The edges alive in each time bin, as one tidy table. Useful for exporting a slice, for feeding a layout routine, or for checking by eye what the metric verbs are seeing.
Usage
snapshots(
dn,
at = NULL,
sessions = c("bounded", "collapse", "separate"),
sample = NULL,
start = NULL,
end = NULL,
step = NULL,
window = NULL,
plot = FALSE
)Arguments
- dn
A temporal network from
dynet().- at
Optional single time, narrowing the result to the bins that cover it. A network built from dates may be addressed with a date. With the default disjoint tiling that is one bin; with an overlapping
windowevery bin containing the time is returned. A time outside every bin falls back to the nearest bin rather than failing, but that bin may itself hold no active tie, in which case the result is a zero-row frame with the documented columns.- sessions
How to treat sessions, as in
centrality_series():"bounded"(the default),"collapse"or"separate"."separate"needs a network built with a session column and raisesdynet_no_sessionsotherwise.- sample
Deprecated.
"instant"is equivalent towindow = 0;"window"uses the current positive/default window.- start, end
First and last time at which to measure. Default to the observed range. A network built from dates may be addressed with dates.
- step
How often to measure. Defaults to the interval the network was built with.
- window
How much time each measurement covers. Defaults to
step, which tiles the period into disjoint bins. A larger value slides an overlapping window;0samples the network at each point in time."all"measures the whole observed period as one window, closed on the right so an event at the final instant is inside it; it cannot be combined withstep, and undersessions = "separate"or discontinuous observation it gives one window per session or observed component.- plot
Whether to draw the result as well as return it. Drawing is a side effect in the manner of
graphics::hist(): the verb still returns its tidy table, invisibly when it has drawn, soplot = TRUEsaves the wrappingplot()call without changing what comes back. Useplot()on the result when the figure needs arguments of its own.
Value
A dynet_snapshot data frame with one row per active edge per bin:
session (when the network has sessions), observation (when
observation is discontinuous, naming the observed component the bin falls
in), time, from, to, weight and n_spells.
print() shows a header and the first rows, summary() collapses to one
row per bin, plot() draws how many ties each bin holds, and
as.data.frame() returns the plain table. A pair
joined by more than one spell in the same bin is one edge, with
n_spells recording how many spells were collapsed – so the edge counts
here agree with those from metrics(). weight is the sum of those
spells' full weights: a spell counts its whole weight in every bin it
touches, as networkDynamic::network.collapse() does. Snapshot
"strength" in centrality_series() instead splits a spell's weight by
the share of its duration inside the bin. Eligible isolates have no
synthetic edge row; use centrality_series() or metrics() when the
eligible population itself is required.
Conditions
Errors: dynet_no_sessions (sessions = "separate" without a session
column), dynet_outside_observation (the requested range misses observed
support; it also carries dynet_bad_input), and dynet_bad_input for
every other broken contract – dn not
a dynet, an at, start or end that is not a single finite time, and
an out-of-range step or window.
Warning: dynet_deprecated for the retired sample argument.
Examples
dn <- dynet(school_contacts)
snapshots(dn, at = 3)
#> # Snapshot edges | 1 bin | 12 tie rows | time in step
#> time from to weight n_spells
#> 1 3 Ana Jonas 1 1
#> 2 3 Kira Leo 1 1
#> 3 3 Leo Finn 1 1
#> 4 3 Nils Eve 1 1
#> 5 3 Ben Jonas 1 1
#> 6 3 Ben Eve 1 1
#> 7 3 Dan Ana 1 1
#> 8 3 Dan Jonas 1 1
#> 9 3 Dan Eve 1 1
#> 10 3 Gita Jonas 1 1
#> # 2 more rows. summary() counts them by bin.