| Version: | 0.0.5 |
| Date: | 2024-12-19 |
| Title: | Analysis of Continuous Glucose Monitor Data |
| Author: | Evan Olawsky [aut, cre], Yuan Zhang [ctb], Lynn Eberly [ctb] |
| Maintainer: | Evan Olawsky <evanolawsky@gmail.com> |
| Depends: | R (≥ 3.2.0), chron |
| Description: | Reads in continuous glucose monitor data of many different formats, calculates a host of glycemic variability metrics, and plots glucose over time. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| Suggests: | testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| RoxygenNote: | 7.1.1 |
| NeedsCompilation: | no |
| Packaged: | 2024-12-19 21:00:51 UTC; evanolawsky |
| Repository: | CRAN |
| Date/Publication: | 2024-12-19 21:10:02 UTC |
Calculate all glycemic variability metrics
Description
Calculate all glycemic variability metrics
Usage
GV(
x,
times,
unit = "mg",
m_index = 120,
k = 60,
s = 1,
conga_n = 1,
interval = 1,
thresh = 100,
event_thresh = 55
)
Arguments
x |
vector of glucose readings |
times |
vector of corresponding times, in minutes |
unit |
"mg" if the units are mg/dL or "mmol" if the units are mmol/L. Null value is "mg". |
m_index |
a value to be considered a 'standard' blood glucose value for calculation of M-value, in mg/dL. Null value is 120. |
k |
length of time (in minutes) used to find partners. Null value is 60. |
s |
number of minutes of slack used when searching for partners. Null value is 1. |
conga_n |
number of hours between "partner" observations. Null value is 1. |
interval |
size (in hours) of the moving window to be used if overall is false. Null value is 1. |
thresh |
threshold above (or below) which you wish to calculate the AUC. Default is 100. |
event_thresh |
glucoses below this threshold are considered as part of an episode. Default is 55 |
Value
A data frame containing the entire suite of rGV metrics for the given dataset.
Examples
GV(x=c(rep(100, 10), rep(120, 10), 105, 85), times=seq(0, 1260, 60), unit='mg',
m_index=120, k=60, s=1, conga_n=1, interval=1, thresh=100, event_thresh=55)
Calculate Average Daily Risk Range (ADRR)
Description
Calculate Average Daily Risk Range (ADRR)
Usage
adrr(x, times, unit = "mg", method = "manuscript")
Arguments
x |
vector of glucose readings |
times |
vector of corresponding times, in minutes |
unit |
"mg" if the units are mg/dL or "mmol" if the units are mmol/L. Null value is "mg". |
method |
"manuscript", "corrected", or "easy". Null value is "manuscript". |
Value
The numeric ADRR value for a given dataset of glucose measurements and times.
Examples
adrr(x=c(rep(100, 10), rep(120, 10), 105, 85), times=seq(0, 1260, 60),
unit="mg", method='manuscript')
Calculate Low / High Blood Glucose Index (LBGI, HBGI)
Description
Calculate Low / High Blood Glucose Index (LBGI, HBGI)
Usage
bgi(x, unit = "mg", method = "manuscript")
Arguments
x |
vector of glucose readings |
unit |
"mg" if the units are mg/dL or "mmol" if the units are mmol/L. Null value is "mg". |
method |
"manuscript", "corrected", or "easy". Null value is "manuscript". |
Value
A list containing the LBGI and HBGI values for a given dataset of glucose measurements.
Examples
bgi(x=c(rep(100, 10), rep(120, 10), 105, 85), unit='mg', method='manuscript')
Calculate area under the curve (AUC)
Description
Calculate area under the curve (AUC)
Usage
cgm_auc(x, times, thresh = 100, above = TRUE)
Arguments
x |
vector of glucose readings |
times |
vector of corresponding times, in minutes |
thresh |
threshold above (or below) which you wish to calculate the AUC. Default is 100. |
above |
logical indicating whether you wish to calculate area above the threshold value (TRUE) or below it (FALSE). Default is TRUE. |
Value
The numeric area under the curve value for a given dataset of glucose measurements and times.
Examples
cgm_auc(x=c(rep(100, 10), rep(120, 10), 105, 85), times=seq(0, 1260, 60), thresh=110, above=TRUE)
Plot glucose values over time
Description
Plot glucose values over time
Usage
cgm_plot(x, times, unit = "mg")
Arguments
x |
vector of glucose readings |
times |
vector of corresponding times, in minutes |
unit |
"mg" if the units are mg/dL or "mmol" if the units are mmol/L. Null value is "mg". |
Value
A plot of glucose values over time.
Examples
cgm_plot(x=c(rep(100, 10), rep(120, 10), 105, 85), times=seq(0, 1260, 60), unit='mg')
Calculate continuous overall net glycemic action (CONGA)
Description
Calculate continuous overall net glycemic action (CONGA)
Usage
conga(x, times, n = 1, s = 1, method = "manuscript")
Arguments
x |
vector of glucose readings |
times |
vector of corresponding times, in minutes |
n |
number of hours between "partner" observations. Null value is 1. |
s |
number of minutes of slack used when searching for partners. Null value is 1. |
method |
"manuscript" or "easy". Null value is "manuscript". |
Value
The numeric CONGA value for a given dataset of glucose measurements and times.
Examples
conga(x=c(rep(100, 10), rep(120, 10), 105, 85), times=seq(0, 1260, 60),
n=1, s=1, method="manuscript")
Calculate coefficient of variation (CV)
Description
Calculate coefficient of variation (CV)
Usage
cv(x, times, overall = TRUE, interval = 1)
Arguments
x |
vector of glucose readings |
times |
vector of corresponding times, in minutes |
overall |
a logical, equal to TRUE you want the CV for the entire dataset, or equal to FALSE if you would prefer many CV values over a moving window |
interval |
size (in hours) of the moving window to be used if overall is false. Null value is 1. |
Value
Either a numeric coefficient of variation over the entire dataset or a vector of CV values over windows of the data.
Examples
cv(x=c(rep(100, 10), rep(120, 10), 105, 85), times=seq(0, 1260, 60), overall=TRUE)
Plots glucose changes over time
Description
Plots glucose changes over time
Usage
diff_plot(x, times, n = 1, s = 1, unit = "mg")
Arguments
x |
vector of glucose readings |
times |
vector of corresponding times, in minutes |
n |
number of hours between "partner" observations. Null value is 1. |
s |
number of minutes of slack used when searching for partners. Null value is 1. |
unit |
"mg" if the units are mg/dL or "mmol" if the units are mmol/L. Null value is "mg". |
Value
A plot of n-hour glucose differences over time.
Examples
diff_plot(x=c(rep(100, 10), rep(120, 10), 105, 85), times=seq(0, 1260, 60), n=1, s=1, unit='mg')
Calculate distance travelled
Description
Calculate distance travelled
Usage
dist_travelled(x)
Arguments
x |
vector of glucose readings |
Value
The numeric distance travelled value for a given dataset of glucose measurements.
Examples
dist_travelled(x=c(rep(100, 10), rep(120, 10), 105, 85))
Calculate Glucose Management Indicator (GMI)
Description
Calculate Glucose Management Indicator (GMI)
Usage
gmi(x, unit = "mg")
Arguments
x |
vector of glucose readings |
unit |
"mg" if the units are mg/dL or "mmol" if the units are mmol/L. Null value is "mg". |
Value
The numeric GMI value for a given dataset of glucose measurements.
Examples
gmi(x=c(rep(100, 10), rep(120, 10), 105, 85), unit='mg')
Calculate Glycemic Risk Assessment Diabetes Equation (GRADE)
Description
Calculate Glycemic Risk Assessment Diabetes Equation (GRADE)
Usage
grade(
x,
unit = "mg",
method = "manuscript",
c1 = ifelse(unit == "mg", 70.2, 3.9),
c2 = ifelse(unit == "mg", 140.4, 7.8)
)
Arguments
x |
vector of glucose readings |
unit |
"mg" if the units are mg/dL or "mmol" if the units are mmol/L. Null value is "mg". |
method |
"manuscript" or "easy". Null value is "manuscript". |
c1 |
glucose value below which readings are considered hypoglycemic. Default is 70.2 mg/dL. |
c2 |
glucose value above which readings are considered hyperglycemic. Default is 140.4 mg/dL. |
Value
A list containing the GRADE value and the percentage of the GRADE value due to euglycemia, hypoglycemia, and hyperglycemia for a given dataset of glucose measurements
Examples
grade(x=c(rep(100, 10), rep(120, 10), 105, 85), unit='mg', method='manuscript', c1=70.2, c2=140.4)
Calculate Glycemic Variability Percentage (GVP)
Description
Calculate Glycemic Variability Percentage (GVP)
Usage
gvp(x, times)
Arguments
x |
vector of glucose readings |
times |
vector of corresponding times, in minutes |
Value
The numeric GVP value for a given dataset of glucose measurements and times.
Examples
gvp(x=c(rep(100, 10), rep(120, 10), 105, 85), times=seq(0, 1260, 60))
Calculate J-index
Description
Calculate J-index
Usage
j_index(x, unit = "mg")
Arguments
x |
vector of glucose readings |
unit |
"mg" if the units are mg/dL or "mmol" if the units are mmol/L. Null value is "mg". |
Value
The numeric J-index value for a given dataset of glucose measurements.
Examples
j_index(x=c(rep(100, 10), rep(120, 10), 105, 85), unit='mg')
Calculate the lability index (LI)
Description
Calculate the lability index (LI)
Usage
li(x, times, k = 60, s = 1)
Arguments
x |
vector of glucose readings |
times |
vector of corresponding times, in minutes |
k |
length of time (in minutes) used to find partners. Null value is 60. |
s |
number of minutes of slack used when searching for partners. Null value is 1. |
Value
The numeric value of the lability index for a given dataset of glucose measurements and times.
Examples
li(x=c(rep(100, 10), rep(120, 10), 105, 85), times=seq(0, 1260, 60), k=60, s=1)
Calculate M-value
Description
Calculate M-value
Usage
m_value(x, unit = "mg", index = 120, method = "manuscript")
Arguments
x |
vector of glucose readings |
unit |
"mg" if the units are mg/dL or "mmol" if the units are mmol/L. Null value is "mg". |
index |
value to be considered a 'standard' blood glucose value, in mg/dL. Null value is 120. |
method |
"manuscript", "corrected", or "easy". Null value is "manuscript". |
Value
The numeric M-value for a given dataset of glucose measurements.
Examples
m_value(x=c(rep(100, 10), rep(120, 10), 105, 85), unit='mg', index=120, method='manuscript')
Calculate Mean Absolute Glucose (MAG)
Description
Calculate Mean Absolute Glucose (MAG)
Usage
mag(x, times)
Arguments
x |
vector of glucose readings |
times |
vector of corresponding times, in minutes |
Value
The numeric MAG value for a given dataset of glucose measurements and times.
Examples
mag(x=c(rep(100, 10),rep(120, 10), 105, 85), times=seq(0, 1260, 60))
Calculate Mean Amplitude of Glycemic Excursions (MAGE)
Description
Calculate Mean Amplitude of Glycemic Excursions (MAGE)
Usage
mage(x, times)
Arguments
x |
vector of glucose readings |
times |
vector of corresponding times, in minutes |
Value
The numeric MAGE value for a given dataset of glucose measurements and times.
Examples
mage(x=c(rep(100, 10), rep(120, 10), 105, 85), times=seq(0, 1260, 60))
Calculate Mean of Daily Differences (MODD)
Description
Calculate Mean of Daily Differences (MODD)
Usage
modd(x, times, s = 1, method = "manuscript")
Arguments
x |
vector of glucose readings |
times |
vector of corresponding times, in minutes |
s |
number of minutes of slack used when searching for partners. Null value is 1. |
method |
"manuscript" or "easy". Null value is "manuscript". |
Value
The numeric MODD value for a given dataset of glucose measurements and times.
Examples
modd(x=c(rep(100, 10), rep(120, 10), 105, 85), times=seq(0, 1260, 60), s=1, method='manuscript')
Find number of episodes below a given glucose value for a given amount of time
Description
Find number of episodes below a given glucose value for a given amount of time
Usage
num_events(x, times, thresh = 55, len = 15, gap = 5)
Arguments
x |
vector of glucose readings |
times |
vector of corresponding times, in minutes |
thresh |
glucoses below this threshold are considered as part of an episode. Default is 55 |
len |
minimum length of an episode. Default is 15 |
gap |
typical gap between CGM measurements, in minutes. Default is 5 |
Value
The integer number of events for a given dataset of glucose measurements and times.
Examples
num_events(x=c(rep(100, 10), rep(120, 10), 105, 85), times=seq(0, 1260, 60),
thresh=55, len=15, gap=5)
Read in continuous glucose monitor data
Description
Read in continuous glucose monitor data
Usage
read_cgm(
file,
timezero = "first",
na.rm = TRUE,
skip = 0,
calib_col = NA,
calib_tag,
mult_sensors = FALSE,
sensor_times = NA,
sensor_gap = 120,
time_col,
gluc_col,
time_sep = " ",
time_format = c(dates = "m/d/y", times = "h:m:s"),
high_ind = "High",
high_value = 400,
low_ind = "Low",
low_value = 40
)
Arguments
file |
name of the CSV file to be read in |
timezero |
set to "first" if the first glucose reading should be considered time zero and set to "midnight" if midnight of the day of the first reading should be considered time zero. Default is "first". |
na.rm |
a logical that is TRUE if you wish to exclude all readings that are missing glucose values or time stamps and FALSE if not. Default is TRUE. |
skip |
the number of lines in the data file to skip before beginning to read in data |
calib_col |
the number or name of the column containing information regarding calibration status of each glucose entry |
calib_tag |
the character value used to denote calibration rows in calib_col |
mult_sensors |
a logical that is TRUE if you wish to split the data set into parts corresponding to different CGM sensors and FALSE if not. Default is FALSE. |
sensor_times |
a vector of times (in the same format as the time data) that correspond to the beginning of a new CGM sensor. These times are used to split the data between multiple sensors if mult_sensors is TRUE. If sensor_times is NA, the data is split automatically at every gap of sensor_gap or more minutes. |
sensor_gap |
a number specifying the minimum gap (in minutes) for which we should split the data into two pieces. Default is 120. |
time_col |
the number or name of the column containing time data |
gluc_col |
the number or name of the column containing glucose data |
time_sep |
character that separates date from time in your time data |
time_format |
specify date and time formats according to the specification used in the chron package. Default is c(dates = "m/d/y", times = "h:m:s"). |
high_ind |
character value that identifies high glucose values in the data. Default is "High". |
high_value |
numeric value by which to replace glucose values equal to "high_ind". Default is 400. |
low_ind |
character value that identifies low glucose values in the data. Default is "Low". |
low_value |
numeric value by which to replace glucose values equal to "low_ind". Default is 40. |
Value
A data frame with two columns: glucose values and time. This data frame can then be used with other rGV functions to calculate CGM metrics.
Calculate standard deviation (SD)
Description
Calculate standard deviation (SD)
Usage
st_dev(x, times, overall = TRUE, interval = 1)
Arguments
x |
vector of glucose readings |
times |
vector of corresponding times, in minutes |
overall |
a logical, equal to TRUE you want the CV for the entire dataset, or equal to FALSE if you would prefer many CV values over a moving window |
interval |
size (in hours) of the moving window to be used if overall is false. Null value is 1. |
Value
Either a numeric standard deviation over the entire dataset or a vector of SD values over windows of the data.
Examples
st_dev(x=c(rep(100, 10), rep(120, 10), 105, 85), times=seq(0, 1260, 60), overall=TRUE)
Plot the symmetrized glucose values
Description
Plot the symmetrized glucose values
Usage
symm_plot(x, times, unit = "mg")
Arguments
x |
vector of glucose readings |
times |
vector of corresponding times, in minutes |
unit |
"mg" if the units are mg/dL or "mmol" if the units are mmol/L. Null value is "mg". |
Value
A plot of symmetrized glucose values over time. These symmetrized values are used in the calculation of BGI and ADRR.
Examples
symm_plot(x=c(rep(100, 10),rep(120, 10), 105, 85), times=seq(0, 1260, 60), unit='mg')
Calculate amount of time that the CGM was active
Description
Calculate amount of time that the CGM was active
Usage
time_on(times, gap = 5)
Arguments
times |
vector of corresponding times, in minutes |
gap |
typical gap between CGM measurements, in minutes. Default is 5 |
Value
The numeric amount of time that the CGM device was active in a given dataset.
Examples
time_on(times=seq(0, 1260, 60), gap=5)
Calculate time in range (TIR)
Description
Calculate time in range (TIR)
Usage
tir(x, low = 70, high = 180)
Arguments
x |
vector of glucose readings |
low |
lower bound of the range. Default is 70 |
high |
upper bound of the range. Default is 180 |
Value
The numeric time in range value for a given dataset of glucose measurements and times.
Examples
tir(x=c(rep(100, 10), rep(120, 10), 105, 85), low=70, high=80)