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Override a variable's dataType in a spec

Source: R/set_type.R
set_type.Rd

Return a new artoo_spec with one or more variables retyped. This is the supported, in-R way to correct a spec when the data disagrees with its declared dataType (e.g. a variable typed integer whose extract holds fractional values): fix it here rather than editing the source workbook, then drive apply_spec() with the corrected spec. The spec is immutable, so the original is never changed.

Usage

set_type(spec, dataset, ...)

Arguments

spec

The specification to amend. <artoo_spec>: required.

dataset

The dataset whose variables to retype. <character(1)>: required. Must name a dataset in spec.

...

Named variable = type pairs. Each name is a variable in dataset; each value is a CDISC dataType ("string", "integer", "decimal", "float", "double", "boolean", "date", "datetime", "time", "URI") or a recognised spelling of one. At least one pair is required and every argument must be named.

Tip: to undo an integer dataType that the data does not satisfy, set "float" (IEEE double) or "decimal" (exact, exchanged as text).

Value

A new <artoo_spec> with the named variables retyped, ready for apply_spec() or write_spec(). The input spec is unchanged.

Details

Per-dataset scope. A type is set only on the named dataset's row. A variable that appears in several datasets keeps its other rows' types; call set_type() once per dataset to change them all. Spec-wide consequences (a variable typed inconsistently across datasets) are a validate_spec() concern, not a construction error here.

Canonicalised, then validated. Each supplied type is mapped through the closed CDISC dataType vocabulary, so "Float", "decimal", and "text" all resolve; an unrecognised token aborts with artoo_error_type. The rebuilt spec is re-validated, so an override that would break the spec aborts with artoo_error_spec.

See also

Auto-repair: repair_spec() to apply every integer_fraction / integer_overflow fix from a findings frame at once.

Workflow: apply_spec() to conform with the corrected spec; write_spec() to persist it; check_spec() to find the mismatches.

Examples

# ---- Example 1: retype one variable the data disagrees with ----
#
# The bundled adam_spec types ADSL.AGE as integer. If an extract stored it
# with fractional values, retype it to float so apply_spec() coerces
# without loss. set_type() returns a new spec; the original is untouched.
fixed <- set_type(adam_spec, "ADSL", AGE = "float")
v <- spec_variables(fixed, "ADSL")
v[v$variable == "AGE", c("variable", "data_type")]
#>    variable data_type
#> 16      AGE     float

# ---- Example 2: retype several at once, original left intact ----
#
# Pass any number of variable = type pairs; canonical dataTypes and common
# spellings both resolve. The source spec is immutable, so adam_spec still
# reports AGE as its original type.
patched <- set_type(adam_spec, "ADSL", AGE = "decimal", TRTSDT = "date")
spec_variables(adam_spec, "ADSL")$data_type[
  spec_variables(adam_spec, "ADSL")$variable == "AGE"
]
#> [1] "integer"