mlr3 (version 0.10.0)

as_data_backend.data.frame: Create a Data Backend

Description

Wraps a DataBackend around data. mlr3 ships with methods for data.frame (converted to a DataBackendDataTable and Matrix from package Matrix (converted to a DataBackendMatrix).

Additional methods are implemented in the package mlr3db, e.g. to connect to real DBMS like PostgreSQL (via dbplyr) or DuckDB (via DBI/duckdb).

Usage

# S3 method for data.frame
as_data_backend(data, primary_key = NULL, keep_rownames = FALSE, ...)
# S3 method for Matrix
as_data_backend(data, primary_key = NULL, dense = NULL, ...)
as_data_backend(data, primary_key = NULL, ...)

Arguments

data

any Data to create a DataBackend from. For a data.frame() (this includes tibble() from tibble and data.table::data.table()), a DataBackendDataTable is created. For objects of type Matrix (from package Matrix), a DataBackendMatrix is returned. See methods("as_data_backend") for all possible input formats.

primary_key

(character(1) | integer()) Name of the primary key column, or integer vector of row ids.

keep_rownames

(logical(1) | character(1)) If TRUE or a single string, keeps the row names of data as a new column. The column is named like the provided string, defaulting to "..rownames" for keep_rownames == TRUE. Note that the created column will be used as a regular feature by the task unless you manually change the column role. Also see data.table::as.data.table().

...

(any) Additional arguments passed to the respective DataBackend method.

dense

(data.frame()). Dense data.

Value

DataBackend.

See Also

Other DataBackend: DataBackendDataTable, DataBackendMatrix, DataBackend

Examples

# NOT RUN {
# create a new backend using the iris data:
as_data_backend(iris)
# }