matter-class | R Documentation |
The matter
class and its subclasses are designed for easy on-demand read/write access to virtual memory data structures stored in files and/or shared memory and allow working with them as vectors, matrices, arrays, and lists.
## Instance creation
matter(data, ...)
# Check if an object is a matter object
is.matter(x)
# Coerce an object to a matter object
as.matter(x)
# Check if an object uses shared memory
is.shared(x)
# Coerce an object to use shared memory
as.shared(x)
## Additional methods documented below
data |
Data passed to the subclasse constructor. |
... |
Arguments passed to subclasses. |
x |
An object to check if it is a matter object or coerce to a matter object. |
An object of class matter
.
data
:This slot stores any information necessary to access the data for the object (which may include the data itself and/or paths to file locations, etc.).
type
:The storage mode of the accessed data when read into R. This is a 'factor' with levels 'raw', 'logical', 'integer', 'numeric', or 'character'.
dim
:Either 'NULL' for vectors, or an integer vector of length one of more giving the maximal indices in each dimension for matrices and arrays.
names
:The names of the data elements for vectors.
dimnames
:Either 'NULL' or the names for the dimensions. If not 'NULL', then this should be a list of character vectors of the length given by 'dim' for each dimension. This is always 'NULL' for vectors.
matter
is a virtual class and cannot be instantiated directly, but instances of its subclasses can be created through matter()
.
Class-specific methods:
atomdata(x)
:Access the 'data' slot.
adata(x)
:An alias for atomdata(x).
type(x), type(x) <- value
:Get or set data 'type'.
Standard generic methods:
length(x), length(x) <- value
:Get or set length.
dim(x), dim(x) <- value
:Get or set 'dim'.
names(x), names(x) <- value
:Get or set 'names'.
dimnames(x), dimnames(x) <- value
:Get or set 'dimnames'.
Kylie A. Bemis
matter_arr
,
matter_mat
,
matter_vec
,
matter_fct
,
matter_list
,
matter_str
## Create a matter_vec vector
x <- matter(1:100, length=100)
x
## Create a matter_mat matrix
y <- matter(1:100, nrow=10, ncol=10)
y
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