Deep Dive: Building Synthetic Data Tools with R
2022-2024

Key Resources

• Books:

• R for Data Science by Garrett Grolemund and Hadley Wickham (essential for R programming).
• Statistical Modeling in R by Tormod Naes and Solve Kjolstad.
• Hadley Wickham!

• Research Papers:

• Evaluating Synthetic Data Utility in R by Katherine Crook and Brian Dalessandro.
• Best Practices in Synthetic Data Generation by Scott Lundberg et al.

• Tools:

• R packages: synthpop, simstudy, caret (synthetic data generation and validation).
• Tutorials: Comprehensive resources on RStudio’s website.

• Online Courses:

• Datacamp: Generating Synthetic Data in R.
• EdX: Foundations of R Programming.

TLDR

Building synthetic data platforms in R = you want the most scalable, replicable pipelines for generating data to support machine learning, analytics, and simulation. R’s ecosystem provides specialized packages and tools for generating, validating, and visualizing synthetic data that aligns with real-world distributions and characteristics. This is mostly for use with R + R Shiny, would try Python again.

• Purpose of Synthetic Data: Synthetic data is artificially generated data that mimics the statistical properties of real-world datasets. It is best used when there are privacy concerns, to enable data sharing, and overcomes limitations posed by restricted or incomplete datasets.
• The challenge was to create synthetic datasets that preserve the statistical integrity of the original data while minimizing the risk of exposing sensitive information.

Baseline Knowledge

R as a Platform for Synthetic Data

Subtopics:

1. Key R Packages for Synthetic Data:

• synthpop

• Provides tools for creating synthetic versions of real datasets.
• Includes utility metrics for evaluating synthetic data quality.

• simstudy

• Enables simulation of correlated data for research or testing.
• Useful for generating multi-variable datasets with specified distributions.

• caret

• Helps preprocess and generate data tailored for machine learning applications.

• Ggplot2
• Dplyr, tidyr

2. How to Generate Data

• Rule-based Generation: Defined constraints and deterministic rules.
• Statistical Modeling: Used regression models, Gaussian Mixture Models (GMMs), and Bayesian Networks.
• Machine Learning: Exploratory work with Generative Adversarial Networks (GANs) for tabular data.

3. Statistical Methods in R:

• Using generalized linear models to create synthetic predictors.
• Random sampling techniques for balanced dataset creation.
• Techniques like imputation for partially synthetic data.

4. Pipeline Design:

• Steps to create synthetic data:

• Define distributions (e.g., Gaussian, binomial).
• Generate synthetic samples using R scripts.
• Evaluate similarity metrics (e.g., Wasserstein distance, KL divergence).

• Automating synthetic data pipelines with R Markdown or Shiny.

Workflow and Methodology

• Data Preparation:

• Cleaned raw datasets using tidyverse and dplyr workflows.
• Addressed missing data with imputation techniques.

• Modeling Approaches:

• Applied Bayesian inference for hierarchical data structures.
• Experimented with GMMs for clustering and probabilistic generation.
• Simulated data using rule-based systems - use simstudy

• Validation Framework:

• Measured statistical similarity using correlation matrices.
• Trained downstream machine learning models on both synthetic and real datasets to compare performance.

• Tools for Automation:

• Built repeatable pipelines with custom R scripts.
• Automated validation workflows using parameter sweeps and benchmarking.

Challenges Encountered

• Privacy vs. Utility Trade-off: Struggled with achieving high fidelity while ensuring anonymization, especially in small datasets. We got a lot of crap for this.
• Scalability: Processing large datasets led to memory constraints in R.
• Edge Cases: Extreme outliers and sparse datasets affected model accuracy.
• Technical Bottlenecks: Runtime inefficiencies while iterating over large parameter spaces.

Technical Takeaways:

• R excels in statistical modeling but struggles with scalability in high-dimensional data.

• Validation is as critical as generation.

• Theoretical Insights: Balancing privacy, fidelity, and downstream model performance is an art.

Questions

1. Best Practices:

• How do you validate the representativeness of synthetic datasets compared to real data? We ran into this issue with consumer replication
• What are the risks of overfitting to synthetic datasets in model training? Also common question

2. Emerging Tools:

• Can R’s synthetic data methods integrate with Python? Most companies already have datasets and modelling that are in things like python (the most technical) but more commonly things like Tableau (not even really a language). How do we extract from that/integrate with that?