By Douglas Luke

Providing a complete source for the mastery of community research in R, the aim of community research with R is to introduce sleek community research concepts in R to social, actual, and wellbeing and fitness scientists. The mathematical foundations of community research are emphasised in an available means and readers are guided during the simple steps of community reviews: community conceptualization, info assortment and administration, community description, visualization, and development and checking out statistical versions of networks. as with every of the books within the Use R! sequence, each one bankruptcy includes broad R code and targeted visualizations of datasets. Appendices will describe the R community applications and the datasets utilized in the publication. An R package deal constructed in particular for the publication, to be had to readers on GitHub, includes suitable code and real-world community datasets in addition.

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Extra info for A User's Guide to Network Analysis in R (Use R!)

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Also, this demonstrates that if the matrix has identical row and column names, they will be used as the labels for the nodes. We can also see that this is the same network as the earlier example by plotting it (Fig. 2). col = 2, displaylabels = TRUE) The same network can be created using an edge list format. This will often be more convenient than adjacency matrices. Not only are edge lists smaller than sociomatrices, but network data are often obtained naturally in this format. For example, email communications can be analyzed as networks, where each email corresponds to a tie from the email sender to the receiver.

The ties themselves are stored as a binary indicator in the network object, while the values of those ties are stored in an edge attribute. We can see how this works for the DHHS Collaboration network. First, we examine the network ties for the first six members of the network. Then we determine where the collaboration values are stored, and then use that to view the tie values for the same set of six actors. 4 Common Network Data Tasks ## ## ## ## ## ## ## ACF-1 ACF-2 AHRQ-1 AHRQ-2 AHRQ-3 AHRQ-4 37 ACF-1 ACF-2 AHRQ-1 AHRQ-2 AHRQ-3 AHRQ-4 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 3 3 3 0 0 3 0 3 2 0 0 3 3 0 3 0 0 3 2 3 0 The summary of the network object tells us that there are 447 ties in the DHHS network.

We have seen that to create network objects in R we can use a workflow that takes data in a number of basic matrix formats and transforms them into the network class 24 3 Network Data Management in R object. However, statnet also includes a number of tools that allow you to reverse this workflow, by coercing network data into other matrix formats. matrix(). It can be used to produce a sociomatrix or an edgelist matrix. type = "edgelist") ## ## ## ## ## ## ## ## ## ## ## [,1] [,2] [1,] 1 2 [2,] 3 2 [3,] 1 3 [4,] 2 3 [5,] 5 3 [6,] 2 4 attr(,"n") [1] 5 attr(,"vnames") [1] "A" "B" "C" "D" "E" This ability to go back and forth between network objects and more fundamental data structures such as sociomatrices and edgelist matrices gives the analyst great power and flexibility when managing network data.

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