R Package Mapping

OMOPy reimplements the OHDSI / DARWIN-EU R package ecosystem as a single Python monorepo package. The table below shows how each R package maps to an OMOPy module.

For the full development history, design decisions, and technical details behind each module, see the Audit Trail.

Package ↔ Module mapping

OHDSI R Package	OMOPy Module	Phase	Description
omopgenerics	omopy.generics	0	Core type system — CDM schema, codelists, cohort tables, summarised results
CDMConnector	omopy.connector	1–2	Database connection, CDM reference, cohort generation, CIRCE engine, subsetting, snapshots
PatientProfiles	omopy.profiles	3A	Patient-level enrichment — demographics, intersections (flag/count/date/days), death
CodelistGenerator	omopy.codelist	3B	Vocabulary-based code list generation, hierarchy traversal, diagnostics
visOmopResults	omopy.vis	3C	Formatting, tabulation, and plotting of SummarisedResult objects
CohortCharacteristics	omopy.characteristics	4A	Cohort characterisation — summarise, table, and plot functions for demographics, timing, overlap
IncidencePrevalence	omopy.incidence	4B	Denominator generation, incidence rate and prevalence estimation with confidence intervals
DrugUtilisation	omopy.drug	5A	Drug cohort generation, daily dose, utilisation metrics, indication, treatment, dose coverage
CohortSurvival	omopy.survival	5B	Kaplan-Meier and Aalen-Johansen competing-risk survival analysis
TreatmentPatterns	omopy.treatment	6A	Sequential treatment pathway computation, Sankey/sunburst visualisation
DrugExposureDiagnostics	omopy.drug_diagnostics	6B	12 diagnostic checks on drug exposure records (missingness, duration, dose, etc.)
PregnancyIdentifier	omopy.pregnancy	7A	HIPPS algorithm for pregnancy episode identification
TestGenerator	omopy.testing	8A	Synthetic OMOP CDM test data generation

Key technology differences

The table below summarises the main technology substitutions made in the Python rewrite:

Concern	R ecosystem	OMOPy (Python)
Lazy SQL	dbplyr	Ibis
DataFrames	tibble / data.frame	Polars
Data models	S4 classes / R6	Pydantic BaseModel
Plotting	ggplot2 + plotly	Plotly
Tables	gt	great_tables
Survival	survival (R)	lifelines + custom Aalen-Johansen
Statistics	stats (R)	SciPy
Package manager	renv	uv

Design philosophy

1. Single package. All 13 R packages are consolidated into one installable Python package (pip install omopy) with sub-modules.
2. Clean-room implementation. Code was written against specifications and documentation only — no R source code was consulted.
3. Lazy by default. Database queries are built as Ibis expressions and only executed when .collect() is called.
4. Standardised output. All analytics produce SummarisedResult objects (the Python equivalent of summarised_result in omopgenerics), enabling consistent downstream formatting, tabulation, and plotting.