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Create Interactive Network Visualisation

Source: R/mc_network.R
mc_network.Rd

[Experimental]

Generates an interactive network visualisation using visNetwork library. The visualisation includes interactive features for exploring model structure and relationships.

By default, nodes are colored as:

  • inputs (light blue, #B0DFF9): Input datasets, data frames, files, and columns

  • in_node (blue, #6ABDEB): Input nodes and scalar values

  • out_node (green, #A4CF96): Output nodes

  • filter (light purple, #E8A5E5): Filtered nodes created with mc_filter()

  • compare (medium purple, #D88FD5): Comparison nodes created with mc_compare()

  • trials_info (light orange, #FAE4CB): Trial, subset, and related information nodes

  • total (orange, #F39200): Total nodes created with at_least_one()

  • agg_total (dark orange, #C17816): Aggregated total nodes created with agg_variates()

Usage

mc_network(
  mcmodule,
  variate = 1,
  color_pal = NULL,
  color_by = NULL,
  legend = FALSE,
  inputs = FALSE,
  percentages = TRUE
)

Arguments

mcmodule

(mcmodule object). Module containing network to visualise.

variate

(positive integer). Variate (row) to visualise. The same row number is used across the module; this argument does not filter variates by key values. See get_node_table(). Default: 1.

color_pal

(character vector, optional). Custom colour palette for nodes. Default: NULL.

color_by

(character, optional). Column name to determine node colours. Default: NULL.

legend

(logical). If TRUE, show colours legend. Default: FALSE.

inputs

(logical). If TRUE, show non-node inputs. Default: FALSE.

percentages

(logical). If TRUE, eligible stochastic nodes whose values do not exceed 1 are displayed as percentages. If FALSE, all stochastic nodes are displayed as numbers. Default: TRUE.

Value

An interactive visNetwork object with highlighting of connected nodes, node selection and filtering by module, directional arrows, hierarchical layout, and draggable nodes.

See also

get_node_table() and get_edge_table() for the underlying tables, mc_filter() for selecting variates by conditions, and visNetwork::visNetwork() for the underlying visualisation.

Examples

# \donttest{
network <- mc_network(mcmodule = imports_mcmodule)

# Select a variate by its key values rather than its original row number.
# The filtered node's first retained variate is displayed with variate = 1.
selected_module <- mc_filter(
  imports_mcmodule,
  "w_prev",
  pathogen == "b",
  origin == "south",
  name = "w_prev_selected"
)
selected_network <- mc_network(selected_module, variate = 1)
# }