Package 'r4subcore'

Standard ADRG Section Catalog

Description

Returns the standard top-level and key subsections of the Analysis Data Reviewer's Guide (ADRG), following the PHUSE ADRG completion guideline. This is the checklist adrg_to_evidence() scores a guide against.

Usage

adrg_sections()

Value

A tibble with columns section_id, section_name, and required.

Examples

adrg_sections()

---

Convert ADRG Section Coverage to Evidence

Description

Scores an Analysis Data Reviewer's Guide for completeness against the standard section catalog and emits R4SUB evidence rows, one per section.

Usage

adrg_to_evidence(
  present,
  ctx,
  source_name = "adrg",
  source_version = NULL,
  standard = adrg_sections()
)

Arguments

present	A character vector of section_ids the guide contains, or a data.frame with a section_id column and a logical present column.
ctx	An r4sub_run_context.
source_name	Source label. Default "adrg"; use "sdrg" for a Study Data Reviewer's Guide.
source_version	Optional source version string.
standard	The section catalog to score against. Defaults to adrg_sections().

Details

ADRG and SDRG documents are Word or PDF, and reliable text extraction from them is out of scope for this package. adrg_to_evidence() therefore takes a structured summary of which sections a guide contains, which a document pipeline (or a human reviewer) can produce, and turns it into evidence. A present section passes. A missing required section fails at high severity; a missing recommended section warns at low severity. The rows land in the usability pillar, since the reviewer's guide is reviewer-facing documentation.

Value

An evidence data.frame, one row per standard section.

Examples

ctx <- suppressMessages(r4sub_run_context("STUDY1", "DEV"))
ev <- suppressMessages(adrg_to_evidence(c("1", "2", "3", "4", "5"), ctx))
ev[, c("indicator_id", "severity", "result")]

---

Aggregate Indicator Scores

Description

Computes summary scores from an evidence table, grouped by one or more columns.

Usage

aggregate_indicator_score(
  ev,
  by = "indicator_id",
  method = c("mean", "min", "weighted")
)

Arguments

ev	A valid evidence data.frame.
by	Character vector of column names to group by. Default: c("indicator_id").
method	Aggregation method: "mean", "min", or "weighted". The "weighted" method uses severity_to_weight() and result_to_score().

Value

A data.frame with grouping columns plus score (0–1) and n_evidence (count of rows).

Examples

ctx <- suppressMessages(r4sub_run_context("STUDY1", "DEV"))
ev <- suppressMessages(as_evidence(
  data.frame(
    asset_type = rep("validation", 3), asset_id = rep("ADSL", 3),
    source_name = rep("pinnacle21", 3),
    indicator_id = c("SD0001", "SD0001", "SD0002"),
    indicator_name = c("SD0001", "SD0001", "SD0002"),
    indicator_domain = rep("quality", 3),
    severity = c("high", "medium", "low"),
    result = c("fail", "warn", "pass"),
    stringsAsFactors = FALSE
  ),
  ctx = ctx
))
aggregate_indicator_score(ev, by = "indicator_id", method = "weighted")

---

Coerce to Evidence Table

Description

Takes a data.frame and coerces it into a valid evidence table. Fills in missing nullable columns with NA of the correct type and validates controlled vocabulary columns.

Usage

as_evidence(x, ctx = NULL, ...)

Arguments

x	A data.frame (or tibble) with at least the required evidence columns.
ctx	An optional r4sub_run_context. If provided, run_id and study_id are filled from the context when missing.
...	Additional columns to set (e.g., asset_type = "validation").

Value

A data.frame conforming to the evidence schema.

Examples

ctx <- r4sub_run_context("STUDY1", "DEV")
df <- data.frame(
  asset_type = "validation",
  asset_id = "ADSL",
  source_name = "pinnacle21",
  indicator_id = "P21-001",
  indicator_name = "Missing variable",
  indicator_domain = "quality",
  severity = "high",
  result = "fail",
  message = "Variable AGEU missing",
  stringsAsFactors = FALSE
)
ev <- as_evidence(df, ctx = ctx)

---

Bind Evidence Tables

Description

Row-binds multiple evidence data.frames after validating each one.

Usage

bind_evidence(...)

Arguments

...	Evidence data.frames to bind.

Value

A single combined evidence data.frame.

Examples

ctx <- suppressMessages(r4sub_run_context("STUDY1", "DEV"))
make_ev <- function(ind_id) {
  suppressMessages(as_evidence(
    data.frame(
      asset_type = "validation", asset_id = "ADSL",
      source_name = "pinnacle21", indicator_id = ind_id,
      indicator_name = ind_id, indicator_domain = "quality",
      severity = "low", result = "pass",
      stringsAsFactors = FALSE
    ),
    ctx = ctx
  ))
}
ev1 <- make_ev("IND-001")
ev2 <- make_ev("IND-002")
combined <- suppressMessages(bind_evidence(ev1, ev2))
nrow(combined)

---

Canonical Result Values

Description

Maps common result/status labels to the canonical set: pass, fail, warn, na.

Usage

canon_result(x)

Arguments

x	Character vector of result values.

Value

Character vector with canonical result labels.

Examples

canon_result(c("PASS", "Failed", "Warning", "N/A"))

---

Canonical Severity Values

Description

Maps common severity labels (case-insensitive) to the canonical set.

Usage

canon_severity(x)

Arguments

x	Character vector of severity values.

Value

Character vector with canonical severity labels.

Examples

canon_severity(c("HIGH", "Low", "warning", "Error"))

---

Check Define-XML Against Dataset Contents

Description

Compares the variables declared in Define-XML against the variables actually present in the datasets, and emits evidence for each: a match, a variable documented in Define but absent from the data, or a variable present in the data but not documented. Drift between Define-XML and the data is a common submission problem that reviewers notice quickly, and this makes it a scored indicator. The comparison is case-insensitive on dataset and variable names.

Usage

check_define_consistency(
  define,
  data,
  ctx,
  source_name = "define_vs_data",
  source_version = NULL
)

Arguments

define	Either a path to a Define-XML file, or a data.frame with dataset and variable columns (as returned by define_variables()).
data	A data.frame with dataset and variable columns listing the variables actually present in each dataset.
ctx	A r4sub_run_context.
source_name, source_version	Provenance recorded on the evidence.

Value

A data.frame conforming to the evidence schema, one row per variable checked, under indicator Q-DEFINE-CONSIST.

See Also

define_variables(), define_xml_to_evidence().

Examples

ctx <- suppressMessages(r4sub_run_context("STUDY1", "DEV"))
define <- data.frame(
  dataset  = c("ADSL", "ADSL", "ADSL"),
  variable = c("USUBJID", "AGE", "SEX")
)
data <- data.frame(
  dataset  = c("ADSL", "ADSL", "ADSL"),
  variable = c("USUBJID", "AGE", "RACE")  # SEX missing, RACE undocumented
)
ev <- suppressMessages(check_define_consistency(define, data, ctx))
ev[, c("asset_id", "result", "message")]

---

Extract the Variables Declared in a Define-XML File

Description

Returns one row per variable declared in Define-XML, following the ItemGroupDef to ItemRef to ItemDef structure: the dataset name comes from the ItemGroupDef, and the variable name from the ItemDef its ItemRef points at. Elements are matched by local name, so the result does not depend on how the file mixes the ODM and ⁠def:⁠ namespaces.

Usage

define_variables(file)

Arguments

file	Path to a Define-XML file.

Value

A data.frame with columns dataset and variable.

See Also

check_define_consistency().

---

Parse Define-XML to Evidence

Description

Reads a Define-XML 2.0/2.1 file and extracts dataset, variable, and derivation completeness checks into the standard evidence table format. Three indicators are evaluated for each asset:

Q-DEFINE-001

Dataset is present and has a non-empty label.

Q-DEFINE-002

Variable is documented (has label and dataType).

Q-DEFINE-003

Derivation text is present for derived variables.

Usage

define_xml_to_evidence(file, ctx, source_version = "2.1")

Arguments

file	Character. Path to a Define-XML file (.xml).
ctx	An r4sub_run_context providing run and study metadata.
source_version	Character. Version label for the Define-XML standard. Default: "2.1".

Value

A data.frame conforming to the evidence schema, one row per dataset-level check (Q-DEFINE-001), per variable check (Q-DEFINE-002), and per derivation check (Q-DEFINE-003).

Examples

# Build a minimal Define-XML 2.1 document
xml_txt <- '<?xml version="1.0" encoding="UTF-8"?>
<ODM xmlns="http://www.cdisc.org/ns/odm/v1.3"
     xmlns:def="http://www.cdisc.org/ns/def/v2.1">
  <Study OID="STUDY001">
    <MetaDataVersion OID="MDV.001" Name="Define-XML 2.1">
      <def:ItemGroupDef OID="IG.ADSL" Name="ADSL" SASDatasetName="ADSL"
                        Repeating="No" Purpose="Analysis"
                        def:Label="Subject-Level Analysis Dataset">
        <ItemRef ItemOID="IT.ADSL.USUBJID" Mandatory="Yes"/>
        <ItemRef ItemOID="IT.ADSL.AGE" Mandatory="Yes"/>
      </def:ItemGroupDef>
      <ItemDef OID="IT.ADSL.USUBJID" Name="USUBJID"
               DataType="text" Length="20"
               SASFieldName="USUBJID">
        <Description><TranslatedText>Unique Subject Identifier</TranslatedText></Description>
      </ItemDef>
      <ItemDef OID="IT.ADSL.AGE" Name="AGE"
               DataType="integer" Length="8"
               SASFieldName="AGE">
        <Description><TranslatedText>Age</TranslatedText></Description>
        <def:Origin Type="Derived">
          <def:Description><TranslatedText>Derived from RFSTDTC</TranslatedText></def:Description>
        </def:Origin>
      </ItemDef>
    </MetaDataVersion>
  </Study>
</ODM>'
tmp <- tempfile(fileext = ".xml")
writeLines(xml_txt, tmp)
ctx <- r4sub_run_context("STUDY001", "DEV")
ev <- define_xml_to_evidence(tmp, ctx)
nrow(ev)

---

Compare Two Evidence Tables

Description

Reports what changed between two evidence tables, for example the same submission checked at two points in time. Rows are matched on a key (by default the indicator applied to a specific asset) and classified as added, removed, changed, or unchanged, with a direction that says whether each change made the submission better or worse.

Usage

evidence_diff(
  old,
  new,
  key = c("indicator_id", "asset_id"),
  include_unchanged = FALSE
)

Arguments

old, new	Valid evidence data.frames, as produced by as_evidence(). old is the earlier state and new the later one.
key	Character vector of columns that identify a comparable row. Default c("indicator_id", "asset_id").
include_unchanged	Logical. If FALSE (default) unchanged matched rows are dropped from the result.

Details

When a key appears on more than one row within a table (an indicator that emits several messages for the same asset, say), the rows are collapsed to the worst one for that key before comparison: lowest result score first (fail beats warn beats pass), then highest severity. This keeps the diff at the indicator level, which is what a submission team acts on.

Value

A data.frame with the key columns plus indicator_name, indicator_domain, change (added, removed, changed, or unchanged), direction (improved, regressed, or neutral), result_from, result_to, severity_from, severity_to, metric_from, and metric_to. Rows are ordered with the most actionable first: regressions, then new findings, then resolved findings, then improvements. The returned object carries a "summary" attribute with the count in each change category.

Examples

ctx <- suppressMessages(r4sub_run_context("STUDY1", "DEV"))
mk <- function(id, result, severity) {
  suppressMessages(as_evidence(
    data.frame(
      asset_type = "validation", asset_id = "ADSL",
      source_name = "pinnacle21", indicator_id = id,
      indicator_name = id, indicator_domain = "quality",
      severity = severity, result = result,
      stringsAsFactors = FALSE
    ),
    ctx = ctx
  ))
}
old <- suppressMessages(bind_evidence(mk("A", "fail", "high"), mk("B", "pass", "low")))
new <- suppressMessages(bind_evidence(mk("A", "pass", "high"), mk("C", "fail", "medium")))
evidence_diff(old, new)

---

Evidence Table Schema Definition

Description

Returns the column specification for the R4SUB evidence table. Each element describes a column's expected R type and, where applicable, the set of allowed values.

Usage

evidence_schema()

Value

A named list. Each element is a list with type (character) and optionally allowed (character vector) or nullable (logical).

Examples

str(evidence_schema())

---

Evidence Schema Specification

Description

Returns the R4SUB evidence schema as a tidy table: one row per column with its type, whether it is required, the controlled vocabulary (when the column has one), and a short description. This is the published, machine-readable form of the schema that evidence_schema() encodes, suitable for documentation, a data dictionary, or a Define-XML style reference.

Usage

evidence_schema_spec()

Value

A tibble with columns column, type, required, allowed_values, and description. allowed_values is a comma-separated string, or NA when the column is free text.

Examples

evidence_schema_spec()

---

Evidence Schema Version

Description

The evidence table carries a schema version so a table written by one release of the ecosystem can be recognized, validated, and migrated by another. evidence_schema_version() with no argument returns the version this release writes. Given an evidence table, it returns the version stamped on that table, or NA when the table predates versioning.

Usage

evidence_schema_version(x = NULL)

Arguments

x	Optional. An evidence data.frame. When supplied, the stamped version of that table is returned instead of the current package version.

Value

A single version string, or NA_character_ for an unstamped table.

See Also

migrate_evidence() to upgrade an older table.

Examples

evidence_schema_version()

ctx <- suppressMessages(r4sub_run_context("STUDY1", "DEV"))
ev <- suppressMessages(as_evidence(
  data.frame(
    asset_type = "validation", asset_id = "ADSL",
    source_name = "pinnacle21", indicator_id = "SD0001",
    indicator_name = "SD0001", indicator_domain = "quality",
    severity = "high", result = "fail",
    stringsAsFactors = FALSE
  ),
  ctx = ctx
))
evidence_schema_version(ev)

---

Supported and Planned Evidence Sources

Description

A reference table of the evidence sources R4SUB can ingest, their status, the format they come from, and the pillar they feed. Use it to see at a glance what is wired up today and what is on the roadmap.

Usage

evidence_sources()

Value

A tibble with columns source, status, format, pillar, and entry_point.

Examples

evidence_sources()

---

Summarize Evidence

Description

Returns a summary data.frame with counts grouped by domain, severity, result, and source.

Usage

evidence_summary(ev)

Arguments

ev	A valid evidence data.frame.

Value

A data.frame with columns: indicator_domain, severity, result, source_name, and n.

Examples

ctx <- suppressMessages(r4sub_run_context("STUDY1", "DEV"))
ev <- suppressMessages(as_evidence(
  data.frame(
    asset_type = "validation", asset_id = "ADSL",
    source_name = "pinnacle21", indicator_id = "SD0001",
    indicator_name = "SD0001", indicator_domain = "quality",
    severity = "high", result = "fail",
    stringsAsFactors = FALSE
  ),
  ctx = ctx
))
evidence_summary(ev)

---

Export Evidence Table to File

Description

Validates the evidence table then writes it to disk in the requested format. Metadata attributes (exported_at, r4subcore_version, nrow) are attached to the returned path value for traceability.

Usage

export_evidence(evidence, file, format = c("csv", "rds", "json"))

Arguments

evidence	A valid evidence data.frame (as produced by as_evidence()).
file	Character. Destination file path (including extension).
format	Character. One of "csv", "rds", or "json". Default: "csv".

Value

Invisibly returns file with attributes:

exported_at

POSIXct timestamp of export.

r4subcore_version

Package version string.

nrow

Number of rows written.

Examples

ctx <- suppressMessages(r4sub_run_context("STUDY1", "DEV"))
ev <- suppressMessages(as_evidence(
  data.frame(
    asset_type = "validation", asset_id = "ADSL",
    source_name = "pinnacle21", indicator_id = "SD0001",
    indicator_name = "SD0001", indicator_domain = "quality",
    severity = "high", result = "fail",
    stringsAsFactors = FALSE
  ),
  ctx = ctx
))
tmp_csv <- tempfile(fileext = ".csv")
out <- suppressMessages(export_evidence(ev, tmp_csv, format = "csv"))
file.exists(out)

---

Generate a Stable Hash ID

Description

Creates a deterministic hash from one or more character inputs, hashed in memory with rlang::hash().

Usage

hash_id(..., prefix = NULL)

Arguments

...	Character values to hash together. Concatenated with "|".
prefix	Optional prefix prepended to the hash (e.g., "RUN", "IND").

Value

A character string of the form prefix-hexhash or just hexhash.

Examples

hash_id("ADSL", "rule_001")
hash_id("my_study", "2024-01-01", prefix = "RUN")

---

Import Evidence Table from File

Description

Reads an evidence table that was previously saved by export_evidence(), then validates it against the evidence schema.

Usage

import_evidence(file, format = c("csv", "rds", "json"))

Arguments

file	Character. Path to the file to read.
format	Character. One of "csv", "rds", or "json". Default: "csv".

Value

A validated evidence data.frame conforming to the evidence schema.

Examples

ctx <- suppressMessages(r4sub_run_context("STUDY1", "DEV"))
ev <- suppressMessages(as_evidence(
  data.frame(
    asset_type = "validation", asset_id = "ADSL",
    source_name = "pinnacle21", indicator_id = "SD0001",
    indicator_name = "SD0001", indicator_domain = "quality",
    severity = "high", result = "fail",
    stringsAsFactors = FALSE
  ),
  ctx = ctx
))
tmp_rds <- tempfile(fileext = ".rds")
suppressMessages(export_evidence(ev, tmp_rds, format = "rds"))
ev2 <- suppressMessages(import_evidence(tmp_rds, format = "rds"))
nrow(ev2)

---

Safely Serialize to JSON String

Description

Converts an R object to a valid JSON string. Returns "{}" on failure or for NULL/empty inputs.

Usage

json_safely(x)

Arguments

x	An R object to serialize.

Value

A single character string containing valid JSON.

Examples

json_safely(list(a = 1, b = "hello"))
json_safely(NULL)

---

Migrate an Evidence Table to a Newer Schema Version

Description

Upgrades an evidence table written by an older release to the requested schema version, applying each intervening migration step in order. A table with no stamp is treated as the earliest known version. A table already at the target is returned unchanged apart from the version stamp.

Usage

migrate_evidence(x, to = current_evidence_schema_version())

Arguments

x	An evidence data.frame.
to	Target schema version. Defaults to the current version.

Value

The migrated evidence data.frame, stamped with to.

See Also

evidence_schema_version().

Examples

ctx <- suppressMessages(r4sub_run_context("STUDY1", "DEV"))
ev <- suppressMessages(as_evidence(
  data.frame(
    asset_type = "validation", asset_id = "ADSL",
    source_name = "pinnacle21", indicator_id = "SD0001",
    indicator_name = "SD0001", indicator_domain = "quality",
    severity = "high", result = "fail",
    stringsAsFactors = FALSE
  ),
  ctx = ctx
))
ev <- migrate_evidence(ev)
evidence_schema_version(ev)

---

Normalize to 0–1 Range

Description

Applies min-max normalization to a numeric vector, optionally clamping values to [0, 1].

Usage

normalize_01(x, direction = c("higher_better", "lower_better"), clamp = TRUE)

Arguments

x	Numeric vector.
direction	Character. "higher_better" (default) maps max to 1; "lower_better" maps min to 1.
clamp	Logical. If TRUE, clamp output to [0, 1].

Value

Numeric vector normalized to 0–1.

Examples

normalize_01(c(10, 20, 30, 40, 50))
normalize_01(c(10, 20, 30), direction = "lower_better")

---

Parse Pinnacle21 Output to Evidence

Description

Converts a data.frame of Pinnacle21-style validation results into the standard evidence table format. Column names are detected case-insensitively.

Usage

p21_to_evidence(
  p21_df,
  ctx,
  asset_type = "validation",
  source_version = NULL,
  default_domain = "quality"
)

Arguments

p21_df	A data.frame containing Pinnacle21 validation output. Expected columns (case-insensitive): Rule (or ⁠Rule ID⁠), Message, Severity, Dataset, Variable, Result (or Status).
ctx	A r4sub_run_context providing run and study metadata.
asset_type	Character. Asset type label. Default: "validation".
source_version	Character or NULL. Version of the P21 tool.
default_domain	Character. Indicator domain. Default: "quality".

Value

A data.frame conforming to the evidence schema.

Examples

p21_raw <- data.frame(
  Rule = c("SD0001", "SD0002"),
  Message = c("Missing variable label", "Invalid format"),
  Severity = c("Error", "Warning"),
  Dataset = c("ADSL", "ADAE"),
  Variable = c("AGE", "AESTDTC"),
  Status = c("Failed", "Warning"),
  stringsAsFactors = FALSE
)
ctx <- r4sub_run_context("STUDY1", "DEV")
ev <- p21_to_evidence(p21_raw, ctx)

---

Create a Run Context

Description

A run context captures metadata for a particular evidence collection run. It provides a unique run_id, study identifier, environment label, and timestamps used throughout evidence ingestion.

Usage

r4sub_run_context(
  study_id,
  environment = c("DEV", "UAT", "PROD"),
  user = NULL,
  run_id = NULL,
  timestamp = Sys.time()
)

Arguments

study_id	Character. Study identifier (e.g., "ABC123").
environment	Character. One of "DEV", "UAT", "PROD".
user	Character or NULL. Username; defaults to system user.
run_id	Character or NULL. If NULL, a unique ID is generated.
timestamp	POSIXct. Defaults to current time.

Value

A list of class r4sub_run_context with elements: run_id, study_id, environment, user, created_at.

Examples

ctx <- r4sub_run_context(study_id = "STUDY001", environment = "DEV")
ctx$run_id
ctx$study_id

---

Read a Pinnacle 21 Report from Disk

Description

Loads a Pinnacle 21 validation report (the export most teams already produce for every submission) into a data.frame, ready for p21_to_evidence(). CSV exports are read with base R. Excel exports are read with the readxl package when it is installed; if it is not, export the report as CSV instead.

Usage

read_p21_report(path, format = c("auto", "csv", "xlsx"), sheet = NULL)

Arguments

path	Path to the report file.
format	One of "auto" (infer from the file extension), "csv", or "xlsx". Default "auto".
sheet	For Excel reports, the sheet to read. When NULL (the default), a sheet whose name looks like the findings list (matching "detail", "issue", or "finding") is used, otherwise the first sheet.

Details

This function only reads the file; it does not interpret the columns. Pass the result to p21_to_evidence(), which detects the rule, message, severity, dataset, variable, and result columns case-insensitively.

Value

A data.frame of the report rows with the original column names.

See Also

p21_to_evidence() to convert the result into evidence.

Examples

# Simulate a small CSV export and read it back.
p21 <- data.frame(
  "Rule ID" = c("SD0001", "SD0002"),
  Message   = c("Missing variable label", "Invalid format"),
  Severity  = c("Error", "Warning"),
  Dataset   = c("ADSL", "ADAE"),
  Variable  = c("AGEU", "AESEQ"),
  Status    = c("Failed", "Warning"),
  check.names = FALSE
)
csv <- tempfile(fileext = ".csv")
utils::write.csv(p21, csv, row.names = FALSE)
report <- read_p21_report(csv)
report

---

Register an Indicator

Description

Adds an indicator definition to the local in-memory registry.

Usage

register_indicator(
  indicator_id,
  domain,
  description,
  expected_inputs = character(0),
  default_thresholds = numeric(0),
  tags = character(0)
)

Arguments

indicator_id	Character. Stable identifier for the indicator.
domain	Character. One of "quality", "trace", "risk", "usability".
description	Character. Human-readable description.
expected_inputs	Character vector. Evidence source types this indicator expects.
default_thresholds	Named numeric vector. Optional thresholds.
tags	Character vector. Optional tags (e.g., "define", "adam").

Value

The indicator definition list, invisibly.

Examples

register_indicator(
  indicator_id = "P21-001",
  domain = "quality",
  description = "Required variable is missing from dataset"
)

---

Map Result to Numeric Score

Description

Converts canonical result labels to numeric scores.

Usage

result_to_score(result)

Arguments

result

Character vector of canonical result values (pass, fail, warn, na).

Value

Numeric vector: pass=1, warn=0.5, fail=0, na=NA.

Examples

result_to_score(c("pass", "fail", "warn", "na"))

---

Map Severity to Numeric Weight

Description

Converts canonical severity labels to numeric penalty multipliers on a 0–1 scale.

Usage

severity_to_weight(severity)

Arguments

severity

Character vector of canonical severity values (info, low, medium, high, critical).

Details

Default mapping:

• info = 0.00

• low = 0.25

• medium = 0.50

• high = 0.75

• critical = 1.00

Value

Numeric vector of weights.

Examples

severity_to_weight(c("low", "high", "critical"))

---

Validate Evidence Table

Description

Checks that a data.frame conforms to the evidence schema. Verifies column presence, types, and controlled vocabulary values.

Usage

validate_evidence(ev)

Arguments

ev	A data.frame to validate.

Value

TRUE invisibly if valid; throws an error otherwise.

Examples

ctx <- suppressMessages(r4sub_run_context("STUDY1", "DEV"))
ev <- suppressMessages(as_evidence(
  data.frame(
    asset_type = "validation", asset_id = "ADSL",
    source_name = "pinnacle21", indicator_id = "SD0001",
    indicator_name = "SD0001", indicator_domain = "quality",
    severity = "high", result = "fail",
    stringsAsFactors = FALSE
  ),
  ctx = ctx
))
validate_evidence(ev)

---

Validate Indicator Metadata

Description

Checks that an indicator definition list is well-formed.

Usage

validate_indicator(indicator)

Arguments

indicator

A list with required fields: indicator_id, domain, description. Optional fields: expected_inputs, default_thresholds, tags.

Value

TRUE invisibly if valid; throws an error otherwise.

Examples

validate_indicator(list(
  indicator_id = "P21-001",
  domain = "quality",
  description = "Missing required variable"
))

---