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
- data
DataFrame object with column names "id", "time", and "gl", or numeric vector of glucose values.
- targets_above
Numeric vector of glucose thresholds. Glucose values from data argument will be compared to each value in the targets_above vector. Default list is (140, 180, 250).
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.
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.
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)
above_percent(example_data_1_subject)
#> # A tibble: 1 × 4
#> id above_140 above_180 above_250
#> <fct> <dbl> <dbl> <dbl>
#> 1 Subject 1 26.1 8.20 0.377
above_percent(example_data_1_subject, targets_above = c(100, 150, 180))
#> # A tibble: 1 × 4
#> id above_100 above_150 above_180
#> <fct> <dbl> <dbl> <dbl>
#> 1 Subject 1 72.7 20.1 8.20
data(example_data_5_subject)
above_percent(example_data_5_subject)
#> # A tibble: 5 × 4
#> id above_140 above_180 above_250
#> <fct> <dbl> <dbl> <dbl>
#> 1 Subject 1 26.1 8.20 0.377
#> 2 Subject 2 96.6 73.6 26.1
#> 3 Subject 3 49.8 18.3 5.68
#> 4 Subject 4 32.0 4.61 0
#> 5 Subject 5 69.8 37.8 11.3
above_percent(example_data_5_subject, targets_above = c(70, 170))
#> # A tibble: 5 × 3
#> id above_170 above_70
#> <fct> <dbl> <dbl>
#> 1 Subject 1 10.4 99.9
#> 2 Subject 2 82.3 100
#> 3 Subject 3 26.5 99.6
#> 4 Subject 4 8.19 99.6
#> 5 Subject 5 44.7 99.9