Check one dataset, without needing a study folder

For when you are writing the code that builds a domain and want to know what is wrong with it right now. Give it the data frame you already have open, or the path to a single file.

Usage

check_dataset(
  x,
  domain = NULL,
  standard = NULL,
  version = NULL,
  use_case = NULL,
  max_records = 1000,
  include_deprecated = FALSE,
  ct_package = NULL
)

Arguments

x

A data frame (or data.table::data.table()), or the path to one .xpt, .sas7bdat or .csv file.

domain

Two-letter domain code, e.g. "AE". Left as NULL, coreval takes it from your DOMAIN column, or the file name if there isn't one - so ae1.xpt from a split dataset is still checked as AE. Set it yourself if that guess is wrong.

standard

The standard the data follows, e.g. "SDTMIG" or "SENDIG". Rules are scoped to it, which is usually what you want - a SENDIG rule has nothing to say about an SDTM study.

It is not free, though, and CDISC's coverage is uneven. The general "dates must be valid ISO 8601" rule (CORE-000547) is published for SENDIG and TIG but not for SDTMIG, whose only equivalents are TSVAL-specific or deprecated. So standard = "SDTMIG" can stop a malformed RFSTDTC being reported at all. The report says how many rules were set aside; leave standard unset to see everything.

version

The standard's version, e.g. "3-4".

use_case

Optional use case (e.g. "INDH"), as in list_rules().

max_records

Most records to keep per rule, default 1000. A rule that flags every row of a large dataset would otherwise produce more findings than anyone can read or Excel can hold. The true count is kept in truncated. Use Inf for every record.

include_deprecated

Also run rules CDISC has deprecated. FALSE by default: a deprecated rule has a published replacement, so running both reports the same defect twice.

ct_package

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.

Value

An object of class coreval_result: a list of the three tables findings, skipped and truncated, the same shape check_study() returns, so write_findings() and filter_findings() work on it unchanged. Because it has a class, typing the result's name prints a readable report rather than dumping the list.

skipped carries a reason for every rule that did not run - a dataset you did not supply, a missing Define-XML, or (for 9 rules) CDISC's controlled terminology, if you did not say which package the study follows - see ct_package. Nothing skipped is ever counted as a pass.

Provenance rides along as attributes: checks_run (how many rules were evaluated), domains, and excluded_by_standard (how many rules the standard/version filter set aside). write_findings() writes these into the file it saves.

What it cannot check on its own

Plenty of CDISC rules compare one dataset against another - an adverse event date against the subject's reference dates in DM, a visit against the trial design. Hand over a single dataset and those questions cannot be answered.

coreval does not guess. Those rules are skipped, and $skipped names the dataset each one wanted. Running them anyway would compare your data against columns that are not there and report problems that do not exist.

Most rules still run - across AE, DM, LB and VS, 76-84% of the applicable ones work on a single dataset. But the ones that cannot are the cross-dataset checks, which are often the ones that matter.

So a short $findings table here does not mean the data is clean. It is a quick first pass, not a verdict. Run check_study() on the whole folder before drawing conclusions.

Examples