This layout is a bit unusual in that it shows nodes as horizontal line ranges
end edges as evenly spaced vertical spans connecting the nodes. As with the
matrix layout the strength comes from better scalability but its use require
some experience recognising the patterns that different connectivity features
gives rise to. As with matrix layouts the ordering of nodes have huge power
over the look of the plot. The
node_rank_fabric() mimics the default
ordering from the original BioFabric implementation, but other ranking
algorithms from tidygraph can be used with the
sort.by argument as well.
Fabric layouts tend to become quite wide as the graph grows which is
something that should be handled with care - e.g. by only zooming in on a
layout_tbl_graph_fabric( graph, circular = FALSE, sort.by = NULL, shadow.edges = FALSE ) node_rank_fabric()
An expression providing the sorting of the nodes. If
the nodes will be ordered by their index in the graph.
Should shadow edges be shown.
A data.frame with the columns
well as any information stored as node variables in the tbl_graph object.
Further, the edges of the graph will gain a
edge_x variable giving the
horizontal position of the edge as well as a
shadow_edge variable denoting
whether the edge is a shadow edge added by the layout.
BioFabric website: http://www.biofabric.org
Longabaugh, William J.R. (2012). Combing the hairball with BioFabric: a new approach for visualization of large networks. BMC Bioinformatics, 13: 275. doi:10.1186/1471-2105-13-275