Inspired by the awesome R list that I mentioned a few months ago, I have started the awesome-network-analysis list, which features a large section on R packages.

Building a list specifically dedicated to network analysis presents the opportunity to cite more R packages that focus on that task, such as the rapidly expanding list of packages to estimate exponential random graph models with R.

The list is intended to join the list of awesome lists that abide to the content and stylistic guidelines of the awesome manifesto. It could also feed into a CRAN Task View on network analysis, which is something I might come back to.

It is perhaps unsurprising that R has become so crucially important for network analysis:

• R is at the centre of many efforts to build statistical models for network data: the Statnet project uses R packages, and SIENA switched from being a Windows program to becoming an R package in 2011.
• Because R interfaces very well with other languages such as JavaScript, it is surprisingly easy to use networks built using R with visualization tools such as the d3.js, Sigma.js and vis.js libraries, or Gephi.
• Last, but perhaps most importantly from a development perspective, R packages are intended to be built and released as free and open source software that can be extended, improved, or simply read and understood by anyone.

The incredible amount of books, courses and tutorials on how to analyze networks with R is also contributing to that trend, and building the awesome list on network analysis is helping me to organize my many bookmarks on the topic.

More ideas on how to document the universe of R (and Python) packages for network analysis are currently being discussed on the SOCNET mailing-list, where subscribers have circulated publicly editable Google Spreadsheets to that effect.