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)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)
#> [1] 0.000000 1.570796 0.000000 0.000000 2.500000 0.000000 0.000000
#> [8] 0.000000 0.000000 0.050000
sparse_multiplication_scalar(x_sparse, 2)
#> [1] 0.000000 6.283185 0.000000 0.000000 10.000000 0.000000
#> [7] 0.000000 0.000000 0.000000 0.200000
sparse_addition_scalar(x_sparse, 2)
#> [1] 2.000000 5.141593 2.000000 2.000000 7.000000 2.000000 2.000000
#> [8] 2.000000 2.000000 2.100000
sparse_subtraction_scalar(x_sparse, 2)
#> [1] -2.000000 1.141593 -2.000000 -2.000000 3.000000 -2.000000
#> [7] -2.000000 -2.000000 -2.000000 -1.900000