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‎index.qmd‎

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---
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title: "pharmaverse examples"
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title: "`pharmaverse` examples"
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---
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The true beauty of pharmaverse (and open source in general) is when efforts from
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The true beauty of `pharmaverse` (and open source in general) is when efforts from
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various different developers come together to complement each other as a whole
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greater than the sum of the individual parts. By design in R, no single package
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will ever completely cover all your needs, but by piecing them together we can
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make complex tasks increasingly simple.
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This book contains end-to-end examples of using pharmaverse packages together to
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This book contains end-to-end examples of using `pharmaverse` packages together to
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achieve common clinical reporting analyses, such as SDTM, ADaM, and
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Tables/Listings/Graphs. The examples use consistent source test raw datasets from
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`{pharmaverseraw}`, SDTMs from `{pharmaversesdtm}`, and ADaMs from
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`{pharmaverseadam}` respectively.
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::: {.callout-tip}
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## Try it in the cloud
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We'll endeavour to include a selection of examples here over time, e.g. to help
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users when trying out the packages for PK/PD or Therapeutic Area specific
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(such as Oncology or Vaccines) analyses.
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Note that this examples book should only be used to show how collections of
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packages can be used in conjunction - more thorough examples of individual
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package usages would always be covered in the package site vignettes and no
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need to repeat here.
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## Running the examples
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### Posit Cloud
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::: {.callout-tip}
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[Launch Posit Cloud](https://posit.cloud/content/7279124) to run any example
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interactively without a local R installation. Open **File → Open File** and
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choose whichever example script you'd like to explore.
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interactively without a local R installation. Each example can be explored via a live and interactive Posit Cloud environment
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(preconfigured with all required package installations).
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You can do this by clicking **File → Open File** and then choosing whichever example script,
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e.g. `adam/adsl.R`. Feel free to try out customizing any of the examples to better fit any of
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your own internal clinical reporting workflows!
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:::
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### Locally
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To run examples locally, download the [repository](https://github.com/pharmaverse/examples) and run the following in the R console inside the project folder to install dependencies:
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```r
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if(!require(pak)) {
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install.packages("pak")
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}
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pak::pak()
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```
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*Note: the R scripts are generated from `.qmd` files automatically using [`knitr::purl`](https://bookdown.org/yihui/rmarkdown-cookbook/purl.html).*
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---
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## Getting Started
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The examples are organized by the stage of the clinical data pipeline they cover.
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If you're new to pharmaverse, working through them in order gives a natural end-to-end
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If you're new to `pharmaverse`, working through them in order gives a natural end-to-end
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view of how the ecosystem fits together.
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| Section | What it covers | Good starting point if… |
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|---------|---------------|--------------------------|
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| **[SDTM](sdtm/index.qmd)** | Converting raw collected data to CDISC SDTM domains | You work upstream of ADaM or want to understand the data origin |
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| **[ADaM](adam/index.qmd)** | Building analysis datasets (ADSL, ADTTE, ADPC, etc.) from SDTM | You're implementing analysis-ready datasets for reporting |
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| **[ADaM](adam/index.qmd)** | Building analysis datasets (`ADSL`, `ADTTE`, `ADPC`, etc.) from SDTM | You're implementing analysis-ready datasets for reporting |
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| **[TLG](tlg/index.qmd)** | Creating tables, listings, and graphs for clinical study reports | You focus on outputs — demographics, AEs, survival, PK summaries |
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If you're primarily interested in a specific output type, jump directly to the
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[TLG section](tlg/index.qmd), which is self-contained and uses pre-built ADaM
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datasets from `{pharmaverseadam}`.
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The site also includes introductory examples for several additional workflow areas:
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- **[Interactive](interactive/index.qmd)** — building clinical data exploration apps with `{teal}`
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- **[Documents](documents/index.qmd)** — automated report generation from ADaM data
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- **[Logs](logs/index.qmd)** — logging and traceability for clinical R workflows
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- **[Documents](digit_files/index.qmd)** — automated report generation from ADaM data
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- **[Logging](logging/index.qmd)** — logging and traceability for clinical R workflows
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- **[eSub](esub/index.qmd)** — preparing outputs for electronic regulatory submission
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---
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### Data Packages
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:::{tbl-colwidths="[15,60,25]"}
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| Package | Description | Example |
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|---------|-------------|---------|
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| [`{pharmaverseraw}`](https://pharmaverse.github.io/pharmaverseraw/) | Source raw datasets (AE, DM, EX, DS domains) used as the starting point for all SDTM examples | [SDTM](sdtm/index.qmd) |
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| [`{pharmaversesdtm}`](https://pharmaverse.github.io/pharmaversesdtm/) | CDISC SDTM example datasets generated from `{pharmaverseraw}` | [SDTM](sdtm/index.qmd) / [ADaM](adam/index.qmd) |
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| [`{pharmaverseadam}`](https://pharmaverse.github.io/pharmaverseadam/) | CDISC ADaM example datasets (ADSL, ADTTE, ADPC, etc.) used across TLG and PK examples | [TLG](tlg/index.qmd) / [PK](tlg/pharmacokinetic.qmd) |
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### SDTM Packages
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| Package | Description | Example |
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|---------|-------------|---------|
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| [`{pharmaverseraw}`](https://pharmaverse.github.io/pharmaverseraw/) | Source raw datasets (`AE`, `DM`, `EX`, `DS` domains) used as the starting point for all SDTM examples | [SDTM](sdtm/index.qmd) |
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| [`{sdtm.oak}`](https://pharmaverse.github.io/sdtm.oak/) | SDTM programming in R — maps raw collected data to CDISC SDTM domains using an algorithm-based engine | [SDTM](sdtm/index.qmd) |
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| [`{pharmaversesdtm}`](https://pharmaverse.github.io/pharmaversesdtm/) | CDISC SDTM example datasets generated from `{pharmaverseraw}` | [SDTM](sdtm/index.qmd) / [ADaM](adam/index.qmd) |
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| [`{pharmaverseadam}`](https://pharmaverse.github.io/pharmaverseadam/) | CDISC ADaM example datasets (`ADSL`, `ADTTE`, `ADPC`, etc.) used across TLG and PK examples | [TLG](tlg/index.qmd) / [PK](tlg/pharmacokinetic.qmd) |
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:::
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### ADaM Packages
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:::{tbl-colwidths="[15,60,25]"}
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| Package | Description | Example |
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|---------|-------------|---------|
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| [`{admiral}`](https://pharmaverse.github.io/admiral/) | ADaM dataset construction in R — the core package for building ADSL, ADAE, ADTTE, and other standard domains | [ADaM](adam/index.qmd) |
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| [`{admiral}`](https://pharmaverse.github.io/admiral/) | ADaM dataset construction in R — the core package for building `ADSL`, `ADAE`, `ADTTE`, and other standard domains | [ADaM](adam/index.qmd) |
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| [`{admiralonco}`](https://pharmaverse.github.io/admiralonco/) | Oncology-specific ADaM extensions for `{admiral}` — adds functions for tumor response, DOR, and TTE oncology endpoints | [ADaM](adam/index.qmd) |
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| [`{metacore}`](https://atorus-research.github.io/metacore/) | Stores and validates dataset metadata (variable definitions, codelists) from specs in a consistent R object | [ADaM](adam/index.qmd) |
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| [`{metatools}`](https://pharmaverse.github.io/metatools/) | Utilities for working with `{metacore}` objects — checking variables, applying codelists, and deriving variables from metadata | [ADaM](adam/index.qmd) |
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| [`{xportr}`](https://atorus-research.github.io/xportr/) | Applies SAS XPT metadata (types, labels, lengths, formats) and exports submission-ready `.xpt` transport files | [ADaM](adam/index.qmd) |
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:::
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### TLG Packages
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:::{tbl-colwidths="[15,60,25]"}
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| Package | Description | Example |
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|---------|-------------|---------|
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|-----------|-------------|---------|
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| [`{rtables}`](https://insightsengineering.github.io/rtables/) | Framework for building complex nested tables with flexible layouts — the foundation of the NEST TLG stack | [Demographics](tlg/demographic.qmd) / [AE](tlg/adverse_events.qmd) / [PK](tlg/pharmacokinetic.qmd) |
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| [`{tern}`](https://insightsengineering.github.io/tern/) | Analysis functions for clinical trial TLGs built on `{rtables}` — covers demographics, AEs, survival, PK summaries | [Demographics](tlg/demographic.qmd) / [AE](tlg/adverse_events.qmd) / [PK](tlg/pharmacokinetic.qmd) |
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| [`{rlistings}`](https://insightsengineering.github.io/rlistings/) | Creates regulatory-ready listings from ADaM datasets | [PK](tlg/pharmacokinetic.qmd) |
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| [`{gtsummary}`](https://www.danieldsjoberg.com/gtsummary/) | Publication-ready summary and analytical tables — used here for survival median and time-point tables | [Oncology Survival](tlg/oncology_survival.qmd) |
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| [`{ggsurvfit}`](https://www.danieldsjoberg.com/ggsurvfit/) | Publication-ready Kaplan-Meier and time-to-event figures built on `{ggplot2}`, with native CDISC ADTTE support via `Surv_CNSR()` | [Oncology Survival](tlg/oncology_survival.qmd) |
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| [`{gtsummary}`](https://www.danieldsjoberg.com/gtsummary/) | Publication-ready summary and analytical tables — ideal for demographics and analysis summaries | [Demographics](tlg/demographic.qmd) |
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| [`{ggsurvfit}`](https://www.danieldsjoberg.com/ggsurvfit/) | Publication-ready Kaplan-Meier and time-to-event figures built on `{ggplot2}` | [TLG](tlg/index.qmd) |
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| [`{cards}`](https://insightsengineering.github.io/cards/) | Creates Analysis Results Datasets (ARDs) following the emerging CDISC Analysis Results Standard | [Demographics](tlg/demographic.qmd) / [AE](tlg/adverse_events.qmd) |
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| [`{tfrmt}`](https://gsk-biostatistics.github.io/tfrmt/) | Transforms ARDs into publication-ready tables with precise formatting control — particularly suited to RTF regulatory outputs | [Demographics](tlg/demographic.qmd) / [AE](tlg/adverse_events.qmd) |
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:::
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without writing the code yourself, open an issue and the team will review it for
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the backlog.
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You can see a list of current contributors via our [GitHub repo DESCRIPTION file](https://github.com/pharmaverse/examples/blob/main/DESCRIPTION).
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While you're there, consider giving the packages you use a
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[⭐ GitHub star](https://github.com/pharmaverse/examples) — it helps maintainers
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demonstrate community adoption and keeps the ecosystem visible to new users.
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demonstrate community adoption and keeps the ecosystem visible to new users.
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While you're there, please also consider adding a star to the Examples GitHub repo!
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:::

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