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Position the labels of clusters in network and alluvial plots

Source: R/prepare_label_networks.R
prepare_label_networks.Rd

prepare_label_networks() computes, for each category of cluster_label_column, the mean coordinates of its nodes, where the label of the cluster will be drawn by plot_networks(): label_x and label_y are the centre of gravity of the cluster.

prepare_label_alluvial() chooses the time window where the label of each cluster of an alluvial will be drawn: the median window of the windows where the cluster exists. The chosen row gets the label in a label_x column, which plot_alluvial() uses.

Usage

prepare_label_networks(
  graphs,
  x = "x",
  y = "y",
  cluster_label_column = "cluster_label"
)

prepare_label_alluvial(
  alluv_dt,
  window_column = "window",
  cluster_label_column = "cluster_label"
)

Arguments

graphs

A tbl_graph or a list of tbl_graphs with coordinates (see layout_networks()).

x, y

Character scalars, the node columns with the coordinates ("x" and "y" by default).

cluster_label_column

Character scalar, the column with the labels: "cluster_label" (name_clusters()) by default, or a cluster column.

alluv_dt

A data frame created with networks_to_alluv().

window_column

Character scalar, the column of alluv_dt with the time windows ("window" by default, as created by networks_to_alluv()).

Value

prepare_label_networks(): the same object as graphs with node columns label_x and label_y. prepare_label_alluvial(): alluv_dt with a label_x column, holding the label on the rows of the chosen window and missing elsewhere.

Examples

network <- build_network(
  nodes = subset(Nodes_stagflation, source_type == "Stagflation"),
  directed_edges = Ref_stagflation,
  source_id = "source_id",
  target_id = "target_id",
  method = "similarity",
  edges_threshold = 1
)
#> ℹ Coupling (`biblionetwork::biblio_coupling()`) with "similarity": ties
#>   weighted by the idf-weighted cosine of the two lists of targets, after Shen
#>   et al. (2019), between source_id entities sharing target_id entities.
#> ℹ Two source_id are linked when they share at least 1 target_id
#>   (`edges_threshold`).
#> ℹ Nodes without any edge are removed (`keep_singleton = FALSE`).
network <- add_clusters(
  network,
  clustering_method = "leiden",
  objective_function = "modularity",
  seed = 123,
  verbose = FALSE
)
set.seed(1)
network <- layout_networks(network, node_id = "source_id", layout = "fr")
network <- prepare_label_networks(network, cluster_label_column = "cluster_leiden")
unique(tidygraph::as_tibble(network, active = "nodes")[c("cluster_leiden", "label_x", "label_y")])
#> # A tibble: 4 × 3
#>   cluster_leiden label_x label_y
#>   <chr>            <dbl>   <dbl>
#> 1 02                2.79   -1.13
#> 2 01               -6.64    1.38
#> 3 04                1.39    2.49
#> 4 03                2.39    4.06