Synthetic Data Generation with Synthpop in R

Synthetic data generation has become a novel tool for researchers and analysts who need to work with sensitive data while safeguarding privacy. The synthpop package in R provides a robust and flexible framework for creating synthetic datasets that closely mirror the statistical properties of real-world data without compromising individual privacy. This approach is particularly valuable in fields like healthcare, finance, and social sciences, where protecting confidential information is paramount.

At its core, synthpop generates synthetic data by modeling the relationships within the original dataset and creating new, artificial records that preserve these patterns. The package uses a sequential synthesis process, where each variable is generated based on conditional distributions derived from the original data. By default, it employs Classification and Regression Trees (CART) to model these relationships, but users can customize the synthesis method for each variable, offering significant flexibility. The process begins by sampling the first variable from the observed data, then iteratively generates subsequent variables, using previously synthesized variables as predictors to maintain the dataset’s internal structure. This ensures that the synthetic data captures the statistical characteristics and inter-variable relationships of the original data while containing no actual individual records, thus minimizing disclosure risks.

A key strength of synthpop is its built-in tools for evaluating the quality of synthetic datasets. Researchers can compare the synthetic data to the original to assess how well it preserves statistical properties, such as distributions and correlations. This is critical for ensuring that analyses conducted on synthetic data produce results comparable to those from the original dataset. The package also supports model fitting on synthetic data, allowing users to validate results against those obtained from the original data. As of June 2025, the latest version of synthpop remains actively maintained, with ongoing updates enhancing its functionality for privacy-preserving data analysis.

The Synthpop Package

The synthpop package in R offers a practical solution for creating synthetic versions of sensitive microdata, enabling researchers to share datasets with fewer privacy concerns. It generates synthetic data that closely mimics the statistical properties of the original, making it a valuable tool for statistical disclosure control. What sets synthpop apart is its adaptability, where researchers can customize the synthesis process to match the unique characteristics of their dataset, adjusting settings for individual variables to handle complex data structures effectively. The package supports a range of synthesis methods, from advanced techniques like Classification and Regression Trees (CART) to parametric approaches, accommodating diverse data types and user needs. Beyond data generation, synthpop provides robust tools for evaluation. Its comparison functions allow users to assess how well the synthetic data preserves the statistical integrity of the original, ensuring both utility and privacy. Researchers can also fit linear models to synthetic datasets and compare their performance against models built on the original data, offering insights into the synthetic data’s analytical value. Additionally, the package includes diagnostic tools that deliver detailed summaries, correlation analyses, and visualizations to evaluate data quality. These features are especially useful for confirming that multivariate relationships are accurately captured. Overall, synthpop is a comprehensive and user-friendly tool for researchers aiming to balance data privacy with analytical accuracy in their work.

Generating Synthetic Data: Step-by-Step Examples

# Install synthpop if not already installed
if (!requireNamespace("synthpop", quietly = TRUE)) {
  install.packages("synthpop")
}

library(synthpop)

# Load the built-in SD2011 dataset from synthpop
data(SD2011, package = "synthpop")

head(SD2011)

# Generate synthetic data using default settings
synthetic_data <- syn(SD2011, m = 1, seed = 123)  # Set seed for reproducibility

head(synthetic_data$syn)

You can customize the synthesis method for specific variables. For instance, you might use the CART method for categorical variables.

# Customize synthesis methods for specific variables
# Specify methods for a subset of variables (e.g., age, income, marital)
methods <- rep("cart", ncol(SD2011))  # Default to CART for all variables
names(methods) <- colnames(SD2011)
methods["age"] <- "norm"  # Use normal distribution for age
methods["marital"] <- "polyreg"  # Use polytomous regression for marital status

# Synthesize with custom methods
syn_control <- syn(SD2011, method = methods, seed = 123)

# View synthetic data with custom methods
head(syn_control$syn)

method: Defines the synthesis method for each variable (e.g., "norm" for normal distribution, "logreg" for logistic regression).

To evaluate the similarity between the original and synthetic datasets, use diagnostic plots.

# Compare distributions of original and synthetic data

compare(synthetic_data, SD2011, vars = c("age", "income", "marital"))
compare(syn_control, SD2011, vars = c("age", "income", "marital"))

compare: generates side-by-side visualizations of distributions in the original and synthetic datasets.

The synthpop package also handles missing values by automatically modeling them during synthesis.

# Introduce missing values in the dataset (e.g., 50 random NAs in age)
SD2011$age[sample(1:nrow(SD2011), 50)] <- NA

syn_missing <- syn(SD2011, seed = 123)

head(syn_missing$syn)

To generate multiple synthetic datasets, specify the m parameter.

multi_syn <- syn(SD2011, m = 5, seed = 123)

# Access the first synthetic dataset
head(multi_syn$syn[[1]]) 

Conclusion

Synthetic data generation with the synthpop package is a tool for privacy-preserving data analysis, testing, and sharing. By retaining key statistical properties of the original data a wide range of applications become available without compromising confidentiality. With careful implementation and validation, synthetic data can change how we work with sensitive datasets. By understanding how to generate and evaluate synthetic datasets, researchers can use this tool to overcome challenges of using sensitive or limited real-world data. For more information on synthpop and its applications, refer to its documentation or explore additional resources on synthetic data generation.

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Synthetic Data Generation with Synthpop in R