Detect potential data quality issues

Scans a data frame for common issues like missing data, outliers, constant columns, high cardinality, etc.

Usage

cassidy_detect_issues(
  data,
  missing_threshold = 20,
  outlier_method = c("iqr", "zscore"),
  outlier_threshold = 3
)

Arguments

data

A data frame

missing_threshold

Percent missing to flag (default: 20)

outlier_method

Method for outlier detection: "iqr" or "zscore"

outlier_threshold

Threshold for outlier detection (default: 3 for IQR, 3 for z-score)

Value

A list with detected issues

Examples

if (FALSE) { # \dontrun{
  # Check for issues
  issues <- cassidy_detect_issues(mtcars)

  # More sensitive missing data detection
  issues <- cassidy_detect_issues(mtcars, missing_threshold = 10)
} # }