Package 'iglu'

Description

The function above_percent produces a tibble object with values equal to the percentage of glucose measurements above target values. The output columns correspond to the subject id followed by the target values, and the output rows correspond to the subjects. The values will be between 0 (no measurements) and 100 (all measurements).

Usage

above_percent(data, targets_above = c(140, 180, 250))

Arguments

Details

A tibble object with 1 row for each subject, a column for subject id and column for each target value is returned. NA's will be omitted from the glucose values in calculation of percent.

Value

If a DataFrame object is passed, then a tibble object with a column for subject id and then a column for each target value is returned. If a vector of glucose values is passed, then a tibble object without the subject id is returned. Wrap 'as.numeric()' around the latter to output a numeric vector.

References

Rodbard (2009) Interpretation of continuous glucose monitoring data: glycemic variability and quality of glycemic control, Diabetes Technology and Therapeutics 11 .55-67, doi:10.1089/dia.2008.0132.

See Also

plot_ranges()

Examples

data(example_data_1_subject)

above_percent(example_data_1_subject)
above_percent(example_data_1_subject, targets_above = c(100, 150, 180))

data(example_data_5_subject)

above_percent(example_data_5_subject)
above_percent(example_data_5_subject, targets_above = c(70, 170))

Description

The function 'active_percent' produces the

Usage

active_percent(data, dt0 = NULL, tz = "",
range_type = "automatic", ndays = 14, end_date = NULL)

Arguments

Details

The function 'active_percent' produces a tibble object with values equal to the percentage of time the CGM was active, the total number of observed days, the start date, and the end date. For example, if a CGM's (5 min frequency) times were 0, 5, 10, 15 and glucose values were missing at time 5, then percentage of time the CGM was active is 75 The output columns correspond to the subject id, the percentage of time for which the CGM was active, the number of days of measurements, the start date and the end date of measurements. The output rows correspond to the subjects. The values of 'active_percent' are always between 0

Value

A tibble object with five columns: subject id, corresponding active_percent value, duration of measurement period in days, start date, and end date.

Author(s)

Pratik Patel, Irina Gaynanova

References

Danne et al. (2017) International Consensus on Use of Continuous Glucose Monitoring Diabetes Care 40 .1631-1640, doi:10.2337/dc17-1600.

Examples

data(example_data_1_subject)

active_percent(example_data_1_subject)

data(example_data_5_subject)

active_percent(example_data_5_subject)
active_percent(example_data_5_subject, dt0 = 5, tz = 'GMT')

Description

The function 'adrr' produces ADRR values in a tibble object.

Usage

adrr(data)

Arguments

Details

A tibble object with 1 row for each subject, a column for subject id and a column for ADRR values is returned. 'NA' glucose values are omitted from the calculation of the ADRR values.

ADRR is the average sum of HBGI corresponding to the highest glucose value and LBGI corresponding to the lowest glucose value for each day, with the average taken over the daily sums. If there are no high glucose or no low glucose values, then 0 will be substituted for the HBGI value or the LBGI value, respectively, for that day.

Value

A tibble object with two columns: subject id and corresponding ADRR value.

References

Kovatchev et al. (2006) Evaluation of a New Measure of Blood Glucose Variability in, Diabetes Diabetes care 29 .2433-2438, doi:10.2337/dc06-1085.

Examples

data(example_data_1_subject)
adrr(example_data_1_subject)

data(example_data_5_subject)
adrr(example_data_5_subject)

Description

Display Ambulatory Glucose Profile (AGP) statistics for selected subject

Usage

agp(data, maxd = 14, inter_gap = 45, dt0 = NULL, tz = "", daily = TRUE)

Arguments

Value

A plot displaying glucose measurements range, selected glucose statistics (average glucose, Glucose Management Indicator,

References

Johnson et al. (2019) Utilizing the Ambulatory Glucose Profile to Standardize and Implement Continuous Glucose Monitoring in Clinical Practice, Diabetes Technology and Therapeutics 21:S2 S2-17-S2-25, doi:10.1089/dia.2019.0034.

Examples

data(example_data_1_subject)
agp(example_data_1_subject, daily = FALSE)

Description

The function 'agp_metrics' runs the following functions and combines them into a tibble object: 'active_percent', 'mean_glu', 'gmi', 'cv_glu', 'below_percent', 'in_range_percent', 'above_percent'.

Usage

agp_metrics(data, shinyformat = FALSE, tz = '')

Arguments

Details

The function uses recommended cutoffs of 54, 70, 180, and 250 mg/dL for calculation.

If 'shinyformat == FALSE' (default), returns a tibble object with 1 row for each subject, and 12 columns: a column for subject id ('id'), a column for start date ('start_date'), a column for end date ('end_date'), a column for number of days ('ndays'), a column for active percent ('active_percent'), a column for mean value ('mean'), a column for GMI value ('GMI'), a column for CV value ('CV'), a column for a column for a column for a column for a column for

If 'shinyformat == TRUE', a tibble with 2 columns: metric and value, is returned. This output is used when generating the single subject AGP shiny page.

Value

By default, a tibble object with 1 row for each subject, and 13 columns is returned: a column for subject id, a column for start date, a column for end date, a column for number of days, a column for active_percent, a column for Mean value, a column for gmi value, a column for cv value, a column for below_54 value, a column for below_70 value, a column for in_range_70_180 value, a column for above_180 value, a column for above_250 value,

References

Johnson et al. (2019) Utilizing the Ambulatory Glucose Profile to Standardize and Implement Continuous Glucose Monitoring in Clinical Practice, Diabetes Technology and Therapeutics 21:S2 S2-17-S2-25, doi:10.1089/dia.2019.0034.

Examples

data(example_data_1_subject)
agp_metrics(example_data_1_subject)

Description

The function all_metrics runs all of the iglu metrics, and returns the results with one column per metric.

Usage

all_metrics(
  data,
  dt0 = NULL,
  inter_gap = 45,
  tz = "",
  timelag = 15,
  lag = 1,
  metrics_to_include = c("all", "consensus_only")
)

Arguments

Details

All iglu functions are calculated within the all_metrics function, and the resulting tibble is returned with one row per subject and a column for each metric. Time dependent functions are calculated together using the function optimized_iglu_functions with two exceptions: PGS and episodes are calculated within all_metrics because their structure does not align with optimized_iglu_functions. Note that episodes related outputs included in all_metrics are only average episodes per day. To get the average duration and glucose, please use the standalone episodes function

For metric specific information, please see the corresponding function documentation.

Value

A tibble object with 1 row per subject and one column per metric is returned.

References

Battelino T, Alexander CM, Amiel SA, et al. Continuous glucose monitoring and metrics for clinical trials: an international consensus statement. Lancet Diabetes Endocrinol. 2023;11(1):42-57. doi:10.1016/S2213-8587(22)00319-9.

# Specify the meter frequency and change the interpolation gap to 30 min all_metrics(example_data_1_subject, dt0 = 5, inter_gap = 30)

Examples

data(example_data_1_subject)
all_metrics(example_data_1_subject)

Description

The function auc produces hourly average AUC for each subject.

Usage

auc(data, tz="")

Arguments

Details

A tibble object with 1 row for each subject, a column for subject id and a column for hourly average AUC values is returned. NA glucose values are omitted from the calculation of the AUC.

AUC is calculated using the formula: (dt0/60) * ((gl[2:length(gl)] + gl[1:(length(gl)-1)])/2), where dt0/60 is the frequency of the cgm measurements in hours and gl are the glucose values.

This formula is based off the Trapezoidal Rule: (time[2]-time[1] * ((glucose[1]+glucose[2])/2)).

Value

If a data.frame object is passed, then a tibble object with two columns: subject id and corresponding hourly average AUC value is returned.

AUC is calculated for every hour using the trapezoidal rule, then hourly average AUC is calculated for each 24 hour period, then the mean of hourly average AUC across all 24 hour periods is returned as overall hourly average AUC.

References

Danne et al. (2017) International Consensus on Use of Continuous Glucose Monitoring, Diabetes Care 40 .1631-1640, doi:10.2337/dc17-1600.

Examples

data(example_data_1_subject)
auc(example_data_1_subject)

Description

The function below_percent produces a tibble object with values equal to the percentage of glucose measurements below target values. The output columns correspond to the subject id followed by the target values and the output rows correspond to the subjects. The values will be between 0 (no measurements) and 100 (all measurements).

Usage

below_percent(data, targets_below = c(54, 70))

Arguments

Details

A tibble object with 1 row for each subject, a column for subject id and column for each target value is returned. NA's will be omitted from the glucose values in calculation of percent.

Value

If a data.frame object is passed, then a tibble object with a column for subject id and then a column for each target value is returned. If a vector of glucose values is passed, then a tibble object without the subject id is returned. as.numeric() can be wrapped around the latter to output a numeric vector.

References

Rodbard (2009) Interpretation of continuous glucose monitoring data: glycemic variability and quality of glycemic control, Diabetes Technology and Therapeutics 11 .55-67, doi:10.1089/dia.2008.0132.

See Also

plot_ranges()

Examples

data(example_data_1_subject)

below_percent(example_data_1_subject)
below_percent(example_data_1_subject, targets_below = c(50, 100, 180))

data(example_data_5_subject)

below_percent(example_data_5_subject)
below_percent(example_data_5_subject, targets_below = c(80, 180))

Description

This function applies a given function to a subset of data filtered by time of day.

Usage

calculate_sleep_wake(
  data,
  FUN,
  sleep_start = 0,
  sleep_end = 6,
  calculate = c("sleep", "wake", "both"),
  ...
)

Arguments

Details

An object of the same return type as FUN, with the same column names as FUN will be returned. If calculate = "both", there will be columns for FUN applied to both inside and outside values, with either "in range" or "out of range" append to signify whether the statistic was calculated on values which were inside the time range or outside the range.

FUN is found by a call to match.fun and typically is either a function or a character string specifying a function to be searched for from the environment of the call to apply. Arguments in ... cannot have the same name as any of the other arguments, and care may be needed to avoid partial matching to FUN. FUN is applied to the data after the data is filtered based on whether its hour falls within the given range. If sleep_start is an integer, all times within that hour will be included in the range, but if sleep_end is an integer only times up to that hour will be included in the range. If sleep_start is after sleep_end, the data will be filtered to include all hours after sleep_start and all times before sleep_end.

Value

An object of the same return type as FUN, with columns corresponding to the values returned by FUN. Separated for values inside or outside the time range, if calculate = both.

Examples

data(example_data_1_subject)
calculate_sleep_wake(example_data_1_subject, sd_glu, calculate = "sleep")

data(example_data_5_subject)
calculate_sleep_wake(example_data_5_subject, cogi, targets = c(80, 150),
weights = c(.3,.2,.5), calculate = "wake")
calculate_sleep_wake(example_data_5_subject, sd_measures, sleep_start = 2,
sleep_end = 8, calculate = "both")

Description

Interpolate glucose value on an equally spaced grid from day to day

Usage

CGMS2DayByDay(data, dt0 = NULL, inter_gap = 45, tz = "")

Arguments

Value

A list with

Examples

CGMS2DayByDay(example_data_1_subject)

Description

The function COGI produces cogi values in a tibble object.

Usage

cogi(data, targets = c(70, 180), weights = c(.5,.35,.15))

Arguments

Details

A tibble object with 1 row for each subject, a column for subject id and column for each target value is returned. NA's will be omitted from the glucose values in calculation of cogi.

Value

If a data.frame object is passed, then a tibble object with a column for subject id and then a column for each target value is returned. If a vector of glucose values is passed, then a tibble object without the subject id is returned. as.numeric() can be wrapped around the latter to output a numeric vector.

References

Leelarathna (2020) Evaluating Glucose Control With a Novel Composite Continuous Glucose Monitoring Index, Diabetes Technology and Therapeutics 14(2) 277-284, doi:10.1177/1932296819838525.

Examples

data(example_data_1_subject)

cogi(example_data_1_subject)
cogi(example_data_1_subject, targets = c(50, 140), weights = c(.3,.6,.1))

data(example_data_5_subject)

cogi(example_data_5_subject)
cogi(example_data_5_subject, targets = c(80, 180), weights = c(.2,.4,.4))

Description

The function conga produces CONGA values a tibble object for any n hours apart.

Usage

conga(data, n = 24, tz = "")

Arguments

Details

A tibble object with 1 row for each subject, a column for subject id and a column for the CONGA values is returned.

Missing values will be linearly interpolated when close enough to non-missing values.

CONGA is the standard deviation of the difference between glucose values that are exactly n hours apart. CONGA is computed by taking the standard deviation of differences in measurements separated by n hours.

Value

A tibble object with two columns: subject id and corresponding CONGA value.

References

McDonnell et al. (2005) : A novel approach to continuous glucose analysis utilizing glycemic variation Diabetes Technology and Therapeutics 7 .253-263, doi:10.1089/dia.2005.7.253.

Examples

data(example_data_1_subject)
conga(example_data_1_subject)

data(example_data_5_subject)
conga(example_data_5_subject)

Description

The function cv_glu produces CV values in a tibble object.

Usage

cv_glu(data)

Arguments

Details

A tibble object with 1 row for each subject, a column for subject id and a column for CV values is returned. NA glucose values are omitted from the calculation of the CV.

CV (Coefficient of Variation) is calculated by $100 * sd(G) / mean(G)$ Where G is the list of all Glucose measurements for a subject.

Value

If a data.frame object is passed, then a tibble object with two columns: subject id and corresponding CV value is returned. If a vector of glucose values is passed, then a tibble object with just the CV value is returned. as.numeric() can be wrapped around the latter to output just a numeric value.

References

Rodbard (2009) Interpretation of continuous glucose monitoring data: glycemic variability and quality of glycemic control, Diabetes Technology and Therapeutics 11 .55-67, doi:10.1089/dia.2008.0132.

Examples

data(example_data_1_subject)
cv_glu(example_data_1_subject)

data(example_data_5_subject)
cv_glu(example_data_5_subject)

Description

The function cv_measures produces CV subtype values in a tibble object.

Usage

cv_measures(data, dt0 = NULL, inter_gap = 45, tz = "" )

Arguments

Details

A tibble object with 1 row for each subject, a column for subject id and a column for each cv subtype values is returned.

Missing values will be linearly interpolated when close enough to non-missing values.

1. CVmean:

   Calculated by first taking the coefficient of variation of each day's glucose measurements, then taking the mean of all the coefficient of variation. That is, for x days we compute cv_1 ... cv_x daily coefficient of variations and calculate $1/x * \sum [(cv_i)]$

2. CVsd:

   Calculated by first taking the coefficient of variation of each day's glucose measurements, then taking the standard deviation of all the coefficient of variations. That is, for d days we compute cv_1 ... cv_d daily coefficient of variations and calculate SD([cv_1, cv_2, ... cv_d])

Value

When a data.frame object is passed, then a tibble object with three columns: subject id and corresponding CV subtype values is returned.

References

Umpierrez, et.al. (2018) Glycemic Variability: How to Measure and Its Clinical Implication for Type 2 Diabetes The American Journal of Medical Sciences 356 .518-527, doi:10.1016/j.amjms.2018.09.010.

Examples

data(example_data_1_subject)
cv_measures(example_data_1_subject)

data(example_data_5_subject)
cv_measures(example_data_5_subject)

Description

The function ea1c produces eA1C values in a tibble object.

Usage

ea1c(data)

Arguments

Details

A tibble object with 1 row for each subject, a column for subject id and a column for eA1C values is returned. NA glucose values are omitted from the calculation of the eA1C.

eA1C score is calculated by $(46.7+mean(G))/28.7$ where G is the vector of Glucose Measurements (mg/dL).

Value

If a data.frame object is passed, then a tibble object with two columns: subject id and corresponding eA1C is returned. If a vector of glucose values is passed, then a tibble object with just the eA1C value is returned. as.numeric() can be wrapped around the latter to output just a numeric value.

Author(s)

Marielle Hicban

References

Nathan (2008) Translating the A1C assay into estimated average glucose values Hormone and Metabolic Research 31 .1473-1478, doi:10.2337/dc08-0545.

Examples

data(example_data_1_subject)
ea1c(example_data_1_subject)

data(example_data_5_subject)
ea1c(example_data_5_subject)

Description

Display Episode Calculation statistics for selected subject

Usage

epicalc_profile(
  data,
  lv1_hypo = 70,
  lv2_hypo = 54,
  lv1_hyper = 180,
  lv2_hyper = 250,
  dur_length = 15,
  end_length = 15,
  subject = NULL,
  dt0 = NULL,
  inter_gap = 45,
  tz = ""
)

Arguments

Value

A plot displaying (1) the statistics for the episodes and (2) the episodes colored by level.

Author(s)

Johnathan Shih, Jung Hoon Seo, Elizabeth Chun

See Also

episode_calculation()

Examples

epicalc_profile(example_data_1_subject)

Description

The function determines episodes or events, calculates summary statistics, and optionally returns data with episode label columns added

Usage

episode_calculation(
  data,
  lv1_hypo = 70,
  lv2_hypo = 54,
  lv1_hyper = 180,
  lv2_hyper = 250,
  dur_length = 15,
  end_length = 15,
  return_data = FALSE,
  dt0 = NULL,
  inter_gap = 45,
  tz = ""
)

Arguments

Details

We follow the definition of episodes given in the 2023 consensus by Battelino et al. Note we have classified lv2 as a subset of lv1 since we find the consensus to be slightly ambiguous. For lv1 exclusive of lv2, please see lv1_excl which summarises episodes that were exclusively lv1 and did not cross the lv2 threshold. Also note, hypo extended refers to episodes that are >120 consecutive minutes below lv1 hypo and ends with at least 15 minutes of normoglycemia. For more details on each category please see the reference below (Battelino et al 2023).

Value

If return_data is FALSE, a single dataframe with columns:

If return_data is TRUE, returns a list where the first entry is the episode summary dataframe (see above) and the second entry is the input data with episode labels added. Note the data returned here has been interpolated using the CGMS2DayByDay() function. Mostly for use with epicalc_profile function. Format of the second list entry is:

Author(s)

Elizabeth Chun, Jung Hoon Seo, Johnathan Shih

References

Battelino et al. (2023): Continuous glucose monitoring and metrics for clinical trials: an international consensus statement Lancet Diabetes & Endocrinology 11(1) .42-57, doi:10.1016/s2213-8587(22)00319-9.

See Also

epicalc_profile()

Examples

episode_calculation(example_data_5_subject, lv1_hypo=100, lv1_hyper= 120)

Description

Dexcom G4 CGM measurements from 1 subject with Type II diabetes, this is a subset of example_data_5_subject.

Usage

example_data_1_subject

Format

A data.frame with 2915 rows and 3 columns, which are:

id

identifier of subject

time

5-10 minute time value

gl

glucose level

Description

Dexcom G4 CGM measurements for 5 subjects with Type II diabetes. These data are part of a larger study sample that consisted of patients with Type 2 diabetes recruited from the general community. To be eligible, patients with Type 2 diabetes, not using insulin therapy and with a glycosylated hemoglobin ($HbA_{1c}$) value at least 6.5

Usage

example_data_5_subject

Format

A data.frame with 13866 rows and 3 columns, which are:

id

identifier of subject

time

date and time stamp

gl

glucose level as measured by CGM (mg/dL)

Description

Dexcom G4 CGM measurements for 19 subjects from the Hall publicly available dataset. Chosen as a subset of all subjects to be only those with diabetes or pre-diabetes. Primarily intended for use with example_meals_hall

Usage

example_data_hall

Format

a data.frame with 34890 rows and 4 columns, which are:

id

identifier of subject

time

date and time stamp

gl

glucose level as measured by CGM (mg/dL)

diagnosis

character indicating diabetes diagnosis: diabetic or pre-diabetic

Details

This dataset can be used along with the example_meals_hall dataset in this package to calculate meal_metrics.

References

Hall et al. (2018) : Glucotypes reveal new patterns of glucose dysregulation Plos Biology 16 (7): 3:e2005143 doi:10.1371/journal.pbio.2005143.

Description

Example of mealtimes data format for meal_metrics function, corresponds to example_data_hall data.

Usage

example_meals_hall

Format

A data.frame with 9 rows and 3 columns, which are:

id

identifier of subject

meal

meal type identifier

mealtime

time of meal

Details

There are 3 types of meals available: Cereal Flakes (CF), Peanut Butter Sandwich (PB), and Protein Bar (Bar). The number after the abbreviation refers to the replication number for the original study. For more details on nutritional differences, please see the original study reference.

This dataset should be used along with example_data_hall to calculate meal_metrics.

References

Hall et al. (2018) : Glucotypes reveal new patterns of glucose dysregulation Plos Biology 16 (7): 3:e2005143 doi:10.1371/journal.pbio.2005143.

Description

The function gmi produces GMI values in a tibble object.

Usage

gmi(data)

Arguments

Details

A tibble object with 1 row for each subject, a column for subject id and a column for GMI values is returned. NA glucose values are omitted from the calculation of the GMI.

GMI score is calculated by $3.31 + (.02392*mean(G))$ where G is the vector of Glucose Measurements (mg/dL).

Value

If a data.frame object is passed, then a tibble object with two columns: subject id and corresponding GMI is returned. If a vector of glucose values is passed, then a tibble object with just the GMI value is returned. as.numeric() can be wrapped around the latter to output just a numeric value.

References

Bergenstal (2018) Glucose Management Indicator (GMI): A New Term for Estimating A1C From Continuous Glucose Monitoring Hormone and Metabolic Research 41 .2275-2280, doi:10.2337/dc18-1581.

Examples

data(example_data_1_subject)
gmi(example_data_1_subject)

data(example_data_5_subject)
gmi(example_data_5_subject)

Description

The function grade produces GRADE score values in a tibble object.

Usage

grade(data)

Arguments

Details

A tibble object with 1 row for each subject, a column for subject id and a column for GRADE values is returned. NA glucose values are omitted from the calculation of the GRADE.

GRADE score is calculated by $1/n * \sum [425 * (log(log(G_i / 18)) + .16)^2]$ Where $G_i$ is the ith Glucose measurement and n is the total number of measurements.

Value

If a data.frame object is passed, then a tibble object with two columns: subject id and corresponding GRADE value is returned. If a vector of glucose values is passed, then a tibble object with just the GRADE value is returned. as.numeric() can be wrapped around the latter to output just a numeric value.

References

Hill et al. (2007): A method for assessing quality of control from glucose profiles Diabetic Medicine 24 .753-758, doi:10.1111/j.1464-5491.2007.02119.x.

Examples

data(example_data_1_subject)
grade(example_data_1_subject)

data(example_data_5_subject)
grade(example_data_5_subject)

Description

The function grade_eugly produces %GRADE euglycemia values in a tibble object.

Usage

grade_eugly(data, lower = 70, upper = 140)

Arguments

Details

A tibble object with 1 row for each subject, a column for subject id and a column for %GRADE euglycemia values is returned. NA glucose values are omitted from the calculation of the %GRADE euglycemia values.

%GRADE euglycemia is determined by calculating the percentage of GRADE score (see grade function) attributed to values in the target range, i.e. values not below hypoglycemic or above hyperglycemic cutoffs.

Value

If a data.frame object is passed, then a tibble object with two columns: subject id and corresponding %GRADE euglycemia value is returned. If a vector of glucose values is passed, then a tibble object with just the %GRADE euglycemia value is returned. as.numeric() can be wrapped around the latter to output just a numeric value.

References

Hill et al. (2007): A method for assessing quality of control from glucose profiles Diabetic Medicine 24 .753-758, doi:10.1111/j.1464-5491.2007.02119.x.

Examples

data(example_data_1_subject)
grade_eugly(example_data_1_subject)
grade_eugly(example_data_1_subject, lower = 80, upper = 180)

data(example_data_5_subject)
grade_eugly(example_data_5_subject)
grade_eugly(example_data_5_subject, lower = 80, upper = 160)

Description

The function grade_hyper produces %GRADE hyperglycemia values in a tibble object.

Usage

grade_hyper(data, upper = 140)

Arguments

Details

A tibble object with 1 row for each subject, a column for subject id and a column for %GRADE hyperglycemia values is returned. NA glucose values are omitted from the calculation of the %GRADE hyperglycemia values.

%GRADE hyperglycemia is determined by calculating the percentage of GRADE score (see grade function) attributed to hyperglycemic glucose values.

Value

If a data.frame object is passed, then a tibble object with two columns: subject id and corresponding %GRADE hyperglycemia value is returned. If a vector of glucose values is passed, then a tibble object with just the %GRADE hyperglycemia value is returned. as.numeric() can be wrapped around the latter to output just a numeric value.

References

Hill et al. (2007): A method for assessing quality of control from glucose profiles Diabetic Medicine 24 .753-758, doi:10.1111/j.1464-5491.2007.02119.x.

Examples

data(example_data_1_subject)
grade_hyper(example_data_1_subject)
grade_hyper(example_data_1_subject, upper = 180)

data(example_data_5_subject)
grade_hyper(example_data_5_subject)
grade_hyper(example_data_5_subject, upper = 160)

Description

The function grade_hypo produces %GRADE hypoglycemia values in a tibble object.

Usage

grade_hypo(data, lower = 80)

Arguments

Details

A tibble object with 1 row for each subject, a column for subject id and a column for %GRADE hypoglycemia values is returned. NA glucose values are omitted from the calculation of the %GRADE hypoglycemia values.

%GRADE hypoglycemia is determined by calculating the percentage of GRADE score (see grade function) attributed to hypoglycemic glucose values.

Value

If a data.frame object is passed, then a tibble object with two columns: subject id and corresponding %GRADE hypoglycemia value is returned. If a vector of glucose values is passed, then a tibble object with just the %GRADE hypoglycemia value is returned. as.numeric() can be wrapped around the latter to output just a numeric value.

References

Hill et al. (2007): A method for assessing quality of control from glucose profiles Diabetic Medicine 24 .753-758, doi:10.1111/j.1464-5491.2007.02119.x.

Examples

data(example_data_1_subject)
grade_hypo(example_data_1_subject)
grade_hypo(example_data_1_subject, lower = 70)

data(example_data_5_subject)
grade_hypo(example_data_5_subject)
grade_hypo(example_data_5_subject, lower = 65)

Description

The function gri produces a tibble object with values equal to the glycemia risk index (GRI) metric. The output columns are subject id and GRI value. ' The output rows correspond to subjects.

Usage

gri(data, tz = "")

Arguments

Details

A tibble object with 1 row for each subject, a column for subject id and column for GRI is returned. The formula for GRI is as follows:

$(3.0 × VLow) + (2.4 × Low) + (1.6 × VHigh) + (0.8 × High)$

where VLow, Low, VHigh, and High correspond to the percent of glucose values in the ranges <54 mg/dL, 54-70 mg/dL, >250 mg/dL, and 180-250 mg/dL respectively. The maximum allowed value for GRI is 100%, any calculated values higher than 100 are capped.

Value

A tibble object with columns for subject id and GRI value. Rows correspond to individual subjects.

Author(s)

Elizabeth Chun

References

Klonoff et al. (2022) A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings. J Diabetes Sci Technol doi:10.1177/19322968221085273.

Examples

data(example_data_1_subject)
gri(example_data_1_subject)

data(example_data_5_subject)
gri(example_data_5_subject, tz = 'GMT')

Description

The function mad produces GVP values in a tibble object.

Usage

gvp(data)

Arguments

Details

A tibble object with 1 row for each subject, a column for subject id and a column for GVP values is returned. NA glucose values are omitted from the calculation of the GVP.

GVP is calculated by dividing the total length of the line of the glucose trace by the length of a perfectly flat trace. The formula for this is $sqrt(diff^2+dt0^2)/(n*dt0)$, where diff is the change in Glucose measurements from one reading to the next, dt0 is the time gap between measurements and n is the number of glucose readings

Value

A tibble object with two columns: subject id and corresponding GVP value.

Author(s)

David Buchanan, Mary Martin

References

Peyser et al. (2017) Glycemic Variability Percentage: A Novel Method for Assessing Glycemic Variability from Continuous Glucose Monitor Data. Diabetes Technol Ther 20(1):6–16, doi:10.1089/dia.2017.0187.

Examples

data(example_data_1_subject)
gvp(example_data_1_subject)

data(example_data_5_subject)
gvp(example_data_5_subject)

Description

The function hbgi produces HBGI values in a tibble object.

Usage

hbgi(data)

Arguments

Details

A tibble object with 1 row for each subject, a column for subject id and a column for HBGI values is returned. NA glucose values are omitted from the calculation of the HBGI.

HBGI is calculated by $1/n * \sum (10 * fg_i ^2)$, where $fg_i = max(0, 1.509 * (log(G_i)^{1.084} - 5.381)$, G_i is the ith Glucose measurement for a subject, and n is the total number of measurements for that subject.

Value

If a data.frame object is passed, then a tibble object with two columns: subject id and corresponding HBGI value is returned. If a vector of glucose values is passed, then a tibble object with just the HBGI value is returned. as.numeric() can be wrapped around the latter to output just a numeric value.

References

Kovatchev et al. (2006) Evaluation of a New Measure of Blood Glucose Variability in, Diabetes Diabetes care 29 .2433-2438, doi:10.2337/dc06-1085.

Examples

data(example_data_1_subject)
hbgi(example_data_1_subject)

data(example_data_5_subject)
hbgi(example_data_5_subject)

Description

The function hist_roc produces a histogram plot of ROC values

Usage

hist_roc(data, subjects = NULL, timelag = 15, dt0 = NULL, inter_gap = 45, tz = "")

Arguments

Details

For the default, a histogram is produced for each subject displaying the ROC values colored by ROC categories defined as follows. The breaks for the categories are: c(-Inf, -3, -2, -1, 1, 2, 3, Inf) where the glucose is in mg/dl and the ROC values are in mg/dl/min. A ROC of -5 mg/dl/min will thus be placed in the first category and colored accordingly.

Value

A histogram of ROC values per subject

Author(s)

Elizabeth Chun, David Buchanan

References

Clarke et al. (2009) Statistical Tools to Analyze Continuous Glucose Monitor Data, Diabetes Diabetes Technology and Therapeutics 11 S45-S54, doi:10.1089/dia.2008.0138.

See Also

plot_roc for reference paper on ROC categories.

Examples

data(example_data_1_subject)
hist_roc(example_data_1_subject)

data(example_data_5_subject)
hist_roc(example_data_5_subject)
hist_roc(example_data_5_subject, subjects = 'Subject 3')

Description

The function hyper_index produces Hyperglycemia Index values in a tibble object.

Usage

hyper_index(data, ULTR = 140, a = 1.1, c = 30)

Arguments

Details

A tibble object with 1 row for each subject, a column for subject id and a column for the Hyperglycemia Index values is returned. NA glucose values are omitted from the calculation of the Hyperglycemia Index values.

Hyperglycemia Index is calculated by $\frac{1}{n \cdot c} \sum \left(hyperBG_j - ULTR \right)^a$. Here n is the total number of Glucose measurements (excluding NA values), $hyperBG_j$ is the jth Glucose measurement above the ULTR cutoff, a is an exponent, and c is a scaling factor.

Value

If a data.frame object is passed, then a tibble object with two columns: subject id and corresponding Hyperglycemia Index value is returned. If a vector of glucose values is passed, then a tibble object with just the Hyperglycemia Index value is returned. as.numeric() can be wrapped around the latter to output just a numeric value.

References

Rodbard (2009) Interpretation of continuous glucose monitoring data: glycemic variability and quality of glycemic control, Diabetes Technology and Therapeutics 11 .55-67, doi:10.1089/dia.2008.0132.

Examples

data(example_data_1_subject)
hyper_index(example_data_1_subject)
hyper_index(example_data_1_subject, ULTR = 160)

data(example_data_5_subject)
hyper_index(example_data_5_subject)
hyper_index(example_data_5_subject, ULTR = 150)

Description

The function hypo_index produces Hypoglycemia index values in a tibble object.

Usage

hypo_index(data, LLTR = 80, b = 2, d = 30)

Arguments

Details

A tibble object with 1 row for each subject, a column for subject id and a column for the Hypoglycemia Index values is returned. NA glucose values are omitted from the calculation of the Hypoglycemia Index values.

Hypoglycemia Index is calculated by $\frac{1}{n \cdot d} \sum \left(ULTR - hyperBG_j \right)^b$. Here n is the total number of Glucose measurements (excluding NA values), and $hypoBG_j$ is the jth Glucose measurement below the LLTR cutoff, b is an exponent, and d is a scaling factor.

Value

If a data.frame object is passed, then a tibble object with two columns: subject id and corresponding Hypoglycemia Index value is returned. If a vector of glucose values is passed, then a tibble object with just the Hypoglycemia Index value is returned. as.numeric() can be wrapped around the latter to output just a numeric value.

References

Rodbard (2009) Interpretation of continuous glucose monitoring data: glycemic variability and quality of glycemic control, Diabetes Technology and Therapeutics 11 .55-67, doi:10.1089/dia.2008.0132.

Examples

data(example_data_1_subject)
hypo_index(example_data_1_subject, LLTR = 60)

data(example_data_5_subject)
hypo_index(example_data_5_subject)
hypo_index(example_data_5_subject, LLTR = 70)

Description

The function igc produces IGC values in a tibble object.

Usage

igc(data, LLTR = 80, ULTR = 140, a = 1.1, b = 2, c = 30, d = 30)

Arguments

Details

A tibble object with 1 row for each subject, a column for subject id and a column for the IGC values is returned.

IGC is calculated by taking the sum of the Hyperglycemia Index and the Hypoglycemia index. See hypo_index and hyper_index.

Value

A tibble object with two columns: subject id and corresponding IGC value.

References

Rodbard (2009) Interpretation of continuous glucose monitoring data: glycemic variability and quality of glycemic control, Diabetes Technology and Therapeutics 11 .55-67, doi:10.1089/dia.2008.0132.

Examples

data(example_data_1_subject)
igc(example_data_1_subject)
igc(example_data_1_subject, ULTR = 160)

data(example_data_5_subject)
igc(example_data_5_subject)
igc(example_data_5_subject, LLTR = 75, ULTR = 150)

Description

Run IGLU Shiny App

Usage

iglu_shiny()

Description

The function in_range_percent produces a tibble object with values equal to the percentage of glucose measurements in ranges of target values. The output columns correspond to subject id followed by the target value ranges, and the rows correspond to the subjects. The values will be between 0 (no measurements) and 100 (all measurements).

Usage

in_range_percent(data, target_ranges = list(c(70, 180), c(63, 140)))

Arguments

Details

A tibble object with 1 row for each subject, a column for subject id and column for each range of target values is returned. NA's will be omitted from the glucose values in calculation of percent.

in_range_percent will only work properly if the target_ranges argument is a list of paired values in the format list(c(a1,b1), c(a2,b2), ...). The paired values can be ordered (min, max) or (max, min). See the Examples section for proper usage.

Value

If a data.frame object is passed, then a tibble object with a column for subject id and then a column for each target value is returned. If a vector of glucose values is passed, then a tibble object without the subject id is returned. as.numeric() can be wrapped around the latter to output a numeric vector.

References

Rodbard (2009) Interpretation of continuous glucose monitoring data: glycemic variability and quality of glycemic control, Diabetes Technology and Therapeutics 11 .55-67, doi:10.1089/dia.2008.0132.

Battelino et al. (2019) Clinical targets for continuous glucose monitoring data interpretation: recommendations from the international consensus on time in range. Diabetes Care 42(8):1593-603, doi:10.2337/dci19-0028

See Also

plot_ranges()

Examples

data(example_data_1_subject)

in_range_percent(example_data_1_subject)
in_range_percent(example_data_1_subject, target_ranges = list(c(50, 100), c(200,
300), c(80, 140)))

data(example_data_5_subject)

in_range_percent(example_data_5_subject)
in_range_percent(example_data_1_subject, target_ranges = list(c(60, 120), c(140,
250)))

Description

The function iqr_glu outputs the distance between the 25th percentile and the 25th percentile of the glucose values in a tibble object.

Usage

iqr_glu(data)

Arguments

Details

A tibble object with 1 row for each subject, a column for subject id and a column for the IQR values is returned. NA glucose values are omitted from the calculation of the IQR.

Value

If a data.frame object is passed, then a tibble object with two columns: subject id and corresponding IQR value is returned. If a vector of glucose values is passed, then a tibble object with just the IQR value is returned. as.numeric() can be wrapped around the latter to output just a numeric value.

Examples

data(example_data_1_subject)
iqr_glu(example_data_1_subject)

data(example_data_5_subject)
iqr_glu(example_data_5_subject)

Description

The function j_index produces J-Index values a tibble object.

Usage

j_index(data)

Arguments

Details

A tibble object with 1 row for each subject, a column for subject id and a column for J-Index values is returned. NA glucose values are omitted from the calculation of the J-Index.

J-Index score is calculated by $.001 * [mean(G) + sd(G)]^2$ where G is the list of Glucose Measurements.

Value

If a data.frame object is passed, then a tibble object with two columns: subject id and corresponding J-Index value is returned. If a vector of glucose values is passed, then a tibble object with just the J-Index value is returned. as.numeric() can be wrapped around the latter to output just a numeric value.

References

Wojcicki (1995) "J"-index. A new proposition of the assessment of current glucose control in diabetic patients Hormone and Metabolic Research 27 .41-42, doi:10.1055/s-2007-979906.

Examples

data(example_data_1_subject)
j_index(example_data_1_subject)

data(example_data_5_subject)
j_index(example_data_5_subject)

Description

The function lbgi produces LBGI values in a tibble object.

Usage

lbgi(data)

Arguments

Details

A tibble object with 1 row for each subject, a column for subject id and a column for LBGI values is returned. NA glucose values are omitted from the calculation of the LBGI.

LBGI is calculated by $1/n * \sum (10 * fbg_i ^2)$, where $fbg_i = min(0, 1.509 * (log(G_i)^{1.084} - 5.381)$, G_i is the ith Glucose measurement for a subject, and n is the total number of measurements for that subject.

Value

If a data.frame object is passed, then a tibble object with two columns: subject id and corresponding LBGI value is returned. If a vector of glucose values is passed, then a tibble object with just the LBGI value is returned. as.numeric() can be wrapped around the latter to output just a numeric value.

References

Kovatchev et al. (2006) Evaluation of a New Measure of Blood Glucose Variability in, Diabetes Diabetes care 29 .2433-2438, doi:10.2337/dc06-1085.

Examples

data(example_data_1_subject)
lbgi(example_data_1_subject)

data(example_data_5_subject)
lbgi(example_data_5_subject)

Description

Calculates the M-value of Schlichtkrull et al. (1965) for each subject in the data, where the M-value is the mean of the logarithmic transformation of the deviation from a reference value. Produces a tibble object with subject id and M-values.

Usage

m_value(data, r = 90)

Arguments

Details

A tibble object with 1 row for each subject, a column for subject id and a column for the M-values is returned. NA glucose values are omitted from the calculation of the M-value.

M-value is computed by averaging the transformed glucose values, where each transformed value is equal to $|10 * log_{10}(glucose/r)|^3$, where r is the specified reference value.

Value

If a data.frame object is passed, then a tibble object with two columns: subject id and corresponding M-value is returned. If a vector of glucose values is passed, then a tibble object with just the M-value is returned. as.numeric() can be wrapped around the latter to output just a numeric value.

References

Schlichtkrull J, Munck O, Jersild M. (1965) The M-value, an index of blood-sugar control in diabetics. Acta Medica Scandinavica 177 .95-102. doi:10.1111/j.0954-6820.1965.tb01810.x.

Examples

data(example_data_5_subject)

m_value(example_data_5_subject)
m_value(example_data_5_subject, r = 100)

Description

The function mad produces MAD values in a tibble object.

Usage

mad_glu(data, constant = 1.4826)

Arguments

Details

A tibble object with 1 row for each subject, a column for subject id and a column for MAD values is returned. NA glucose values are omitted from the calculation of the MAD.

MAD is calculated by taking the median of the difference of the glucose readings from their median and multiplying it by a scaling factor $1.4826*median(|gl-median(gl)|)$, where gl is the list of Glucose measurements.

Value

If a data.frame object is passed, then a tibble object with two columns: subject id and corresponding MAD value is returned. If a vector of glucose values is passed, then a tibble object with just the MAD value is returned. as.numeric() can be wrapped around the latter to output just a numeric value.

Author(s)

David Buchanan, Marielle Hicban

Examples

data(example_data_1_subject)
mad_glu(example_data_1_subject)

data(example_data_5_subject)
mad_glu(example_data_5_subject)

Description

The function mag calculates the mean absolute glucose or MAG.

Usage

mag(data, n = 60, dt0 = NULL, inter_gap = 45, tz = "")

Arguments

Details

A tibble object with a column for subject id and a column for MAG values is returned.

The glucose values are linearly interpolated over a time grid starting at the beginning of the first day of data and ending on the last day of data. Then, MAG is calculated as $\frac{|\Delta G|}{\Delta t}$ where $|\Delta G|$ is the sum of the absolute change in glucose calculated for each interval as specified by n, default n = 60 for hourly change in glucose. The sum is then divided by $\Delta t$ which is the total time in hours.

Value

A tibble object with two columns: subject id and MAG value

Author(s)

Elizabeth Chun

References

Hermanides et al. (2010) Glucose Variability is Associated with Intensive Care Unit Mortaility, Critical Care Medicine 38(3) 838-842, doi:10.1097/CCM.0b013e3181cc4be9

Examples

data(example_data_1_subject)
mag(example_data_1_subject)

data(example_data_5_subject)
mag(example_data_5_subject)

Description

The function calculates MAGE values and can optionally return a plot of the glucose trace.

Usage

mage(
  data,
  version = c("ma", "naive"),
  sd_multiplier = 1,
  short_ma = 5,
  long_ma = 32,
  return_type = c("num", "df"),
  direction = c("avg", "service", "max", "plus", "minus"),
  tz = "",
  inter_gap = 45,
  max_gap = 180,
  plot = FALSE,
  title = NA,
  xlab = NA,
  ylab = NA,
  show_ma = FALSE,
  show_excursions = TRUE
)

Arguments

Details

If version 'ma' is selected, the function computationally emulates the manual method for calculating the mean amplitude of glycemic excursions (MAGE) first suggested in "Mean Amplitude of Glycemic Excursions, a Measure of Diabetic Instability", (Service, 1970). For this version, glucose values will be interpolated over a uniform time grid prior to calculation.

'ma' is a more accurate algorithm that uses the crosses of a short and long moving average to identify intervals where a peak/nadir might exist. Then, the height from one peak/nadir to the next nadir/peak is calculated from the _original_ (not moving average) glucose values. (Note: this function internally uses CGMS2DayByDay with dt0 = 5. Thus, all CGM data is linearly interpolated to 5 minute intervals. See the MAGE vignette for more details.)

'naive' algorithm calculates MAGE by taking the mean of absolute glucose differences (between each value and the mean) that are greater than the standard deviation. A multiplier can be added to the standard deviation using the sd_multiplier argument.

Value

A tibble object with two columns: the subject id and corresponding MAGE value. If a vector of glucose values is passed, then a tibble object with just the MAGE value is returned.

In version = "ma", if plot = TRUE, a list of ggplots will be returned with one plot per subject. To return an interactive plot, use iglu::mage_ma_single with static_or_gui='plotly' on each subject individually.

References

Service et al. (1970) Mean amplitude of glycemic excursions, a measure of diabetic instability Diabetes 19 .644-655, doi:10.2337/diab.19.9.644.

Fernandes, Nathaniel J., et al. "Open-source algorithm to calculate mean amplitude of glycemic excursions using short and long moving averages." Journal of diabetes science and technology 16.2 (2022): 576-577. doi:10.1177/19322968211061165

Examples

data(example_data_5_subject)
mage(example_data_5_subject, version = 'ma')
mage(example_data_5_subject, return_type='df')

Description

This function is an internal function used 'mage'. The function will calculate the Mean Amplitude of Glycemic Excursions (MAGE) on all the values of the inputted data set regardless of subject. To calculate separate MAGE values for a group of subjects, use the 'mage' function.

Usage

mage_ma_single(
  data,
  short_ma = 5,
  long_ma = 32,
  return_type = c("num", "df"),
  direction = c("avg", "service", "max", "plus", "minus"),
  tz = "",
  inter_gap = 45,
  max_gap = 180,
  plot = FALSE,
  title = NA,
  xlab = NA,
  ylab = NA,
  show_ma = FALSE,
  show_excursions = TRUE,
  static_or_gui = c("plotly", "ggplot")
)

Arguments

Details

See 'mage'.

Value

A ggplot or Plotly chart if plot = TRUE, depending on static_or_gui. Otherwise, a numeric MAGE value for the inputted glucose trace or a DataFrame with the MAGE values on each segment, depending on return_type.

Author(s)

Nathaniel J. Fernandes

Examples

data(example_data_1_subject)
mage_ma_single(
   example_data_1_subject,
   short_ma = 4,
   long_ma = 24,
   direction = 'plus')

mage_ma_single(
   example_data_1_subject,
   inter_gap = 300)

mage_ma_single(
   example_data_1_subject,
   plot=TRUE,
   static_or_gui='ggplot',
   title="Patient X",
   xlab="Time",
   ylab="Glucose Level (mg/dL)",
   show_ma=FALSE)

Description

The function meal_metrics calculates three simple glucose meal metrics

Usage

meal_metrics(data, mealtimes, before_win = 1, after_win = 3,
recovery_win = 1, interpolate = TRUE, adjust_mealtimes = TRUE, dt0 = NULL,
inter_gap = 45, tz = "", glucose_times = FALSE)

Arguments

Details

A tibble object is returned with three metrics calculated for each mealtime. The last three columns of the output correspond to the three metrics: deltag refers to $\Delta G$, deltat is $\Delta T$, and basereco is % Baseline recovery. If no meal column is given in the original data, one will be automatically generated with a unique number for each meal.

Value

By default tibble object with 6 columns will be returned: id, time, meal, deltag, deltat, and basereco. If glucose_times = TRUE then 5 more columns are returned for baseline glucose (basegl), peak glucose (peakgl), recover glucose (recovergl), peak timestamp (peaktime), and recovery timestamp (recovertime)

References

Service, F. John. (2013) Glucose Variability, Diabetes 62(5): 1398-1404, doi:10.2337/db12-1396

See Also

plot_meals()

Examples

data(example_data_hall)
data(example_meals_hall)
meal_metrics(example_data_hall, example_meals_hall)

Description

The function mean_glu is a wrapper for the base function mean(). Output is a tibble object with subject id and mean values.

Usage

mean_glu(data)

Arguments

Details

A tibble object with 1 row for each subject, a column for subject id and a column for the mean values is returned. NA glucose values are omitted from the calculation of the mean.

Value

If a data.frame object is passed, then a tibble object with two columns: subject id and corresponding mean value is returned. If a vector of glucose values is passed, then a tibble object with just the mean value is returned. as.numeric() can be wrapped around the latter to output just a numeric value.

Examples

data(example_data_1_subject)
mean_glu(example_data_1_subject)

data(example_data_5_subject)
mean_glu(example_data_5_subject)

Description

The function median_glu is a wrapper for the base function median(). Output is a tibble object with subject id and median values.

Usage

median_glu(data)

Arguments

Details

A tibble object with 1 row for each subject, a column for subject id and a column for the median values is returned. NA glucose values are omitted from the calculation of the median.

Value

If a data.frame object is passed, then a tibble object with two columns: subject id and corresponding median value is returned. If a vector of glucose values is passed, then a tibble object with just the median value is returned. as.numeric() can be wrapped around the latter to output just a numeric value.

Examples

data(example_data_1_subject)
median_glu(example_data_1_subject)

data(example_data_5_subject)
median_glu(example_data_5_subject)

Description

Create a heatmap of metric values by subject based on hierarchical clustering order

Usage

metrics_heatmap(
  data = NULL,
  metrics = NULL,
  metric_cluster = 6,
  clustering_method = "complete",
  clustering_distance_metrics = "correlation",
  clustering_distance_subjects = "correlation",
  tz = ""
)

Arguments

Value

A heatmap of metrics by subjects generated via pheatmap

Examples

# Using pre-calculated sd metrics only rather than default (all metrics)
mecs = sd_measures(example_data_5_subject)
metrics_heatmap(metrics = mecs)

Description

The function modd produces MODD values in a tibble object.

Usage

modd(data, lag = 1, tz = "")

Arguments

Details

A tibble object with 1 row for each subject, a column for subject id and a column for the MODD values is returned.

Missing values will be linearly interpolated when close enough to non-missing values.

MODD is calculated by taking the mean of absolute differences between measurements at the same time 1 day away, or more if lag parameter is set to an integer > 1.

Value

A tibble object with two columns: subject id and corresponding MODD value.

References

Service, F. J. & Nelson, R. L. (1980) Characteristics of glycemic stability. Diabetes care 3 .58-62, doi:10.2337/diacare.3.1.58.

Examples

data(example_data_1_subject)
modd(example_data_1_subject)
modd(example_data_1_subject, lag = 2)

data(example_data_5_subject)
modd(example_data_5_subject, lag = 2)

Description

The function optimized_iglu_functions optimizes the calculation of all time dependent iglu metrics by extracting the CGMS2DayByDay calculation and passing the result into each function.

Usage

optimized_iglu_functions(data, dt0 = NULL, inter_gap = 45, tz = "", timelag = 15, lag = 1)

Arguments

Details

Returns a tibble object with 1 row for each subject and a column for each metric. This function includes time dependent iglu metrics only. For metric specific information, please see the corresponding function documentation.

Value

If a data.frame object is passed, then a tibble object with 1 row for each subject and one column for each metric is returned.

Examples

data(example_data_1_subject)
optimized_iglu_functions(example_data_1_subject)

# Pass some arguments to possibly change the defaults
optimized_iglu_functions(example_data_1_subject, dt0 = 5, inter_gap = 30)

data(example_data_5_subject)
optimized_iglu_functions(example_data_5_subject)

Description

The function mad produces PGS values in a tibble object.

Usage

pgs(data, dur_length = 20, end_length = 30)

Arguments

Details

A tibble object with 1 row for each subject, a column for subject id and a column for PGS values is returned. NA glucose values are omitted from the calculation. The formula for PGS is as follows, where GVP = glucose variability percentage, MG = mean glucose, PTIR = percent time in range, and N54, N70 are the number of hypoglycemic episodes per week in the ranges <54 mg/dL and 54 to <70 mg/dL level respectively.

$PGS = f(GVP) + g(MG) + h(PTIR) + j(N54, N70)$

The component functions are listed below.

$\newline f(GVP) = 1 + \frac{9}{1+\exp(-0.049(GVP - 65.47))} \newline g(MG) = 1 + 9(\frac{1}{1+\exp(0.1139(MG - 72.08))} + \frac{1}{1+\exp(-0.09195(MG - 157.57))}) \newline h(PTIR) = 1+\frac{9}{1+\exp(0.0833(PTIR - 55.04))} \newline j(N54, N70) = a(N54) + b(N70) \newline a(N54) = 0.5+4.5(1-\exp(-0.91093N54)$

and b(N70) is defined such that b(N70) = $0.5714N70 + 0.625$ if N70 <= 7.65, and b(N70) = 5 otherwise.

Note that the duration thresholds for episodes are NOT the same as the episode_calculation defaults. The defaults chosen for PGS are those that match the original PGS paper definition, while the episode_calculation defaults match the consensus.

Value

A tibble object with two columns: subject id and corresponding PGS value.

Author(s)

Elizabeth Chun

References

Hirsch et al. (2017): A Simple Composite Metric for the Assessment of Glycemic Status from Continuous Glucose Monitoring Data: Implications for Clinical Practice and the Artificial Pancreas Diabetes Technol Ther 19(S3) .S38-S48, doi:10.1089/dia.2017.0080.

See Also

episode_calculation()

Examples

data(example_data_1_subject)
pgs(example_data_1_subject)

Description

The function plot_agp produces an AGP plot that collapses all data into a single 24 hr "modal day".

Usage

plot_agp(data, LLTR = 70, ULTR = 180, smooth = TRUE, span = 0.3, dt0 = NULL,
inter_gap = 45, tz = "", title = FALSE)

Arguments

Details

Only a single subject's data may be plotted. If smooth = TRUE, then the quantiles are loess smoothed with the specified span before plotting. The horizontal green lines represent the target range, default is 70-180 mg/dL. The black line is the median glucose value for each time of day. The dark blue shaded area represents 50% of glucose values - those between the 25th and 75 quantiles. The light blue shaded area shows 90% of the glucose values - those between the 5th and 95th quantiles. Additionally, the percents shown on the right hand side of the plot show which quantiles each line refers to - e.g. the line ending at 95% is the line corresponding to the 95th quantiles of glucose values.

Value

Plot of a 24 hr modal day collapsing all data to a single day.

Author(s)

Elizabeth Chun

References

Johnson et al. (2019) Utilizing the Ambulatory Glucose Profile to Standardize and Implement Continuous Glucose Monitoring in Clinical Practice, Diabetes Technology and Therapeutics 21:S2 S2-17-S2-25, doi:10.1089/dia.2019.0034.

Examples

data(example_data_1_subject)
plot_agp(example_data_1_subject)

Description

The function 'plot_daily' plots daily glucose time series profiles for a single subject.

Usage

plot_daily(data, maxd = 14, LLTR = 70, ULTR = 180, inter_gap = 45, tz = "")

Arguments

Details

Only a single subject's data may be plotted. The black line shows the glucose values. The shaded gray area shows the target range, default 70-180 mg/dL. Areas of the curve above the ULTR are shaded yellow, while areas below the LLTR are shaded red.

Value

Daily glucose time series plots for a single subject

Author(s)

Elizabeth Chun

References

Johnson et al. (2019) Utilizing the Ambulatory Glucose Profile to Standardize and Implement Continuous Glucose Monitoring in Clinical Practice, Diabetes Technology and Therapeutics 21:S2 S2-17-S2-25, doi:10.1089/dia.2019.0034.

Examples

data(example_data_1_subject)
plot_daily(example_data_1_subject)
plot_daily(example_data_1_subject, LLTR = 100, ULTR = 140)

Description

The function 'plot_glu' supports several plotting methods for both single and multiple subject data.

Usage

plot_glu(
  data,
  plottype = c("tsplot", "lasagna"),
  datatype = c("all", "average", "single"),
  lasagnatype = c("unsorted", "timesorted"),
  LLTR = 70,
  ULTR = 180,
  subjects = NULL,
  inter_gap = 45,
  tz = "",
  color_scheme = c("blue-red", "red-orange"),
  log = F,
  static_or_gui = c("ggplot", "plotly")
)

Arguments

Details

For the default option 'plottype = tsplot', a time series graph for each subject is produced with hypo- and hyperglycemia cutoffs shown as horizontal red lines. The time series plots for all subjects chosen (all by default) are displayed on a grid.

The 'lasagna' plot type works best when the datatype argument is set to average.

Value

Any output from the plot object

Examples

data(example_data_1_subject)
plot_glu(example_data_1_subject)

data(example_data_5_subject)
plot_glu(example_data_5_subject, subjects = 'Subject 2')
plot_glu(example_data_5_subject, plottype = 'tsplot', tz = 'EST', LLTR = 70, ULTR = 150)
plot_glu(example_data_5_subject, plottype = 'lasagna', lasagnatype = 'timesorted')

Description

Lasagna plot of glucose values for multiple subjects

Usage

plot_lasagna(
  data,
  datatype = c("all", "average"),
  lasagnatype = c("unsorted", "timesorted", "subjectsorted"),
  maxd = 14,
  limits = c(50, 500),
  midpoint = 105,
  LLTR = 70,
  ULTR = 180,
  dt0 = NULL,
  inter_gap = 45,
  tz = "",
  color_scheme = c("blue-red", "red-orange"),
  log = F,
  static_or_gui = c("ggplot", "plotly")
)

Arguments

Value

A ggplot object corresponding to lasagna plot

References

Swihart et al. (2010) Lasagna Plots: A Saucy Alternative to Spaghetti Plots, Epidemiology 21(5), 621-625, doi:10.1097/EDE.0b013e3181e5b06a

Examples

plot_lasagna(example_data_5_subject, datatype = "average", lasagnatype = 'timesorted', tz = "EST")
plot_lasagna(example_data_5_subject, lasagnatype = "subjectsorted", LLTR = 100, tz = "EST")

Description

Lasagna plot of glucose values for 1 subject aligned across times of day

Usage

plot_lasagna_1subject(
  data,
  lasagnatype = c("unsorted", "timesorted", "daysorted"),
  limits = c(50, 500),
  midpoint = 105,
  LLTR = 70,
  ULTR = 180,
  dt0 = NULL,
  inter_gap = 45,
  tz = "",
  color_scheme = c("blue-red", "red-orange"),
  log = F,
  static_or_gui = c("ggplot", "plotly")
)

Arguments

Value

A ggplot object corresponding to lasagna plot

References

Swihart et al. (2010) Lasagna Plots: A Saucy Alternative to Spaghetti Plots, Epidemiology 21(5), 621-625, doi:10.1097/EDE.0b013e3181e5b06a

Examples

plot_lasagna_1subject(example_data_1_subject)
plot_lasagna_1subject(example_data_1_subject, color_scheme = 'red-orange')
plot_lasagna_1subject(example_data_1_subject, lasagnatype = 'timesorted')
plot_lasagna_1subject(example_data_1_subject, lasagnatype = 'daysorted')
plot_lasagna_1subject(example_data_1_subject, log = TRUE)

Description

The function plot_meals produces a visual for meals data

Usage

plot_meals(data, mealtimes, plot_type=c('ggplot','plotly'), tz = "")

Arguments

Details

Only a single subject's data may be plotted. The solid black line is the glucose trace. Vertical dashed red lines show the mealtimes, and the horizontal blue lines show the baseline for each meal. Purple triangles are plotted to illustrate the 3 meal_metrics Namely the three vertices show the baseline, peak, and 1hr post-peak recovery. If plot_type = 'plotly', plotly is used to display an interactive visual that allows one to zoom into specific areas of the plot.

Value

Plot to visualize meals data.

Author(s)

Elizabeth Chun

References

Service, F. John. (2013) Glucose Variability, Diabetes 62(5): 1398-1404, doi:10.2337/db12-1396

See Also

meal_metrics()

Examples

select_subject = example_data_hall[example_data_hall$id == "2133-018", ]
select_meals = example_meals_hall[example_meals_hall$id == "2133-018", ]
plot_meals(select_subject, select_meals, tz = 'GMT')

Description

The function plot_ranges produces a barplot showing the percent of time in glucose ranges.

Usage

plot_ranges(data)

Arguments

Details

Only a single subject's data may be used. There are four ranges: very low (below 54 mg/dL), low (54-69 mg/dL), target range (70-180 mg/dL), high (181-250 mg/dL), and very high (above 250 mg/dL). This plot is meant to be used as part of the Ambulatory Glucose Profile (AGP)

Value

Single subject bar chart showing percent in different glucose ranges.

Author(s)

Elizabeth Chun

References

Johnson et al. (2019) Utilizing the Ambulatory Glucose Profile to Standardize and Implement Continuous Glucose Monitoring in Clinical Practice, Diabetes Technology and Therapeutics 21:S2 S2-17-S2-25, doi:10.1089/dia.2019.0034.

Examples

data(example_data_1_subject)
plot_ranges(example_data_1_subject)

Description

The function plot_roc produces a time series plot of glucose values colored by categorized rate of change values

Usage

plot_roc(data, subjects = NULL, timelag = 15, dt0 = NULL, inter_gap = 45, tz = "")

Arguments

Details

For the default, a time series is produced for each subject in which the glucose values are plotted and colored by ROC categories defined as follows. The breaks for the categories are: c(-Inf, -3, -2, -1, 1, 2, 3, Inf) where the glucose is in mg/dl and the ROC values are in mg/dl/min. A ROC of -5 mg/dl/min will thus be placed in the first category and colored accordingly. The breaks for the categories come from the reference paper below.

Value

A time series of glucose values colored by ROC categories per subject

Author(s)

Elizabeth Chun, David Buchanan

References

Klonoff, D. C., & Kerr, D. (2017) A Simplified Approach Using Rate of Change Arrows to Adjust Insulin With Real-Time Continuous Glucose Monitoring. Journal of Diabetes Science and Technology 11(6) 1063-1069, doi:10.1177/1932296817723260.

Examples

data(example_data_1_subject)
plot_roc(example_data_1_subject)

data(example_data_5_subject)
plot_roc(example_data_5_subject, subjects = 'Subject 5')

Description

A helper function to assist in pre-processing the user-supplied input data for use with other functions. Typically, this function will process the data and return another DataFrame. This function ensures that the returned data will be compatible with every function within the iglu package. All NAs will be removed. See Vignette for further details.

Usage

process_data(data, id, timestamp, glu, time_parser = as.POSIXct)

Arguments

Details

A dataframe with the columns "id", "time", and "gl" will be returned. All NAs will be removed.

If "mmol/l" in the glucose column name, the glucose values will be multipled by 18 to convert to mg/dL.

Based on John Schwenck's data_process for his bp package "https://github.com/johnschwenck/bp".

Value

A processed DataFrame object that cooperates with every other function within the iglu package - all column names and formats comply.

Author(s)

David Buchanan, John Schwenck

Examples

data("example_data_1_subject")

# Process example data
processed <- process_data(example_data_1_subject, id = "id", timestamp = "time", glu = "gl")

processed

data("example_data_5_subject")

# Process example data
processed_5subj <- process_data(example_data_5_subject, id = "id", timestamp = "time", glu = "gl")

processed_5subj

Description

The function quantile_glu is a wrapper for the base function quantile(). Output is a tibble object with columns for subject id and each of the quantiles.

Usage

quantile_glu(data, quantiles = c(0, 25, 50, 75, 100))

Arguments

Details

A tibble object with 1 row for each subject, a column for subject id and a column for each quantile is returned. NA glucose values are omitted from the calculation of the quantiles.

The values are scaled from 0-1 to 0-100 to be consistent in output with above_percent, below_percent, and in_range_percent.

The command quantile_glu(...) / 100 will scale each element down from 0-100 to 0-1.

Value

If a DataFrame object is passed, then a tibble object with a column for subject id and then a column for each quantile value is returned. If a vector of glucose values is passed, then a tibble object without the subject id is returned. as.numeric() can be wrapped around the latter to output a numeric vector.

Examples

data(example_data_1_subject)

quantile_glu(example_data_1_subject)
quantile_glu(example_data_1_subject, quantiles = c(0, 33, 66, 100))

data(example_data_5_subject)

quantile_glu(example_data_5_subject)
quantile_glu(example_data_5_subject, quantiles = c(0, 10, 90, 100))

Description

The function range_glu outputs the distance between minimum and maximum glucose values per subject in a tibble object.

Usage

range_glu(data)

Arguments

Details

A tibble object with 1 row for each subject, a column for subject id and a column for the range values is returned. NA glucose values are omitted from the calculation of the range.

Value

If a DataFrame object is passed, then a tibble object with two columns: subject id and corresponding range value is returned. If a vector of glucose values is passed, then a tibble object with just the range value is returned. as.numeric() can be wrapped around the latter to output just a numeric value.

Examples

data(example_data_1_subject)
range_glu(example_data_1_subject)

data(example_data_5_subject)
range_glu(example_data_5_subject)

Description

Helper function to assist in reading data directly from sensor outputs. Should return a dataframe in correct format for use with the rest of the iglu package. Assumes all data will be readable with base R read.csv() function.

Usage

read_raw_data(
  filename,
  sensor = c("dexcom", "libre", "librepro", "asc", "ipro"),
  id = "filename",
  tz = ""
)

Arguments

Details

A DataFrame object with the columns "id", "time" and "gl" and one row per reading will be returned. For the libre reader, if the phrase "mmol/l" is found in the column names, the glucose values will be multiplied by 18.

Assumes .csv format for all data.

Sensor formats change with ongoing development, so these functions may become deprecated. If any issues are encountered, contact the package maintainer: this is currently Irina Gaynanova, who can be reached at [email protected].

Note: this function is heavily derived from the readers avaiable in the cgmanalysis package's cleandata function.

Value

A dataframe containing the data read from the named file.

Author(s)

David Buchanan

References

Vigers et al. (2019) cgmanalysis: An R package for descriptive analysis of continuous glucose monitor data PLoS ONE 14(10): e0216851, doi:10.1371/journal.pone.0216851

Description

The function roc produces rate of change values in a tibble object.

Usage

roc(data, timelag = 15, dt0 = NULL, inter_gap = 45, tz = "")

Arguments

Details

A tibble object with a column for subject id and a column for ROC values is returned. A ROC value is returned for each time point for all the subjects. Thus multiple rows are returned for each subject. If the rate of change cannot be calculated, the function will return NA for that point.

The glucose values are linearly interpolated over a time grid starting at the beginning of the first day of data and ending on the last day of data. Because of this, there may be many NAs at the beginning and the end of the roc values for each subject. These NAs are a result of interpolated time points that do not have recorded glucose values near them because recording had either not yet begun for the day or had already ended.

The ROC is calculated as $\frac{G(t_i) - G(t_{i-1})}{t_i - t_{i-1}}$ where $G_i$ is the Glucose measurement at time $t_i$ and $G_{i-1}$ is the Glucose measurement at time $t_{i-1}$. The time difference between the points, $t_i - t_{i-1}$, is selectable and set at a default of 15 minutes.

Value

A tibble object with two columns: subject id and rate of change values

Author(s)

Elizabeth Chun, David Buchanan

References

Clarke et al. (2009) Statistical Tools to Analyze Continuous Glucose Monitor Data, Diabetes Diabetes Technology and Therapeutics 11 S45-S54, doi:10.1089/dia.2008.0138.

Examples

data(example_data_1_subject)
roc(example_data_1_subject)
roc(example_data_1_subject, timelag = 10)

data(example_data_5_subject)
roc(example_data_5_subject)

Description

The function sd_glu is a wrapper for the base function sd(). Output is a tibble object with subject id and sd values.

Usage

sd_glu(data)

Arguments

Details

A tibble object with 1 row for each subject, a column for subject id and a column for the sd values is returned. NA glucose values are omitted from the calculation of the sd.

Value

If a data.frame object is passed, then a tibble object with two columns: subject id and corresponding sd value is returned. If a vector of glucose values is passed, then a tibble object with just the sd value is returned. as.numeric() can be wrapped around the latter to output just a numeric value.

Examples

data(example_data_1_subject)
sd_glu(example_data_1_subject)

data(example_data_5_subject)
sd_glu(example_data_5_subject)

Description

The function sd_measures produces SD subtype values in a tibble object with a row for each subject and columns corresponding to id followed by each SD subtype.

Usage

sd_measures(data,dt0 = NULL, inter_gap = 45, tz = "")

Arguments

Details

A tibble object with 1 row for each subject, a column for subject id and a column for each SD subtype values is returned.

Missing values will be linearly interpolated when close enough to non-missing values.

1. SDw - vertical within days:

   Calculated by first taking the standard deviation of each day's glucose measurements, then taking the mean of all the standard deviations. That is, for d days we compute $SD_1 ... SD_d$ daily standard deviations and calculate $1/d * \sum [(SD_i)]$

2. SDhhmm - between time points:

   Also known as SDhh:mm. Calculated by taking the mean glucose values at each time point in the grid across days, and taking the standard deviation of those mans. That is, for t time points we compute $X_t$ means for each time point and then compute $SD([X_1, X_2, ... X_t])$.

3. SDwsh - within series:

   Also known as SDws h. Calculated by taking the hour-long intervals starting at every point in the interpolated grid, computing the standard deviation of the points in each hour-long interval, and then finding the mean of those standard deviations. That is, for n time points compute $SD_1 ... SD_n$, where $SD_i$ is the standard deviation of the glucose values $[X_i, X_{i+1}, ... X_{i+k}]$ corresponding to hour-long window starting at observation $X_i$, the number of observations in the window k depends on CGM meter frequency. Then, take $1/n * \sum [(SD_i)]$.

4. SDdm - horizontal sd:

   Calculated by taking the daily mean glucose values, and then taking the standard deviation of those daily means. That is, for d days we take $X_1 ... X_d$ daily means, and then compute $SD([X_1, X_2, ... X_d])$.

5. SDb - between days, within timepoints:

   Calculated by taking the standard deviation of the glucose values across days for each time point, and then taking the mean of those standard deviations. That is, for t time points take $SD_1 ... SD_t$ standard deviations, and then compute $1/t * \sum[(SD_i)]$

6. SDbdm - between days, within timepoints, corrected for changes in daily means:

   Also known as SDb // dm. Calculated by subtracting the daily mean from each glucose value, then taking the standard deviation of the corrected glucose values across days for each time point, and then taking the mean of those standard deviations. That is, for t time points take $SD_1 ... SD_t$ standard deviations, and then compute $1/t * \sum[(SD_i)]$. where $SD_i$ is the standard deviation of d daily values at the 1st time point, where each value is the dth measurement for the ith time point subtracted by the mean of all glucose values for day d.

Value

A tibble object with a column for id and a column for each of the six SD subtypes.

References

Rodbard (2009) New and Improved Methods to Characterize Glycemic Variability Using Continuous Glucose Monitoring Diabetes Technology and Therapeutics 11 .551-565, doi:10.1089/dia.2009.0015.

Examples

data(example_data_1_subject)
sd_measures(example_data_1_subject)

Description

The function sd_roc produces the standard deviation of the rate of change values in a tibble object.

Usage

sd_roc(data, timelag = 15, dt0 = NULL, inter_gap = 45, tz = "")

Arguments

Details

A tibble object with one row for each subject, a column for subject id and a column for the standard deviation of the rate of change.

When calculating rate of change, missing values will be linearly interpolated when close enough to non-missing values.

Calculated by taking the standard deviation of all the ROC values for each individual subject. NA rate of change values are omitted from the standard deviation calculation.

Value

A tibble object with two columns: subject id and standard deviation of the rate of change values for each subject.

Author(s)

Elizabeth Chun, David Buchanan

References

Clarke et al. (2009) Statistical Tools to Analyze Continuous Glucose Monitor Data, Diabetes Diabetes Technology and Therapeutics 11 S45-S54, doi:10.1089/dia.2008.0138.

Examples

data(example_data_1_subject)
sd_roc(example_data_1_subject)
sd_roc(example_data_1_subject, timelag = 10)

data(example_data_5_subject)
sd_roc(example_data_5_subject)
sd_roc(example_data_5_subject, timelag = 10)

Description

The function 'summary_glu' is a wrapper for the base function 'summary()'. Output is a tibble object with subject id and the summary value: Minimum, 1st Quantile, Median, Mean, 3rd Quantile and Max.

Usage

summary_glu(data)

Arguments

Details

A tibble object with 1 row for each subject, a column for subject id and a column for each of summary values is returned. 'NA' glucose values are omitted from the calculation of the summary values.

Value

If a DataFrame object is passed, then a tibble object with a column for subject id and then a column for each summary value is returned. If a vector of glucose values is passed, then a tibble object without the subject id is returned. 'as.numeric()' can be wrapped around the latter to output a numeric vector with values in order of Min, 1st Quantile, Median, Mean, 3rd Quantile and Max.

Examples

data(example_data_1_subject)
summary_glu(example_data_1_subject)

data(example_data_5_subject)
summary_glu(example_data_5_subject)