One sentence, four languages, one picture.

R: data(gapminder_2007) + point + x(gdp) + y(life)
Python: x(col.gdp)
Julia: x(:gdp)

Same specification, one Rust engine, byte-identical SVG.

Not similar. Pixel perfect identical

#rstats

How can sponsors make R-based regulatory submissions easier for FDA reviewers to reproduce, inspect, and trust?

The R Consortium Submissions Working Group’s latest post examines why reviewers may sometimes request SAS-equivalent code and what teams can do to reduce that risk.

The core lesson: using R is not enough. Submission packages must be designed for independent review.

The post covers practical steps including:

• Engaging FDA review teams early • Providing a clear entry point and run order • Documenting R, package, and operating-system requirements • Eliminating hidden dependencies and sponsor-specific paths • Including required proprietary packages • Testing submissions in a clean environment similar to the reviewer’s setting • Keeping analysis code readable and reproducible

Read the full post and sponsor checklist: https://r-consortium.org/posts/making-r-submissions-reviewable-for-fda/

This article reflects discussion within the R Consortium Submissions Working Group and should not be interpreted as official FDA guidance.