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Interpolate CGM Data

Source: R/cgmguru-functions-docs.R
interpolate_cgm.Rd

Interpolates continuous glucose monitoring (CGM) data onto the same iglu-compatible, midnight-aligned full-day grid used internally by cgmguru's event-detection functions. The interpolation is implemented in C++ and is intended for users who want to inspect or reuse the preprocessed grid behind detect_all_events, detect_hyperglycemic_events, and detect_hypoglycemic_events.

For each subject, interpolate_cgm() builds an equally spaced grid at reading_minutes intervals. If reading_minutes is omitted, it is inferred per subject from the median positive timestamp difference. Glucose values are linearly interpolated only across gaps up to inter_gap; larger gaps are treated as missing and removed from the returned data, preserving segment boundaries used by event calculation.

The GRID-family functions grid, maxima_grid, and excursion do not call this helper automatically; they operate on the rows supplied by the user unless the caller explicitly passes an interpolated dataset.

Usage

interpolate_cgm(df, reading_minutes = NULL, sort_time = FALSE,
 inter_gap = 45)

Arguments

df

A dataframe containing CGM data with columns:

  • id: Subject identifier

  • time: POSIXct measurement timestamp

  • gl: Glucose value in mg/dL

reading_minutes

Time interval for the interpolation grid in minutes. If omitted or NULL, it is calculated automatically per id as the median positive time difference in the data.

sort_time

Logical. If TRUE, sort rows within each id by time in C++ before interpolation. Defaults to FALSE.

inter_gap

Maximum gap in minutes to interpolate across. Defaults to 45; larger gaps split the event-detection grid.

Value

A tibble with columns id, interpolated time, and interpolated gl. Rows inside gaps larger than inter_gap are omitted.

See also

detect_all_events, detect_hyperglycemic_events, detect_hypoglycemic_events

Examples

df <- data.frame(
  id = "A",
  time = as.POSIXct(c("2026-01-01 00:15:00", "2026-01-01 00:25:00"),
                    tz = "UTC"),
  gl = c(100, 120)
)
interpolate_cgm(df)
#> # A tibble: 1 × 3
#>   id    time                   gl
#>   <chr> <dttm>              <dbl>
#> 1 A     2026-01-01 00:20:00   110