Network

Description

Draw a network graph showing relationships between entities. Requires an igraph object with nodes (vertices) and links (edges). Node size can represent counts or other metrics.

Usage

po_network(d3po, ..., data = NULL, inherit_daes = TRUE)

Arguments

Value

Appends nodes arguments to a network-specific 'htmlwidgets' object

Examples

if (interactive()) {
  trade_network <- d3po::trade[d3po::trade$year == 2023L, ]
  trade_network <- aggregate(
    trade ~
      reporter_iso + partner_iso + reporter_continent + partner_continent,
    data = trade_network, FUN = sum
  )

  # subset to 10 largest connection per reporter country
  trade_network <- do.call(
    rbind,
    lapply(
      split(trade_network, trade_network$reporter_iso),
      function(df) head(df[order(-df$trade), ], 10)
    )
  )

  # Create vertex (node) attributes for coloring and sizing
  # Get unique countries with their continents and trade volumes
  vertices <- unique(rbind(
    data.frame(
      name = trade_network$reporter_iso,
      continent = trade_network$reporter_continent,
      stringsAsFactors = FALSE
    ),
    data.frame(
      name = trade_network$partner_iso,
      continent = trade_network$partner_continent,
      stringsAsFactors = FALSE
    )
  ))

  # Remove duplicates
  vertices <- vertices[!duplicated(vertices$name), ]

  # Calculate total trade volume per country (as reporter)
  trade_volume <- aggregate(trade ~ reporter_iso, data = trade_network, FUN = sum)
  colnames(trade_volume) <- c("name", "trade_volume")

  # Merge trade volume with vertices
  vertices <- merge(vertices, trade_volume, by = "name", all.x = TRUE)
  vertices$trade_volume[is.na(vertices$trade_volume)] <- 0

  # Assign colors to continents
  my_pal <- tintin::tintin_pal(option = "The Blue Lotus")(7)

  names(my_pal) <- c(
    "Africa", "Antarctica", "Asia",
    "Europe", "North America", "Oceania", "South America"
  )

  # Add color column based on continent
  vertices$color <- my_pal[vertices$continent]

  # Create igraph object with vertex attributes
  g <- graph_from_data_frame(trade_network, directed = TRUE, vertices = vertices)

  # Create the network visualization
  d3po(g, width = 800, height = 600) %>%
    po_network(daes(size = trade_volume, color = color, layout = "fr")) %>%
    po_labels(title = "Trade Network by Country in 2023")
}