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Validate Data Frame Columns Against Specified Validations

Source: R/validation-options.R
validateColumns.Rd

This function checks if the columns of a given data frame adhere to specified validations defined in columnSpecs. It validates each column's type, value range, allowed values, and handles NULL and NA values based on the configurations provided in columnSpecs.

Usage

validateColumns(object, columnSpecs)

Arguments

object

#' @param object A data frame, tibble, or data.table whose columns are to be validated.

columnSpecs

A list specifying the valid configurations for each column in object. Each entry in columnSpecs should be a list containing the type, allowedValues, valueRange, nullAllowed, and naAllowed parameters for the column it corresponds to.

Value

The function does not explicitly return a value. It completes without error if all columns are valid according to columnSpecs. If any validation fails, it generates a descriptive error message, reporting all failures if multiple validations fail.

Details

The function iterates through each column name in columnSpecs, retrieves the corresponding column from object, and validates it using validateVector. The validation includes:

  • Type correctness according to type.

  • Whether the column values are within the specified valueRange (if applicable).

  • Whether the column values are among the allowedValues (if specified).

  • Handling of NULL and NA based on nullAllowed and naAllowed flags.

Examples

df <- data.frame(
  age = c(25L, 30L, NA),
  BMI = c(22.5, 27.3, 24.9),
  gender = c("M", "F", "F"),
  smoker = c(TRUE, FALSE, NA)
)

columnSpecs <- list(
  age = list(type = "integer", valueRange = c(18L, 65L), naAllowed = TRUE),
  BMI = list(type = "numeric", valueRange = c(15.0, 40.0), naAllowed = TRUE),
  gender = list(type = "character", allowedValues = c("M", "F"), naAllowed = TRUE),
  smoker = list(type = "logical", naAllowed = TRUE)
)

validateColumns(df, columnSpecs)
#> NULL