Implicit to Explicit Missingness and Linear Interpolation of Glucose Data.

Transforms implicit gaps in CGM timestamps into explicit missing glucose values, and linearly interpolate glucose values if needed.

Usage

cgmInterpolate(
  DataFrame,
  Interpolate = TRUE,
  MinGap = 120,
  MaxGap = 1800,
  Granularity = 60
)

Arguments

DataFrame: A dataframe of CGM data which will be filled and interpolated where specified. Must contain columns: id, cgm_timestamp and glucose.
Interpolate: Logical value (TRUE/FALSE) which determines whether linear interpolation should be conducted. If FALSE, implicit gaps in CGM timestamps will be turned into explicit gaps and no further changes to the data will be made.D Default is TRUE so that linear interpolation is conducted.
MinGap: Numeric value which defines the minimum duration in seconds of gaps to be filled and interpolated. Default is 120 seconds, i.e. 2 minutes
MaxGap: Numeric value which defines the maximum duration in seconds of gaps to be filled and interpolated. Default is 1800 seconds, i.e. 30 mines
Granularity: Numeric value which defines the granularity of the Glucose data desired in seconds. Typically closely linked to the MinGap parameter e.g. if granularity of data needed is 60 seconds, MinGap would be 120 so that gap is wide enough to add extra CGM timestamps. Default is 60 seconds or 1 min. i.e. the resulting data frame will have CGM data at the 1 -min level.

Value

A dataframe of CGM data interpolated. Gaps above the maximum duration are left as is.

Details

This functions firstly turns implicit gaps in CGM timestamps into explicit missing glucose values. Secondly, if needed, it interpolates missing glucose value as defined by the MinGap, MaxGap and Granularity parameters.

Examples

if (FALSE) { # \dontrun{
hypometrics::cgmInterpolate(DataFrame)
} # }