Reduce a target — reduce_cluster • partition

reduce_cluster() and map_cluster() apply the data reduction to the targets found in the director step. They only do so if the metric is above the threshold, however. reduce_cluster() is for functions that return vectors while map_cluster() is for functions that return data.frames. If you're using as_reducer(), there's no need to call these functions directly.

reduce_cluster(.partition_step, .f, first_match = FALSE)

map_cluster(.partition_step, .f, rewind = FALSE, first_match = FALSE)

Arguments

.partition_step: a partition_step object
.f: a function to reduce the data to either a vector or a data.frame
first_match: logical. Should the partition algorithm stop when it finds a reduction that is equal to the threshold? Default is TRUE for reducers that return a data.frame and FALSE for reducers that return a vector
rewind: logical. Should the last target be used instead of the current target?

Value

a partition_step object

Examples


reduce_row_means <- function(.partition_step, .data) {
  reduce_cluster(.partition_step, rowMeans)
}

replace_partitioner(
  part_icc,
  reduce = reduce_row_means
)
#>    Director: Minimum Distance (Pearson) 
#>    Metric: Intraclass Correlation 
#>    Reducer: <custom reducer>