Create Kanji Similarity Network — wk_kanji

Creates an interactive network diagram centered on a specific kanji, showing all similarity relationships in a force-directed graph layout.

Usage

wk_kanji_network(
  kanji_df,
  center_kanji,
  max_depth = 2,
  layout = "force",
  node_size_by = "connections",
  show_meanings = TRUE,
  interactive = TRUE
)

Arguments

kanji_df: Data frame of kanji data with similarity information
center_kanji: Character string of the kanji to center the network on, or an English meaning (e.g., "Water" for 水)
max_depth: Integer indicating how many degrees of separation to include (default: 2)
layout: Layout algorithm: "force", "circle", "tree", "grid" (default: "force")
node_size_by: Size nodes by: "level", "connections", "fixed" (default: "connections")
show_meanings: Logical indicating whether to show meanings in node labels (default: TRUE)
interactive: Logical indicating whether to create an interactive plot (default: TRUE)

Value

A network visualization (plotly object if interactive, ggplot if not)

Examples

if (FALSE) { # \dontrun{
# Get kanji data with similarity
subjects <- wk_subjects(types = "kanji", levels = 1:10, all_pages = TRUE)
kanji_df <- wk_extract_kanji(subjects$data, include_similarity = TRUE)
kanji_resolved <- wk_resolve_similarity_details(kanji_df, subjects$data)

# Create network centered on a specific kanji
wk_kanji_network(kanji_resolved, center_kanji = "水")

# Or use English meaning instead of kanji
wk_kanji_network(kanji_resolved, center_kanji = "Water")

# Create a more detailed network with 3 degrees of separation
wk_kanji_network(kanji_resolved, center_kanji = "Person", max_depth = 3, 
                 node_size_by = "level", layout = "force")
} # }