network.extraction: Extraction and Replacement Operators for Network Objects
In network: Classes for Relational Data

Description Usage Arguments Details Value Author(s) References See Also Examples

Various operators which allow extraction or replacement of various components of a network object.

## S3 method for class 'network'
x[i, j, na.omit = FALSE]
## S3 replacement method for class 'network'
x[i, j, names.eval=NULL, add.edges=FALSE] <- value
x %e% attrname
x %e% attrname <- value
x %eattr% attrname
x %eattr% attrname <- value

x %n% attrname
x %n% attrname <- value
x %nattr% attrname
x %nattr% attrname <- value

x %v% attrname
x %v% attrname <- value
x %vattr% attrname
x %vattr% attrname <- value

`x`	an object of class `network`.
`i, j`	indices of the vertices with respect to which adjacency is to be tested. Empty values indicate that all vertices should be employed (see below).
`na.omit`	logical; should missing edges be omitted (treated as no-adjacency), or should `NA`s be returned? (Default: return `NA` on missing.)
`names.eval`	optionally, the name of an edge attribute to use for assigning edge values.
`add.edges`	logical; should new edges be added to `x` where edges are absent and the appropriate element of `value` is non-zero?
`value`	the value (or set thereof) to be assigned to the selected element of `x`.
`attrname`	the name of a network or vertex attribute (as appropriate).

Indexing for edge extraction operates in a manner analogous to matrix objects. Thus, x[,] selects all vertex pairs, x[1,-5] selects the pairing of vertex 1 with all vertices except for 5, etc. Following this, it is acceptable for i and/or j to be logical vectors indicating which vertices are to be included. During assignment, an attempt is made to match the elements of value to the extracted pairs in an intelligent way; in particular, elements of value will be replicated if too few are supplied (allowing expressions like x[1,]<-1). Where names.eval==NULL, zero and non-zero values are taken to indicate the presence of absence of edges. x[2,4]<-6 thus adds a single (2,4) edge to x, and x[2,4]<-0 removes such an edge (if present). If x is multiplex, assigning 0 to a vertex pair will eliminate all edges on that pair. Pairs are taken to be directed where is.directed(x)==TRUE, and undirected where is.directed(x)==FALSE.

If an edge attribute is specified using names.eval, then the provided values will be assigned to that attribute. When assigning values, only extant edges are employed (unless add.edges==TRUE); in the latter case, any non-zero assignment results in the addition of an edge where currently absent. If the attribute specified is not present on a given edge, it is added. Otherwise, any existing value is overwritten. The %e% operator can also be used to extract/assign edge values; in those roles, it is respectively equivalent to get.edge.value(x,attrname) and set.edge.value(x,attrname=attrname,value=value). Note that the assignment operator takes edge values input in adjacency matrix form.

The %n% and %v% operators serve as front-ends to the network and vertex extraction/assignment functions (respectively). In the extraction case, x %n% attrname is equivalent to get.network.attribute(x,attrname), with x %v% attrname corresponding to get.vertex.attribute(x,attrname). In assignment, the respective equivalences are to set.network.attribute(x,attrname,value) and set.vertex.attribute(x,attrname,value). Note that the “%%” assignment forms are generally slower than the named versions of the functions beause they will trigger an additional internal copy of the network object.

The %eattr%, %nattr%, and %vattr% operators are equivalent to %e%, %n%, and %v% (respectively). The short forms are more succinct, but may produce less readable code.

The extracted data, or none.

Carter T. Butts buttsc@uci.edu

Butts, C. T. (2008). “network: a Package for Managing Relational Data in R.” Journal of Statistical Software, 24(2). http://www.jstatsoft.org/v24/i02/

is.adjacent, as.sociomatrix, attribute.methods, add.edges, network.operators, and get.inducedSubgraph

  #Create a random graph (inefficiently)
  g<-network.initialize(10)
  g[,]<-matrix(rbinom(100,1,0.1),10,10)
  plot(g)
  
  #Demonstrate edge addition/deletion
  g[,]<-0
  g[1,]<-1
  g[2:3,6:7]<-1
  g[,]
  
  #Set edge values
  g[,,names.eval="boo"]<-5
  as.sociomatrix(g,"boo")
  g %e% "hoo" <- "wah"
  g %e% "hoo"
  #Assignment input should be as adjacency matrix
  g %e% "age" <- matrix(1:100, 10, 10)
  g %e% "age"
  as.sociomatrix(g,"age")

  #Set/retrieve network and vertex attributes
  g %n% "blah" <- "Pork!"                 #The other white meat?
  g %n% "blah" == "Pork!"                 #TRUE!
  g %v% "foo" <- letters[10:1]            #Letter the vertices
  g %v% "foo" == letters[10:1]            #All TRUE