The function `active_percent` produces the
Usage
active_percent(data, dt0 = NULL, tz = "",
range_type = "automatic", ndays = 14, end_date = NULL)Arguments
- data
DataFrame object with column names "id", "time", and "gl".
- dt0
The time frequency for interpolated aligned grid in minutes, the default will match the CGM meter's frequency (e.g. 5 min for Dexcom).
- tz
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.- range_type
range_type = "automatic". A character string indicating the type of range ('automatic' or 'manual').
- ndays
ndays = 14. An integer specifying the number of days to consider in the calculation.
- end_date
end_date = NULL. A Date object or NULL indicating the end date for the calculation.
Value
A tibble object with five columns: subject id, corresponding active_percent value, duration of measurement period in days, start date, and end date.
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
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)
#> # A tibble: 1 × 5
#> id active_percent ndays start_date end_date
#> <fct> <dbl> <drtn> <dttm> <dttm>
#> 1 Subject 1 79.8 12.7 days 2015-06-06 16:50:27 2015-06-19 08:59:36
data(example_data_5_subject)
active_percent(example_data_5_subject)
#> # A tibble: 5 × 5
#> id active_percent ndays start_date end_date
#> <fct> <dbl> <drtn> <dttm> <dttm>
#> 1 Subject 1 79.8 12.7 days 2015-06-06 16:50:27 2015-06-19 08:59:36
#> 2 Subject 2 58.9 16.7 days 2015-02-24 17:31:29 2015-03-13 09:38:01
#> 3 Subject 3 92.1 5.8 days 2015-03-10 15:36:26 2015-03-16 10:11:05
#> 4 Subject 4 98.7 12.9 days 2015-03-13 12:44:09 2015-03-26 10:01:58
#> 5 Subject 5 95.8 10.6 days 2015-02-28 17:40:06 2015-03-11 08:04:28
active_percent(example_data_5_subject, dt0 = 5, tz = 'GMT')
#> # A tibble: 5 × 5
#> id active_percent ndays start_date end_date
#> <fct> <dbl> <drtn> <dttm> <dttm>
#> 1 Subject 1 79.8 12.7 days 2015-06-06 16:50:27 2015-06-19 08:59:36
#> 2 Subject 2 58.9 16.7 days 2015-02-24 17:31:29 2015-03-13 09:38:01
#> 3 Subject 3 92.1 5.8 days 2015-03-10 15:36:26 2015-03-16 10:11:05
#> 4 Subject 4 98.7 12.9 days 2015-03-13 12:44:09 2015-03-26 10:01:58
#> 5 Subject 5 95.8 10.6 days 2015-02-28 17:40:06 2015-03-11 08:04:28