tw_network: Creates graph (directed)
In gvegayon/twitterreport: Out-of-the-Box Analysis and Reporting Tools for Twitter

Description Usage Arguments Details Value Author(s) Examples

Using the output of tw_extract() and the author of the message, creates a graph

1 2	tw_network(source, target, only.from = FALSE, exclude.self = TRUE, min.interact = 1, group = NULL, size = NULL, ignore.case = TRUE)

`source`	Vector of screen_name
`target`	List of vectors of mentions (output from `tw_extract`)
`only.from`	Whether to filter the links to those only where source and target are in the `source` vector
`exclude.self`	Whether to exclude self-links
`min.interact`	Minimun number of interactions to consider (links below this number will be excluded)
`group`	Data frame with two columns: name & group
`size`	A data frame with two columns: name & size
`ignore.case`	When `TRUE` converts all of `source` and `target` to lower-case.

The value column in the links dataframe (see Value) is computed as the number of connexions between the source and the target.

A two-element list containing two data.frames, nodes and links of class tw_Class_graph (to be used with plot.tw_Class_graph(). The nodes data.frame includes two columns, id, name and group. The links data.frame includes three columns, source, target and value.

George G. Vega Yon

## Not run: 
# Loading sample data and retrieving mentions
data(senate_tweets)
mentions <- tw_extract(senate_tweets$text, obj="mention")$mention

# Preparing data for size. Here we are just setting a random size for
# each vertex.
usrs<- tolower(senate_tweets$screen_name)
size <- data.frame(name=unique(usrs),
                   size=exp(runif(length(unique(usrs)))*5))

# Creating the graph
graph <- tw_network(
  usrs, mentions, min.interact = 5, size=size)

# Visualizing the graph
plot(graph)

## End(Not run)