Narrow a result to the findings you care about — filter

Saves writing subset() over $findings by hand, and - because it returns a result rather than a plain table - what comes back still prints as a readable report.

Usage

filter_findings(
  result,
  triage = NULL,
  dataset = NULL,
  rule = NULL,
  variable = NULL
)

Arguments

result

A result from check_dataset() or check_study().

triage

Keep only these triage levels. One or more of:

"wrong value" - the data contains something that breaks the rule, like a month of 13. Nothing about your study explains it away. Start here.
"missing required" - something the standard marks Required is absent.
"missing optional" - something Expected is absent, or a value is blank. Often legitimate: a screen-failure subject with no reference dates, a variable your raw data does not carry yet.

These are coreval's own triage, not a CDISC severity: the rules carry no severity field, and this does not map onto anyone's scale.

dataset

Keep only these datasets, e.g. "AE".

rule

Keep only these rule ids, e.g. "CORE-000547".

variable

Keep only findings naming these variables, including a finding that lists the variable as missing (a Required variable the dataset lacks).

Value

A coreval_result holding the matching findings. $skipped is left alone: what could not be checked does not become less true because you narrowed what you are looking at.

Examples

ae <- data.frame(
  STUDYID = "S1", DOMAIN = "AE", USUBJID = c("01", "01"),
  AESEQ = c(1, 2), AETERM = c("Headache", "Rash"),
  AESTDTC = c("2024-01-10", "2024-02-30")
)
result <- check_dataset(ae)

# Just the things that are definitely wrong:
filter_findings(result, triage = "wrong value")
#> 
#> ── coreval — AE ────────────────────────────────────────────────────────────────
#> 
#> (filtered - this is a subset of the full result)
#> 
#> 2 problems across 1 record  (159 checks ran)
#> 
#>   wrong value         2   the data breaks the rule - start here
#> 
#> [wrong value]
#> Study Day of Start of Observation (--STDY) variable is missing when Start
#>   Date/Time of Observation (--STDTC) is present.
#>   the dataset · AESTDTC
#>     AESTDTC = "2024-01-10"
#>     CORE-000328  · also FB3202
#> 
#> [wrong value]
#> Variable value is not in correct ISO 8601 date or datetime format
#>   1 record · AESTDTC
#>     row 2     AESTDTC = "2024-02-30"
#>     CORE-000547  · also SEND66, SEND67, SEND68, ...
#> 
#> ────────────────────────────────────────────────────────────────────────────────
#> 48 checks could not run.
#>   24 need other datasets (DM, POOLDEF, SUPPAE, SV, TA, TO, ...)
#>      → run check_study() on the whole folder to cover these
#>   14 ask what the whole study contains
#>   6 need a define.xml
#>   4 for other reasons, see result$skipped
#> 
#> No standard declared, so rules from every standard ran.
#>   Narrow with  standard = "SDTMIG"  (or "SENDIG", "TIG", ...)
#> 
#> Fix what you can, then run this again.
#> To track the rest:  write_findings(result, "issues.xlsx")