Nothing
test_that("pro_applicability_matrix works", {
skip_on_cran() # deprecated
skip_if_not_installed("withr")
withr::local_options(dataquieR.CONDITIONS_WITH_STACKTRACE = TRUE,
dataquieR.ERRORS_WITH_CALLER = TRUE,
dataquieR.WARNINGS_WITH_CALLER = TRUE,
dataquieR.MESSAGES_WITH_CALLER = TRUE)
meta_data <- prep_get_data_frame("meta_data")
study_data <- prep_get_data_frame("study_data")
meta_data2 <-
prep_scalelevel_from_data_and_metadata(study_data = study_data,
meta_data = meta_data)
meta_data[[SCALE_LEVEL]] <-
setNames(meta_data2[[SCALE_LEVEL]], nm = meta_data2[[VAR_NAMES]])[
meta_data[[VAR_NAMES]]
]
for (max_vars_per_plot in list(
1:10, -1, -Inf, NA, NaN, complex(real = 1), "A", letters
)) {
expect_error(
appmatrix <- pro_applicability_matrix(study_data = study_data,
meta_data = meta_data,
label_col = LABEL,
max_vars_per_plot =
max_vars_per_plot),
regexp =
paste("max_vars_per_plot must be one strictly positive",
"non-complex integer value, may be Inf."),
perl = TRUE)
}
for (max_vars_per_plot in list(
20, Inf
)) {
expect_silent(
appmatrix <- pro_applicability_matrix(study_data = study_data,
meta_data = meta_data,
label_col = LABEL,
max_vars_per_plot =
max_vars_per_plot))
}
md0 <- meta_data
md0$DATA_TYPE <- NULL
expect_error(
appmatrix <- pro_applicability_matrix(study_data = study_data,
meta_data = md0,
label_col = LABEL,
max_vars_per_plot =
max_vars_per_plot),
regexp =
paste("Missing columns .+DATA_TYPE.+ from .+meta_data.+"),
perl = TRUE)
md0 <- meta_data
md0$DATA_TYPE[[2]] <- NA
expect_warning(
appmatrix <- pro_applicability_matrix(study_data = study_data,
meta_data = md0,
label_col = LABEL,
max_vars_per_plot =
max_vars_per_plot),
regexp =
paste("yielding .+v00001 = string.+"),
perl = TRUE)
md0 <- meta_data
md0$DATA_TYPE[[2]] <- "Ordinal"
expect_warning(
appmatrix <- pro_applicability_matrix(study_data = study_data,
meta_data = md0,
label_col = LABEL,
max_vars_per_plot =
max_vars_per_plot),
regexp =
paste("yielding .+v00001 = string.+"),
all = TRUE,
perl = TRUE)
expect_silent(
appmatrix <- pro_applicability_matrix(study_data = study_data,
meta_data = meta_data,
label_col = LABEL,
split_segments = TRUE)
)
md0 <- meta_data
if (KEY_STUDY_SEGMENT %in% names(md0))
md0[[KEY_STUDY_SEGMENT]][[2]] <- NA
if (STUDY_SEGMENT %in% names(md0))
md0[[STUDY_SEGMENT]][[2]] <- NA
expect_message(
appmatrix <- pro_applicability_matrix(study_data = study_data,
meta_data = md0,
label_col = LABEL,
split_segments = TRUE),
regexp =
paste("Some .+STUDY_SEGMENT.+ are NA.",
"Will assign those to an artificial segment .+SEGMENT.+"),
all = TRUE,
perl = TRUE
)
expect_message(
appmatrix <- pro_applicability_matrix(study_data = study_data,
meta_data = meta_data,
label_col = LABEL,
split_segments = TRUE,
max_vars_per_plot = 2),
regexp =
paste(".*Will split segemnt",
".+ arbitrarily avoiding too large figures"),
all = TRUE,
perl = TRUE
)
md0 <- meta_data
md0$KEY_STUDY_SEGMENT <- NULL
md0$STUDY_SEGMENT <- NULL
expect_message(
appmatrix <- pro_applicability_matrix(study_data = study_data,
meta_data = md0,
label_col = LABEL,
split_segments = TRUE),
regexp = paste("Stratification for STUDY_SEGMENT is not",
"possible due to missing metadata. Will split arbitrarily",
"avoiding too large figures"),
perl = TRUE,
all = TRUE
)
appmatrix <- pro_applicability_matrix(study_data = study_data,
meta_data = meta_data,
label_col = LABEL)
expect_length(appmatrix$ApplicabilityPlotList, 5)
expect_lt(abs(suppressWarnings(sum(na.rm = TRUE,
as.numeric(as.matrix(appmatrix$SummaryTable))
)) - 3149), 5)
skip_on_cran()
skip_if_not_installed("vdiffr")
# TODO: skip_if_not(capabilities()["long.double"])
vdiffr::expect_doppelganger("appmatrix plot ok",
appmatrix$ApplicabilityPlot)
vdiffr::expect_doppelganger("appmatrix plot for segment v10000 ok",
appmatrix$ApplicabilityPlotList$v10000)
})
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