Easily graph networks with sensible defaults

This function provides users with an easy way to graph (m)any network data for exploration, investigation, inspiration, and communication.

graphr() builds upon {ggplot2} and {ggraph} to offer pretty, easy, and extensible graphing solutions. Just passing the function some network data will often be sufficient to return a reasonable-looking graph.

The function also makes it easy to modify many of the most commonly adapted aspects of a graph, including node and edge size, colour, and shape, as arguments rather than additional functions that you need to remember. These can be defined outright, e.g. node_size = 8, or in reference to an attribute of the network, e.g. node_size = "wealth".

Lastly, graphr() uses {ggplot2}-related theme information, so it is easy to make colour palette and fonts institution-specific and consistent. See e.g. theme_iheid() for more.

To learn more about what can be done visually, try run_tute("Visualisation").

Usage

graphr(
  .data,
  layout = NULL,
  labels = TRUE,
  node_color,
  node_shape,
  node_size,
  node_group,
  edge_color,
  edge_size,
  isolates = c("legend", "caption", "keep"),
  snap = FALSE,
  label_dist = NULL,
  label_repel = TRUE,
  edge_bundle = FALSE,
  ...,
  node_colour,
  edge_colour
)

Arguments

.data: A manynet-consistent object.
layout: An igraph, ggraph, or manynet layout algorithm. If not declared, defaults to "triad" for networks with 3 nodes, "quad" for networks with 4 nodes, "stress" for all other one mode networks, or "hierarchy" for two mode networks. For "hierarchy" layout, one can further split graph by declaring the "center" argument as the "events", "actors", or by declaring a node name. For "concentric" layout algorithm please declare the "membership" as an extra argument. The "membership" argument expects either a quoted node attribute present in data or vector with the same length as nodes to draw concentric circles. For "multilevel" layout algorithm please declare the "level" as extra argument. The "level" argument expects either a quoted node attribute present in data or vector with the same length as nodes to hierarchically order categories. If "level" is missing, function will look for 'lvl' node attribute in data. The "lineage" layout ranks nodes in Y axis according to values. For "lineage" layout algorithm please declare the "rank" as extra argument. The "rank" argument expects either a quoted node attribute present in data or vector with the same length as nodes.
labels: Logical, whether to print node names as labels if present.
node_color, node_colour: Node variable to be used for coloring the nodes. It is easiest if this is added as a node attribute to the graph before plotting. Nodes can also be colored by declaring a color instead.
node_shape: Node variable to be used for shaping the nodes. It is easiest if this is added as a node attribute to the graph before plotting. Nodes can also be shaped by declaring a shape instead.
node_size: Node variable to be used for sizing the nodes. This can be any continuous variable on the nodes of the network. Since this function expects this to be an existing variable, it is recommended to calculate all node-related statistics prior to using this function. Nodes can also be sized by declaring a numeric size or vector instead.
node_group: Node variable to be used for grouping the nodes. It is easiest if this is added as a hull over groups before plotting. Group variables should have a minimum of 3 nodes, if less, number groups will be reduced by merging categories with lower counts into one called "other".
edge_color, edge_colour: Tie variable to be used for coloring the nodes. It is easiest if this is added as an edge or tie attribute to the graph before plotting. Edges can also be colored by declaring a color instead.
edge_size: Tie variable to be used for sizing the edges. This can be any continuous variable on the nodes of the network. Since this function expects this to be an existing variable, it is recommended to calculate all edge-related statistics prior to using this function. Edges can also be sized by declaring a numeric size or vector instead.
isolates: Character scalar, how to treat isolates. "keep" will keep isolates in the graph as they are. "legend" (default) will remove isolates from the graph but note them in the legend. "caption" will remove isolates from the graph but note them in the caption. If there are no isolates, this argument will be ignored. If the default layout ("stress") is used, we recommend that the "legend" option is used to avoid isolates crowding out the giant component.
snap: Logical scalar, whether the layout should be snapped to a grid.
label_dist: Numeric scalar, in points (pt), controlling the extra gap left between labels and node borders – similar to igraph's vertex.label.dist. Node size is always accounted for automatically (larger nodes push labels further away without any extra configuration); label_dist adds further spacing on top of that, and defaults to a small gap (5pt). Set to 0 for labels right at the node border, or to a larger value (e.g. 15) for more spacing. Only used when labels = TRUE and label_repel = TRUE (as the padding passed to the repel algorithm) or label_repel = FALSE (as a fixed nudge away from the node, in the layouts where this makes sense, e.g. "circle"/"concentric", "bipartite"/"railway", "alluvial").
label_repel: Logical scalar, whether labels should be repelled away from each other and from nodes using ggrepel (via ggraph's repel argument). Defaults to TRUE. Set to FALSE to place labels at a fixed offset (see label_dist) without the (sometimes slow, and non-deterministic between runs for some layouts) repelling algorithm.
edge_bundle: Edge bundling, off by default (FALSE). When TRUE (or equivalently "force"), edges are bundled together using ggraph's force-directed edge bundling (geom_edge_bundle_force()), which pulls nearby edges into shared paths to reduce visual clutter in dense networks. Alternative non-hierarchical algorithms can be selected by name: "path" (geom_edge_bundle_path()) or "minimal" (geom_edge_bundle_minimal()). Bundling only makes a visible difference when a network has enough edges; for directed networks arrowheads are retained, but the slight reciprocal-tie curvature used for unbundled edges does not apply.
...: Extra arguments to pass on to the layout algorithm, if necessary.

Value

A ggplot2::ggplot() object. The last plot can be saved to the file system using ggplot2::ggsave().

Examples

graphr(ison_adolescents)

ison_adolescents %>%
  mutate(color = rep(c("introvert","extrovert"), times = 4),
         size = ifelse(netrics::node_is_cutpoint(ison_adolescents), 6, 3)) %>%
  mutate_ties(ecolor = rep(c("friends", "acquaintances"), times = 5)) %>%
  graphr(node_color = "color", node_size = "size",
         edge_size = 1.5, edge_color = "ecolor")

graphr(ison_southern_women, labels = TRUE, label_dist = 10)

graphr(ison_southern_women, labels = TRUE, label_repel = FALSE)

graphr(manynet::generate_random(40, 0.1), edge_bundle = TRUE)

Usage

Arguments

Value

See also

Examples