Virtual useR! 2024

An important part of writing software revolves around functionality and features. A sometimes overlooked part is what happens when something goes wrong. There are many reasons something can go wrong. Faulty input, user error or deprecation are a few examples. Regardless of what the reason is, we should thrive towards letting the user know as soon and informative as possible so they can get back on track with what they are doing. This talks showcases how to make better error messages with the packages rlang and cli.

The use of containers across organizations and their GxP R environments is quite common. The rationale could differ from providing isolated project specific R compute environments based on a standard validated container or it is simply a cheap and easily accessible virtual environment in a pre-existing IT architecture. In most circumstances, this approach relies on maintaining different frozen validated container images, sometimes referred to as golden images, for each particular use case. If instead we utilize container image provenance and some very simple controls in our validation strategy, we could create one validated container image that is then used as the basis for any container images designed for particular use cases. We consider how the approach impacts and, in many ways, simplifies the four classic use cases of Posit Workbench for interactive development, {shiny} for apps, {plumber} for APIs, and a back-end compute environment for batch processing.

I will be talking about {rix}, a new package still in development that leverages the powerful Nix package manager. With Nix, it is possible to create project-specific environments that contain a project-specific version of R and R packages (as well as other tools or languages, if needed). You can use rix and Nix to replace renv and Docker with one single tool. rix provides functions to help you write and deploy Nix expressions (written in the Nix language). These expressions will be the inputs for the Nix package manager, to build sets of software packages and provide them in a reproducible development environment. These environments can be used for interactive data analysis, or reproduced when running pipelines in CI/CD systems. Environments contain R and all the required packages that you need for your project. The Nix R ecosystem currently includes almost the entirety of CRAN and Bioconductor packages. Like with any other programming language and software, it is also possible to install older releases of R packages, or install packages from GitHub at defined states.

The WHO's Health Economic Assessment Tool (HEAT) for walking and cycling is a publicly available health impact calculator employing R as its primary language. Originally an Excel sheet, it evolved into an HTML web tool before transitioning to R/Shiny six years ago for improved transparency and collaboration. A prototype of the application is accessible at https://heatwalkingcycling.org/HEAT_langs_dev/tool, soon to be integrated into the main production. Our development insights include tailored packages for translation and custom features. HEAT consists of three components: input data, health impact and carbon modeling (via `heatr`), and the UI, each utilizing distinct R libraries. Data integrity is upheld through workflows with `targets` and `assertr` packages, addressing issues like choosing between ISO 3166-1 alpha-3 and alpha-2. The UI, focusing on user-friendly data input options, leverages R Shiny, and inserts default inputs dynamically from spreadsheets. The custom-built `translator` package supports translation independently from the Shiny ecosystem. Our `templater` package streamlines text and reduces editorial errors, enabling conditioned templates with locale support.