r4subdata provides realistic example datasets for the R4SUB clinical submission readiness ecosystem, designed for demos, vignettes, and package testing, like nycflights13 or palmerpenguins.
Installation
install.packages("r4subdata")Development version:
pak::pak("R4SUB/r4subdata")
Datasets
| Dataset | Rows | Description |
|---|---|---|
evidence_pharma |
250 | Full evidence table for study CDISCPILOT01 across 4 domains |
adam_metadata |
36 | ADaM variable metadata for ADSL, ADAE, ADLB |
sdtm_metadata |
43 | SDTM variable metadata for DM, AE, LB |
trace_mapping |
25 | ADaM-to-SDTM traceability mapping with confidence scores |
risk_register_pharma |
18 | FMEA risk register with P/I/D scores and mitigations |
regulatory_indicators |
30 | Indicator definitions across quality, trace, risk, usability |
oncology_metadata |
32 | ADaM variable metadata for ONCO-2025-001 (ADSL, ADRS, ADTTE) |
oncology_evidence |
29 | Evidence table for ONCO-2025-001 across all 4 pillars |
library(r4subdata)
list_datasets()Quick Start
library(r4subdata) # Explore available datasets list_datasets() # Load and inspect data(evidence_pharma) table(evidence_pharma$indicator_domain) # Column dictionary dataset_dictionary("evidence_pharma")
Usage with R4SUB Packages
library(r4subcore) library(r4subscore) library(r4subdata) # Score the pharma evidence scores <- compute_indicator_scores(evidence_pharma) pillars <- compute_pillar_scores(evidence_pharma) sci <- compute_sci(pillars) # Traceability library(r4subtrace) model <- build_trace_model(adam_metadata, sdtm_metadata, trace_mapping) # Risk analysis library(r4subrisk) rr <- create_risk_register(risk_register_pharma) # Oncology study scores_onco <- compute_indicator_scores(oncology_evidence)
License
MIT