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OmopStudyBuilder

The OmopStudyBuilder package helps a study prepare for network studies using the OMOP Common Data Model (CDM). The package sets up an R project for your study with a default folder structure and template code typically required for network studies. This allows you to focus on the parts of the analysis that are specific to your study design.

In addition to project setup, the package reviews code and dependencies, supports renv locking for consistent package versions, and can build and run a Docker image for reproducible execution aligned with best practices.

The package is highly opinionated and designed to align with the OxInfer study code checklist. For further details, please refer to the documentation.

Installation

You can install the development version of the package from GitHub:

# install.packages("remotes")
remotes::install_github("oxford-pharmacoepi/OmopStudyBuilder")

Quick Start

The main entry point is initStudy(), which creates the study folder structure and template files.

library(OmopStudyBuilder)

initStudy(here::here("SampleStudy"))

Once the study has been created, you can review the generated code and dependencies:

reviewStudyCode(here::here("SampleStudy", "studyCode"))
reviewStudyDependencies(here::here("SampleStudy", "studyCode"))

If you want reproducible package versions, initialise renv in the study folder and snapshot the environment:

renv::init(here::here("SampleStudy", "studyCode"))
install.packages(c("dplyr", "CDMConnector", "IncidencePrevalence"))
renv::snapshot(here::here("SampleStudy", "studyCode"))

Optional: Docker for reproducible execution

This package supports building and running study code in Docker for reproducible execution. Docker is optional and only needed if you want to build and run the study in a containerised workflow. Install Docker and confirm it is running before using the Docker-based functions.

General checks (all operating systems)

• Install Docker.
• Start Docker (e.g., Docker Desktop).
• Verify Docker is available:
  • docker --version
  • docker info

macOS

• Install Docker Desktop for Mac: https://docs.docker.com/desktop/setup/install/mac-install/
• Open Docker Desktop and wait until it reports “Docker Desktop is running”.

Windows

• Install Docker Desktop for Windows (WSL 2 recommended): https://docs.docker.com/desktop/setup/install/windows-install/
• Ensure WSL 2 is enabled (Docker Desktop installer will guide you).
• Open Docker Desktop and wait until it is running.

Linux

• Install Docker Engine using your distribution instructions: https://docs.docker.com/engine/install/
• Start/enable the Docker service (varies by distro).
• If you want to run Docker without sudo, follow: https://docs.docker.com/engine/install/linux-postinstall/

Example Usage

After the study has been created and configured, you can optionally build a Docker image from the study folder:

dockeriseStudy(path = here::here("SampleStudy", "studyCode"))

Run the study interactively in RStudio Server or as an automated script. If you use a .env file for credentials, pass env_file = ".env".

runRStudio()
runStudy()

Use optional inputs only if you need them:

runStudy(
  image_name = "omop-study-study-code",
  data_path = "path/to/data",
  results_path = "./results"
)

To distribute the study, share the study folder created by initStudy() (including studyCode/ and renv.lock). Partners can build and run using the same commands.

install.packages("OmopStudyBuilder")
library(OmopStudyBuilder)

dockeriseStudy(path = here::here("SampleStudy", "studyCode"))
runStudy()
runRStudio()
stopStudy()

To push a built image to Docker Hub, use the helper and enter your credentials when prompted (the tag defaults to latest, and the image name defaults to the current folder name):

pushDockerImage(
  repo = "yourname/omop-study-study-code"
)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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