Easily animate dynamic networks with sensible defaults

This function provides users with an easy way to graph dynamic network data for exploration and presentation.

It builds upon this package's graphr() function, and inherits all the same features and arguments. See graphr() for more. However, it uses the {gganimate} package to animate the changes between successive iterations of a network. This is useful for networks in which the ties and/or the node or tie attributes are changing.

A progress bar is shown if it takes some time to encoding all the .png files into a .gif.

grapht(
  tlist,
  keep_isolates = TRUE,
  layout,
  labels = TRUE,
  node_color,
  node_shape,
  node_size,
  edge_color,
  edge_size,
  ...,
  node_colour,
  edge_colour
)

Source

https://blog.schochastics.net/posts/2021-09-15_animating-network-evolutions-with-gganimate/

Arguments

tlist

The same migraph-compatible network listed according to a time attribute, waves, or slices.

keep_isolates

Logical, whether to keep isolate nodes in the graph. TRUE by default. If FALSE, removes nodes from each frame they are isolated in.

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.

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.

...

Extra arguments to pass on to the layout algorithm, if necessary.

Value

Shows a .gif image. Assigning the result of the function saves the gif to a temporary folder and the object holds the path to this file.

See also

Other mapping: map_graphr, map_graphs, map_layout_configuration, map_layout_partition

Examples

#ison_adolescents %>%
#  mutate_ties(year = sample(1995:1998, 10, replace = TRUE)) %>%
#  to_waves(attribute = "year", cumulative = TRUE) %>%
#  grapht()
#ison_adolescents %>% 
#  mutate(gender = rep(c("male", "female"), times = 4),
#         hair = rep(c("black", "brown"), times = 4),
#         age = sample(11:16, 8, replace = TRUE)) %>%
#  mutate_ties(year = sample(1995:1998, 10, replace = TRUE),
#              links = sample(c("friends", "not_friends"), 10, replace = TRUE),
#              weekly_meetings = sample(c(3, 5, 7), 10, replace = TRUE)) %>%
#  to_waves(attribute = "year") %>%
#  grapht(layout = "concentric", membership = "gender",
#             node_shape = "gender", node_color = "hair",
#             node_size =  "age", edge_color = "links",
#             edge_size = "weekly_meetings")
#grapht(play_diffusion(ison_adolescents, seeds = 5))