Type: Package
Title: Fast, Robust, and High-Quality Synthetic Data Generation with a Tuneable Privacy-Utility Trade-Off
Version: 0.5.0
Maintainer: Mark van der Loo <mark.vanderloo@gmail.com>
Description: Synthesize numeric, categorical, mixed and time series data. Data circumstances including mixed (or zero-inflated) distributions and missing data patterns are reproduced in the synthetic data. A single parameter allows balancing between high-quality synthetic data that represents correlations of the original data and lower quality but more privacy safe synthetic data without correlations. Tuning can be done per variable or for the whole dataset.
License: EUPL version 1.1 | EUPL version 1.2 [expanded from: EUPL]
URL: https://github.com/markvanderloo/synthesizer
Imports: stats
VignetteBuilder: simplermarkdown
Depends: R (≥ 3.5.0)
Suggests: tinytest, simplermarkdown
Encoding: UTF-8
RoxygenNote: 7.3.2
NeedsCompilation: no
Packaged: 2025-07-10 10:24:19 UTC; mplo
Author: Mark van der Loo ORCID iD [aut, cre]
Repository: CRAN
Date/Publication: 2025-07-10 13:10:05 UTC

synthesizer: Fast, Robust, and High-Quality Synthetic Data Generation with a Tuneable Privacy-Utility Trade-Off

Description

Synthesize numeric, categorical, mixed and time series data. Data circumstances including mixed (or zero-inflated) distributions and missing data patterns are reproduced in the synthetic data. A single parameter allows balancing between high-quality synthetic data that represents correlations of the original data and lower quality but more privacy safe synthetic data without correlations. Tuning can be done per variable or for the whole dataset.

Author(s)

Maintainer: Mark van der Loo mark.vanderloo@gmail.com (ORCID)

See Also

Useful links:

Create a function that generates synthetic data

Description

Create a function that accepts a non-negative integer n, and that returns synthetic data sampled from the emperical (multivariate) distribution of x.

Usage

make_synthesizer(x, ...)

## S3 method for class 'numeric'
make_synthesizer(x, na.rm = FALSE, ...)

## S3 method for class 'integer'
make_synthesizer(x, na.rm = FALSE, ...)

## S3 method for class 'logical'
make_synthesizer(x, na.rm = FALSE, ...)

## S3 method for class 'factor'
make_synthesizer(x, na.rm = FALSE, ...)

## S3 method for class 'character'
make_synthesizer(x, na.rm = FALSE, ...)

## S3 method for class 'ts'
make_synthesizer(x, ...)

## S3 method for class 'data.frame'
make_synthesizer(x, na.rm = FALSE, ...)

Arguments

x

[vector|data.frame] Template data to be synthesized.

...

arguments passed to other methods

na.rm

[logical] Remove missing values before creating a synthesizer

Value

A function accepting a single integer argument: the number of synthesized values or records to return. For objects of class ts n must be equal to the length of the original data (this is set as the default).

See Also

Other synthesis: synthesize()

Examples

synth <- make_synthesizer(cars$speed)
synth(10)


synth <- make_synthesizer(iris)
synth(6)
synth(150)
synth(250)

Create synthetic version of a dataset

Description

Create n values or records based on the emperical (multivariate) distribution of y. For data frames it is possible to decorrelate synthetic from the original variables by lowering the value for the rankcor parameter.

Usage

synthesize(x, na.rm = FALSE, n = NROW(x), rankcor = 1)

Arguments

x

[vector|data.frame] data to synthesize.

na.rm

[logical] Remove missing values before creating a synthesizer. Set to TRUE to avoid synthesizing missing values.

n

[integer] Number of values or records to synthesize.

rankcor

[numeric] in [0,1]. Either a single rank correlation value that is applied to all variables, or a vector of the form c(variable1=ut1lity1,...). Variables not explicitly mentioned will have rankcor=1. See also the note below. Ignored for all types of x, except for objects of class data.frame.

Value

A data object of the same type and structure as x.

Note

The utility of a synthetic variable is lowered by decorelating the rank correlation between the real and synthetic data. If rankcor=1, the synthetic data will ordered such that it has the same rank order as the original data. If rankcor=0, no such reordering will take place. For values between 0 and 1, blocks of data are randomly selected and randomly permuted iteratively until the rank correlation between original and synthetic data drops below the parameter.

See Also

Other synthesis: make_synthesizer()

Examples

synthesize(cars$speed,10)
synthesize(cars)
synthesize(cars,25)

s1 <- synthesize(iris, rankcor=1)
s2 <- synthesize(iris, rankcor=0.5)
s3 <- synthesize(iris, rankcor=c("Species"=0.5))

oldpar <- par(mfrow=c(2,2), pch=16, las=1)
plot(Sepal.Length ~ Sepal.Width, data=iris, col=iris$Species, main="Iris")
plot(Sepal.Length ~ Sepal.Width, data=s1, col=s1$Species, main="Synthetic Iris")
plot(Sepal.Length ~ Sepal.Width, data=s2, col=s2$Species, main="Low utility Iris")
plot(Sepal.Length ~ Sepal.Width, data=s3, col=s3$Species, main="Low utility Species")
par(oldpar)