add_gnp_graph {DiagrammeR} | R Documentation

## Add a G(n, p) Erdos-Renyi graph

### Description

To an existing graph object, add a graph built according to the Erdos-Renyi G(n, p) model, which uses a constant probability when creating edges.

### Usage

    add_gnp_graph(graph, n, p, loops = FALSE, type = NULL, label = TRUE,
      rel = NULL, node_aes = NULL, edge_aes = NULL, node_data = NULL,
      edge_data = NULL, set_seed = NULL)

### Arguments

`graph` | a graph object of class `dgr_graph`.
`n` | the number of nodes comprising the generated graph.
`p` | the probability of creating an edge between two arbitrary nodes.
`loops` | a logical value (default is `FALSE`) that governs whether loops are allowed to be created.
`type` | an optional string that describes the entity type for all the nodes to be added.
`label` | a boolean value where setting to `TRUE` ascribes node IDs to the label and `FALSE` yields a blank label.
`rel` | an optional string for providing a relationship label to all edges to be added.
`node_aes` | an optional list of named vectors comprising node aesthetic attributes. The helper function `node_aes()` is strongly recommended for use here as it contains arguments for each of the accepted node aesthetic attributes (e.g., `shape`, `style`, `color`, `fillcolor`).
`edge_aes` | an optional list of named vectors comprising edge aesthetic attributes. The helper function `edge_aes()` is strongly recommended for use here as it contains arguments for each of the accepted edge aesthetic attributes (e.g., `shape`, `style`, `penwidth`, `color`).
`node_data` | an optional list of named vectors comprising node data attributes. The helper function `node_data()` is strongly recommended for use here as it helps bind data specifically to the created nodes.
`edge_data` | an optional list of named vectors comprising edge data attributes. The helper function `edge_data()` is strongly recommended for use here as it helps bind data specifically to the created edges.
`set_seed` | supplying a value sets a random seed of the `Mersenne-Twister` implementation.

### Examples

    # Create an undirected GNP
    # graph with 100 nodes using
    # a probability value of 0.05
    gnp_graph <-
      create_graph(
        directed = FALSE) %>%
      add_gnp_graph(
        n = 100,
        p = 0.05)

    # Get a count of nodes
    gnp_graph %>%
      count_nodes()

    # Get a count of edges
    gnp_graph %>%
      count_edges()

[Package DiagrammeR version 1.0.1 Index]