Coerce a grouped transition network to a tidy data frame

A grouped model is a collection of networks, one per group. This method is the tidy view of it: every table it returns carries a group column, so a comparison across groups is a data frame you can read, sort, or join rather than a set of objects you have to reach into one at a time.

Usage

# S3 method for class 'ts_tna_group'
as.data.frame(
  x,
  row.names = NULL,
  optional = FALSE,
  what = c("edges", "series", "groups"),
  ...
)

Arguments

x

A ts_tna_group result from ts_tna() and friends, built with their group argument.

row.names

Optional row names.

optional

Ignored.

what

Which table to return: "edges" (one row per group and state pair, the default), "series" (the per-observation source table for every group), or "groups" (a one-row-per-group index of how much data backs each network).

...

Ignored.

Value

A base data frame whose first column is group.

See also

ts_tna() for the group argument that builds these models.

Examples

data(motivation)
networks <- ts_tna(
  motivation,
  series = "pleasure", group = "task_context_type",
  labels = c("low", "mid", "high")
)
as.data.frame(networks, what = "groups")
#>      group type sequences observations states edges
#> 1     Home  tna       832         1324      3     9
#> 2    Other  tna         2            3      3     1
#> 3 Personal  tna       822         1309      3     9
#> 4     Work  tna       976         2235      3     9
head(as.data.frame(networks))
#>   group from  to    weight
#> 1  Home  low low 0.7616099
#> 2  Home  mid low 0.4366197
#> 3  Home high low 0.4814815
#> 4  Home  low mid 0.1981424
#> 5  Home  mid mid 0.4718310
#> 6  Home high mid 0.3703704