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From API Responses to Data Model with R

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From API Responses to Data Model with R

Overview

Converting complex nested JSON data from APIs into normalized relational data models is a common challenge in data engineering and analysis workflows.

The R packages dm and tibblify provide complementary capabilities that streamline this transformation process, each addressing different aspects of the data modeling pipeline.

Understanding the Challenge

When working with REST APIs, JSON responses often arrive in deeply nested, hierarchical structures that mirror the denormalized nature of document-oriented data. This structure optimizes for flexible data retrieval but complicates analytical workflows that benefit from normalized, relational schemas. The transformation requires:

  • Parsing nested JSON into tabular R structures
  • Identifying entities and relationships within the hierarchical data
  • Normalizing the data by separating concerns into distinct tables
  • Establishing primary and foreign key constraints to maintain referential integrity
  • Deploying the model to databases when needed

Tibblify: Rectangling Nested JSON

tibblify specializes in “rectangling”, or the process of converting deeply nested lists (typically from JSON or XML sources) into tidy tibbles. This package serves as the critical first step in the transformation pipeline.

Some of the key features of tibblify are:

  • Automatic Schema Detection: tibblify can automatically infer the structure of nested JSON and convert it to a tibble without requiring explicit specifications.

  • Explicit Field Specifications: For more control, you can define type specifications using tspec_*() to dictate exactly how nested fields should be extracted and typed.

  • Handling Complex Nesting: tibblify supports various nested structures including arrays of objects, nested data frames, and optional fields. You can use functions like tib_df() for nested data frames and tib_row() for single nested objects.

dm: Building Relational Data Models

Once you’ve rectangled your JSON data using tibblify, dm enables you to organize multiple related tibbles into a cohesive relational data model with explicit relationships.

  • Creating Data Models from Tibbles: dm wraps multiple data frames or tibbles into a single object that represents a relational model.

Appendix

Note created on 2025-12-29 and last modified on 2025-12-29.

See Also

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