Assess Data Against Acceptance Criteria

Evaluates a set of values against acceptance criteria and returns a detailed assessment table with pass/fail status.

Usage

measure_assess(data, criteria, action = c("return", "warn", "error"))

Arguments

data

A named list or data frame containing the values to assess. Names must match criterion names.

criteria

A measure_criteria() object defining the acceptance criteria.

action

What to do on failure: "return" (default) returns the assessment table, "warn" issues a warning for failures, "error" stops on any critical failures.

Value

A tibble with class measure_assessment containing:

• criterion: Name of the criterion

• value: The observed value

• threshold: The threshold value(s)

• operator: The comparison operator

• pass: Logical indicating pass/fail

• priority: Priority level of the criterion

• description: Human-readable description

See also

measure_criteria() for creating criteria, criterion() for individual criteria.

Examples

# Define criteria
crit <- measure_criteria(
  cv_qc = list("<", 15),
  r_squared = list(">=", 0.99),
  recovery = list("between", c(80, 120))
)

# Assess results
results <- list(cv_qc = 12.5, r_squared = 0.995, recovery = 98.2)
measure_assess(results, crit)
#> <measure_assessment> [PASS]
#>   3 passed, 0 failed
#> 
#>   ✓ cv_qc: 12.5 (< 15)
#>   ✓ r_squared: 0.995 (>= 0.99)
#>   ✓ recovery: 98.2 (between [80, 120])

# Assess with some failures
results_bad <- list(cv_qc = 18.3, r_squared = 0.985, recovery = 75)
measure_assess(results_bad, crit)
#> <measure_assessment> [FAIL]
#>   0 passed, 3 failed
#> 
#>   ✗ cv_qc: 18.3 (< 15)
#>   ✗ r_squared: 0.985 (>= 0.99)
#>   ✗ recovery: 75 (between [80, 120])