Temporal Network Analysis with R

Key Info
Description - a brief synopsis, abstract or summary of what the learning resource is about: 

This tutorial introduces methods for visualizing and analyzing temporal networks using several libraries written for the statistical programming language R. With the rate at which network analysis is developing, there will soon be more user-friendly ways to produce similar visualizations and analyses, as well as entirely new metrics of interest. For these reasons, this tutorial focuses as much on the principles behind creating, visualizing, and analyzing temporal networks (the “why”) as it does on the particular technical means by which we achieve these goals (the “how”). It also highlights some of the unhappy oversimplifications that historians may have to make when preparing their data for temporal network analysis, an area where our discipline may actually suggest new directions for temporal network analysis research.

One of the most basic forms of historical argument is to identify, describe, and analyze changes in a phenomenon or set of phenomena as they occur over a period of time. The premise of this tutorial is that when historians study networks, we should, insofar as it is possible, also be acknowledging and investigating how networks change over time.

Lesson Goals
In this tutorial you will learn:
-The types of data necessary to model a temporal network
-How to visualize a temporal network using the NDTV package in R
-How to quantify and visualize some important network-level and node-level metrics that describe temporal networks using the TSNA package in R.

This tutorial assumes that you have:
- a basic familiarity with static network visualization and analysis, which you can get from excellent tutorials on the Programming Historian such as From Hermeneutics to Data to Networks: Data Extraction and Network Visualization of Historical Sources and Exploring and Analyzing Network Data with Python
- RStudio with R version 3.0 or higher
- A basic understanding of how R can be used to modify data. You may want to review the excellent tutorial on R Basics with Tabular Data found at:  https://programminghistorian.org/en/lessons/r-basics-with-tabular-data.

Authoring Person(s) Name: 
Alex Brey
Authoring Organization(s) Name: 
The Programming Historian
License - link to legal statement specifying the copyright status of the learning resource: 
Creative Commons Attribution 4.0 International - CC BY 4.0
Access Cost: 
No fee
Citation - format of the preferred citation for the learning resource: 
Alex Brey, "Temporal Network Analysis with R," The Programming Historian 7 (2018), https://doi.org/10.46430/phen0080.
Primary language(s) in which the learning resource was originally published or made available: 
Also available in - other languages in which the learning resource has been translated or made available other than the primary: 
More info about
Keywords - short phrases describing what the learning resource is about: 
Data analysis
Data formats
Data modeling
Data visualization
Data visualization tools
Digital humanities
Humanities data
Network analysis
R software
Subject Discipline - subject domain(s) toward which the learning resource is targeted: 
Arts and Humanities
Arts and Humanities: Digital Humanities
Arts and Humanities: History
Arts and Humanities: History of Art, Architecture, and Archaeology
Published / Broadcast: 
Sunday, November 4, 2018
ID - identifier that provides the means to locate the learning resource or its citation: 
Type - namespace prefix for the citable locator, if any: 
Publisher - organization credited with publishing or broadcasting the learning resource: 
The Programming Historian
Media Type - designation of the form in which the content of the learning resource is represented, e.g., moving image: 
Interactive Resource - requires a user to take action or make a request in order for the content to be understood, executed or experienced.
Educational Info
Purpose - primary educational reason for which the learning resource was created: 
Instruction - detailed information about aspects or processes related to data management or data skills.
Learning Resource Type - category of the learning resource from the point of view of a professional educator: 
Learning Activity - guided or unguided activity engaged in by a learner to acquire skills, concepts, or knowledge that may or may not be defined by a lesson. Examples: data exercises, data recipes.
Target Audience - intended audience for which the learning resource was created: 
Data manager
Data professional
Data supporter
Software engineer
Technology expert group
Intended time to complete - approximate amount of time the average student will take to complete the learning resource: 
More than 1 hour (but less than 1 day)