resample_mnirs: Re-sample an _mnirs_ data frame

In mnirs: Muscle Near-Infrared Spectroscopy Processing and Analysis

Re-sample an mnirs data frame

Description

Up- or down-sample an "mnirs" data frame to a new sample rate, filling new samples via nearest-neighbour matching or interpolation.

Usage

resample_mnirs(
  data,
  time_channel = NULL,
  sample_rate = NULL,
  resample_rate = sample_rate,
  method = c("none", "linear", "locf"),
  verbose = TRUE
)

Arguments

data	A data frame of class "mnirs" containing time series data and metadata.
time_channel	A character string naming the time or sample column. Must match a column name in data exactly. If NULL (default), the time_channel metadata attribute of data is used.
sample_rate	A numeric sample rate in Hz. If NULL (default), the sample_rate metadata attribute of data will be used if detected, or the sample rate will be estimated from time_channel.
resample_rate	An optional sample rate (Hz) for the output data frame. If NULL (default) resamples to the existing sample_rate, which regularises any irregular samples without changing the rate.
method	A character string specifying how new samples are filled. Default is "none". Filling must be opted into explicitly (see Details): "none" Matches each new sample to the nearest original time_channel value without any interpolation, to within tolerance of half a sample-interval. New samples are returned as NA. "locf" ("Last observation carried forward"). Fills new and missing samples with the most recent valid non-NA value to the left, or the nearest valid value to the right for leading NAs. Safe for numeric, integer, and character columns. "linear" Fills new and missing samples via linear interpolation using stats::approx(). Suitable for numeric columns only; non-numeric columns will fall back to "locf" behaviour.
verbose	Logical. Default is TRUE. Display or silence (if FALSE) warnings and information messages helpful for troubleshooting. Ad global default can be set via options(mnirs.verbose = FALSE).

Details

This function uses replace_missing() (based on stats::approx()) to interpolate across new samples in the resampled data range.

Sample rate and time channel

time_channel and sample_rate are retrieved automatically from data of class "mnirs", if not defined explicitly.

Otherwise, sample_rate will be estimated from the values in time_channel. However, this may return unexpected values, and it is safer to define sample_rate explicitly or retrieve it from "mnirs" metadata.

Default behaviour

When resample_rate is omitted, the output has the same sample_rate as the input but with a regular, evenly-spaced time_channel. This is useful for regularising data that contains missing or repeated samples without changing the nominal rate.

Column handling

Numeric columns are interpolated according to method (see ?replace_missing). Non-numeric columns (e.g. character event labels, integer lap numbers) are always filled by last-observation-carried-forward, regardless of method:

• For method = "none", existing rows are matched to the nearest original values of time_channel without interpolation or filling, meaning newly created samples and any NAs in the original data are returned as NA.

• When down-sampling, numeric columns use time-weighted averaging. Non-numeric columns use the first valid value in each output bin.

Value

A tibble of class "mnirs". Metadata are stored as attributes and can be accessed with attributes(data).

Examples

## read example data
data <- read_mnirs(
    file_path = example_mnirs("moxy_ramp"),
    nirs_channels = c(smo2 = "SmO2 Live"),
    time_channel = c(time = "hh:mm:ss"),
    verbose = TRUE
)

## note warning about irregular sampling
data

data_resampled <- resample_mnirs(
    data,               ## blank channels will be retrieved from metadata
    resample_rate = 2,  ## blank by default will resample to `sample_rate`
    method = "linear",  ## linear interpolation across resampled indices
    verbose = TRUE
)

## note the altered `time` values resolving the above warning
data_resampled

mnirs documentation built on May 15, 2026, 9:07 a.m.