# Difference between revisions of "R console (tutorial)"

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− | This [[ | + | This [[Tutorials|tutorial]] illustrates how to send network data from visone to R and back. [http://www.r-project.org/ The R project] for statistical computing offers a rich set of methods for data analysis and modeling which becomes accessible from visone through the R console. We assume that you have installed the R connection as it is explained in the [[Installation_(tutorial)#Installing_the_R_connection|installation tutorial]]. This tutorial assumes that you have basic understanding about how to work with visone as it is, for instance, explained in the tutorial on [[Visualization_and_analysis_(tutorial)|visualization and analysis]]. You do ''not'' need to have any previous knowledge about R to follow this tutorial; nevertheless, to exploit the full potential offered by R you could consult documentation and tutorials linked from the [http://www.r-project.org/ R-project page]. |

− | To follow the steps illustrated in this | + | To follow the steps illustrated in this tutorial you should download the network file ''Egonet.graphml'' which is linked from and explained in the page [[Egoredes_(data)]]. Further you should remove all ties that are not rated as ''very likely'' in the same manner as it is explained in the last section of the [[Visualization_and_analysis_(tutorial)#Selecting_elements_dependent_on_attribute_values|visualization and analysis tutorial]]. |

==Sending networks from visone to R== | ==Sending networks from visone to R== | ||

− | To send the network from visone to R | + | You can open the [[Console|R console]] by clicking on the [[File:console.png|link=console]] icon in the [[GUI#toolbar|toolbar]]; then click on the ''R console'' tab. |

− | + | To send the network from visone to R choose a name in the textfield right of the '''send''' button (you might just accept visone's suggestion for this name, which should be ''egonet''), and click on the '''send''' button. After clicking on '''send''' visone starts the Rserve connection and sends the current network to R. If this does not work you should check the settings of the [[Option_dialog#R_-_connection|R connection options]] accessible via the '''file, options''' menu. If it works you should get a message like | |

− | After clicking on '''send''' visone starts the Rserve connection and | ||

− | |||

visone: sendActiveNet egonet | visone: sendActiveNet egonet | ||

done | done | ||

− | + | in the message field of the R console. | |

− | |||

− | in the message field of the R console. | ||

[[File:R_console_ls.png]] | [[File:R_console_ls.png]] | ||

You can list all variables that are in the R workspace by typing | You can list all variables that are in the R workspace by typing | ||

− | + | ls() | |

− | in the input field | + | in the input field of the R console (the input field is the text field just above the '''send''' button) and pressing the Enter-Key (currently there is one object called ''egonet''). |

− | + | Typing | |

+ | class(egonet) | ||

+ | prints the class of the <code>egonet</code> object which is <code>igraph</code>. '''igraph''' is an R package obtainable from the [http://cran.r-project.org/ CRAN Website] (this site also gives you access to R tutorials and documentation). The igraph package is documented in more detail on [http://igraph.sourceforge.net/ http://igraph.sourceforge.net/]. | ||

+ | |||

+ | ==Getting basic statistics about an igraph object== | ||

+ | |||

+ | The igraph documentation linked above gives a complete list of all methods available for this class. In the following we describe how to inspect what is encoded in the given object and how to get simple summary statistics. | ||

+ | Executing the command | ||

+ | summary(egonet) | ||

+ | outputs basic information such as the number of vertices and edges, the names of vertex and edge attributes, as well as a list of all edges. | ||

+ | |||

+ | [[File:R_console_summary.png]] | ||

+ | |||

+ | === Exploring attributes === | ||

+ | |||

+ | The names of all vertex or edge attributes are printed with | ||

+ | list.vertex.attributes(egonet) | ||

+ | list.edge.attributes(egonet) | ||

+ | To see the values of the vertex or edge attributes one needs to understand the concept of ''vertex iterators'' and ''edge iterators'' in igraph. The vertex iterator of graph ''egonet'' is returned by typing the command | ||

+ | V(egonet) | ||

+ | When executing this you see just the list of vertex ''name''s which are here the numbers from 1 to 45. To obtain the values of an attribute (e.g., ''Afrm'') type | ||

+ | V(egonet)$Afrm | ||

+ | which returns the vector of countries of origin of the various actors. | ||

+ | |||

+ | Useful summary statistics include information about how many actors originate from the various countries. However, typing the | ||

+ | command | ||

+ | summary(V(egonet)$Afrm) | ||

+ | just outputs information about the class and size of the list of countries of origin: | ||

+ | Length Class Mode | ||

+ | 45 character character | ||

+ | which is not very informative. To obtain the list of unique values for the ''Afrm'' attribute, you can type | ||

+ | unique(V(egonet)$Afrm) | ||

+ | which returns the names of five different countries. To count the number of actors in each of the countries, it is most convenient to convert the vector of character strings into a ''factor'' and save this factor in a new variable (e.g., called ''from'') by typing the command | ||

+ | from <- as.factor(V(egonet)$Afrm) | ||

+ | Finally the command | ||

+ | summary(from) | ||

+ | returns the list of unique values along with the number of actors in each of the classes. In our example this is | ||

+ | Colombia Dominican Republic Puerto Rico Spain United States | ||

+ | 2 14 5 1 23 | ||

+ | |||

+ | === Indexing of vertex and edge iterators === | ||

+ | |||

+ | Vertex iterators can be restricted to subsets by specifying a logical vector (or a command that produces one) in square brackets after the iterator. For instance, the command | ||

+ | V(egonet)[2:5] | ||

+ | returns the values 3 to 6. This seeming contradiction is explained by the fact that counting of indices in vertex or edge iterators starts at zero; thus, the name of the vertex at position 0 is 1, the name of the vertex at position 2 is 3, and so on. | ||

+ | The result of such a restriction operation on an vertex iterator is itself a vertex iterator and, thus, provides access to vertex attributes. For instance, typing | ||

+ | V(egonet)[2:5]$Afrm | ||

+ | returns the countries of origin of actors indexed by 2 to 5 (i.e., named 3 to 6). The command | ||

+ | V(egonet)[Acit == "new york"]$Afrm | ||

+ | gives you the countries of origin of all actors whose attribute ''Acit'' (encoding the city of residence) equals ''new york'', and so on. | ||

+ | |||

+ | An edge iterator is returned via the command | ||

+ | E(egonet) | ||

+ | and offers access to edge attributes similar as for vertices. | ||

+ | |||

+ | Vertex iterators and edge iterators can be indexed by more complex conditions. Actually, any logical vector whose length equals the number of vertices (respectively edges) can be used as an argument in the square brackets following vertex iterators (respectively edge iterators). The following lines illustrate such indexing tasks. To select all actors whose origin is in the US and save this iterator in a variable ''actors.from.usa'' type | ||

+ | actors.from.usa <- V(egonet)[Afrm == "United States"] | ||

+ | All edges connecting two actors from the US are obtained by | ||

+ | edges.within.usa <- E(egonet)[actors.from.usa %--% actors.from.usa] | ||

+ | The command ''%--%'' is a special command used in edge iterators between two vertex iterators; it selects all edges connecting vertices from the two specified subsets (which might be identical, as in the example above). | ||

+ | Edges with at least one actor from the US are selected by | ||

+ | edges.incident.usa <- E(egonet)[adj(actors.from.usa)] | ||

+ | |||

+ | To select all edges within any of the classes defined by Afrm we first construct a logical vector for edges that is true if and only if the ''Afrm'' attribute of the two connected vertices is identical and then use it as an argument in ''E(egonet)[...]''. Therefore type | ||

+ | el <- get.edgelist(egonet) +1 | ||

+ | within.class.edges <- V(egonet)[el[,1]]$Afrm == V(egonet)[el[,2]]$Afrm | ||

+ | E(egonet)[within.class.edges] | ||

+ | The variable ''el'' is just a matrix with two columns containing the vertex ids of adjacent vertices. The ''+1'' in the first line is necessary because ids start with zero. | ||

+ | |||

+ | Vertex and edge iterators can be restricted in various other ways; see the [http://igraph.sourceforge.net/ igraph documentation for details]. | ||

+ | |||

+ | == Analyzing distributions of centralities in igraph == | ||

+ | |||

+ | The igraph package offers methods to compute various established centrality measures (refer to the [http://igraph.sourceforge.net/ igraph documentation] for a complete list of available methods). While many of these could also be directly computed in visone without the detour via the R console, R directly offers statistical descriptions and analysis of the computed values. This is demonstrated in the following. The vertex degrees are returned by the command | ||

+ | degree(egonet) | ||

+ | Let's save this vector in a variable ''d'' by typing | ||

+ | d <- degree(egonet) | ||

+ | Mean, standard deviation, and summary statistics (including min, max, quartiles, and median) are computed by | ||

+ | mean(d) | ||

+ | sd(d) | ||

+ | summary(d) | ||

+ | |||

+ | To display these (or other) statistics separately for each class of actors defined by the country of origin (or any other attribute), execute the command | ||

+ | tapply(d, from, summary) | ||

+ | The three arguments of ''tapply'' have the following meaning: ''d'' is the vector of values to which the function should be applied, ''from'' is the factor whose unique values determine the different classes, and ''summary'' is the function to be computed (instead of ''summary'', you could also type ''mean'', ''sd'', and so on). | ||

+ | |||

+ | [[File:R_console_tapply.png]] | ||

+ | |||

+ | == Loading networks from R into visone == | ||

+ | |||

+ | As networks can be sent from visone to R, you can also load objects of class ''igraph'' into visone. Loading the current R object ''egonet'' would be of no use since this network is already in visone and has not been modified. Loading networks from R into visone is useful when some values that have been computed in R and attached to the igraph object should be accessible as vertex or edge attributes in visone. This offers numerous possibilities to transform attributes, as it will be demonstrated in the following. | ||

+ | |||

+ | First let's copy ''egonet'' into a new igraph object named ''g'' by executing | ||

+ | g <- egonet | ||

+ | (This rather serves to demonstrate how new variables for igraph objects can be loaded into visone; we could also have attached the new data directly to ''egonet''.) To attach a new attribute named ''Degree'' that encodes the previously computed node degrees type | ||

+ | V(g)$Degree <- d | ||

+ | Degrees could have been computed in visone as well. However, R offers methods to transform such values that are not implemented directly in visone. For instance, the variable ''DegreeCentered'' computed and attached via | ||

+ | V(g)$DegreeCentered <- d-mean(d) | ||

+ | encodes the differences between the individual degrees and the mean (so that nodes with relatively small degrees get negative values and nodes with relatively high degrees get positive values). Likewise | ||

+ | V(g)$LogDegree <- log(d) | ||

+ | attaches the logarithmized degrees to the graph (this transformation is quite useful in networks with skewed degree distributions, e.g., [[Random_networks_generation#preferential|preferential attachment]] graphs). Note that while visone directly offers some possibilities to [[Managing_attributes_(tutorial)#Transforming_attributes|transform attributes]], logarithmic transformations are not implemented; in contrast, R as a programming language, imposes no such restrictions. | ||

+ | |||

+ | [[File:R_console_load.png]] | ||

+ | |||

+ | Finally, loading the network along with all old and new attributes into visone can be done as explained in the following. Select the variable <code>g</code> in the drop-down menu right of the '''load network''' button; pushing the '''load network''' button opens a new network tab with a network called ''g''. You can inspect the newly attached attributes via the [[attribute manager]]. | ||

+ | |||

+ | == File import and export in R == | ||

+ | |||

+ | === Saving and loading R objects === | ||

+ | |||

+ | Variables in the R workspace can be saved to and read from the disk. If you just want to save the igraph objects (the network together with all attributes) you could as well load them into visone and save them as [[GraphML]]. However, your R workspace might contain other variables that are not of class ''igraph''. To save objects it is convenient to set the working directory. The path to the current working directory can be obtained by | ||

+ | getwd() | ||

+ | which currently outputs in many cases the directory where the ''Rserve.exe'' file is located (see the [[Option_dialog#R_-_connection| R connection settings]]). To set it to a different directory type | ||

+ | setwd("<path_to_directory>") | ||

+ | (Chose a directory to which you have write-access.) Saving an R object (for instance, the network ''egonet'') can be done by | ||

+ | save(egonet, file="egonet.rda") | ||

+ | This creates a file ''egonet.rda'' in the specified working directory. (Here, ''egonet.rda'' is an arbitray filename that you can chose as you wish. ) All objects in the workspace can be saved by | ||

+ | save.image(file="myWorkspace.rda") | ||

+ | Conversely, to read objects from a file in the current working directory type | ||

+ | load(file="egonet.rda") | ||

+ | (This makes sense if you have closed the R connection and you want to recover the object ''egonet''.) R objects that have been saved to disk can, of course, also been imported into any other R environment (see [http://www.r-project.org/ http://www.r-project.org/] for more information). | ||

+ | |||

+ | === Plotting to a PDF file === | ||

+ | |||

+ | To plot a diagram, such as the degrees of a network, to a PDF file type | ||

+ | pdf("myDiagram.pdf") | ||

+ | plot(d, type="b") | ||

+ | dev.off() | ||

+ | This writes a PDF file ''myDiagram.pdf'' to the current working directory; this file can be viewed, e.g., with Adobe's Acrobat Reader, printed, or used as a figure in some other document. The R plot command is a very general and powerful tool to create statistical graphics; for more information see the documentation linked from the [http://www.r-project.org/ R Project Website]. | ||

+ | |||

+ | === Executing R code from a file === | ||

+ | |||

+ | Especially if you are working on a larger project it is convenient not just to save the data but also the R code to recompute some of the results or to modify some analyses. R code can be executed from text-files by the ''source'' command. For instance, | ||

+ | source("myRCode.R") | ||

+ | executes all commands from the file ''myRCode.R'' located in the current working directory. Note that this file must be a '''plain text file''' (see [http://en.wikipedia.org/wiki/Text_editor http://en.wikipedia.org/wiki/Text_editor] for explanation) containing commands like the examples provided in typewriter font in the boxes on this page. Conversely, | ||

+ | sink("myROutput.txt") | ||

+ | directs the R output to a file ''myROutput.txt'' in the current working directory. This is convenient when executing R commands that produce complex output. | ||

+ | |||

+ | == Fitting exponential random graph models (ERGMs) == | ||

+ | |||

+ | Since networks can be sent from visone to R you get the possibility to use many powerful contributed R packages for network analysis and modeling. In this section we illustrate the use of some methods provided by the '''statnet''' package written by Mark S. Handcock, David R. Hunter, Carter T. Butts, Steven M. Goodreau, and Martina Morris; see the [http://statnetproject.org/ statnet project Website] for additional information, including documentation and tutorials. In particular, we are going to demonstrate how to fit an exponential random graph model (ERGM) to a network sent from visone to R. ERGMs are sophisticated statistical network models that can deal with complex dependencies among observations, including homophily, reciprocity, preferential attachment, and triangular closure. Note that ERGMs are mainly applied to time-independent networks; an option for modeling network dynamics is the [[RSiena|'''RSiena''']] package that can be used from within visone as it is illustrated in the [[RSiena_(tutorial)| RSiena tutorial]]. | ||

+ | |||

+ | === Installing new packages === | ||

+ | |||

+ | To estimate an ERGM we need to install the R packages '''network''', '''sna''', and '''ergm''', all of which are part of the statnet package. To install these packages separately type (after appropriate replacement of <code><path to R library dir></code> and <code><url of R mirror site></code>) | ||

+ | install.packages("network", lib = "<path to R library dir>", repos = "<url of R mirror site>") | ||

+ | library(network, lib.loc = "<path to R library dir>") | ||

+ | |||

+ | install.packages("sna", lib = "<path to R library dir>", repos = "<url of R mirror site>") | ||

+ | library(sna, lib.loc = "<path to R library dir>") | ||

+ | |||

+ | install.packages("ergm", lib = "<path to R library dir>", repos = "<url of R mirror site>") | ||

+ | library(ergm, lib.loc = "<path to R library dir>") | ||

+ | |||

+ | For the R library you can set the same path as in the [[Option_dialog#R_-_connection| R connection settings]]; a list of mirror sites can be found at [http://cran.r-project.org/mirrors.html http://cran.r-project.org/mirrors.html] (copy the URL of any mirror site, including the http:// prefix, and use it in quotes as a value for the '''repos''' argument in the commands above). | ||

+ | |||

+ | Since <code>igraph</code> and <code>network</code> are both packages for networks, they define some methods with identical names. This can lead to problems when a method from one package is executed on an object from the other. A workaround for such problems would be to save the objects that you want to work with, close the R connection, open it again, install only the package that you need, and reload the data objects. | ||

+ | |||

+ | === Converting an igraph to a network object === | ||

+ | |||

+ | The <code>ergm</code> package works on network objects from the package <code>network</code>. Thus, the current <code>igraph</code> object first has to be converted into this class. This can be done in two steps: getting the adjacency matrix for the ''egonet'' object by | ||

+ | adj <- get.adjacency(egonet) | ||

+ | and then creating a network from this adjacency matrix by executing the following line (note that the <code>directed</code> attribute must be set to <code>FALSE</code>, otherwise the network will be directed) | ||

+ | net <- as.network(adj, directed=FALSE) | ||

+ | Executing | ||

+ | summary(net) | ||

+ | shows that the ties have been correctly converted from the <code>igraph</code> package to <code>network</code> but, so far, no vertex or edge attributes are attached to the <code>net</code> object. This can be done by, e.g., | ||

+ | set.vertex.attribute(net,"From", V(egonet)$Afrm) | ||

+ | set.vertex.attribute(net,"City", V(egonet)$Acit) | ||

+ | |||

+ | Conversely, to create an igraph from an adjacency matrix use the command <code>graph.adjacency(adj, mode="undirected")</code> (we don't need this at the moment). | ||

+ | |||

+ | === Computing observed statistics === | ||

+ | |||

+ | The '''ergm''' package enables the computation of the observed values of various network statistics, such as number of triangles, number of k-stars, and many more. To get these values call the <code>summary</code> method on an ERGM formula object. For instance, | ||

+ | summary(net ~ triangle + kstar(2) + nodematch("City")) | ||

+ | returns the number of triangles in the network, the number of 2-stars, and the number of edges connecting actors that live in the same city. In our example, this is | ||

+ | triangle kstar2 nodematch.City | ||

+ | 543 1857 143 | ||

+ | To see other statistics that can be used in an ERGM formula see the section on <code>ergm.terms</code> in the statnet documentation linked from [http://statnetproject.org http://statnetproject.org]. | ||

+ | |||

+ | === Estimating ERGMs === | ||

+ | |||

+ | Exponential random graph models (ERGMs) assign a network <math>G</math> a probability of the form | ||

+ | |||

+ | <math>P(G)=\frac{1}{\kappa(\theta)}\exp\left(\sum_{i=1}^k\theta_i\cdot s_i(G)\right)</math>, where | ||

+ | * the <math>s_i</math> are functions mapping from the set of networks (the ''population'' of the random graph model) to the real numbers; the <math>s_i</math> are called '''(network) statistics''' and are typically chosen by the researcher based on theory | ||

+ | * <math>\theta=(\theta_1,\dots,\theta_k)</math> is a vector of free '''parameters''' associated with statistics; the parameters are typically estimated via maximum likelihood estimation (MLE) given an observed network | ||

+ | * <math>\kappa(\theta)</math> is a normalization constant for the random graph model | ||

+ | |||

+ | The statistics are typically (functions of) counts of small subgraphs such as edges, stars, triangles, or edges connecting actors that have specific attribute values. The interpretation of the estimated parameters is as follwing. If, for instance, the parameter associated with the triangle count statistic is (significantly) positive, then networks with more triangles have higher probability - assuming that all other statistics remain constant. This would demonstrate a tendency for transitive closure as described in the saying ''the friend of a friend is a friend''. The specification, estimation, and interpretation of ERGMs is quite involved and cannot be sufficiently treated in this page; for more information we refer to the statnet tutorial linked from [http://statnetproject.org http://statnetproject.org]. In the following, we illustrate merely ''how'' ERGMs can be specified and estimated in statnet. | ||

+ | |||

+ | Computing maximum likelihood estimates for ERGM parameters can be done by calling the <code>ergm</code> on an ERGM formula. For instance, | ||

+ | model <- ergm(net ~ edges + nodematch("From") + nodematch("City") + gwesp(0.1, fixed=TRUE), , MCMCsamplesize=20000) | ||

+ | estimates the parameters of a model with four statistics: number of edges, numbers of edges connecting actors with the same country of origin, respectively the same city of residence, and the so-called ''geometrically weighted edgewise shared partners'' statistic. The latter has a similar interpretation as the triangle statistic but is less likely to lead to degenerate models; see the statnet documentation for details. Calling | ||

+ | summary(model) | ||

+ | prints basic information about the model, including estimated parameters and standard errors. In our example, we get (note that the results might change from call to call, since the estimation algorithm itself is probabilistic) | ||

+ | ========================== | ||

+ | Summary of model fit | ||

+ | ========================== | ||

+ | Formula: net ~ edges + nodematch("From") + nodematch("City") + gwesp(0.1, fixed = TRUE) | ||

+ | Newton-Raphson iterations: 6 | ||

+ | MCMC sample of size 20000 | ||

+ | Monte Carlo MLE Results: | ||

+ | Estimate Std. Error MCMC s.e. p-value | ||

+ | edges -4.1438 3.8443 0.193 0.281 | ||

+ | nodematch.From 0.8223 0.1459 0.006 <1e-04 *** | ||

+ | nodematch.City 0.8212 0.1553 0.002 <1e-04 *** | ||

+ | gwesp.fixed.0.1 3.0107 3.4763 0.175 0.387 | ||

+ | --- | ||

+ | Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 | ||

+ | From these results we can conclude that ties are more likely between actors that stem from the same countries of origin (significantly positive estimate for the parameter associated with the <code>nodematch.From</code> statistic) and ties are more likely between actors that live in the same city (<code>nodematch.City</code>). We could not find evidence for transitive closure - controlling for country of origin and city of residence - since the parameter associated with <code>gwesp.fixed.0.1</code> is not significant. | ||

+ | |||

+ | == References == | ||

+ | |||

+ | Gábor Csárdi and Tamás Nepusz. The '''igraph''' library. [http://igraph.sourceforge.net/ http://igraph.sourceforge.net/]. | ||

+ | |||

+ | Mark S. Handcock, David R. Hunter, Carter T. Butts, Steven M. Goodreau, and Martina Morris (2003). '''statnet''': Software tools for the Statistical Modeling of Network Data. URL [http://statnetproject.org http://statnetproject.org]. | ||

− | + | The R project for statistical computing [http://www.r-project.org/ http://www.r-project.org/] |

## Latest revision as of 13:24, 7 March 2012

This tutorial illustrates how to send network data from visone to R and back. The R project for statistical computing offers a rich set of methods for data analysis and modeling which becomes accessible from visone through the R console. We assume that you have installed the R connection as it is explained in the installation tutorial. This tutorial assumes that you have basic understanding about how to work with visone as it is, for instance, explained in the tutorial on visualization and analysis. You do *not* need to have any previous knowledge about R to follow this tutorial; nevertheless, to exploit the full potential offered by R you could consult documentation and tutorials linked from the R-project page.

To follow the steps illustrated in this tutorial you should download the network file *Egonet.graphml* which is linked from and explained in the page Egoredes_(data). Further you should remove all ties that are not rated as *very likely* in the same manner as it is explained in the last section of the visualization and analysis tutorial.

## Contents

## Sending networks from visone to R

You can open the R console by clicking on the icon in the toolbar; then click on the *R console* tab.
To send the network from visone to R choose a name in the textfield right of the **send** button (you might just accept visone's suggestion for this name, which should be *egonet*), and click on the **send** button. After clicking on **send** visone starts the Rserve connection and sends the current network to R. If this does not work you should check the settings of the R connection options accessible via the **file, options** menu. If it works you should get a message like

visone: sendActiveNet egonet done

in the message field of the R console.

You can list all variables that are in the R workspace by typing

ls()

in the input field of the R console (the input field is the text field just above the **send** button) and pressing the Enter-Key (currently there is one object called *egonet*).
Typing

class(egonet)

prints the class of the `egonet`

object which is `igraph`

. **igraph** is an R package obtainable from the CRAN Website (this site also gives you access to R tutorials and documentation). The igraph package is documented in more detail on http://igraph.sourceforge.net/.

## Getting basic statistics about an igraph object

The igraph documentation linked above gives a complete list of all methods available for this class. In the following we describe how to inspect what is encoded in the given object and how to get simple summary statistics. Executing the command

summary(egonet)

outputs basic information such as the number of vertices and edges, the names of vertex and edge attributes, as well as a list of all edges.

### Exploring attributes

The names of all vertex or edge attributes are printed with

list.vertex.attributes(egonet) list.edge.attributes(egonet)

To see the values of the vertex or edge attributes one needs to understand the concept of *vertex iterators* and *edge iterators* in igraph. The vertex iterator of graph *egonet* is returned by typing the command

V(egonet)

When executing this you see just the list of vertex *name*s which are here the numbers from 1 to 45. To obtain the values of an attribute (e.g., *Afrm*) type

V(egonet)$Afrm

which returns the vector of countries of origin of the various actors.

Useful summary statistics include information about how many actors originate from the various countries. However, typing the command

summary(V(egonet)$Afrm)

just outputs information about the class and size of the list of countries of origin:

Length Class Mode 45 character character

which is not very informative. To obtain the list of unique values for the *Afrm* attribute, you can type

unique(V(egonet)$Afrm)

which returns the names of five different countries. To count the number of actors in each of the countries, it is most convenient to convert the vector of character strings into a *factor* and save this factor in a new variable (e.g., called *from*) by typing the command

from <- as.factor(V(egonet)$Afrm)

Finally the command

summary(from)

returns the list of unique values along with the number of actors in each of the classes. In our example this is

Colombia Dominican Republic Puerto Rico Spain United States 2 14 5 1 23

### Indexing of vertex and edge iterators

Vertex iterators can be restricted to subsets by specifying a logical vector (or a command that produces one) in square brackets after the iterator. For instance, the command

V(egonet)[2:5]

returns the values 3 to 6. This seeming contradiction is explained by the fact that counting of indices in vertex or edge iterators starts at zero; thus, the name of the vertex at position 0 is 1, the name of the vertex at position 2 is 3, and so on. The result of such a restriction operation on an vertex iterator is itself a vertex iterator and, thus, provides access to vertex attributes. For instance, typing

V(egonet)[2:5]$Afrm

returns the countries of origin of actors indexed by 2 to 5 (i.e., named 3 to 6). The command

V(egonet)[Acit == "new york"]$Afrm

gives you the countries of origin of all actors whose attribute *Acit* (encoding the city of residence) equals *new york*, and so on.

An edge iterator is returned via the command

E(egonet)

and offers access to edge attributes similar as for vertices.

Vertex iterators and edge iterators can be indexed by more complex conditions. Actually, any logical vector whose length equals the number of vertices (respectively edges) can be used as an argument in the square brackets following vertex iterators (respectively edge iterators). The following lines illustrate such indexing tasks. To select all actors whose origin is in the US and save this iterator in a variable *actors.from.usa* type

actors.from.usa <- V(egonet)[Afrm == "United States"]

All edges connecting two actors from the US are obtained by

edges.within.usa <- E(egonet)[actors.from.usa %--% actors.from.usa]

The command *%--%* is a special command used in edge iterators between two vertex iterators; it selects all edges connecting vertices from the two specified subsets (which might be identical, as in the example above).
Edges with at least one actor from the US are selected by

edges.incident.usa <- E(egonet)[adj(actors.from.usa)]

To select all edges within any of the classes defined by Afrm we first construct a logical vector for edges that is true if and only if the *Afrm* attribute of the two connected vertices is identical and then use it as an argument in *E(egonet)[...]*. Therefore type

el <- get.edgelist(egonet) +1 within.class.edges <- V(egonet)[el[,1]]$Afrm == V(egonet)[el[,2]]$Afrm E(egonet)[within.class.edges]

The variable *el* is just a matrix with two columns containing the vertex ids of adjacent vertices. The *+1* in the first line is necessary because ids start with zero.

Vertex and edge iterators can be restricted in various other ways; see the igraph documentation for details.

## Analyzing distributions of centralities in igraph

The igraph package offers methods to compute various established centrality measures (refer to the igraph documentation for a complete list of available methods). While many of these could also be directly computed in visone without the detour via the R console, R directly offers statistical descriptions and analysis of the computed values. This is demonstrated in the following. The vertex degrees are returned by the command

degree(egonet)

Let's save this vector in a variable *d* by typing

d <- degree(egonet)

Mean, standard deviation, and summary statistics (including min, max, quartiles, and median) are computed by

mean(d) sd(d) summary(d)

To display these (or other) statistics separately for each class of actors defined by the country of origin (or any other attribute), execute the command

tapply(d, from, summary)

The three arguments of *tapply* have the following meaning: *d* is the vector of values to which the function should be applied, *from* is the factor whose unique values determine the different classes, and *summary* is the function to be computed (instead of *summary*, you could also type *mean*, *sd*, and so on).

## Loading networks from R into visone

As networks can be sent from visone to R, you can also load objects of class *igraph* into visone. Loading the current R object *egonet* would be of no use since this network is already in visone and has not been modified. Loading networks from R into visone is useful when some values that have been computed in R and attached to the igraph object should be accessible as vertex or edge attributes in visone. This offers numerous possibilities to transform attributes, as it will be demonstrated in the following.

First let's copy *egonet* into a new igraph object named *g* by executing

g <- egonet

(This rather serves to demonstrate how new variables for igraph objects can be loaded into visone; we could also have attached the new data directly to *egonet*.) To attach a new attribute named *Degree* that encodes the previously computed node degrees type

V(g)$Degree <- d

Degrees could have been computed in visone as well. However, R offers methods to transform such values that are not implemented directly in visone. For instance, the variable *DegreeCentered* computed and attached via

V(g)$DegreeCentered <- d-mean(d)

encodes the differences between the individual degrees and the mean (so that nodes with relatively small degrees get negative values and nodes with relatively high degrees get positive values). Likewise

V(g)$LogDegree <- log(d)

attaches the logarithmized degrees to the graph (this transformation is quite useful in networks with skewed degree distributions, e.g., preferential attachment graphs). Note that while visone directly offers some possibilities to transform attributes, logarithmic transformations are not implemented; in contrast, R as a programming language, imposes no such restrictions.

Finally, loading the network along with all old and new attributes into visone can be done as explained in the following. Select the variable `g`

in the drop-down menu right of the **load network** button; pushing the **load network** button opens a new network tab with a network called *g*. You can inspect the newly attached attributes via the attribute manager.

## File import and export in R

### Saving and loading R objects

Variables in the R workspace can be saved to and read from the disk. If you just want to save the igraph objects (the network together with all attributes) you could as well load them into visone and save them as GraphML. However, your R workspace might contain other variables that are not of class *igraph*. To save objects it is convenient to set the working directory. The path to the current working directory can be obtained by

getwd()

which currently outputs in many cases the directory where the *Rserve.exe* file is located (see the R connection settings). To set it to a different directory type

setwd("<path_to_directory>")

(Chose a directory to which you have write-access.) Saving an R object (for instance, the network *egonet*) can be done by

save(egonet, file="egonet.rda")

This creates a file *egonet.rda* in the specified working directory. (Here, *egonet.rda* is an arbitray filename that you can chose as you wish. ) All objects in the workspace can be saved by

save.image(file="myWorkspace.rda")

Conversely, to read objects from a file in the current working directory type

load(file="egonet.rda")

(This makes sense if you have closed the R connection and you want to recover the object *egonet*.) R objects that have been saved to disk can, of course, also been imported into any other R environment (see http://www.r-project.org/ for more information).

### Plotting to a PDF file

To plot a diagram, such as the degrees of a network, to a PDF file type

pdf("myDiagram.pdf") plot(d, type="b") dev.off()

This writes a PDF file *myDiagram.pdf* to the current working directory; this file can be viewed, e.g., with Adobe's Acrobat Reader, printed, or used as a figure in some other document. The R plot command is a very general and powerful tool to create statistical graphics; for more information see the documentation linked from the R Project Website.

### Executing R code from a file

Especially if you are working on a larger project it is convenient not just to save the data but also the R code to recompute some of the results or to modify some analyses. R code can be executed from text-files by the *source* command. For instance,

source("myRCode.R")

executes all commands from the file *myRCode.R* located in the current working directory. Note that this file must be a **plain text file** (see http://en.wikipedia.org/wiki/Text_editor for explanation) containing commands like the examples provided in typewriter font in the boxes on this page. Conversely,

sink("myROutput.txt")

directs the R output to a file *myROutput.txt* in the current working directory. This is convenient when executing R commands that produce complex output.

## Fitting exponential random graph models (ERGMs)

Since networks can be sent from visone to R you get the possibility to use many powerful contributed R packages for network analysis and modeling. In this section we illustrate the use of some methods provided by the **statnet** package written by Mark S. Handcock, David R. Hunter, Carter T. Butts, Steven M. Goodreau, and Martina Morris; see the statnet project Website for additional information, including documentation and tutorials. In particular, we are going to demonstrate how to fit an exponential random graph model (ERGM) to a network sent from visone to R. ERGMs are sophisticated statistical network models that can deal with complex dependencies among observations, including homophily, reciprocity, preferential attachment, and triangular closure. Note that ERGMs are mainly applied to time-independent networks; an option for modeling network dynamics is the **RSiena** package that can be used from within visone as it is illustrated in the RSiena tutorial.

### Installing new packages

To estimate an ERGM we need to install the R packages **network**, **sna**, and **ergm**, all of which are part of the statnet package. To install these packages separately type (after appropriate replacement of `<path to R library dir>`

and `<url of R mirror site>`

)

install.packages("network", lib = "<path to R library dir>", repos = "<url of R mirror site>") library(network, lib.loc = "<path to R library dir>")

install.packages("sna", lib = "<path to R library dir>", repos = "<url of R mirror site>") library(sna, lib.loc = "<path to R library dir>")

install.packages("ergm", lib = "<path to R library dir>", repos = "<url of R mirror site>") library(ergm, lib.loc = "<path to R library dir>")

For the R library you can set the same path as in the R connection settings; a list of mirror sites can be found at http://cran.r-project.org/mirrors.html (copy the URL of any mirror site, including the http:// prefix, and use it in quotes as a value for the **repos** argument in the commands above).

Since `igraph`

and `network`

are both packages for networks, they define some methods with identical names. This can lead to problems when a method from one package is executed on an object from the other. A workaround for such problems would be to save the objects that you want to work with, close the R connection, open it again, install only the package that you need, and reload the data objects.

### Converting an igraph to a network object

The `ergm`

package works on network objects from the package `network`

. Thus, the current `igraph`

object first has to be converted into this class. This can be done in two steps: getting the adjacency matrix for the *egonet* object by

adj <- get.adjacency(egonet)

and then creating a network from this adjacency matrix by executing the following line (note that the `directed`

attribute must be set to `FALSE`

, otherwise the network will be directed)

net <- as.network(adj, directed=FALSE)

Executing

summary(net)

shows that the ties have been correctly converted from the `igraph`

package to `network`

but, so far, no vertex or edge attributes are attached to the `net`

object. This can be done by, e.g.,

set.vertex.attribute(net,"From", V(egonet)$Afrm) set.vertex.attribute(net,"City", V(egonet)$Acit)

Conversely, to create an igraph from an adjacency matrix use the command `graph.adjacency(adj, mode="undirected")`

(we don't need this at the moment).

### Computing observed statistics

The **ergm** package enables the computation of the observed values of various network statistics, such as number of triangles, number of k-stars, and many more. To get these values call the `summary`

method on an ERGM formula object. For instance,

summary(net ~ triangle + kstar(2) + nodematch("City"))

returns the number of triangles in the network, the number of 2-stars, and the number of edges connecting actors that live in the same city. In our example, this is

triangle kstar2 nodematch.City 543 1857 143

To see other statistics that can be used in an ERGM formula see the section on `ergm.terms`

in the statnet documentation linked from http://statnetproject.org.

### Estimating ERGMs

Exponential random graph models (ERGMs) assign a network a probability of the form

, where

- the are functions mapping from the set of networks (the
*population*of the random graph model) to the real numbers; the are called**(network) statistics**and are typically chosen by the researcher based on theory - is a vector of free
**parameters**associated with statistics; the parameters are typically estimated via maximum likelihood estimation (MLE) given an observed network - is a normalization constant for the random graph model

The statistics are typically (functions of) counts of small subgraphs such as edges, stars, triangles, or edges connecting actors that have specific attribute values. The interpretation of the estimated parameters is as follwing. If, for instance, the parameter associated with the triangle count statistic is (significantly) positive, then networks with more triangles have higher probability - assuming that all other statistics remain constant. This would demonstrate a tendency for transitive closure as described in the saying *the friend of a friend is a friend*. The specification, estimation, and interpretation of ERGMs is quite involved and cannot be sufficiently treated in this page; for more information we refer to the statnet tutorial linked from http://statnetproject.org. In the following, we illustrate merely *how* ERGMs can be specified and estimated in statnet.

Computing maximum likelihood estimates for ERGM parameters can be done by calling the `ergm`

on an ERGM formula. For instance,

model <- ergm(net ~ edges + nodematch("From") + nodematch("City") + gwesp(0.1, fixed=TRUE), , MCMCsamplesize=20000)

estimates the parameters of a model with four statistics: number of edges, numbers of edges connecting actors with the same country of origin, respectively the same city of residence, and the so-called *geometrically weighted edgewise shared partners* statistic. The latter has a similar interpretation as the triangle statistic but is less likely to lead to degenerate models; see the statnet documentation for details. Calling

summary(model)

prints basic information about the model, including estimated parameters and standard errors. In our example, we get (note that the results might change from call to call, since the estimation algorithm itself is probabilistic)

========================== Summary of model fit ========================== Formula: net ~ edges + nodematch("From") + nodematch("City") + gwesp(0.1, fixed = TRUE) Newton-Raphson iterations: 6 MCMC sample of size 20000 Monte Carlo MLE Results: Estimate Std. Error MCMC s.e. p-value edges -4.1438 3.8443 0.193 0.281 nodematch.From 0.8223 0.1459 0.006 <1e-04 *** nodematch.City 0.8212 0.1553 0.002 <1e-04 *** gwesp.fixed.0.1 3.0107 3.4763 0.175 0.387 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

From these results we can conclude that ties are more likely between actors that stem from the same countries of origin (significantly positive estimate for the parameter associated with the `nodematch.From`

statistic) and ties are more likely between actors that live in the same city (`nodematch.City`

). We could not find evidence for transitive closure - controlling for country of origin and city of residence - since the parameter associated with `gwesp.fixed.0.1`

is not significant.

## References

Gábor Csárdi and Tamás Nepusz. The **igraph** library. http://igraph.sourceforge.net/.

Mark S. Handcock, David R. Hunter, Carter T. Butts, Steven M. Goodreau, and Martina Morris (2003). **statnet**: Software tools for the Statistical Modeling of Network Data. URL http://statnetproject.org.

The R project for statistical computing http://www.r-project.org/