pharmaverse examples

The true beauty of pharmaverse (and open source in general) is when efforts from various different developers come together to complement each other as a whole greater than the sum of the individual parts. By design in R, no single package will ever completely cover all your needs, but by piecing them together we can make complex tasks increasingly simple.

This book contains end-to-end examples of using pharmaverse packages together to achieve common clinical reporting analyses, such as SDTM, ADaM, and Tables/Listings/Graphs. The examples use consistent source test raw datasets from {pharmaverseraw}, SDTMs from {pharmaversesdtm}, and ADaMs from {pharmaverseadam} respectively.

We’ll endeavour to include a selection of examples here over time, e.g. to help users when trying out the packages for PK/PD or Therapeutic Area specific (such as Oncology or Vaccines) analyses.

Note that this examples book should only be used to show how collections of packages can be used in conjunction - more thorough examples of individual package usages would always be covered in the package site vignettes and no need to repeat here.

Running the examples

Posit Cloud

Tip

Launch Posit Cloud to run any example interactively without a local R installation. Each example can be explored via a live and interactive Posit Cloud environment (preconfigured with all required package installations).

You can do this by clicking File → Open File and then choosing whichever example script, e.g. adam/adsl.R. Feel free to try out customizing any of the examples to better fit any of your own internal clinical reporting workflows!

Locally

To run examples locally, download the repository and run the following in the R console inside the project folder to install dependencies:

if(!require(pak)) {
  install.packages("pak")
}
pak::pak()

Note: the R scripts are generated from .qmd files automatically using knitr::purl.

Getting Started

The examples are organized by the stage of the clinical data pipeline they cover. If you’re new to pharmaverse, working through them in order gives a natural end-to-end view of how the ecosystem fits together.

flowchart LR
    RAW["**Raw Data** {pharmaverseraw}"]
    SDTM["**SDTM** {sdtm.oak}"]
    ADAM["**ADaM** {admiral}"]
    TLG["**TLG** {rtables} · {tern}"]
    INT["**Interactive** {teal}"]
    DOC["**Documents** {autoslideR}"]
    ESUB["**eSub**"]
    LOG["**Logging**"]

    RAW --> SDTM --> ADAM --> TLG
    TLG <--> INT
    TLG --> DOC
    TLG --> ESUB

    RAW -..-> LOG
    SDTM -..-> LOG
    ADAM -..-> LOG
    TLG -..-> LOG

    style RAW  fill:#d6eaf8,stroke:#2e86c1,color:#000
    style SDTM fill:#2e86c1,stroke:#1a5276,color:#fff
    style ADAM fill:#2e86c1,stroke:#1a5276,color:#fff
    style TLG  fill:#2e86c1,stroke:#1a5276,color:#fff
    style INT  fill:#d5f5e3,stroke:#1e8449,color:#000
    style DOC  fill:#d5f5e3,stroke:#1e8449,color:#000
    style ESUB fill:#d5f5e3,stroke:#1e8449,color:#000
    style LOG  fill:#fdebd0,stroke:#ca6f1e,color:#000

The pharmaverse clinical data pipeline

Section What it covers Good starting point if…
SDTM Converting raw collected data to CDISC SDTM domains You work upstream of ADaM or want to understand the data origin
ADaM Building analysis datasets (ADSL, ADTTE, ADPC, etc.) from SDTM You’re implementing analysis-ready datasets for reporting
TLG Creating tables, listings, and graphs for clinical study reports You focus on outputs — demographics, AEs, survival, PK summaries

The site also includes introductory examples for several additional workflow areas:

  • Interactive — building clinical data exploration apps with {teal}
  • Documents — automated report generation from ADaM data
  • Logging — logging and traceability for clinical R workflows
  • eSub — preparing outputs for electronic regulatory submission

Packages Used in These Examples

The table below covers every package used across the examples site, grouped by the part of the pipeline where it appears. Clicking a package name opens its documentation; the Example column links to where it is used on this site.

Data Packages

Package Description Example
{pharmaverseraw} Source raw datasets (AE, DM, EX, DS domains) used as the starting point for all SDTM examples SDTM
{sdtm.oak} SDTM programming in R — maps raw collected data to CDISC SDTM domains using an algorithm-based engine SDTM
{pharmaversesdtm} CDISC SDTM example datasets generated from {pharmaverseraw} SDTM / ADaM
{pharmaverseadam} CDISC ADaM example datasets (ADSL, ADTTE, ADPC, etc.) used across TLG and PK examples TLG / PK

ADaM Packages

Package Description Example
{admiral} ADaM dataset construction in R — the core package for building ADSL, ADAE, ADTTE, and other standard domains ADaM
{admiralonco} Oncology-specific ADaM extensions for {admiral} — adds functions for tumor response, DOR, and TTE oncology endpoints ADaM
{metacore} Stores and validates dataset metadata (variable definitions, codelists) from specs in a consistent R object ADaM
{metatools} Utilities for working with {metacore} objects — checking variables, applying codelists, and deriving variables from metadata ADaM
{xportr} Applies SAS XPT metadata (types, labels, lengths, formats) and exports submission-ready .xpt transport files ADaM

TLG Packages

Package Description Example
{rtables} Framework for building complex nested tables with flexible layouts — the foundation of the NEST TLG stack Demographics / AE / PK
{tern} Analysis functions for clinical trial TLGs built on {rtables} — covers demographics, AEs, survival, PK summaries Demographics / AE / PK
{rlistings} Creates regulatory-ready listings from ADaM datasets PK
{gtsummary} Publication-ready summary and analytical tables — ideal for demographics and analysis summaries Demographics
{ggsurvfit} Publication-ready Kaplan-Meier and time-to-event figures built on {ggplot2} TLG
{cards} Creates Analysis Results Datasets (ARDs) following the emerging CDISC Analysis Results Standard Demographics / AE
{tfrmt} Transforms ARDs into publication-ready tables with precise formatting control — particularly suited to RTF regulatory outputs Demographics / AE

Interactive Packages

Package Description Example
{teal} Shiny-based framework for building interactive clinical trial data exploration apps — supports filter panels, reproducibility code, and a reporter for exporting results Interactive
NoteContributing

Have an example that others would benefit from? Open an issue with your idea — new issues are automatically added to our project board for the team to review and prioritize. If you’re willing to contribute the code yourself, say so in the issue and the team will provide repo access.

A note on dependencies: there is no renv here. If your example needs a new package, add it to Imports in the root DESCRIPTION file and the deployment pipeline will install the latest CRAN release automatically.

You can see a list of current contributors via our GitHub repo DESCRIPTION file. If ever unsure, tag the team in your issue using @pharmaverse/examples.

Consider giving the Examples repo and any of the package repos listed in the tables above a ⭐ GitHub star — it helps maintainers demonstrate community adoption and keeps the ecosystem visible to new users.