kdsort: Sort multidimensional data
In kdtools: Tools for Working with Multidimensional Data

Description Usage Arguments Details Value See Also Examples

Sort multidimensional data

kd_sort(x, ...)

## S3 method for class 'matrix'
kd_sort(x, cols = NULL, parallel = TRUE, ...)

## S3 method for class 'arrayvec'
kd_sort(x, inplace = FALSE, parallel = TRUE, ...)

## S3 method for class 'data.frame'
kd_sort(x, cols = NULL, parallel = TRUE, ...)

## S3 method for class 'sf'
kd_sort(x, cols = NULL, parallel = TRUE, ...)

kd_order(x, ...)

## S3 method for class 'matrix'
kd_order(x, cols = NULL, parallel = TRUE, ...)

## S3 method for class 'arrayvec'
kd_order(x, inplace = FALSE, parallel = TRUE, ...)

## S3 method for class 'data.frame'
kd_order(x, cols = NULL, parallel = TRUE, ...)

kd_is_sorted(x, ...)

`x`	a matrix or arrayvec object
`...`	ignored
`cols`	integer or character vector or formula indicating columns
`parallel`	use multiple threads if true
`inplace`	sort as a side-effect if true

The algorithm used is a divide-and-conquer quicksort variant that recursively partitions an range of tuples using the median of each successive dimension. Ties are resolved by cycling over successive dimensions. The result is an ordering of tuples matching their order if they were inserted into a kd-tree.

kd_order returns permutation vector that will order the rows of the original matrix, exactly as order. If inplace is true, then kd_order will also sort the arrayvec object as a side effect. This can be more efficient when many subsequent queries are required.

kd_sort and kd_order have been extended to work directly on R native data.frame and matrix types. All vector column types are supported (even lists of objects as long as equality and comparison operators are defined). Additional, the user can specify a sequence of column indices that will be used for sorting. These can be a subset of columns and given in any order.

`kd_sort`	the table sorted in kd-tree order
`kd_order`	a permutation vector
`kd_is_sorted`	a boolean

arrayvec

if (has_cxx17()) {
z <- data.frame(real = runif(10), lgl = runif(10) > 0.5,
                int = as.integer(rpois(10, 2)), char = sample(month.name, 10),
                stringsAsFactors = FALSE)
kd_sort(z)
x <- matrix(runif(200), 100)
y <- kd_sort(x)
kd_is_sorted(y)
kd_order(x)
plot(y, type = "o", pch = 19, col = "steelblue", asp = 1)
}