Optimized Calculations of Time Dependent iglu Metrics — optimized_iglu

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

data: DataFrame object with column names "id", "time", and "gl".
dt0: The time frequency for interpolation in minutes, the default will match the CGM meter's frequency (e.g. 5 min for Dexcom).
inter_gap: The maximum allowable gap (in minutes) for interpolation. The values will not be interpolated between the glucose measurements that are more than inter_gap minutes apart. The default value is 45 min.
tz: A character string specifying the time zone to be used. System-specific (see as.POSIXct), but " " is the current time zone, and "GMT" is UTC (Universal Time, Coordinated). Invalid values are most commonly treated as UTC, on some platforms with a warning.
timelag: Integer indicating the time period (# minutes) over which rate of change is calculated. Default is 15, e.g. rate of change is the change in glucose over the past 15 minutes divided by 15.
lag: Integer indicating which lag (# days) to use. Default is 1.

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.

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.

Examples

data(example_data_1_subject)
optimized_iglu_functions(example_data_1_subject)
#> # A tibble: 1 × 15
#>   id        Conga   GVP  MODD SD.Roc CV_Measures_Mean CV_Measures_SD   AUC   MAG
#>   <fct>     <dbl> <dbl> <dbl>  <dbl>            <dbl>          <dbl> <dbl> <dbl>
#> 1 Subject 1  37.0  1.17  27.8  0.620             21.1           7.80  123.  17.0
#> # ℹ 6 more variables: SDw <dbl>, SDhhmm <dbl>, SDwsh <dbl>, SDdm <dbl>,
#> #   SDb <dbl>, SDbdm <dbl>

# Pass some arguments to possibly change the defaults
optimized_iglu_functions(example_data_1_subject, dt0 = 5, inter_gap = 30)
#> # A tibble: 1 × 15
#>   id        Conga   GVP  MODD SD.Roc CV_Measures_Mean CV_Measures_SD   AUC   MAG
#>   <fct>     <dbl> <dbl> <dbl>  <dbl>            <dbl>          <dbl> <dbl> <dbl>
#> 1 Subject 1  36.3  1.17  27.4  0.621             21.0           7.81  123.  17.0
#> # ℹ 6 more variables: SDw <dbl>, SDhhmm <dbl>, SDwsh <dbl>, SDdm <dbl>,
#> #   SDb <dbl>, SDbdm <dbl>

data(example_data_5_subject)
optimized_iglu_functions(example_data_5_subject)
#> # A tibble: 5 × 15
#>   id        Conga   GVP  MODD SD.Roc CV_Measures_Mean CV_Measures_SD   AUC   MAG
#>   <fct>     <dbl> <dbl> <dbl>  <dbl>            <dbl>          <dbl> <dbl> <dbl>
#> 1 Subject 1  37.0  1.17  27.8  0.620             21.1           7.80  123.  17.0
#> 2 Subject 2  60.6  1.21  44.1  0.642             17.0           6.41  218.  18.9
#> 3 Subject 3  63.4  1.26  48.2  0.831             27.1           9.40  154.  27.1
#> 4 Subject 4  33.5  1.17  24.9  0.617             18.4           5.70  130.  16.7
#> 5 Subject 5  73.8  1.38  59.4  1.05              29.0           7.56  174.  35.3
#> # ℹ 6 more variables: SDw <dbl>, SDhhmm <dbl>, SDwsh <dbl>, SDdm <dbl>,
#> #   SDb <dbl>, SDbdm <dbl>