Tidy tables from a temporal network

Usage

# S3 method for class 'dynet'
as.data.frame(
  x,
  row.names = NULL,
  optional = FALSE,
  what = c("edges", "nodes", "bins", "network", "observations", "observed_edges",
    "vertex_spells"),
  measure = NULL,
  sessions = c("bounded", "collapse", "separate"),
  start = NULL,
  end = NULL,
  ...
)

Arguments

x: A temporal network from dynet().
row.names: Ignored; present for compatibility with the generic.
optional: Ignored; present for compatibility with the generic.
what: Which table to return: "edges", the default, for raw edge spells, "observed_edges" for derived observation fragments, "observations" for canonical observed support, "vertex_spells" for canonical declared vertex activity, "nodes" for the vertex table, "bins" for the measurement grid, or "network" for the aggregate edge list cograph renders.
measure: Optional centrality measures to annotate the vertex table with, valid only for what = "nodes". Each becomes one column holding the value over the whole observed period, so the vertex table can be filtered or ranked without a second call. Any measure centrality_series() accepts at snapshot scope is allowed, plus "indegree" and "outdegree"; anything else raises a dynet_unknown_measure error, and a measure that is not a character vector raises dynet_bad_input. Naming it for any other what raises a dynet_bad_input error too.
sessions: How sessions are treated while measure is computed: "bounded" (the default), "collapse" or "separate", as in centrality_series(). Ignored when measure is not given.
start, end: Measurement bounds passed to centrality_series() when measure is given, and ignored otherwise. Default to the observed range.
...: Ignored.

Value

A plain data.frame, one row per whatever what names.

"edges": one row per unchanged raw spell, with from, to, start, end, duration and weight. A session column is present when the network was built with sessions, onset_censored and terminus_censored when interval censoring was declared explicitly, and any column the construction carried through – thread for a threaded log, group for a co-presence log.

"observations": one row per canonical observation component, with observation, start, end, duration and instant.

"observed_edges": one row per derived observation fragment, with raw_spell, observation and fragment locating it, from, to, start, end (clipped to the observation), raw_start, raw_end (as supplied), weight, instant, the strict left_observation_censored and right_observation_censored flags, and duration. session and the explicit onset_censored/terminus_censored flags are copied unchanged from the raw spell when the network carries them.

"vertex_spells": one row per maximal declared activity component, with vertex_spell, node, start, end, duration, instant, session, onset_censored and terminus_censored. Half-open positive spells and exact points are both representable. Undeclared vertices are implicitly always active and receive no synthetic rows, so this table is empty for a network with no declared vertex activity.

"nodes": one row per vertex, with name, any static attributes supplied at construction, and one column per measure named in measure.

"bins": one row per measurement window, with bin, lo, hi, time (the bin's representative time) and closed (whether the upper bound is included). Bins are component-qualified under discontinuous observation.

"network": one row per aggregate vertex pair, with from, to and the summed weight cograph renders.

Examples

dn <- dynet(school_contacts)
spells <- as.data.frame(dn)
head(spells)
#>    from   to start  end duration weight
#> 1 Jonas  Dan  0.00 1.10     1.10      1
#> 2  Gita  Ana  0.14 0.98     0.84      1
#> 3   Leo Mira  0.15 0.42     0.27      1
#> 4   Leo Iris  0.15 0.96     0.81      1
#> 5  Kira  Ben  0.33 0.69     0.36      1
#> 6   Leo Iris  0.38 0.50     0.12      1
as.data.frame(dn, what = "nodes")
#>     name
#> 1    Ana
#> 2    Ben
#> 3   Cara
#> 4    Dan
#> 5    Eve
#> 6   Finn
#> 7   Gita
#> 8   Hugo
#> 9   Iris
#> 10 Jonas
#> 11  Kira
#> 12   Leo
#> 13  Mira
#> 14  Nils
as.data.frame(dn, what = "vertex_spells")
#> [1] vertex_spell      node              start             end              
#> [5] duration          instant           session           onset_censored   
#> [9] terminus_censored
#> <0 rows> (or 0-length row.names)

# Annotate the vertex table so it can be filtered without a second call.
busy <- as.data.frame(dn, what = "nodes",
                      measure = c("degree", "indegree", "outdegree"))
subset(busy, degree > 15)
#>     name degree indegree outdegree
#> 1    Ana     16        8         8
#> 3   Cara     17        7        10
#> 4    Dan     18        8        10
#> 5    Eve     17       10         7
#> 10 Jonas     18        9         9
#> 11  Kira     17        9         8
#> 13  Mira     16        8         8
#> 14  Nils     16        9         7