Map Activity Data to Normalized Quantiles

Provides functions mapping physical activity measures to normalized quantiles. Currently, only maps the 'NHANES' quantiles, but other quantiles will be integrated.

actiquantiles

actiquantiles maps physical activity values to NHANES-based quantiles. The package exposes an acti_-prefixed wrapper around mapnhanespa::map_nhanes_pa_quantiles() so the public API can stay stable while new mapping backends are added later.

Core entry points:

• acti_map_nhanes() for participant-level quantile mapping
• mapnhanespa::nhanes_pa_quantile() for a single value lookup
• mapnhanespa::nhanes_pa_age_category() for age binning

Installation

You can install actiquantiles from GitHub with:

# install.packages("remotes")
remotes::install_github("jhuwit/actiquantiles")

The package depends on mapnhanespa, which provides the NHANES mapping tables and lookup logic.

Quick Start

example_data <- data.frame(
  id = c("A", "B"),
  age = c(25, 62),
  sex = c("Female", "Male"),
  measure = c("mims", "ssl_steps"),
  value = c(15000, 7500)
)

mapped <- acti_map_nhanes(example_data)
mapped
#>   id age    sex   measure value acti_pa_quantile
#> 1  A  25 Female      mims 15000        0.5349443
#> 2  B  62   Male ssl_steps  7500        0.3527381

The wrapper keeps the input columns and adds acti_pa_quantile:

head(mapped)
#>   id age    sex   measure value acti_pa_quantile
#> 1  A  25 Female      mims 15000        0.5349443
#> 2  B  62   Male ssl_steps  7500        0.3527381

For a single value, use the scalar helper from mapnhanespa directly:

mapnhanespa::nhanes_pa_quantile(
  value = 15000,
  age = 25,
  sex = "Female",
  measure = "mims"
)
#> [1] 0.5349443