Package 'FakeDataR'

Detect sensitive columns by name

Description

Uses a broad, configurable regex library to match likely PII columns. You can extend it with extra_patterns (they get ORed in) or replace everything with a single override_regex.

Usage

detect_sensitive_columns(x_names, extra_patterns = NULL, override_regex = NULL)

Arguments

x_names	Character vector of column names to check.
extra_patterns	Character vector of additional regexes to OR in. Examples: c("MRN", "NHS", "Aadhaar", "passport")
override_regex	Optional single regex string that fully replaces the defaults (case-insensitive). When supplied, extra_patterns is ignored.

Value

Character vector of names from x_names that matched.

Examples

detect_sensitive_columns(c("id","email","home_phone","zip","notes"))
detect_sensitive_columns(names(mtcars), extra_patterns = c("^vin$", "passport"))

---

Save a fake dataset to disk

Description

Save a data.frame to CSV, RDS, or Parquet based on the file extension.

Usage

export_fake(x, path)

Arguments

x	A data.frame (e.g., output of generate_fake_data()).
path	File path. Supported extensions: .csv, .rds, .parquet.

Value

(Invisibly) the path written.

---

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)

---

Generate fake data from a DB schema data.frame

Description

Generate fake data from a DB schema data.frame

Usage

generate_fake_from_schema(sch_df, n = 30, seed = NULL)

Arguments

sch_df	A data.frame returned by schema_from_db().
n	Number of rows to generate.
seed	Optional integer seed for reproducibility.

Value

A base data.frame with n rows and one column per schema entry. Column classes follow the schema type values (integer, numeric, character, logical, Date, POSIXct); missingness is injected when nullable is TRUE.

---

Generate a Fake POSIXct Column

Description

Create synthetic timestamps either by mimicking an existing POSIXct vector (using its range and NA rate) or by sampling uniformly between start and end.

Usage

generate_fake_posixct_column(
  like = NULL,
  n = NULL,
  start = NULL,
  end = NULL,
  tz = "UTC",
  na_prop = NULL
)

Arguments

like	Optional POSIXct vector to mimic. If supplied, n defaults to length(like), the output range matches range(like, na.rm = TRUE), and the NA rate is copied unless you override with na_prop.
n	Number of rows to generate. Required when like is NULL.
start, end	Optional POSIXct bounds to sample between when like is NULL.
tz	Timezone to use if like has no tzone (default "UTC").
na_prop	Optional NA proportion to enforce in the output (0–1). If NULL and like is provided, it copies the NA rate from like. If like is NULL, defaults to 0.

Value

A POSIXct vector of length n.

---

Generate fake data with privacy controls

Description

Generates a synthetic copy of data, then optionally detects/handles sensitive columns by name. Detection uses the ORIGINAL column names and maps to output via attr(fake, "name_map") if present.

Usage

generate_fake_with_privacy(
  data,
  n = 30,
  level = c("low", "medium", "high"),
  seed = NULL,
  sensitive = NULL,
  sensitive_detect = TRUE,
  sensitive_strategy = c("fake", "drop"),
  normalize = TRUE,
  sensitive_patterns = NULL,
  sensitive_regex = NULL
)

Arguments

data	A data.frame (or coercible) to mirror.
n	Rows to generate (default same as input if NULL).
level	One of "low","medium","high".
seed	Optional RNG seed.
sensitive	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" or "drop".
normalize	Logical; lightly normalize inputs.
sensitive_patterns	Optional named list of patterns to treat as sensitive (e.g., list(id = "...", email = "...", phone = "...")). Overrides defaults.
sensitive_regex	Optional fully-combined regex (single string) to detect sensitive columns by name. If supplied, it is used instead of defaults.

Details

Generate fake data with privacy controls

Value

data.frame with attributes: sensitive_columns, dropped_columns, name_map

---

Create a copy-paste prompt for LLMs

Description

Create a copy-paste prompt for LLMs

Usage

generate_llm_prompt(
  fake_path,
  schema_path = NULL,
  notes = NULL,
  write_file = TRUE,
  path = dirname(fake_path),
  filename = "README_FOR_LLM.txt"
)

Arguments

fake_path	Path to the fake data file (CSV/RDS/Parquet).
schema_path	Optional path to the JSON schema.
notes	Optional extra notes to append for the analyst/LLM.
write_file	Write a README txt next to the files? Default TRUE.
path	Output directory for the README if write_file = TRUE.
filename	README file name. Default "README_FOR_LLM.txt".

Value

The prompt string (invisibly returns the file path if written).

---

Create a fake-data bundle for LLM workflows

Description

Generates fake data, writes files (CSV/RDS/Parquet), writes a scrubbed JSON schema, and optionally writes a README prompt and a single ZIP file containing everything.

Usage

llm_bundle(
  data,
  n = 30,
  level = c("medium", "low", "high"),
  formats = c("csv", "rds"),
  path = tempdir(),
  filename = "fake_bundle",
  seed = NULL,
  write_prompt = TRUE,
  zip = FALSE,
  prompt_filename = "README_FOR_LLM.txt",
  zip_filename = NULL,
  sensitive = NULL,
  sensitive_detect = TRUE,
  sensitive_strategy = c("fake", "drop"),
  normalize = FALSE
)

Arguments

data	A data.frame (or coercible) to mirror.
n	Number of rows in the fake dataset (default 30).
level	Privacy level: "low", "medium", or "high". Controls stricter defaults.
formats	Which data files to write: any of "csv","rds","parquet".
path	Folder to write outputs. Default: tempdir().
filename	Base file name (no extension). Example: "demo_bundle". This becomes files like "demo_bundle.csv", "demo_bundle.rds", etc.
seed	Optional RNG seed for reproducibility.
write_prompt	Write a README_FOR_LLM.txt next to the data? Default TRUE.
zip	Create a single zip archive containing data + schema + README? Default FALSE.
prompt_filename	Name for the README file. Default "README_FOR_LLM.txt".
zip_filename	Optional custom name for the ZIP file (no path). If NULL (default), it is derived as paste0(filename, ".zip"), e.g. "demo_bundle.zip".
sensitive	Character vector of column names to treat as sensitive (optional).
sensitive_detect	Logical, auto-detect common sensitive columns (id/email/phone). Default TRUE.
sensitive_strategy	"fake" (replace with realistic fakes) or "drop". Default "fake".
normalize	Logical; if TRUE, attempt light auto-normalization before faking.

Details

Tips Avoid using angle brackets in examples; prefer plain tokens like NAME or FILE_NAME. If you truly want bracket glyphs, use Unicode ⟨name⟩ ⟩name⟨.

Value

List with paths: $data_paths (named), $schema_path, $readme_path (optional), $zip_path (optional), and $fake (data.frame).

---

Build an LLM bundle directly from a database table

Description

Reads just the schema from table on conn, synthesizes n fake rows, writes a schema JSON, fake dataset(s), and a README prompt, and optionally zips them into a single archive.

Usage

llm_bundle_from_db(
  conn,
  table,
  n = 30,
  level = c("medium", "low", "high"),
  formats = c("csv", "rds"),
  path = tempdir(),
  filename = "fake_from_db",
  seed = NULL,
  write_prompt = TRUE,
  zip = FALSE,
  zip_filename = NULL,
  sensitive_strategy = c("fake", "drop")
)

Arguments

conn	A DBI connection.
table	Character scalar: table name to read.
n	Number of rows in the fake dataset (default 30).
level	Privacy level: "low", "medium", or "high". Controls stricter defaults.
formats	Which data files to write: any of "csv","rds","parquet".
path	Folder to write outputs. Default: tempdir().
filename	Base file name (no extension). Example: "demo_bundle". This becomes files like "demo_bundle.csv", "demo_bundle.rds", etc.
seed	Optional RNG seed for reproducibility.
write_prompt	Write a README_FOR_LLM.txt next to the data? Default TRUE.
zip	Create a single zip archive containing data + schema + README? Default FALSE.
zip_filename	Optional custom name for the ZIP file (no path). If NULL (default), it is derived as paste0(filename, ".zip"), e.g. "demo_bundle.zip".
sensitive_strategy	"fake" (replace with realistic fakes) or "drop". Default "fake".

Value

Invisibly, a list with useful paths:

• schema_path – schema JSON

• files – vector of written fake-data files

• zip_path – zip archive path (if zip = TRUE)

Examples

if (requireNamespace("DBI", quietly = TRUE) &&
    requireNamespace("RSQLite", quietly = TRUE)) {
  con <- DBI::dbConnect(RSQLite::SQLite(), ":memory:")
  on.exit(DBI::dbDisconnect(con), add = TRUE)
  DBI::dbWriteTable(con, "cars", head(cars, 20), overwrite = TRUE)
  out <- llm_bundle_from_db(
    con, "cars",
    n = 100, level = "medium",
    formats = c("csv","rds"),
    path = tempdir(), filename = "db_bundle",
    seed = 1, write_prompt = TRUE, zip = TRUE
  )
}

---

Prepare Input Data: Coerce to data.frame and (optionally) normalize values

Description

Converts common tabular objects to a base data.frame, and if normalize = TRUE it applies light, conservative value normalization:

• Converts common date/time strings to POSIXct (best-effort across several formats)

• Converts percent-like character columns (e.g. "85%") to numeric (85)

• Maps a configurable set of "NA-like" strings to NA, while keeping common survey responses like "not applicable" or "prefer not to answer" as real levels

• Normalizes yes/no character columns to an ordered factor c("no","yes")

Usage

prepare_input_data(
  data,
  normalize = TRUE,
  na_strings = c("", "NA", "N/A", "na", "No data", "no data"),
  keep_as_levels = c("not applicable", "prefer not to answer", "unsure"),
  percent_detect_threshold = 0.6,
  datetime_formats = c("%m/%d/%Y %H:%M:%S", "%m/%d/%Y %H:%M",
    "%Y-%m-%d %H:%M:%S", "%Y-%m-%d %H:%M", "%Y-%m-%dT%H:%M:%S",
    "%Y-%m-%dT%H:%M", "%m/%d/%Y", "%Y-%m-%d")
)

Arguments

data	An object coercible to data.frame (data.frame/tibble/data.table/matrix/list, etc.)
normalize	Logical, run value normalization step (default TRUE).
na_strings	Character vector that should become NA (default: c("", "NA", "N/A", "na", "No data", "no data")).
keep_as_levels	Character vector that should be kept as values (not NA), e.g., survey choices (default: c("not applicable", "prefer not to answer", "unsure")). Matching is case-insensitive.
percent_detect_threshold	Proportion of non-missing values that must contain ⁠%⁠ before converting a character column to numeric (default 0.6).
datetime_formats	Candidate formats tried (in order) when parsing date-times strings. The best-fitting format (most successful parses) is used. Defaults cover ⁠mm/dd/yyyy HH:MM(:SS)?⁠, ISO-8601, and date-only.

Value

A base data.frame.

---

Extract a table schema from a DB connection

Description

Returns a data frame describing the columns of a database table.

Usage

schema_from_db(conn, table, level = c("medium", "low", "high"))

Arguments

conn	A DBI connection.
table	Character scalar: table name to introspect.
level	Privacy preset to annotate in schema metadata: one of "low", "medium", "high". Default "medium".

Value

A data.frame with column metadata (e.g., name, type).

Examples

if (requireNamespace("DBI", quietly = TRUE) &&
    requireNamespace("RSQLite", quietly = TRUE)) {
  con <- DBI::dbConnect(RSQLite::SQLite(), ":memory:")
  on.exit(DBI::dbDisconnect(con), add = TRUE)
  DBI::dbWriteTable(con, "mtcars", mtcars[1:3, ])
  sc <- schema_from_db(con, "mtcars")
  head(sc)
}

---

Validate a fake dataset against the original

Description

Compares classes, NA/blank proportions, and simple numeric ranges.

Usage

validate_fake(original, fake, tol = 0.15)

Arguments

original	data.frame
fake	data.frame (same columns)
tol	numeric tolerance for proportion differences (default 0.15)

Value

data.frame summary by column

---

Zip a set of files for easy sharing

Description

Zip a set of files for easy sharing

Usage

zip_llm_bundle(files, zipfile)

Arguments

files	Character vector of file paths.
zipfile	Path to the zip file to create.

Value

The path to the created zip file.

---