draft

draft is an R package that launches a local Shiny app to help you write traceable inline results in R Markdown and Quarto documents. It reads live objects from your current R session: models, data frames, and single values: and gives you:

A browsable view of model parameters and data summaries
Copyable inline reference paths (e.g. {params_$wt$estimate})
A live-rendering text editor where you type prose and see actual values
A one-click setup chunk to paste into your .qmd or .Rmd file

The app does not write to your document or interpret results. You still have to do the thinking; draft removes the friction. When combined with automatic table creation (e.g., https://gist.github.com/rbcavanaugh/be76e983fdf3a1920fe1f354d73a5a78) your results section should be built entirely from your R objects and not manually created.

Installation

# Install from GitHub
remotes::install_github("rbcavanaugh/draft")

Quick start

library(draft)

# 1. Run your analysis in the normal way
m1     <- lm(mpg ~ wt + cyl, data = mtcars)
params <- parameters::model_parameters(m1)

# 2. Launch the app: it reads everything in your global environment
launch_app()

In the app:

Environment panel (left): click params to inspect it
Object inspector (center): see formatted parameter estimates with copyable values

Text editor (right): write prose like:

Weight was negatively associated with fuel economy
(b = {params_$wt$estimate}, 95% CI {params_$wt$ci},
p = {params_$wt$p}).

The app suggests params_ as the list name (object name + trailing underscore).

Click Copy inline code: the copied text uses `r expr` syntax, ready to paste into your .qmd or .Rmd.
Click Copy setup chunk: copies the prep_params() call to put in your document’s setup chunk.

Always re-render your full document to confirm results are reproducible.

Standalone helpers

You can also use these functions directly in a .qmd setup chunk without launching the app:

Model parameters: prep_params()

Converts a parameters_model object into a nested named list for inline reporting:

# Setup chunk
params_m1 <- parameters::model_parameters(m1)
results   <- draft::prep_params(params_m1)

# Inline usage
# Weight: b = `r results$wt$estimate` (95% CI `r results$wt$ci`, p = `r results$wt$p`)

Each parameter is a sub-list containing all available formatted fields: estimate, se, ci, ci_low, ci_high, p (frequentist) or pd, rope_pct (Bayesian). Works with any model class supported by parameters::model_parameters(), including correlation matrices.

Data frame summaries: prep_data()

Summarises a data frame into a nested named list for demographics reporting:

# Setup chunk
stats <- draft::prep_data(demo_data)

# Inline usage
# Age: M = `r stats$age$mean` (SD = `r stats$age$sd`)
# Female: `r stats$sex$female$pct`%

Continuous columns return mean, sd, median, min, max, n, n_missing. Categorical columns return n, n_missing, and per-level n and pct.

Marginal means and contrasts: prep_modelbased()

Converts estimate_means, estimate_contrasts, or estimate_slopes objects from the modelbased package:

means_ <- draft::prep_modelbased(modelbased::estimate_means(m1, "cyl"))
# `r means_$x4$mean`, `r means_$x6$mean`, `r means_$x8$mean`

Model performance: prep_performance()

Converts model_performance or compare_performance output from the performance package:

perf_ <- draft::prep_performance(performance::model_performance(m1))
# R2 = `r perf_$r2`, RMSE = `r perf_$rmse`

comp_ <- draft::prep_performance(performance::compare_performance(m1, m2))
# `r comp_$m1$aic` vs `r comp_$m2$aic`

Effect sizes: prep_effectsize()

Converts an effectsize_table object (e.g. cohens_d(), eta_squared(), cramers_v()) into a named list:

es_ <- draft::prep_effectsize(effectsize::cohens_d(mpg ~ am, data = mtcars))
# `r es_$cohens_d`, 95% CI [`r es_$ci_low`, `r es_$ci_high`]

eta_ <- draft::prep_effectsize(effectsize::eta_squared(aov(mpg ~ cyl + gear, mtcars)))
# `r eta_$cyl$eta2`, `r eta_$gear$eta2`

Single-effect objects return a flat list; multi-effect objects (e.g. eta_squared with several predictors) return a nested list keyed by the Parameter column.

Distribution summaries: prep_distribution()

Converts datawizard::describe_distribution() output into a named list for inline reporting:

dist_ <- draft::prep_distribution(datawizard::describe_distribution(mtcars))
# `r dist_$mpg$mean`, SD = `r dist_$mpg$sd`

# With grouping (by argument):
dist_ <- draft::prep_distribution(datawizard::describe_distribution(iris, by = "Species"))
# `r dist_$setosa$sepal_length$mean`

Supports both flat (no by argument) and stratified (with by argument) shapes.

Recommended workflow

Open your .qmd / .Rmd and run all chunks so objects are in globalenv()
Call draft::launch_app()
Browse parameters in the inspector; note list paths
Write prose in the text editor; see live-rendered values
Copy the setup chunk into your document’s setup block
Copy inline code and paste into your document
Re-render the full document to confirm reproducibility

Dependencies

Package	Purpose
`shiny`	App framework
`bslib`	UI layout
`parameters`	`model_parameters()` and value formatting
`insight`	Model detection and value formatting
`dplyr`	Data frame manipulation
`htmltools`	Safe HTML rendering
`effectsize`	Effect size objects (suggested)
`datawizard`	Distribution descriptions (suggested)
`modelbased`	Marginal means and contrasts (suggested)
`performance`	Model performance metrics (suggested)

Roadmap

easystats ecosystem support

Package	Status	Notes
`parameters`	Supported	Core: all model types with a `parameters_model` class
`correlation`	Supported	`correlation()` output inherits `parameters_model`
`modelbased`	Supported	`estimate_means()`, `estimate_contrasts()`, `estimate_slopes()`
`performance`	Supported	`model_performance()` and `compare_performance()`
`effectsize`	Supported	`cohens_d`, `eta_squared`, `cramers_v`, and all `effectsize_table` subclasses
`datawizard`	Supported	`describe_distribution()`: flat and stratified (grouped) output

License

MIT