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Re-scale Data Range

Source: R/rescale_mnirs.R
rescale_mnirs.Rd

Expand or reduce the range (min and max values) of data channels to a new amplitude/dynamic range, e.g. re-scale the range of NIRS data to c(0, 100).

Usage

rescale_mnirs(data, nirs_channels = list(), range, verbose = TRUE)

Arguments

data

A data frame of class "mnirs.data" containing at least one column with numeric time or sample values, and one column with numeric mNIRS values, along with metadata.

nirs_channels

A list() of character vectors indicating the column names for data channels to be re-scaled (see Details).

list("A", "B", "C")

Will re-scale each channel independently, losing the relative scaling between channels.

list(c("A", "B", "C"))

Will re-scale all channels together, preserving the relative scaling between channels.

list(c("A", "B"), c("C", "D"))

Will re-scale channels A and B in one group, and channels C and D in another group, preserving relative scaling within, but not between groups.

Must match column names in data exactly. Will be taken from metadata if not defined explicitly.

range

A numeric vector in the form c(min, max), indicating the range of output values to which data channels will be re-scaled.

verbose

A logical to return (the default) or silence warnings and messages which can be used for data error checking. Abort errors will always be returned.

Value

A tibble of class "mnirs.data" with metadata available with attributes().

Details

nirs_channels = list() can be used to group data channels to preserve absolute or relative scaling.

  • Channels grouped together in a list item will be re-scaled to a common value, and the relative scaling within that group will be preserved.

  • Channels grouped in separate list items will be re-scaled independently, and relative scaling between groups will be lost.

  • Channels (columns) in data not explicitly defined in nirs_channels will be passed through untouched to the output data frame.

nirs_channels can be retrieved automatically from data of class "mnirs.data" which has been processed with {mnirs}, if not defined explicitly. This will default to returning all nirs_channels grouped together, and should be defined explicitly for other grouping arrangements.

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 = FALSE
) |>
    resample_mnirs(verbose = FALSE) |>
    replace_mnirs(
        invalid_values = c(0, 100),
        outlier_cutoff = 3,
        width = 7,
        verbose = FALSE
    ) |>
    filter_mnirs(na.rm = TRUE, verbose = FALSE)

data_rescaled <- rescale_mnirs(
    data,
    # nirs_channels = NULL, ## taken from metadata
    range = c(0, 100),      ## rescale to a 0-100% functional exercise range
    verbose = FALSE
)

if (FALSE) { # \dontrun{
plot(data_rescaled, display_timestamp = TRUE) +
    geom_hline(yintercept = c(0, 100), linetype = "dotted")
} # }