Feature request: Improvements to network_p2p()

[moralec]

Overview

Redesign the network_p2p() function in order to increase functionality with a more streamlined user experience. Among other things, add the ONA calculations from network_summary so they happen automatically when you create a person to person network.

Why?

• Ease of use - makes getting ONA calculations easier to get for non-data science users.
• Consistency - make sure the different returns make sense, regardless of what type of analysis you are doing.

How?

Restructure function so it includes the following parameters.

• hrvar (single variable)
• return: Plot, PDF, Table, Data, Network
• centrality: OFF, Betweenness, Closeness, Degree, eigenvector, Pagerank
• community: OFF, Laiden, Louvain
• REMOVE display, desc_hrvar

Additionally, make the following changes:

• HIDE network_leiden(), network_louvain()
• KEEP netowrk_describe, network_summary,

Example of new functionality

Centrality: OFF & Community: OFF

PLOT -> Returns basic plot with HR attribute
PDF -> Exports plot as pdf file
Table -> HR Var count
Data -> Returns person dataset with HR attributes
Network -> Returns network object

Centrality: ON (any) & Community: OFF

PLOT -> Returns basic plot with HR attribute AND vertices proportional to centrality
PDF -> Exports plot as pdf file
Table -> HR Var count and average centrality
Data -> Returns person dataset with HR attributes and centrality scores (ALL)
Network -> Returns network object with centrality scores (ALL)

Centrality: OFF & Community: ON (Any)

PLOT -> Returns basic plot with community (no hrvar)
PDF -> Exports plot as pdf file
Table -> HR Var x community count
Data -> Returns person dataset with HR attributes and community attribute
Network -> Returns network object with community attribute

Centrality: ON (any) & Community: ON (Any)

PLOT -> Returns basic plot with community AND vertices proportional to centrality
PDF -> Exports plot as pdf file
Table -> HR Var x community count and average centrality
Data -> Returns person dataset with HR attributes, community attribute and centrality scores (ALL)
Network -> Returns network object with community attribute and centrality scores (ALL)

Future Changes

Network_describe

• Leave as an independent function for now. Can be used with the output of return="table"
• In the future, consider how to generalize as a profiling function. So any population (e.g an HR attribute) can be described with 3 others.
• Useful for top_performance analysis.

Create_Sankey

• Leave it as an indepentent function for now.
• In the future, make it run from any person data (sq_data, or return from network_p2p return data)

[moralec]

Another small request:
node_alpha = 1
edge t ickness - at least double.

[moralec]

I think the basic flow is the following:

Step 1: Take data and use Igraph to create network object
Step 2: If Centrality is ON: Use network summary to get the ONA calculations for the different nodes.
Step 3: If Community is ON: Call additional functions for Community > network_leiden() and network_louvain()
Step 4: Prepare data for return (depending on type).

[moralec]

Another add: We should add an minimal example edge list dataset (not to have to generate)