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).
Arguments
- data
DataFrame object with column names "id", "time", and "gl", or numeric vector of glucose values.
- target_ranges
List of target value ranges wrapped in an r 'list' structure. Default list of ranges is ((70, 180), (63, 140)) mg/dL, where the range (70, 180) is recommended to assess glycemic control for subjects with type 1 or type 2 diabetes, and (63, 140) is recommended for assessment of glycemic control during pregnancy; see Battelino et al. (2019)
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 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.
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
Examples
data(example_data_1_subject)
in_range_percent(example_data_1_subject)
#> # A tibble: 1 × 3
#> id in_range_63_140 in_range_70_180
#> <fct> <dbl> <dbl>
#> 1 Subject 1 73.9 91.7
in_range_percent(example_data_1_subject, target_ranges = list(c(50, 100), c(200,
300), c(80, 140)))
#> # A tibble: 1 × 4
#> id in_range_200_300 in_range_50_100 in_range_80_140
#> <fct> <dbl> <dbl> <dbl>
#> 1 Subject 1 3.70 27.3 73.3
data(example_data_5_subject)
in_range_percent(example_data_5_subject)
#> # A tibble: 5 × 3
#> id in_range_63_140 in_range_70_180
#> <fct> <dbl> <dbl>
#> 1 Subject 1 73.9 91.7
#> 2 Subject 2 3.36 26.4
#> 3 Subject 3 50.0 81.3
#> 4 Subject 4 67.9 95.1
#> 5 Subject 5 30.2 62.1
in_range_percent(example_data_1_subject, target_ranges = list(c(60, 120), c(140,
250)))
#> # A tibble: 1 × 3
#> id in_range_140_250 in_range_60_120
#> <fct> <dbl> <dbl>
#> 1 Subject 1 26.3 59.6