The morph/unmorph verbs are used to create temporary representations of
the graph, such as e.g. its search tree or a subgraph. A morphed graph will
accept any of the standard dplyr verbs, and changes to the data is
automatically propagated to the original graph when unmorphing. Tidygraph
comes with a range of morphers, but is it also possible to supply your own.
See Details for the requirement for custom morphers. The crystallise verb
is used to extract the temporary graph representation into a tibble
containing one separate graph per row and a name and graph column holding
the name of each graph and the graph itself respectively. convert() is a
shorthand for performing both morph and crystallise along with extracting
a single tbl_graph (defaults to the first). For morphs were you know they
only create a single graph, and you want to keep it, this is an easy way.
morph(.data, .f, ...)
unmorph(.data)
crystallise(.data)
crystallize(.data)
convert(.data, .f, ..., .select = 1, .clean = FALSE)A tbl_graph or a morphed_tbl_graph
A morphing function. See morphers for a list of provided one.
Arguments passed on to the morpher
The graph to return during convert(). Either an index or the
name as created during crystallise().
Should references to the node and edge indexes in the original
graph be removed when using convert
A morphed_tbl_graph
It is only possible to change and add to node and edge data from a
morphed state. Any filtering/removal of nodes and edges will not result in
removal from the main graph. However, nodes and edges not present in the
morphed state will be unaffected in the main graph when unmorphing (if new
columns were added during the morhped state they will be filled with NA).
Morphing an already morhped graph will unmorph prior to applying the new morph.
During a morphed state, the mapping back to the original graph is stored in
.tidygraph_node_index and .tidygraph_edge_index columns. These are
accesible but protected, meaning that any changes to them with e.g. mutate
will be ignored. Furthermore, if the morph results in the merging of nodes
and/or edges the original data is stored in a .data column. This is
protected as well.
When supplying your own morphers the morphing function should accept a
tbl_graph as its first input. The provided graph will already have nodes
and edges mapped with a .tidygraph_node_index and .tidygraph_edge_index
column. The return value must be a tbl_graph or a list of tbl_graphs and
these must contain either a .tidygraph_node_index column or a
.tidygraph_edge_index column (or both). Note that it is possible for the
morph to have the edges mapped back to the original nodes and vice versa
(e.g. as with to_linegraph). In that case the edge data in the morphed
graph(s) will contain a .tidygraph_node_index column and/or the node data a
.tidygraph_edge_index column. If the morphing results in the collapse of
multiple columns or edges the index columns should be converted to list
columns mapping the new node/edge back to all the nodes/edges it represents.
Furthermore the original node/edge data should be collapsed to a list of
tibbles, with the row order matching the order in the index column element.
create_notable('meredith') %>%
mutate(group = group_infomap()) %>%
morph(to_contracted, group) %>%
mutate(group_centrality = centrality_pagerank()) %>%
unmorph()
#> # A tbl_graph: 70 nodes and 140 edges
#> #
#> # An undirected simple graph with 1 component
#> #
#> # Node Data: 70 × 2 (active)
#> group group_centrality
#> <int> <dbl>
#> 1 1 0.1
#> 2 1 0.1
#> 3 1 0.1
#> 4 1 0.1
#> 5 1 0.1
#> 6 1 0.1
#> 7 1 0.1
#> 8 2 0.1
#> 9 2 0.1
#> 10 2 0.1
#> # ℹ 60 more rows
#> #
#> # Edge Data: 140 × 2
#> from to
#> <int> <int>
#> 1 1 5
#> 2 1 6
#> 3 1 7
#> # ℹ 137 more rows