
dplyr::compute(name = "procedure_occurrence", temporary = FALSE)
expect_warning(summariseMissingData(cdm, "procedure_occurrence", col = "procedure_concept_id", ageGroup = list(c(0, 50))))
expect_no_error(summariseMissingData(cdm, "procedure_occurrence", col = "procedure_concept_id", ageGroup = list(c(0, 50)), sample = 100))
})
test_that("dateRange argument works", {
skip_on_cran()
# Load mock database ----
cdm <- cdmEunomia()
expect_no_error(summariseMissingData(cdm, "condition_occurrence", dateRange = as.Date(c("2012-01-01", "2018-01-01"))))
expect_message(x <- summariseMissingData(cdm, "drug_exposure", dateRange = as.Date(c("2012-01-01", "2025-01-01"))))
observationRange <- cdm$observation_period |>
dplyr::summarise(
minobs = min(.data$observation_period_start_date, na.rm = TRUE),
maxobs = max(.data$observation_period_end_date, na.rm = TRUE)
)
expect_no_error(y <- summariseMissingData(cdm, "drug_exposure", dateRange = as.Date(c("2012-01-01", observationRange |> dplyr::pull("maxobs")))))
expect_equal(x, y, ignore_attr = TRUE)
expect_false(settings(x)$study_period_end == settings(y)$study_period_end)
expect_error(summariseMissingData(cdm, "drug_exposure", dateRange = as.Date(c("2015-01-01", "2014-01-01"))))
expect_warning(z <- summariseMissingData(cdm, "drug_exposure", dateRange = as.Date(c("2020-01-01", "2021-01-01"))))
expect_equal(z, omopgenerics::emptySummarisedResult(), ignore_attr = TRUE)
expect_equal(summariseMissingData(cdm, "drug_exposure", dateRange = as.Date(c("2012-01-01", NA))), y, ignore_attr = TRUE)
checkResultType(z, "summarise_missing_data")
expect_equal(colnames(settings(z)), colnames(settings(x)))
PatientProfiles::mockDisconnect(cdm = cdm)
})
test_that("tableMissingData() works", {
skip_on_cran()
# Load mock database ----
cdm <- cdmEunomia()
# Check that works ----
expect_no_error(x <- tableMissingData(summariseMissingData(cdm, "condition_occurrence")))
expect_true(inherits(x, "gt_tbl"))
expect_no_error(y <- tableMissingData(summariseMissingData(cdm, c(
"observation_period",
"measurement"
))))
expect_true(inherits(y, "gt_tbl"))
expect_warning(t <- summariseMissingData(cdm, "death"))
expect_warning(inherits(tableMissingData(t), "gt_tbl"))
PatientProfiles::mockDisconnect(cdm = cdm)
})
test_that("col not present in table", {
skip_on_cran()
# Load mock database ----
# Load mock database
cdm <- omopgenerics::cdmFromTables(
tables = list(
person = dplyr::tibble(
person_id = as.integer(1:4),
gender_concept_id = c(8507L, 8532L, 8532L, 8507L),
year_of_birth = 2010L,
month_of_birth = 1L,
day_of_birth = 1L,
race_concept_id = 0L,
ethnicity_concept_id = 0L
),
observation_period = dplyr::tibble(
observation_period_id = as.integer(1:8),
person_id = c(1, 1, 1, 2, 2, 3, 3, 4) |> as.integer(),
observation_period_start_date = as.Date(c(
"2020-03-01", "2020-03-25", "2020-04-25", "2020-08-10", "2020-03-10",
"2020-03-01", "2020-04-10", "2020-03-10"
)),
observation_period_end_date = as.Date(c(
"2020-03-20", "2020-03-30", "2020-08-15", "2020-12-31", "2020-03-27",
"2020-03-09", "2020-05-08", "2020-12-10"
)),
period_type_concept_id = 0L
)
),
cdmName = "mock data"
)
cdm <- CDMConnector::copyCdmTo(
con = connection(), cdm = cdm, schema = schema()
)
expect_no_error(expect_message(summariseMissingData(cdm, "person", col = NULL)))
PatientProfiles::mockDisconnect(cdm = cdm)
})
test_that("no tables created", {
skip_on_cran()
# Load mock database ----
cdm <- cdmEunomia()
startNames <- CDMConnector::listSourceTables(cdm)
results <- summariseMissingData(
cdm = cdm,
omopTableName = c("drug_exposure", "condition_occurrence"),
interval = "years",
sex = TRUE,
ageGroup = list(
c(0, 17),
c(18, 65),
c(66, 100)
),
dateRange = as.Date(c("2012-01-01", "2018-01-01")),
sample = 100
)
endNames <- CDMConnector::listSourceTables(cdm)
expect_true(length(setdiff(endNames, startNames)) == 0)
PatientProfiles::mockDisconnect(cdm = cdm)
})
test_that("zero count argument works", {
skip_on_cran()
# Load mock database ----
cdm <- cdmEunomia()
cdm$person <- cdm$person |>
dplyr::mutate(person_id = dplyr::if_else(.data$person_id == 6, 0, .data$person_id))
expect_equal(summariseMissingData(cdm, omopTableName = "person") |>
dplyr::filter(variable_name == "person_id" & estimate_name == "zero_count") |>
dplyr::pull(.data$estimate_value) |> as.integer(), 1L)
columns <- cdm$drug_exposure |> colnames()
columns_zero <- columns[grepl("_id$", columns)]
expect_equal(summariseMissingData(cdm, "drug_exposure") |>
dplyr::filter(estimate_name == "zero_count") |>
dplyr::distinct(variable_name) |>
dplyr::pull() |> sort(), columns_zero |> sort())
PatientProfiles::mockDisconnect(cdm = cdm)
})
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