30 Days of Pharmaverse

Your Friendly Guide to Clinical Data Science with R in Open-Source

Exploration Roadmap

Welcome to 30 Days of Pharmaverse - a free, open-source, hands-on exploration of Clinical Data Science with R and the Pharmaverse ecosystem. Whether you’re a clinical programmer, biostatistician, data scientist, or a curious learner, this project walks you through real-world CDISC workflows one day at a time.

Each day tackles a specific concept - from setting up a working environment to building complex ADaM datasets and producing Tables, Listings, and Figures - using community-driven, open-source R packages like admiral, xportr, metacore, rtables, tern, and many more.

Who Is This For?

Clinical programmers transitioning from SAS to R, looking for a structured, code-first guide to the Pharmaverse
Biostatisticians & data scientists exploring how SDTM, ADaM, and TLF production work end-to-end in R
Students & academics learning clinical trial data standards and reproducible research workflows
Open-source contributors interested in the Pharmaverse - a growing family of validated R packages for clinical reporting

No SAS license or prior CDISC experience required. If you know base R / tidyverse and are curious about clinical data pipelines, start with Day 1.

Week 1: SDTM Fundamentals & Core R Skills

Day	Topic	Deep Dive	Key Packages
Day 1	Environment Setup & First SDTM Code	SDTM Programming Walkthrough	`{admiral}`, `{pharmaversesdtm}`, `{dplyr}`, `{haven}`, `{xportr}`
Day 2	SDTM Domain Structure & Tidyverse Mastery	Understanding SDTM Domain Classes Through Code	`{dplyr}`, `{tidyr}`, `{pharmaversesdtm}`
Day 3	Controlled Terminology & MedDRA Coding	Building AE Codelists in R	`{dplyr}`, `{pharmaversesdtm}`
Day 4	Clinical Date Derivations with lubridate	Study Day, Duration, and Partial Date Imputation	`{lubridate}`, `{dplyr}`, `{pharmaversesdtm}`
Day 5	Advanced Tidyverse: Pivoting & Joining	Reshaping SDTM Data for Analysis	`{dplyr}`, `{tidyr}`, `{pharmaversesdtm}`
Day 6	Introduction to sdtm.oak	EDC-to-SDTM Transformation Patterns	`{sdtm.oak}`, `{dplyr}`, `{pharmaversesdtm}`
Day 7	Week 1 Capstone: End-to-End SDTM Script	Build DM, AE, EX from Scratch with xportr	`{dplyr}`, `{lubridate}`, `{sdtm.oak}`, `{xportr}`, `{haven}`

Week 2: Production SDTM & Validation

Day	Topic	Deep Dive	Key Packages
Day 8	Complex SDTM Domains - LB (Lab Results)	Findings Class with Unit Standardization	`{dplyr}`, `{tidyr}`, `{sdtm.oak}`, `{pharmaversesdtm}`
Day 9	VS (Vital Signs) & Repeated Measures	Visit-Level Data and Positional Readings	`{dplyr}`, `{tidyr}`, `{pharmaversesdtm}`
Day 10	AE Domain Mastery & SAE Logic	Deep Dive into Severity, Causality, and Outcomes	`{dplyr}`, `{lubridate}`, `{sdtm.oak}`
Day 11	Disposition (DS) & Trial Design Domains	Screen Failures, Completers, and Study Structure	`{dplyr}`, `{lubridate}`, `{sdtm.oak}`
Day 12	Data Cuts with datacutr	Applying Clinical Cutoff Dates for Interim & Final Analyses	`{dplyr}`, `{lubridate}`, `{datacutr}`
Day 13	SDTM Validation with sdtmchecks	Running FDA Business Rules Against Your Domains	`{sdtmchecks}`, `{dplyr}`, `{pharmaversesdtm}`
Day 14	Week 2 Capstone - Metadata-Driven SDTM with metacore & xportr	Specification-Driven Workflows for Submission-Ready Data	`{metacore}`, `{metatools}`, `{xportr}`, `{dplyr}`

Week 3: ADaM Deep Dive & Admiral Mastery

Day	Topic	Deep Dive	Key Packages
Day 15	ADaM Architecture & Admiral Core Engine	Week 3, Day 15: Understanding ADaM Structures, Admiral Philosophy, and Core Derivation Patterns	`{admiral}`, `{pharmaversesdtm}`, `{pharmaverseadam}`, `{dplyr}`
Day 16	ADSL Part 1 - Treatment Variables & Dates	Week 3, Day 16: First dose dates, treatment assignment, and study timeline from EX and DS	`{admiral}`, `{dplyr}`, `{lubridate}`, `{pharmaversesdtm}`
Day 17	ADSL Part 2 - Population Flags & Demographics	Week 3, Day 17: SAFFL, ITTFL, demographic groupings, and baseline measurements	`{admiral}`, `{dplyr}`, `{pharmaversesdtm}`
Day 18	ADAE - Adverse Events Analysis Dataset	Week 3, Day 18: OCCDS structure with treatment emergent flags	`{admiral}`, `{dplyr}`, `{lubridate}`
Day 19	ADLB - Lab Analysis Dataset (BDS)	Week 3, Day 19: BDS structure with baseline, change, and toxicity grading	`{admiral}`, `{dplyr}`, `{lubridate}`, `{pharmaversesdtm}`
Day 20	ADVS - Vitals Analysis Dataset (BDS)	Week 3, Day 20: BDS structure with visit windows and multiple readings	`{admiral}`, `{dplyr}`, `{lubridate}`, `{pharmaversesdtm}`
Day 21	ADTTE - Time-to-Event Analysis Dataset	Week 3, Day 21: Survival analysis structure with events and censoring	`{admiral}`, `{dplyr}`, `{lubridate}`, `{pharmaversesdtm}`

Week 4: ADaM Advanced & TLF Production

Day	Topic	Deep Dive	Key Packages
Day 22	Demography Table with gtsummary + gt	First TLF from ADSL	`{admiral}`, `{dplyr}`, `{lubridate}`
Day 23	ADCM and ADRS - Concomitant Meds and Oncology Response	Week 4, Day 23: OCCDS period flags and RECIST 1.1 with admiralonco	`{admiral}`, `{admiralonco}`, `{dplyr}`, `{lubridate}`, `{pharmaversesdtm}`
Day 24	ARD-First Reporting with cards and cardx	CDISC Analysis Results Data - computation decoupled from presentation	`{cards}`, `{cardx}`, `{pharmaverseadam}`, `{dplyr}`, `{ggsurvfit}`
Day 25	gtsummary and tfrmt - ARD-Backed Production Tables	Flexible TLGs from ARD and format-spec driven TLF libraries	`{gtsummary}`, `{tfrmt}`, `{pharmaverseadam}`, `{dplyr}`, `{ggsurvfit}`
Day 26	flextable and officer - Word and RTF Clinical Tables	Formatted TLF output to Word and RTF	`{flextable}`, `{officer}`, `{pharmaverseadam}`, `{dplyr}`
Day 27	rtables, tern, and r2rtf - Structured Clinical Tables	Declarative table layout and RTF output	`{rtables}`, `{tern}`, `{r2rtf}`, `{pharmaverseadam}`, `{dplyr}`
Day 28	Tplyr - Declarative Clinical Table Programming	Grammar of clinical data summaries	`{rlistings}`, `{ggsurvfit}`, `{ggplot2}`, `{dplyr}`, `{purrr}`
Day 29	ggsurvfit + gtsummary - Survival Plots and Clinical Figures	Publication-ready TTE figures in pharmaverse	`{Tplyr}`, `{testthat}`, `{xportr}`, `{logrx}`, `{metacore}`, `{pharmaverseadam}`
Day 30	Capstone - Full Clinical Reporting Workflow	Survival · Safety · Lab · Subgroups across the pharmaverse	`{rtables}`, `{tern}`, `{r2rtf}`, `{flextable}`, `{officer}`, `{gtsummary}`, `{rlistings}`, `{ggsurvfit}`, `{ggplot2}`, `{patchwork}`

What You’ll Explore

This journey covers the essential toolkit for modern clinical programming in R:

SDTM & ADaM Standards: Understanding the structure and intent of CDISC-standardised data.
Pharmaverse Tools: Hands-on work with admiral, pharmaversesdtm, pharmaverseadam, xportr, sdtm.oak, sdtmchecks, metacore, and more.
ADaM Construction: Building ADSL, ADAE, ADLB, ADVS, ADTTE, ADCM, ADEX, and ADRS from SDTM using admiral and admiralonco.
TLF Production: Tables, listings, and figures using rtables/tern, r2rtf, flextable+officer, gtsummary, tfrmt, rlistings, and ggsurvfit.
ARD-First Workflows: Using cards and cardx to produce tidy Analysis Results Datasets that separate computation from formatting.
QC & Traceability: Unit testing with testthat, cell-level traceability with Tplyr, audit logging with logrx, and metadata-driven export with xportr.

The Toolkit

Prerequisites: - Comfort with R and the tidyverse - A curiosity about clinical trials and data standards

Core Packages:

# SDTM & ADaM layer
install.packages(c(
  "admiral",          # ADaM derivations
  "admiralonco",      # Oncology extensions
  "pharmaversesdtm",  # Example SDTM datasets
  "pharmaverseadam",  # Example ADaM datasets
  "sdtm.oak",         # SDTM utilities
  "sdtmchecks",       # SDTM validation
  "datacutr",         # Data cut utilities
  "metacore",         # Metadata management
  "metatools",        # Metadata tools
  "xportr",           # Regulatory export (.xpt)
  "haven",            # SAS interoperability
  "dplyr",            # Data wrangling
  "lubridate"         # Date handling
))

# TLF layer
install.packages(c(
  "rtables", "tern", "r2rtf", "flextable", "officer",
  "gtsummary", "tfrmt", "Tplyr", "rlistings",
  "ggplot2", "ggsurvfit", "patchwork"
))

# ARD & QC
install.packages(c("cards", "cardx", "testthat", "logrx"))

⚠️ Important Disclaimer

This project is a personal initiative created for learning and exploratory purposes only. It is in no way affiliated with, endorsed by, sponsored by, funded by, or assisted by any organisation or company at any capacity.

This project draws heavily from open-source projects, Pharmaverse examples, public repositories, and official CDISC documentation.
Significant portions of the code and content have been created with assistance from Large Language Models (LLMs) and refined through human review and intervention.
Examples and patterns are adapted from established best practices in the R and clinical programming communities.

Learners are strongly encouraged to:

Think critically - Don’t just copy-paste code. Understand why each step exists.
Verify independently - Check code against official Pharmaverse documentation and CDISC standards.
Test thoroughly - Validate any code in your own environment before use in any context.
Consult experts - When implementing for real clinical trials, seek guidance from qualified statisticians and data managers.

The views, opinions, code, and materials shared in this project are solely for exploratory and learning purposes and do not guarantee accuracy, correctness, or compliance with any standard. They do not represent the positions of any external organisation and should not be treated as guidance for any formal or regulated use without independent verification.

Community & Resources

Pharmaverse.org - The central hub for open-source clinical R packages
CDISC.org - The official standards body for clinical data interchange
Admiral Documentation - The flagship ADaM derivation package
Pharmaverse TLG Catalogue - Reference TLF outputs from the pharmaverse stack
r4csr.org - Open-access book: R for Clinical Study Reports (Zhang et al.)
R Consortium R Submissions WG - Pilot projects for R-based regulatory submissions
R for Data Science - The foundation for learning tidyverse
GitHub Repository - Source code, issues, and contributions welcome

Contributing

This is a living document powered with Quarto. Suggestions, corrections, and improvements are always welcome. Feel free to open an issue or submit a pull request on GitHub!

If you find the project useful, please consider starring the repository and sharing it with your network. The more eyes and minds we have, the better this resource can become for everyone exploring clinical data science with R.

Curated by: Indraneel Chakraborty