check_study: Check a whole study against its spec

In artoo: Lossless CDISC-Native Input and Output for Clinical Datasets

Check a whole study against its spec

Description

Run check_spec() over every dataset in a study and return one stacked findings frame. Where check_spec() answers "does this dataset conform?", check_study() answers "is my whole study submittable?" in a single pass, surfacing every dataset's divergences at once instead of one abort at a time. The result is an ordinary findings frame underneath, so filter it by severity or hand it straight to repair_spec().

Usage

check_study(
  spec,
  data,
  decode = c("none", "to_decode", "to_code"),
  checks = NULL
)

Arguments

spec	The specification to check against. ⁠<artoo_spec>: required⁠.
data	The study's datasets. ⁠<named list of data.frame>: required⁠. One entry per dataset, named by the dataset (e.g. list(ADSL = adsl, ADAE = adae)). Every name must be a dataset in spec.
decode	Which codelist column to check against. ⁠<character(1)>⁠. Passed to check_spec(); one of "none" (default), "to_decode", "to_code".
checks	Which conformance dimensions to run. ⁠<artoo_checks> | NULL⁠. Passed to check_spec(); NULL (default) runs every dimension. Build a subset with artoo_checks().

Details

One row per divergence, every dataset stacked. Each dataset's findings carry its name in the dataset column, so the frame is the union of the per-dataset check_spec() results. Printing renders the dataset-by-check count matrix (the study-level summary); the underlying frame is unchanged.

Data-requiring, like check_spec(). check_study() checks data against the spec, so it needs the data frames. For the spec's own structural integrity (no data), use validate_spec().

Value

A ⁠<artoo_study_findings>⁠ data frame with the same columns as check_spec() (check, dimension, severity, dataset, variable, message), one row per divergence across all datasets. Zero rows means the whole study conforms. Print it for the count matrix; treat it as an ordinary data frame otherwise.

See Also

One dataset: check_spec(). Spec structure only: validate_spec().

Repair: repair_spec() to apply the integer fixes the matrix surfaces.

Examples

# ---- Example 1: scan a whole study in one pass ----
#
# Loop the conformance check over every dataset's data. A fractional AGE
# (the spec types it integer) surfaces as an integer_fraction finding; the
# print is a dataset-by-check count matrix.
adsl <- cdisc_adsl
adsl$AGE <- adsl$AGE + 0.5
check_study(adam_spec, list(ADSL = adsl, ADAE = cdisc_adae))

# ---- Example 2: feed the findings straight into repair_spec() ----
#
# The result is an ordinary findings frame, so repair_spec() consumes it to
# flip every integer_fraction / integer_overflow variable across the study.
findings <- check_study(adam_spec, list(ADSL = adsl))
fixed <- repair_spec(adam_spec, findings)
spec_variables(fixed, "ADSL")$data_type[
  spec_variables(fixed, "ADSL")$variable == "AGE"
]

artoo documentation built on July 23, 2026, 1:08 a.m.