iglu
iglu: Interpreting data from Continuous Glucose Monitors (CGMs)
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.
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.
Installation
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)Example
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 x 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 x 4
#> id in_range_70_140 in_range_70_180 in_range_80_200
#> <fct> <dbl> <dbl> <dbl>
#> 1 Subject 1 73.7 91.7 96.0
above_percent(example_data_1_subject, targets = c(80,140,200,250))
#> # A tibble: 1 x 5
#> id above_140 above_200 above_250 above_80
#> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 Subject 1 26.7 3.70 0.446 99.4
j_index(example_data_1_subject)
#> # A tibble: 1 x 2
#> id j_index
#> <fct> <dbl>
#> 1 Subject 1 24.6
conga(example_data_1_subject)
#> # A tibble: 1 x 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')
below_percent(example_data_5_subject, targets = c(80,170,260))
#> # A tibble: 5 x 4
#> id below_170 below_260 below_80
#> <fct> <dbl> <dbl> <dbl>
#> 1 Subject 1 89.6 99.7 0.652
#> 2 Subject 2 17.7 78.9 0
#> 3 Subject 3 73.5 96.0 0.913
#> 4 Subject 4 91.8 100 2.05
#> 5 Subject 5 55.3 90.3 1.13
mage(example_data_5_subject)
#> # A tibble: 5 x 2
#> id mage
#> <fct> <dbl>
#> 1 Subject 1 53.4
#> 2 Subject 2 78.2
#> 3 Subject 3 76.6
#> 4 Subject 4 42.9
#> 5 Subject 5 90.0Shiny App
The 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, the local version will be slightly ahead of the global one.
Shiny Demonstration
For a demonstration of the package in a point-and-click interface, click the link below.