sparsevctrs (version 0.4.0)

sparse-arithmetic-scalar: Scalar arithmetic with sparse vectors

Description

Do Arithmetic on sparse vectors without destroying the sparsity. Note that only multiplication and division preserves the default value.

Usage

sparse_division_scalar(x, val)
sparse_multiplication_scalar(x, val)
sparse_addition_scalar(x, val)
sparse_subtraction_scalar(x, val)

Value

A sparse vector of same type.

Arguments

x

A sparse vector.

val

A single numeric value.

Details

Only minimal checking of the inputs is done, for performance reasons.

sparse_division_scalar() and sparse_multiplication_scalar() are the most used ones, as they preserve the default, which is often what you want to do.

sparse_division_scalar() always produces double vectors, regardless of whether they could be represented as integers or not. Expect when val = 1 as the input is returned unchanged, or val = NA as the input returned will be NA or the appropiate type. When val = 0 the result contains Inf, -Inf and NaN, which sparse vectors can't store, so a regular (dense) double vector is returned.

The other functions return a sparse vector of the same type as x. This means that when x is a sparse integer vector, val must be a whole number. Integer overflow in the stored values produces NA with a warning, as it does for dense integer vectors.

When val is NA or NaN a regular (dense) vector of NA or NaN values is returned.

Results that sparse double vectors can't store, such as stored values that are Inf or NaN, or a default value that is NaN, give an error. The exception is sparse_multiplication_scalar() with val = 0, which keeps NaN for stored Inf and NaN values, since Inf * 0 is NaN.

Examples

x_sparse <- sparse_double(c(pi, 5, 0.1), c(2, 5, 10), 10)

sparse_division_scalar(x_sparse, 2)
sparse_multiplication_scalar(x_sparse, 2)
sparse_addition_scalar(x_sparse, 2)
sparse_subtraction_scalar(x_sparse, 2)