Validate a Data Frame Column with a Validator

Lifts any validator onto one column of a data.frame. Errors keep the column path (e.g. newdata$age: must be in [0, 120]), so one set of steps serves both a standalone argument and a column.

Usage

require_col(restriction, col, validator)

Arguments

restriction

a restriction object.

col

character(1) column name.

validator

a restriction object applied to the column.

Value

The modified restriction object.

Details

The value must be a data.frame containing col, unless validator allows NULL (see allow_null()), in which case a missing column passes. Context passed to the outer validator is visible to formula steps in validator. With .on_fail = "all" each inner failure is reported on its own.

See also

Other composition: allow_null(), require_any(), require_each(), require_fields(), require_valid()

Examples

age <- restrict("age") |>
  require_integer(no_na = TRUE) |>
  require_between(0, 120)

newdata_v <- restrict("newdata") |>
  require_df() |>
  require_col("age", age) |>
  require_col("sex", restrict("sex") |> require_one_of(c("f", "m")))

newdata_v(data.frame(age = 30L, sex = "f"))
validation_errors(newdata_v, data.frame(age = 130L, sex = "x"))
#> [1] "newdata$age: must be in [0, 120]\n  Found: 130"           
#> [2] "newdata$sex: must be one of: \"f\", \"m\"\n  Found: \"x\""