data_column: Define a Column Specification for Schema Checks

In dataverifyr: A Lightweight, Flexible, and Fast Data Validation Package that Can Handle All Sizes of Data

Define a Column Specification for Schema Checks

Description

Creates a single column declaration used in ruleset(..., data_columns = ...). Column declarations are schema checks (column existence, optionality, and declared type), whereas rule() is for row-wise value checks.

Usage

data_column(
  col,
  type = NA_character_,
  optional = FALSE,
  description = NA_character_
)

Arguments

col	column name.
type	optional declared type (for example "int", "double", "str", "logical"). Use NA_character_ for no type declaration.
optional	logical; if FALSE, the column is required.
description	optional free-text description.

Value

A data_column object (list) that can be passed in ruleset(..., data_columns = list(...)).

Examples

rs <- ruleset(
  rule(price >= 0),
  data_columns = list(
    data_column("price", type = "double", optional = FALSE),
    data_column("note", type = "str", optional = TRUE)
  )
)
rs

# combined with row rules and strict schema stopping
order_rules <- ruleset(
  rule(price >= 0, allow_na = FALSE),
  data_columns = list(
    data_column("order_id", type = "int", optional = FALSE),
    data_column("price", type = "double", optional = FALSE),
    data_column("note", type = "str", optional = TRUE)
  )
)

check_data(
  data.frame(order_id = 1:3, price = c(10, 20, 30), note = c("ok", NA, "ok")),
  order_rules,
  stop_on_schema_fail = TRUE
)

dataverifyr documentation built on April 11, 2026, 1:06 a.m.