SynthesizerPlus

Synthetic data generation for R with copulas, built-in quality and disclosure-risk evaluation, ggplot2 visualisation and multi-format I/O.

SynthesizerPlus learns the marginal distribution of every column and the dependence between columns (Gaussian or Student-t copula) from a real data set, and generates new records with the same statistical structure. It handles numeric, integer, categorical, ordinal, logical, date and date-time columns, missing values and their patterns, stratified models, and univariate or multivariate time series.

Installation

# development version
remotes::install_github("bogdanoancea/SynthesizerPlus", build_vignettes = TRUE)

Optional packages enable extra file formats: arrow or nanoparquet (Parquet), arrow (Feather), hdf5r (HDF5), jsonlite (JSON/NDJSON), readxl with writexl or openxlsx (Excel), haven (SPSS, Stata, SAS), fst, and data.table (fast CSV).

Quick start

library(SynthesizerPlus)

fit <- fit_synthesizer(airquality, by = "Month")   # learn
syn <- generate(fit, n = 1000, seed = 1)           # generate

cmp <- compare_synthetic(airquality, syn, seed = 1) # evaluate
cmp
plot(cmp, type = "marginals")
plot(cmp, type = "association")

write_data(syn, "synthetic.parquet")               # deliver

Features

Area	Functions
Synthesis	`fit_synthesizer()`, `generate()`, `synthesize()`, `simulate()`
Disclosure control	`disclosure_risk()` (replicated uniques, correct attribution probability), `dcr()`, simulated `census_sim` data
Closeness control	`generate(closeness = )`, `perturbation()`, `calibrate_closeness()`
ML data augmentation	`augment_data()`, `ts_windows()`
Time series	`fit_synthesizer()` on `ts`: copula-VAR, block and stationary bootstrap
Large data	`generate_to_file()` (chunked, CSV / Parquet / HDF5 / …)
Multivariate distributions	`r_mvnorm()`, `r_mvt()`, `r_mvlnorm()`, `r_mvskewnorm()`, `r_dirichlet()`, `r_mvmixture()`, `r_copula()` (Gaussian, t, Clayton, Gumbel, Frank), `r_mvdist()`, `make_corr()`
Evaluation	`compare_synthetic()`, `marginal_metrics()`, `association_matrix()`, `pmse()`, `discriminator_auc()`, `ci_overlap()`, `dcr()`
Visualisation	`plot_marginals()`, `plot_categorical()`, `plot_association()`, `plot_pairs()`, `plot_qq()`, `plot_ts()`, `plot_acf()`, `plot_dcr()`, `autoplot()`
I/O	`read_data()`, `write_data()`, `supported_formats()`, `match_types()`

Supported file formats: CSV, TSV (optionally .gz/.bz2/.xz), RDS, RData, Parquet, Feather/Arrow IPC, HDF5, JSON, NDJSON, Excel, SPSS, Stata, SAS (sas7bdat read, xpt read/write) and fst.

Example scripts

Runnable scripts are installed with the package:

ex <- function(f) system.file("examples", f, package = "SynthesizerPlus")
source(ex("demo.R"), echo = TRUE)          # a tour of the whole package
source(ex("closeness.R"), echo = TRUE)     # closeness, perturbations, calibration
source(ex("privacy.R"), echo = TRUE)       # releasing confidential microdata
source(ex("augmentation.R"), echo = TRUE)  # more training data for ML models

Vignettes

Getting started — the model, column types, missing data, strata, dependence control
Evaluating and visualising synthetic data — fidelity, utility and disclosure risk
Releasing confidential microdata as synthetic data
Controlling how close synthetic data are to the real data
Augmenting small training sets for machine learning
Regularising high-variance models with synthetic data
Synthetic time series
Generating data from multivariate distributions
Reading and writing data: CSV, Parquet, HDF5 and more

Acknowledgement

The idea of combining empirical quantile functions with a Gaussian copula follows the synthesizer package by Mark van der Loo. SynthesizerPlus is an independent implementation.

License

EUPL (European Union Public Licence)