Type: | Package |
Title: | Analyzing Glucose and Glucose Variability |
Version: | 0.1.0 |
Imports: | dplyr, tidyverse, ggplot2, hms, stats, magrittr |
Description: | Continuous glucose monitoring (CGM) systems provide real-time, dynamic glucose information by tracking interstitial glucose values throughout the day. Glycemic variability, also known as glucose variability, is an established risk factor for hypoglycemia (Kovatchev) and has been shown to be a risk factor in diabetes complications. Over 20 metrics of glycemic variability have been identified. Here, we provide functions to calculate glucose summary metrics, glucose variability metrics (as defined in clinical publications), and visualizations to visualize trends in CGM data. Cho P, Bent B, Wittmann A, et al. (2020) https://diabetes.diabetesjournals.org/content/69/Supplement_1/73-LB.abstract American Diabetes Association (2020) https://professional.diabetes.org/diapro/glucose_calc Kovatchev B (2019) <doi:10.1177/1932296819826111> Kovdeatchev BP (2017) <doi:10.1038/nrendo.2017.3> Tamborlane W V., Beck RW, Bode BW, et al. (2008) <doi:10.1056/NEJMoa0805017> Umpierrez GE, P. Kovatchev B (2018) <doi:10.1016/j.amjms.2018.09.010>. |
License: | MIT License + file LICENSE |
Encoding: | UTF-8 |
LazyData: | true |
RoxygenNote: | 7.1.1 |
Suggests: | testthat (≥ 2.0.0), knitr, rmarkdown |
Config/testthat/edition: | 2, devtools |
VignetteBuilder: | knitr |
Depends: | R (≥ 2.10) |
NeedsCompilation: | no |
Packaged: | 2021-01-29 20:56:52 UTC; mariah |
Author: | Maria Henriquez [aut, com, cph, cre, trl], Brinnae Bent [aut, cph, dtc] |
Maintainer: | Maria Henriquez <marhenriq@gmail.com> |
Repository: | CRAN |
Date/Publication: | 2021-02-05 08:50:02 UTC |
Compute Glycemic Management Indicator
Description
This function computes the estimated GMI
Usage
GMI(df)
Arguments
df |
Data frame read through readfile |
Value
A numeric value representing GMI
Examples
mydatafile <- system.file("extdata", "my_data_file.csv", package = "cgmquantify")
mydata <- readfile(mydatafile)
GMI(mydata)
Compute High Blood Glucose Index
Description
This function computes the high blood glucose index
Usage
HBGI(df)
Arguments
df |
Data frame read through readfile |
Value
A numeric value representing HBGI
Examples
mydatafile <- system.file("extdata", "my_data_file.csv", package = "cgmquantify")
mydata <- readfile(mydatafile)
HBGI(mydata)
Compute J-index
Description
This function computes J-index, a glycemic variability metrix
Usage
J_index(df)
Arguments
df |
Data frame read through readfile |
Value
A numeric value representing J-index
Examples
mydatafile <- system.file("extdata", "my_data_file.csv", package = "cgmquantify")
mydata <- readfile(mydatafile)
J_index(mydata)
Compute Low Blood Glucose Index
Description
This function computes the low blood glucose index
Usage
LBGI(df)
Arguments
df |
Data frame read through readfile |
Value
A numeric value representing LBGI
Examples
mydatafile <- system.file("extdata", "my_data_file.csv", package = "cgmquantify")
mydata <- readfile(mydatafile)
LBGI(mydata)
Compute Low Blood Glucose Index
Description
This function computes the low blood glucose index
Usage
LBGI_HBGI(df)
Arguments
df |
Data frame read through readfile |
Value
A data frame containing both the LBGI and HBGI values
Examples
mydatafile <- system.file("extdata", "my_data_file.csv", package = "cgmquantify")
mydata <- readfile(mydatafile)
LBGI_HBGI(mydata)
Compute Mean of Glycemic Excursions
Description
This function computes the mean of glycemic excursions, glycemic excursions indicated by standard deviation, default = 1
Usage
MGE(df, sd = 1)
Arguments
df |
Data frame read through readfile |
sd |
Standard deviation indicating glycemic excursion, default = 1 |
Value
A numeric value representing MAGE
Examples
mydatafile <- system.file("extdata", "my_data_file.csv", package = "cgmquantify")
mydata <- readfile(mydatafile)
MGE(mydata)
Compute Mean of Normal Glucose
Description
This function computes the mean of normal glucose, glycemic excursions indicated by standard deviation, default = 1
Usage
MGN(df)
Arguments
df |
Data frame read through readfile |
Value
A numeric value representing MGN
Examples
mydatafile <- system.file("extdata", "my_data_file.csv", package = "cgmquantify")
mydata <- readfile(mydatafile)
MGN(mydata)
Compute Percent of Time Outside Range
Description
This function computes the percent of time outside range (range in standard deviations from mean, default = 1).
Usage
POR(df, sd = 1, sr = 5)
Arguments
df |
Data frame read through readfile |
sd |
Standard deviation indicating glycemic excursion, default = 1 |
sr |
Sampling rate inverse in minutes of the CGM (default is Dexcom -> 5 minutes) |
Value
A numeric value representing POR
Examples
mydatafile <- system.file("extdata", "my_data_file.csv", package = "cgmquantify")
mydata <- readfile(mydatafile)
POR(mydata, sd = 1, sr = 5)
Compute Time Inside Range
Description
This function computes the time inside range (range in standard deviations from mean, default = 1).
Usage
TIR(df, sd = 1, sr = 5)
Arguments
df |
Data frame read through readfile |
sd |
Standard deviation indicating glycemic excursions, default = 1 |
sr |
Sampling rate inverse in minutes of the CGM (default is the Dexcom -> 5 minutes) |
Value
A numeric value representing TIR
Examples
mydatafile <- system.file("extdata", "my_data_file.csv", package = "cgmquantify")
mydata <- readfile(mydatafile)
TIR(mydata, sd = 1, sr = 5)
Compute Time Outside Range
Description
This function computes the time outside range (range in standard deviations from mean, default = 1).
Usage
TOR(df, sd = 1, sr = 5)
Arguments
df |
Data frame read through readfile |
sd |
Standard deviation indicating glycemic excursions, default = 1 |
sr |
Sampling rate inverse in minutes of the CGM (default is the Dexcom -> 5 minutes) |
Value
A numeric value representing TOR
Examples
mydatafile <- system.file("extdata", "my_data_file.csv", package = "cgmquantify")
mydata <- readfile(mydatafile)
TOR(mydata, sd = 1, sr = 5)
Compute Estimated A1c
Description
This function computes the estimated A1c, according to the American Diabetes Association calculator
Usage
eA1c(df)
Arguments
df |
Data frame read through readfile |
Value
A numeric value representing eA1c
Examples
mydatafile <- system.file("extdata", "my_data_file.csv", package = "cgmquantify")
mydata <- readfile(mydatafile)
eA1c(mydata)
Compute Interday Coefficient of Variation
Description
This function computes the interday coefficient of variation
Usage
interdaycv(df)
Arguments
df |
Data frame read through readfile |
Value
A numeric value representing interday cv
Examples
mydatafile <- system.file("extdata", "my_data_file.csv", package = "cgmquantify")
mydata <- readfile(mydatafile)
interdaycv(mydata)
Compute Interday Standard Deviation
Description
This function computes the interday standard deviation
Usage
interdaysd(df)
Arguments
df |
Data frame read through readfile |
Value
A numeric value representing interday sd
Examples
mydatafile <- system.file("extdata", "my_data_file.csv", package = "cgmquantify")
mydata <- readfile(mydatafile)
interdaysd(mydata)
Compute Intraday Coefficient of Variation
Description
This function computes the intraday coefficient of variation summary statistics: mean, median, standard deviation of all days in data
Usage
intradaycv(df)
Arguments
df |
Data frame read through readfile |
Value
A data frame containing the mean, median, and standard deviation of the intraday coefficients of variation.
Examples
mydatafile <- system.file("extdata", "my_data_file.csv", package = "cgmquantify")
mydata <- readfile(mydatafile)
intradaycv(mydata)
Compute Intraday Standard Deviation
Description
This function computes the intraday standard deviation summary statistics: mean, median, standard deviation of all days in data
Usage
intradaysd(df)
Arguments
df |
Data frame read through readfile |
Value
A data frame containing the mean, median, and standard deviation of the intraday standard deviations.
Examples
mydatafile <- system.file("extdata", "my_data_file.csv", package = "cgmquantify")
mydata <- readfile(mydatafile)
intradaysd(mydata)
Plot Glucose Data
Description
This function plots glycemic excursions over the time period in which data was collected.
Usage
plot_glucose(df)
Arguments
df |
Data frame read through readfile |
Value
None
Examples
mydatafile <- system.file("extdata", "my_data_file.csv", package = "cgmquantify")
mydata <- readfile(mydatafile)
plot_glucose(mydata)
Read in Data Frame
Description
This function reads in a .csv with variable names Timestamp..YYYY.MM.DDThh.mm.ss and Glucose.Value..mg.dL
Usage
readfile(filename)
Arguments
filename |
.csv file of data frame to be read |
Value
transformed data frame for further analysis
Examples
mydatafile <- system.file("extdata", "my_data_file.csv", package = "cgmquantify")
readfile(mydatafile)
Compute Glucose Summary Statistics
Description
This function computes the mean, median, minimum, maximum, first quartile, and the third quartile of an indidividual's overall glucose levels
Usage
summary_glucose(df)
Arguments
df |
Data frame read through readfile |
Value
A dataframe containing the mean, median, minimum, maximum, quartile1, and quartile3 of glucose levels
Examples
mydatafile <- system.file("extdata", "my_data_file.csv", package = "cgmquantify")
mydata <- readfile(mydatafile)
summary_glucose(mydata)