Tidy table of a temporal trajectory tree
Source:R/path-trajectory.R
as.data.frame.dynet_path_trajectories.RdTidy table of a temporal trajectory tree
Usage
# S3 method for class 'dynet_path_trajectories'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)Arguments
- x
A result from
path_trajectories().- row.names, optional
Ignored.
- ...
Ignored.
Value
A plain data frame with one row per tree node, with the columns
described in path_trajectories() and none of its attributes.
Examples
dn <- dynet(school_contacts)
routes <- paths(dn, from = "Ana")
trajectories <- path_trajectories(routes)
as.data.frame(trajectories)
#> node
#> 1 Ana@0
#> 2 Ana@0 -> [email protected]
#> 3 Ana@0 -> [email protected] -> [email protected]
#> 4 Ana@0 -> [email protected] -> [email protected] -> Iris@10
#> 5 Ana@0 -> [email protected] -> [email protected] -> [email protected]
#> 6 Ana@0 -> [email protected] -> [email protected]
#> 7 Ana@0 -> [email protected] -> [email protected] -> [email protected]
#> 8 Ana@0 -> [email protected] -> [email protected] -> [email protected] -> [email protected]
#> 9 Ana@0 -> [email protected]
#> 10 Ana@0 -> [email protected] -> [email protected]
#> 11 Ana@0 -> [email protected] -> [email protected] -> [email protected]
#> 12 Ana@0 -> [email protected] -> [email protected] -> [email protected] -> [email protected]
#> 13 Ana@0 -> [email protected]
#> 14 Ana@0 -> [email protected] -> [email protected]
#> 15 Ana@0 -> [email protected] -> [email protected] -> [email protected]
#> 16 Ana@0 -> [email protected] -> [email protected] -> [email protected] -> [email protected]
#> 17 Ana@0 -> [email protected]
#> 18 Ana@0 -> [email protected] -> [email protected]
#> 19 Ana@0 -> [email protected] -> [email protected] -> [email protected]
#> 20 Ana@0 -> [email protected] -> [email protected] -> [email protected] -> [email protected]
#> 21 Ana@0 -> [email protected]
#> 22 Ana@0 -> [email protected] -> [email protected]
#> parent depth count probability vertex
#> 1 <NA> 0 19 NA Ana
#> 2 Ana@0 1 7 0.3684211 Cara
#> 3 Ana@0 -> [email protected] 2 3 0.4285714 Finn
#> 4 Ana@0 -> [email protected] -> [email protected] 3 1 0.3333333 Iris
#> 5 Ana@0 -> [email protected] -> [email protected] 3 1 0.3333333 Leo
#> 6 Ana@0 -> [email protected] 2 3 0.4285714 Nils
#> 7 Ana@0 -> [email protected] -> [email protected] 3 2 0.6666667 Hugo
#> 8 Ana@0 -> [email protected] -> [email protected] -> [email protected] 4 1 0.5000000 Dan
#> 9 Ana@0 1 4 0.2105263 Jonas
#> 10 Ana@0 -> [email protected] 2 3 0.7500000 Kira
#> 11 Ana@0 -> [email protected] -> [email protected] 3 2 0.6666667 Ben
#> 12 Ana@0 -> [email protected] -> [email protected] -> [email protected] 4 1 0.5000000 Eve
#> 13 Ana@0 1 3 0.1578947 Jonas
#> 14 Ana@0 -> [email protected] 2 3 1.0000000 Kira
#> 15 Ana@0 -> [email protected] -> [email protected] 3 2 0.6666667 Ben
#> 16 Ana@0 -> [email protected] -> [email protected] -> [email protected] 4 1 0.5000000 Eve
#> 17 Ana@0 1 2 0.1052632 Jonas
#> 18 Ana@0 -> [email protected] 2 2 1.0000000 Kira
#> 19 Ana@0 -> [email protected] -> [email protected] 3 2 1.0000000 Ben
#> 20 Ana@0 -> [email protected] -> [email protected] -> [email protected] 4 1 0.5000000 Eve
#> 21 Ana@0 1 2 0.1052632 Mira
#> 22 Ana@0 -> [email protected] 2 1 0.5000000 Gita
#> time session branch
#> 1 0.00 <NA> 3.15
#> 2 6.67 <NA> 5.75
#> 3 6.96 <NA> 6.50
#> 4 10.00 <NA> 7.00
#> 5 9.65 <NA> 6.00
#> 6 7.51 <NA> 5.00
#> 7 7.98 <NA> 5.00
#> 8 7.98 <NA> 5.00
#> 9 2.12 <NA> 4.00
#> 10 6.12 <NA> 4.00
#> 11 9.59 <NA> 4.00
#> 12 11.66 <NA> 4.00
#> 13 3.43 <NA> 3.00
#> 14 6.12 <NA> 3.00
#> 15 9.59 <NA> 3.00
#> 16 11.66 <NA> 3.00
#> 17 6.68 <NA> 2.00
#> 18 6.68 <NA> 2.00
#> 19 9.59 <NA> 2.00
#> 20 11.66 <NA> 2.00
#> 21 6.36 <NA> 1.00
#> 22 6.36 <NA> 1.00