r4subdata provides realistic example datasets for the R4SUB clinical submission readiness ecosystem — designed for demos, vignettes, and package testing, like nycflights13 or palmerpenguins.
install.packages("r4subdata")Development version:
pak::pak("R4SUB/r4subdata")| 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()library(r4subdata)
# Explore available datasets
list_datasets()
# Load and inspect
data(evidence_pharma)
table(evidence_pharma$indicator_domain)
# Column dictionary
dataset_dictionary("evidence_pharma")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)MIT