Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/kayleedavisgithub/intro-to-networks-with-r
For developing a training course on network studies in R. Follows YouTube guide -
https://github.com/kayleedavisgithub/intro-to-networks-with-r
analysis network network-analysis r
Last synced: 1 day ago
JSON representation
For developing a training course on network studies in R. Follows YouTube guide -
- Host: GitHub
- URL: https://github.com/kayleedavisgithub/intro-to-networks-with-r
- Owner: KayleeDavisGitHub
- License: cc0-1.0
- Created: 2021-06-05T05:36:11.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-10-14T02:38:48.000Z (about 2 years ago)
- Last Synced: 2024-04-16T07:19:21.030Z (7 months ago)
- Topics: analysis, network, network-analysis, r
- Language: R
- Homepage: https://www.youtube.com/playlist?list=PL_ZjndVyoHi49A1rGxlm_qdWgyuNS3ryh
- Size: 53.7 KB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[![No Maintenance Intended](http://unmaintained.tech/badge.svg)](http://unmaintained.tech/)
# Intro-to-Networks-With-R
For developing a training course on network studies in RThese R files are associated with a YouTube series on learning network analysis in R.
https://www.youtube.com/watch?v=bdpazrNEGoQ&list=PL_ZjndVyoHi49A1rGxlm_qdWgyuNS3ryh
*1- Introduction to Network Analysis With R:*
This script goes through the different network structures, how to load in and read through network data, and how to do some basic descriptive statistics (including graphing).
*2- Centrality*
This script goes through how to measure centrality in multiple different ways, and what some of the measures mean - and plotting based upon centrality measures.
*3- Nodes*
Includes properties on nodes, structural equivilance, roles, and positions.
*4- Diad/Triad*
This includes calculations and interpretations of diads and triads and when they might be useful in calculations (a lead in to community detection).
*5- Community*
This includes assortativity and community structure detection and graphing.
*6- Models*
This lecture only briefly covers basic network modeling and statistical interpretation.