sparsevctrs (version 0.4.0)

coerce_to_sparse_data_frame: Coerce sparse matrix or data frame to data frame with sparse columns

Description

Turning a sparse matrix or a data frame into a data frame with sparse columns.

Usage

coerce_to_sparse_data_frame(x, call = rlang::caller_env(0))

Value

data.frame with sparse columns

Arguments

x

sparse matrix, or a data frame or tibble.

call

The execution environment of a currently running function, e.g. caller_env(). The function will be mentioned in error messages as the source of the error. See the call argument of abort() for more information.

Details

The only requirement from a sparse matrix is that it contains column names. Logical sparse matrices give sparse double columns.

When x is a data frame, each column is turned into a sparse vector of the same type using as_sparse_integer(), as_sparse_double(), as_sparse_logical() or as_sparse_character(), with the default value of each function. Columns that are already sparse are left as is. All columns must be integer, double, logical or character vectors. Column names and row names are kept, and the result is a plain data frame even if x is a tibble.

See Also

coerce_to_sparse_tibble() coerce_to_sparse_matrix()

Examples

set.seed(1234)
mat <- matrix(sample(0:1, 100, TRUE, c(0.9, 0.1)), nrow = 10)
colnames(mat) <- letters[1:10]
sparse_mat <- Matrix::Matrix(mat, sparse = TRUE)
sparse_mat

res <- coerce_to_sparse_data_frame(sparse_mat)
res

# All columns are sparse
vapply(res, is_sparse_vector, logical(1))

# Data frames can be used as well
res <- coerce_to_sparse_data_frame(as.data.frame(mat))
vapply(res, is_sparse_vector, logical(1))