drake_slice() is similar to split(). Both functions partition data into disjoint subsets, but whereas split() returns all the subsets, drake_slice() returns just one. In other words, drake_slice(..., index = i) returns split(...)[[i]]. Other features: 1. drake_slice() works on vectors, data frames, matrices, lists, and arbitrary arrays. 2. Like parallel::splitIndices(), drake_slice() tries to distribute the data uniformly across subsets. See the examples to learn why splitting is useful in drake.

drake_slice(data, slices, index, margin = 1L, drop = FALSE)

Arguments

data

A list, vector, data frame, matrix, or arbitrary array. Anything with a length() or dim().

slices

Integer of length 1, number of slices (i.e. pieces) of the whole dataset. Remember, drake_slice(index = i) returns only slice number i.

index

Integer of length 1, which piece of the partition to return.

margin

Integer of length 1, margin over which to split the data. For example, for a data frame or matrix, use margin = 1 to split over rows and margin = 2 to split over columns. Similar to MARGIN in apply().

drop

Logical, for matrices and arrays. If TRUE, the result is coerced to the lowest possible dimension. See ?[` for details.

Value

A subset of data.

Examples

# Simple usage x <- matrix(seq_len(20), nrow = 5) x
#> [,1] [,2] [,3] [,4] #> [1,] 1 6 11 16 #> [2,] 2 7 12 17 #> [3,] 3 8 13 18 #> [4,] 4 9 14 19 #> [5,] 5 10 15 20
drake_slice(x, slices = 3, index = 1)
#> [,1] [,2] [,3] [,4] #> [1,] 1 6 11 16 #> [2,] 2 7 12 17
drake_slice(x, slices = 3, index = 2)
#> [,1] [,2] [,3] [,4] #> [1,] 3 8 13 18 #> [2,] 4 9 14 19
drake_slice(x, slices = 3, index = 3)
#> [,1] [,2] [,3] [,4] #> [1,] 5 10 15 20
drake_slice(x, slices = 3, margin = 2, index = 1)
#> [,1] [,2] #> [1,] 1 6 #> [2,] 2 7 #> [3,] 3 8 #> [4,] 4 9 #> [5,] 5 10
# In drake, you can split a large dataset over multiple targets. if (FALSE) { isolate_example("contain side effects", { plan <- drake_plan( large_data = mtcars, data_split = target( drake_slice(large_data, slices = 32, index = i), transform = map(i = !!seq_len(32)) ) ) plan cache <- storr::storr_environment() make(plan, cache = cache, session_info = FALSE, verbose = FALSE) readd(data_split_1L, cache = cache) readd(data_split_2L, cache = cache) }) }