Validate Analytical Metadata

Validates that a data frame contains the required metadata columns for analytical workflows. This function checks for column presence, correct data types, and valid values (e.g., sample_type levels).

Usage

measure_validate_metadata(
  data,
  require = NULL,
  sample_types = measure_sample_types,
  action = c("error", "warn", "message")
)

Arguments

data

A data frame to validate.

require

Character vector of required columns. Common columns include:

• "sample_type": Sample classification (qc, standard, blank, unknown, reference)

• "run_order": Injection/measurement sequence (integer)

• "batch_id": Batch identifier (character/factor)

• "nominal_conc": Known concentration for standards (numeric)

• "sample_id": Unique sample identifier

• "analyst_id", "day", "instrument_id": Precision study factors

sample_types

Allowed values for sample_type column. Default is measure_sample_types: "qc", "standard", "blank", "unknown", "reference".

action

What to do when validation fails:

• "error" (default): Stop with an informative error

• "warn": Issue warnings but continue

• "message": Issue messages but continue

Value

Invisibly returns a list with validation results:

• valid: Logical, TRUE if all checks passed

• checks: List of individual check results

• data: The original data (unchanged)

Details

Canonical Columns

Milestone 2 functions expect specific column names with specific types:

Column	Type	Description
sample_type	character/factor	Sample classification
run_order	integer	Injection sequence within batch
batch_id	character/factor	Batch identifier
nominal_conc	numeric	Known concentration (standards)
sample_id	character/factor	Unique sample identifier
analyst_id	character/factor	Analyst performing measurement
day	character/Date	Day of measurement
instrument_id	character/factor	Instrument identifier
dilution_factor	numeric	Sample dilution factor

Sample Type Values

The sample_type column must contain only values from measure_sample_types:

• "qc": Quality control sample (pooled QC, system suitability)

• "standard": Calibration standard with known concentration

• "blank": Blank sample (solvent, matrix blank)

• "unknown": Sample with unknown concentration

• "reference": Reference material for batch correction

See also

measure_standardize_sample_type() for converting non-standard sample type values to canonical form.

Examples

# Create sample analytical data
data <- data.frame(
  sample_id = paste0("S", 1:10),
  sample_type = c("qc", "standard", "standard", "unknown", "unknown",
                  "unknown", "qc", "blank", "unknown", "qc"),
  run_order = 1:10,
  batch_id = "B001",
  nominal_conc = c(NA, 10, 50, NA, NA, NA, NA, 0, NA, NA),
  response = rnorm(10, mean = 100)
)

# Validate required columns
measure_validate_metadata(data, require = c("sample_type", "run_order"))

# Validate for calibration workflow
measure_validate_metadata(
  data,
  require = c("sample_type", "nominal_conc")
)

# More lenient validation (warnings only)
measure_validate_metadata(
  data,
  require = c("sample_type", "run_order", "missing_col"),
  action = "warn"
)
#> Warning: Metadata validation issues:
#> ! Missing required column(s): missing_col