Runs every rule that applies to every dataset in the study, including the
ones that compare datasets against each other. Use this once the datasets
exist as files; to check a single dataset while you are still writing the
code that builds it, see check_dataset().
check_study(
study,
standard = NULL,
version = NULL,
use_case = NULL,
max_records = 1000,
include_deprecated = FALSE,
ct_package = NULL
)An object of class coreval_result, holding three tables. Because
it has a class, typing the result's name prints a readable report rather
than dumping the list, and provenance rides along as attributes:
checks_run, domains and excluded_by_standard.
findings - what is wrong. One row per affected record, with Dataset,
Record, Variable, Value, the issue in words, and its triage.
Not in dataset under Value means the rule wanted a variable you do
not have, which is usually the finding itself.
skipped - what could not be checked, with a reason for each. Read
this one: an empty findings table can mean clean data or rules that
never ran, and they look identical otherwise. Reasons include a dataset
you did not supply, a missing Define-XML, and - for 9 rules - CDISC's
controlled terminology when no ct_package was given, which is
not bundled. Nothing skipped is ever counted as a pass.
truncated - rules that flagged more records than max_records kept,
with how many they really found.
A study folder path, or a study object from read_study().
Passing the path is the usual way; reading first is only worth it when
you want to check the same large study more than once without re-reading
it, or to look at what was parsed.
The standard the data follows, e.g. "SDTMIG" or
"SENDIG". Overrides whatever the study declares about itself. Rules
are written per standard, so this cuts the list sharply; leave it
unset to run every standard's rules and see everything.
The standard's version, e.g. "3.4". Needs standard
too, since a bare version is ambiguous across standards.
Optional use case (e.g. "INDH") to further filter
which rules apply, as in list_rules().
Most records to keep per rule, default 1000. A rule can
flag every row - a missing EPOCH on a 200 000-row LB is 200 000
identical findings, more than Excel can hold. The true count is kept in
truncated and the report shows it, so nothing is under-reported. Use
Inf for every record.
Also run rules CDISC has deprecated. FALSE by
default: a deprecated rule has a published replacement, so running both
reports the same defect twice.
Which CDISC Controlled Terminology package the study
follows, e.g. "sdtmct-2026-03-27". Rules that ask whether a value is a
legal term need this, and are skipped with a reason without it - coreval
will not pick a version for you, because terminology changes between
releases and judging a study against one it never declared would both
invent violations and hide real ones. Every published package is bundled;
list_ct_packages() shows them.
A large study takes long enough that silence looks like a hang, so an interactive session shows a progress bar naming the domain being checked and how far through the study it is:
AE 4/7 |===================== | 75%
The percentage is weighted by how many records each domain holds, not by a
plain count of rules, because a check against a 161,600-row AE costs
hundreds of times one against a 200-row SJ. It tracks elapsed time
closely but is still an estimate - rules differ in cost among themselves
too - so treat it as "roughly how far through", not a clock.
It is off in scripts and non-interactive runs, where it would only clutter
a log. Turn it on or off with options(coreval.progress = TRUE) or
FALSE.
Findings come back one row per (dataset, record, variable), pointing at the
exact spot. Some rules ask about a dataset as a whole rather than a
particular row - those leave Record blank. A few ask about the study as a
whole, such as "is DM present at all?"; those are answered once and reported
under Dataset = "STUDY" rather than repeated for every domain.
Rules comparing against a define.xml do run, as long as the study has one and
the xml2 package is installed. Without both, they are skipped with a reason
instead of being run against columns that are not there, which would report
problems that do not exist. The same goes for any rule needing an operator or
join coreval does not implement yet.
dir <- tempfile("coreval_study_")
dir.create(dir)
haven::write_xpt(data.frame(USUBJID = c("1", "2"), AGE = c(30, 65)), file.path(dir, "dm.xpt"))
result <- check_study(dir)
result$findings
unlink(dir, recursive = TRUE)
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