R Packages

sumExtras

sumExtras reduces boilerplate in clinical research table workflows. One call to extras() replaces the five or six gtsummary formatting steps that would otherwise clutter every analysis script.

  • Simplicity: extras() consolidates variable labeling, missing value cleanup, theming, and group header styling into a single function
  • Flexibility: Works with both tbl_summary() and tbl_svysummary() tables
  • Publication-ready: Built-in JAMA compact themes and bold/italic group header formatting
  • Resilient: Warn-and-continue design ensures tables always render, even if individual formatting steps fail

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froggeR

froggeR provides project scaffolding for R and Quarto. Inspired by research compendium principles, it establishes consistent directory structures, pre-configured entry points, and global metadata so you set things up once and reuse across every project.

froggeR gets out of your way so you can focus on the work:

  • Structure: Predictable R/, analysis/, data/, and www/ directories out of the box
  • Consistency: Global configuration for author metadata and branding across all projects
  • Security: .gitignore and pre-commit hooks to keep sensitive files out of version control
  • Reproducibility: Templated Quarto documents with automatic metadata population

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nhanesdata

nhanesdata provides simplified access to the National Health and Nutrition Examination Survey (NHANES), a U.S. public health dataset spanning 1999 to 2023. It solves two persistent pain points: unreliable CDC server access and confusing dataset naming conventions across survey cycles.

  • Reliability: All datasets hosted on cloud storage, no CDC server timeouts
  • Ready to use: Pre-merged datasets with a year column for cycle tracking across demographics, biomarkers, physical measurements, and dietary data
  • Survey-aware: create_design() handles proper weighting out of the box
  • Discoverable: term_search() and var_search() let you find variables by keyword or name

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nascaR.data

nascaR.data provides historical race results from NASCAR’s top three series: Cup (1949-present), Xfinity (1982-present), and Trucks (1995-present). Explore driver, team, and manufacturer performance in a race-by-race, season, or career format. This data has been expertly curated and scraped with permission from DriverAverages.com.

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rUM

This is a collection of R things from your friends at UM (The University of Miami).

rUM includes:

  • A research project template. It creates a new RStudio project that has your choice of an analysis.qmd Quarto file or analysis.Rmd R markdown file with tidyverse and conflicted.

  • Quarto and R Markdown templates which include they YAML header and start up blocks that load the tidyverse and conflicted packages.

  • 💥 NEW in Version 2.0.0 (Overproof Rum) 💥 rUM now can make a package project that includes a paper outline as a vignette. rUM can now add an example table and figure to it’s paper shell.

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