Cooccurrences class for corpus/partition.

The Cooccurrences-class stores the information for all cooccurrences in a corpus. As this data can be bulky, in-place modifications of the data.table in the stat-slot of a Cooccurrences-object are used wherever possible, to avoid copying potentially large objects whenever possible. The class inherits from the textstat-class, so that methods for textstat-objects are inherited (see examples).

# S4 method for Cooccurrences
as.simple_triplet_matrix(x)

# S4 method for Cooccurrences
as_igraph(
  x,
  edge_attributes = c("ll", "ab_count", "rank_ll"),
  vertex_attributes = "count",
  as.undirected = TRUE,
  drop = getOption("polmineR.villainChars")
)

# S4 method for Cooccurrences
subset(x, ..., by)

# S4 method for Cooccurrences
decode(.Object)

# S4 method for Cooccurrences
kwic(
  .Object,
  left = getOption("polmineR.left"),
  right = getOption("polmineR.right"),
  verbose = TRUE,
  progress = TRUE
)

# S4 method for Cooccurrences
as.sparseMatrix(x, col = "ab_count", ...)

# S4 method for Cooccurrences
enrich(.Object)

Arguments

x	A Cooccurrences class object.
edge_attributes	Attributes from stat data.table in x to add to edges.
vertex_attributes	Vertex attributes to add to nodes.
as.undirected	Logical, whether to return directed or undirected graph.
drop	A character vector indicating names of nodes to drop from igraph object that is prepared.
...	Further arguments passed into a further call of subset.
by	A features-class object.
.Object	A Cooccurrences-class object.
left	Number of tokens to the left of the node.
right	Number of tokens to the right of the node.
verbose	Logical.
progress	Logical, whether to show progress bar.
col	A column to extract.

Details

The as.simple_triplet_matrix-method will transform a Cooccurrences object into a sparse matrix. For reasons of memory efficiency, decoding token ids is performed within the method at the as late as possible. It is NOT necessary that decoded tokens are present in the table in the Cooccurrences object.

The as_igraph-method can be used to turn an object of the Cooccurrences-class into an igraph-object.

The subset method, as a particular feature, allows a Coocccurrences-object to be subsetted by a featurs-Object resulting from a features extraction that compares two Cooccurrences objects.

For reasons of memory efficiency, the initial data.table in the slot stat of a Cooccurrences-object will identify tokens by an integer id, not by the string of the token. The decode()-method will replace these integer columns with human-readable character vectors. Due to the reference logic of the data.table object, this is an in-place operation, peformed without copying the table. The modified object is returned invisibly; usually it will not be necessary to catch the return value.

The kwic-method will add a column to the data.table in the stat-slot with the concordances that are behind a statistical finding, and to the data.table in the stat-slot of the partition in the slot partition. It is an in-place operation.

Returns a sparseMatrix based on the counts of term cooccurrences. At this stage, it is required that decoded tokens are present.

The enrich-method will add columns 'a_count' and 'b_count' to the data.table in the 'stat' slot of the Cooccurrences object. If the count for the subcorpus/partition from which the cooccurrences are derived is not yet present, the count is performed first.

Slots

left

Single integer value, number of tokens to the left of the node.

right

Single integer value, number of tokens to the right of the node.

p_attribute

A character vector, the p-attribute(s) the evaluation of the corpus is based on.

corpus

Length-one character vector, the CWB corpus used.

stat

A data.table with the statistical analysis of cooccurrences.

encoding

Length-one character vector, the encoding of the corpus.

partition

The partition that is the basis for computations.

window_sizes

A data.table linking the number of tokens in the context of a token identified by id.

minimized

Logical, whether the object has been minimized.

See also

See the documentation of the Cooccurrences-method (including examples) for procedures to get and filter cooccurrence information. See the documentation for the textstat-class explaining which methods for this superclass of the Cooccurrences-class which are available.

Examples

X <- Cooccurrences("REUTERS", p_attribute = "word", left = 2L, right = 2L)
m <- as.simple_triplet_matrix(X)
if (FALSE) {
X <- Cooccurrences("REUTERS", p_attribute = "word", left = 5L, right = 5L)
decode(X)
sm <- as.sparseMatrix(X)
stm <- as.simple_triplet_matrix(X)
}