In stevebroll/iglu: Interpreting Glucose Data from Continuous Glucose Monitors

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

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)

in_range_percent(example_data_1_subject)

above_percent(example_data_1_subject, targets = c(80,140,200,250))

j_index(example_data_1_subject)

conga(example_data_1_subject)

# 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))

mage(example_data_5_subject)