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Validate Data Quality

Source: R/validate.R
validate_data_quality.Rd

Assesses overall data quality metrics and flags datasets that don't meet minimum quality thresholds.

Usage

validate_data_quality(
  data,
  min_rows = 10,
  max_missing_pct = 50,
  required_cols = c("patient_id", "organism_normalized"),
  stop_on_failure = FALSE
)

Arguments

data

Data frame

min_rows

Numeric. Minimum number of rows required. Default 10.

max_missing_pct

Numeric. Maximum percent of missing values allowed per column (0-100). Default 50.

required_cols

Character vector. Columns that must be present. Default c("patient_id", "organism_normalized").

stop_on_failure

Logical. If TRUE, stops on quality failure. Default FALSE.

Value

List with quality assessment: - passes_quality: Logical - n_rows: Number of rows - n_cols: Number of columns - overall_completeness: Overall proportion non-missing (0-1) - column_completeness: Data frame of per-column completeness - quality_issues: Character vector of identified issues

Examples

if (FALSE) { # \dontrun{
quality <- validate_data_quality(data, min_rows = 100, max_missing_pct = 30)

if (!quality$passes_quality) {
  print(quality$quality_issues)
}
} # }