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excursion function

excursion

Overview

Calculates glucose excursions: >70 mg/dL rise within 2 hours, not preceded by a value <70 mg/dL.

excursion() evaluates the rows supplied in df. It does not automatically call interpolate_cgm() and does not use the full-day event preprocessing grid unless you explicitly pass interpolated data to excursion().

Inputs

Returns

Run documented examples

example(excursion, package = "cgmguru", run.dontrun = FALSE)
#> 
#> excrsn> # Load sample data
#> excrsn> library(iglu)
#> 
#> excrsn> data(example_data_5_subject)
#> 
#> excrsn> data(example_data_hall)
#> 
#> excrsn> # Calculate glucose excursions
#> excrsn> excursion_result <- excursion(example_data_5_subject, gap = 15)
#> 
#> excrsn> print(paste("Excursion vector length:", length(excursion_result$excursion_vector)))
#> [1] "Excursion vector length: 1"
#> 
#> excrsn> print(excursion_result$episode_counts)
#> # A tibble: 5 × 2
#>   id        episode_counts
#>   <chr>              <int>
#> 1 Subject 1              9
#> 2 Subject 2             14
#> 3 Subject 3             11
#> 4 Subject 4             17
#> 5 Subject 5             34
#> 
#> excrsn> # Excursion analysis with different gap
#> excrsn> excursion_30min <- excursion(example_data_5_subject, gap = 30)
#> 
#> excrsn> # Analysis on larger dataset
#> excrsn> large_excursion <- excursion(example_data_hall, gap = 15)
#> 
#> excrsn> print(paste("Excursion vector length in larger dataset:", length(large_excursion$excursion_vector)))
#> [1] "Excursion vector length in larger dataset: 1"
#> 
#> excrsn> print(paste("Total episodes:", sum(large_excursion$episode_counts$episode_counts)))
#> [1] "Total episodes: 111"

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They may not be fully stable and should be used with caution. We make no claims about them.
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