Coerce

as_long(from, ...)

as_count(from)

as_composition(from)

as_abundance(from)

as_incidence(from)

as_occurrence(from)

as_features(from)

as_stratigraphy(from)

# S4 method for ANY
as_count(from)

# S4 method for ANY
as_composition(from)

# S4 method for ANY
as_abundance(from)

# S4 method for ANY
as_incidence(from)

# S4 method for ANY
as_occurrence(from)

# S4 method for ANY
as_stratigraphy(from)

# S4 method for matrix
as_long(from, factor = FALSE, reverse = FALSE)

# S4 method for AbundanceMatrix
as_long(from, factor = FALSE, reverse = FALSE)

# S4 method for AbundanceMatrix
as_features(from)

Arguments

from

An object to be coerced.

...

Currently not used.

factor

A logical scalar: should character string be coerced to factor? Default to FALSE, if TRUE the original ordering is preserved.

reverse

A logical scalar: should the order of factor levels be reversed? Only used if factor is TRUE. Useful for plotting.

Value

A coerced object.

Details

The following methods coerce an object to a *Matrix object:

MethodTargetDetails
as_count()CountMatrixabsolute frequency data
as_composition()CompositionMatrixrelative frequency data
as_incidence()IncidenceMatrixpresence/absence data
as_occurrence()OccurrenceMatrixco-occurrence
as_stratigraphy()StratigraphicMatrixstratigraphic relationships

Note that as_count rounds numeric values to zero decimal places and then coerces to integer as by as.integer().

as_stratigraphy() converts a set of stratigraphic relationships (edges) to a stratigraphic (adjacency) matrix. from can be a matrix, list, or data.frame: the first column/component is assumed to contain the bottom units and the second the top units (adjacency).

MethodTargetDetails
as_long()data.framelong S3 data frame
as_features()data.framewide S3 data frame

as_features() converts a *Matrix object to a collection of features: a data.frame with all informations as extra columns (result may differ according to the class of from).

See also

Author

N. Frerebeau

Examples

## Create a count matrix A0 <- matrix(data = sample(0:10, 100, TRUE), nrow = 10, ncol = 5) ## Coerce to absolute frequencies A1 <- as_count(A0) ## Coerce to relative frequencies B <- as_composition(A1) ## Row sums are internally stored before coercing to relative frequencies ## (use get_totals() to retrieve these values) ## This allows to restore the source data A2 <- as_count(B) all(A1 == A2)
#> [1] TRUE
## Coerce to presence/absence C <- as_incidence(A1) ## Coerce to a co-occurrence matrix D <- as_occurrence(A1) ## Coerce to an S3 matrix or data.frame X <- as.matrix(A1) all(A0 == X)
#> [1] TRUE
Y <- data.frame(A1) head(Y)
#> col1 col2 col3 col4 col5 #> row1 10 10 1 9 1 #> row2 9 0 3 7 5 #> row3 3 4 9 0 10 #> row4 7 1 7 1 0 #> row5 6 8 6 3 2 #> row6 6 0 3 0 1
## Collection of features # set_dates(A1) <- matrix(sample(0:10, 20, TRUE), nrow = 10, ncol = 2) # set_coordinates(A1) <- matrix(sample(0:10, 30, TRUE), nrow = 10, ncol = 3) # as_features(A1)