colmean_group_apply.RdThe function can be applied to spectral collections,
dt_prep_sets. The list-column id_labels with lists of data.tables each
containing a column named group must be present. See also ids_apply().
colmean_group_apply(dt_prep_sets, append_rows = FALSE)A standardized data.table, i.e. returned from specprepper::*_apply()
function. Contains labelled sets of preprocessed spectra:
This argument allows to chain preprocessing in sequential manner, and i.e. apply variable
Savitzky-Golay smoothers with a single function application.
logical whether to append the newly processed rows, when
dt_prep_sets is not NULL.
A "data.table" with as many rows as spectral collections. It contains
at least the following columns:
prep_set: appends "-mean_group" to the exisiting character vector
elements of the input data.
prep_label: appends "mean_group" to the exisiting character vector
elements of the input data.
prep_params: A list-column with 1-row data.table's. Each data.table has
a new column mean_group, contains the string "id_labels$group".
id_labels: This list-column now only contains a sliced version of the
group column, that correspond to the new rows of the aggregated
column means in spc_prep.
spc_prep: A list-column with data.tables that contain aggregated
means of spectra by group for each spectral collection (row of
dt_prep_sets)
A spectral collection typically represents an outcome of one or more
specific preprocessing with methods and possibly associated parameters used.
colmean_group_apply() only accepts collections with structural conventions
of dt_prep_sets. It requires a id_labels list-column with a group
column specifying the lables used for aggregation in each data.table element
(one for each collection). Label columns such as row or id that were
present before will be removed because they are assumed to be aggregated.