generate_fake_data: Generate Fake Data from Real Dataset Structure

In FakeDataR: Privacy-Preserving Synthetic Data for 'LLM' Workflows

Generate Fake Data from Real Dataset Structure

Description

Generate Fake Data from Real Dataset Structure

Usage

generate_fake_data(
  data,
  n = 30,
  category_mode = c("preserve", "generic", "custom"),
  numeric_mode = c("range", "distribution"),
  column_mode = c("keep", "generic", "custom"),
  custom_levels = NULL,
  custom_names = NULL,
  seed = NULL,
  verbose = FALSE,
  sensitive = NULL,
  sensitive_detect = TRUE,
  sensitive_strategy = c("fake", "drop"),
  normalize = TRUE
)

Arguments

data	A tabular object; will be coerced via prepare_input_data().
n	Rows to generate (default 30).
category_mode	One of "preserve","generic","custom". preserve: sample observed categories by empirical frequency (keeps factors) generic: replace categories with "Category A/B/..." custom: use custom_levels[[colname]] if provided
numeric_mode	One of "range","distribution". range: uniform between min/max (integers stay integer-like) distribution: sample observed values with replacement
column_mode	One of "keep","generic","custom". keep: keep original column names var1..varP (mapping in attr(name_map)) custom: use custom_names named vector (old -> new)
custom_levels	optional named list of allowed levels per column (for
custom_names	optional named character vector old->new (for column_mode="custom").
seed	Optional RNG seed.
verbose	Logical; print progress.
sensitive	Optional character vector of original column names to treat as sensitive.
sensitive_detect	Logical; auto-detect common sensitive columns by name.
sensitive_strategy	One of "fake","drop". Only applied if any sensitive columns exist.
normalize	Logical; lightly normalize inputs (trim, %→numeric, short date-times→POSIXct).

Value

A data.frame of n rows with attributes:

• name_map (named chr: original -> output)

• column_mode (chr)

• sensitive_columns (chr; original names)

• dropped_columns (chr; original names that were dropped)

FakeDataR documentation built on Nov. 6, 2025, 1:15 a.m.