sparse-arithmetic-scalar: Scalar arithmetic with sparse vectors

In sparsevctrs: Sparse Vectors for Use in Data Frames

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)

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.

Value

A sparse vector of same type.

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)

sparsevctrs documentation built on Nov. 2, 2026, 1:09 a.m.