(This article was first published on

**Jeromy Anglim's Blog: Psychology, Statistics, & Research Design**, and kindly contributed to R-bloggers)Social Network Analysis is an increasingly popular tool for modelling dependence structures between social actors. In my department researchers are developing new models for representing such dependence structures (MELNET). In 2007 I gave a talk on my consulting experience using social network analysis to provide insights on team dynamics. Since then I have switched to mainly using R for analysing social network datasets.

**R**has many

**advantages**for running social network analyses:

- R packages exist that are devoted to representing, modelling, and plotting networks
- Network data tends to be less standardised than the typical social science dataset. Thus, R’s ability to manipulate and restructure datasets programmatically is particularly useful.
- It’s relatively easy to implement customised functions in R, which are often required in social networks

**Some social network resources for R**include:

- A group at The University of Washington have a number of good packages: I’ve mainly used the sna package for traditional social network analysis, the network package for representing and plotting network data structures, and the ergm package to fit exponential random graph models. Their website also has tutorials and other support material.
- Steve Goodreau and David Hunter posted a 5 video series on social network analysis in R
- Siena for modelling longitudinal network structures is being converted to operate as an R package (RSiena).
- David Smith from Revolution pointed me to a presentation on R and social networks using the igraph package.
- The Social Sciences Task View on Cran lists a couple of packages.

**My own experience using R**: I’ve recently conducted research looking at social networks in the class room. The data includes multiple networks (e.g., friendship, talking), longitudinal measurement, and many different class rooms. R makes it particularly easy to manipulate such data structures and present them graphically. It’s also encouraging that many of the modelling techniques including exponential random graph models and the longitudinal models in Siena have been, or are in the process of being, migrated to R.

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