Synthetic Tabular Data Generation with Gaussian Copulas

Generates synthetic tabular data from real datasets using Gaussian copula models, with parametric marginal selection for numerical columns and a cumulative-frequency embedding that brings categorical and boolean columns into the same joint copula. Includes a metadata system with column types and primary keys, declarative constraints enforced via rejection sampling, conditional sampling, and quality, validity and privacy reports modeled on those of the 'SDMetrics' library. Inspired by the Python 'SDV' (Synthetic Data Vault) library by 'DataCebo'; see Patki, Wedge and Veeramachaneni (2016) "The Synthetic Data Vault" .

rsdv — The R Synthetic Data Vault

Synthetic data generation in R (Gaussian Copula based, extensible to deep generative models)

rsdv is an R implementation of Python’s Synthetic Data Vault (SDV) framework (Patki, Wedge, and Veeramachaneni 2016). It generates synthetic tabular data using Gaussian copula models, with built-in quality and privacy evaluation.

Installation

# Development version
remotes::install_github("kvenkita/rsdv")

Quick start

library(rsdv)
#> 
#> Attaching package: 'rsdv'
#> The following object is masked from 'package:base':
#> 
#>     sample

set.seed(42)

# Describe column types
meta <- metadata(adult_income) |>
  set_column_type("id",         "id") |>
  set_column_type("age",        "numerical") |>
  set_column_type("occupation", "categorical") |>
  set_column_type("income",     "categorical") |>
  set_primary_key("id")

# Fit a GaussianCopula synthesizer
syn       <- gaussian_copula_synthesizer(meta)
syn       <- fit(syn, adult_income)

# Generate 500 synthetic rows
synth_data <- sample(syn, n = 500)

# Evaluate quality
qr <- quality_report(real = adult_income, synthetic = synth_data,
                     metadata = meta)
print(qr)
#> == rsdv Quality Report ==
#> 
#> Column Similarity (KS, numerical):
#>   age                  0.960
#>   fnlwgt               0.936
#>   education_num        0.776
#>   capital_gain         0.468
#>   capital_loss         0.484
#>   hours_per_week       0.724
#> 
#> Column Similarity (TVD, categorical):
#>   workclass            0.973
#>   education            0.942
#>   marital_status       0.988
#>   occupation           0.935
#>   relationship         0.970
#>   race                 0.988
#>   sex                  1.000
#>   native_country       0.956
#>   income               0.972
#> 
#> Property scores:
#>   Column Shapes        0.871
#>   Column Pair Trends   0.893
#>     (correlation 0.965, contingency 0.864)
#> 
#> Overall Score:               0.882

quality_report() aggregates metrics into the two-property hierarchy used by SDMetrics — Column Shapes (per-column marginal fidelity) and Column Pair Trends (correlation similarity for numerical pairs, contingency similarity for categorical pairs) — with the overall score the mean of the two.

diagnostic_report() complements it with structural-validity checks (value ranges, category adherence, key uniqueness), and sample_conditions() generates rows that hold given categorical values fixed:

# Validity checks
diagnostic_report(adult_income, synth_data, meta)

# Conditional generation
sample_conditions(syn, data.frame(income = ">50K", .n = 20))