Description Usage Arguments Details Value Author(s) Examples

Reading matrices from files can be time consuming depending on the size of
the matrix. `read.matrix`

implements a fairly efficient routine to
read in sparse matrices and return dense matrix counterparts.

1 2 3 | ```
read.matrix(file, header = FALSE, skip = 1, row.ids = NULL,
col.ids = NULL, colClasses = c("character", "character", "numeric"),
assign.fn = assign_matrix_dense, filter.fn = NULL, ...)
``` |

`file` |
A file or connection to read from |

`header` |
Whether header lines exist defining all possible rows an columns. If this is false, then the defined triplet elements will produce the complete set of rows and columns. |

`skip` |
The number of rows to skip. This assumes there is a single header line, which is skipped. |

`row.ids` |
If header is TRUE, the row number that defines the row.ids If header == FALSE, the row.ids to use for the matrix |

`col.ids` |
If header is TRUE, the col number that defines the col.ids If header == FALSE, the col.ids to use for the matrix |

`colClasses` |
The classes to use for the columns in the triplet file |

`assign.fn` |
The function to use to construct the sparse representation that is then converted to a dense matrix |

`filter.fn` |
An optional function used to filter/clean the input data and/or row/column ids. The signature of filter.fn must have arguments for data, row.ids, and col.ids |

`...` |
Additional arguments to pass to the construction portion of the implementation |

Matrices that have dimensions on the order of thousands can be slow to load into R. 'read.matrix' provides an efficient implementation for reading sparse matrices in triplet form from a file or other connection. This version removes dependencies from other packages and shows a speed improvement over those methods.

The primary benefit of this function is that named rows and columns can be
used as opposed to integer indexes, as compared to the `slam`

package.
The other main motivation is that if the memory is available, dense matrix
calculations can be faster than their sparse counterparts, not to mention
having a wider range of operators available.

When header == TRUE, the row names and/or column names are read from the file. The names are expected to be comma separated in a single line.

Various methods can be used to construct a sparse matrix representation
that is used as the basis for constructing the dense matrix. Currently only
the `assign_matrix_dense`

function is available, which works well for
matrices in triplet form.

A matrix object generated from sparse triplet data

Brian Lee Yung Rowe

1 2 3 4 5 6 7 8 9 | ```
## Not run:
path <- system.file('sample-data/triplet.csv', package='futile.matrix')
m <- read.matrix(path)
rows <- paste('row', 1:10000, sep='.')
cols <- paste('col', 1:10000, sep='.')
n <- read.matrix(path, row.ids=rows, col.ids=cols)
## End(Not run)
``` |

zatonovo/futile.matrix documentation built on May 4, 2019, 9:11 p.m.

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