The R package ‘iglu’ provides functions for outputting relevant metrics for data collected from Continuous Glucose Monitors (CGM). For reference, see “Interpretation of continuous glucose monitoring data: glycemic variability and quality of glycemic control.” Rodbard (2009). For more information on the package, see package website.
For short tutorial on how to use the package, see Video tutorial on working with CGM data in iglu and corresponding slides.
To cite:
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Broll S, Urbanek J, Buchanan D, Chun E, Muschelli J, Punjabi N and Gaynanova I (2021). Interpreting blood glucose data with R package iglu. PLoS One, Vol. 16, No. 4, e0248560.
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Chun E*, Fernandes JN* and Gaynanova I (2024) An Update on the iglu Software for Interpreting Continuous Glucose Monitoring Data. Diabetes Technology and Therapeutics, Vol. 26, No. 12, 939-950.
iglu comes with two example datasets: example_data_1_subject and example_data_5_subject. These data are collected using Dexcom G4 CGM on subjects with Type II diabetes. Each dataset follows the structure iglu’s functions are designed around. Note that the 1 subject data is a subset of the 5 subject data. See the examples below for loading and using the data.
The R package ‘iglu’ is available from CRAN, use the commands below to install the most recent Github version.
# Plain installation
devtools::install_github("irinagain/iglu") # iglu package
# For installation with vignette
devtools::install_github("irinagain/iglu", build_vignettes = TRUE)library(iglu)
data(example_data_1_subject) # Load single subject data
## Plot data
# Use plot on dataframe with time and glucose values for time series plot
plot_glu(example_data_1_subject)
# Summary statistics and some metrics
summary_glu(example_data_1_subject)
#> # A tibble: 1 × 7
#> # Groups: id [1]
#> id Min. `1st Qu.` Median Mean `3rd Qu.` Max.
#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Subject 1 66 99 112 124. 143 276
in_range_percent(example_data_1_subject)
#> # A tibble: 1 × 3
#> id in_range_63_140 in_range_70_180
#> <fct> <dbl> <dbl>
#> 1 Subject 1 73.9 91.7
above_percent(example_data_1_subject, targets = c(80,140,200,250))
#> # A tibble: 1 × 5
#> id above_140 above_200 above_250 above_80
#> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 Subject 1 26.1 3.40 0.377 99.3
j_index(example_data_1_subject)
#> # A tibble: 1 × 2
#> id J_index
#> <fct> <dbl>
#> 1 Subject 1 24.6
conga(example_data_1_subject)
#> # A tibble: 1 × 2
#> id CONGA
#> <fct> <dbl>
#> 1 Subject 1 37.0
# Load multiple subject data
data(example_data_5_subject)
plot_glu(example_data_5_subject, plottype = 'lasagna', datatype = 'average')
#> Warning: Removed 5 rows containing missing values or values outside the scale range
#> (`geom_tile()`).
below_percent(example_data_5_subject, targets = c(80,170,260))
#> # A tibble: 5 × 4
#> id below_170 below_260 below_80
#> <fct> <dbl> <dbl> <dbl>
#> 1 Subject 1 89.3 99.7 0.583
#> 2 Subject 2 16.8 78.4 0
#> 3 Subject 3 72.7 95.9 0.848
#> 4 Subject 4 91.0 100 1.69
#> 5 Subject 5 54.6 90.1 1.03
mage(example_data_5_subject)
#> Gap found in data for subject id: Subject 2, that exceeds 12 hours.
#> # A tibble: 5 × 2
#> # Rowwise:
#> id MAGE
#> <fct> <dbl>
#> 1 Subject 1 72.4
#> 2 Subject 2 118.
#> 3 Subject 3 116.
#> 4 Subject 4 70.9
#> 5 Subject 5 142.Shiny App can be accessed locally via
library(iglu)
iglu_shiny()or globally at https://irinagain.shinyapps.io/shiny_iglu/. As new functionality gets added, local version will be slightly ahead of the global one.