These functions offer tools for splitting manynet-consistent objects (matrices, igraph, tidygraph, or network objects) into lists of networks.
Not all functions have methods available for all object classes. Below are the currently implemented S3 methods:
| data.frame | diff_model | igraph | matrix | network | tbl_graph | |
| to_components | 1 | 0 | 1 | 1 | 1 | 1 |
| to_egos | 1 | 0 | 1 | 1 | 1 | 1 |
| to_slices | 0 | 0 | 1 | 0 | 0 | 1 |
| to_subgraphs | 0 | 0 | 1 | 0 | 1 | 1 |
| to_waves | 1 | 1 | 1 | 0 | 0 | 1 |
to_egos(.data, max_dist = 1, min_dist = 0)
to_subgraphs(.data, attribute)
to_components(.data)
to_waves(.data, attribute = "wave", panels = NULL, cumulative = FALSE)
to_slices(.data, attribute = "time", slice = NULL)An object of a manynet-consistent class:
matrix (adjacency or incidence) from {base} R
edgelist, a data frame from {base} R or tibble from {tibble}
igraph, from the {igraph} package
network, from the {network} package
tbl_graph, from the {tidygraph} package
The maximum breadth of the neighbourhood. By default 1.
The minimum breadth of the neighbourhood. By default 0. Increasing this to 1 excludes the ego, and 2 excludes ego's direct alters.
One or two attributes used to slice data.
Would you like to select certain waves? NULL by default. That is, a list of networks for every available wave is returned. Users can also list specific waves they want to select.
Whether to make wave ties cumulative. FALSE by default. That is, each wave is treated isolated.
Character string or character list indicating the date(s) or integer(s) range used to slice data (e.g slice = c(1:2, 3:4)).
The returned object will be a list of network objects.
to_egos(): Returns a list of ego (or focal)
networks.
to_subgraphs(): Returns a list of subgraphs
on some given node attribute.
to_components(): Returns a list of the components
in a network.
to_waves(): Returns a network
with some discrete observations over time
into a list of those observations.
to_slices(): Returns a list of a network
with some continuous time variable at some time slice(s).
to_egos(ison_adolescents)
#> $Betty
#> # A labelled, undirected network with 2 nodes and 1 ties
#> # A tibble: 2 × 1
#> name
#> <chr>
#> 1 Betty
#> 2 Sue
#> # A tibble: 1 × 2
#> from to
#> <int> <int>
#> 1 1 2
#>
#> $Sue
#> # A labelled, undirected network with 5 nodes and 6 ties
#> # A tibble: 5 × 1
#> name
#> <chr>
#> 1 Betty
#> 2 Sue
#> 3 Alice
#> 4 Dale
#> 5 Pam
#> # A tibble: 6 × 2
#> from to
#> <int> <int>
#> 1 1 2
#> 2 2 3
#> 3 2 4
#> 4 3 4
#> 5 2 5
#> 6 3 5
#>
#> $Alice
#> # A labelled, undirected network with 5 nodes and 7 ties
#> # A tibble: 5 × 1
#> name
#> <chr>
#> 1 Sue
#> 2 Alice
#> 3 Jane
#> 4 Dale
#> 5 Pam
#> # A tibble: 7 × 2
#> from to
#> <int> <int>
#> 1 1 2
#> 2 2 3
#> 3 1 4
#> 4 2 4
#> 5 3 4
#> 6 1 5
#> # ℹ 1 more row
#>
#> $Jane
#> # A labelled, undirected network with 3 nodes and 3 ties
#> # A tibble: 3 × 1
#> name
#> <chr>
#> 1 Alice
#> 2 Jane
#> 3 Dale
#> # A tibble: 3 × 2
#> from to
#> <int> <int>
#> 1 1 2
#> 2 1 3
#> 3 2 3
#>
#> $Dale
#> # A labelled, undirected network with 4 nodes and 5 ties
#> # A tibble: 4 × 1
#> name
#> <chr>
#> 1 Sue
#> 2 Alice
#> 3 Jane
#> 4 Dale
#> # A tibble: 5 × 2
#> from to
#> <int> <int>
#> 1 1 2
#> 2 2 3
#> 3 1 4
#> 4 2 4
#> 5 3 4
#>
#> $Pam
#> # A labelled, undirected network with 4 nodes and 4 ties
#> # A tibble: 4 × 1
#> name
#> <chr>
#> 1 Sue
#> 2 Alice
#> 3 Pam
#> 4 Carol
#> # A tibble: 4 × 2
#> from to
#> <int> <int>
#> 1 1 2
#> 2 1 3
#> 3 2 3
#> 4 3 4
#>
#> $Carol
#> # A labelled, undirected network with 3 nodes and 2 ties
#> # A tibble: 3 × 1
#> name
#> <chr>
#> 1 Pam
#> 2 Carol
#> 3 Tina
#> # A tibble: 2 × 2
#> from to
#> <int> <int>
#> 1 1 2
#> 2 2 3
#>
#> $Tina
#> # A labelled, undirected network with 2 nodes and 1 ties
#> # A tibble: 2 × 1
#> name
#> <chr>
#> 1 Carol
#> 2 Tina
#> # A tibble: 1 × 2
#> from to
#> <int> <int>
#> 1 1 2
#>
#autographs(to_egos(ison_adolescents,2))
ison_adolescents %>%
mutate(unicorn = sample(c("yes", "no"), 8,
replace = TRUE)) %>%
to_subgraphs(attribute = "unicorn")
#> [[1]]
#> # A labelled, undirected network with 4 nodes and 2 ties
#> # A tibble: 4 × 2
#> name unicorn
#> <chr> <chr>
#> 1 Betty no
#> 2 Sue no
#> 3 Alice no
#> 4 Carol no
#> # A tibble: 2 × 2
#> from to
#> <int> <int>
#> 1 1 2
#> 2 2 3
#>
#> [[2]]
#> # A labelled, undirected network with 4 nodes and 1 ties
#> # A tibble: 4 × 2
#> name unicorn
#> <chr> <chr>
#> 1 Jane yes
#> 2 Dale yes
#> 3 Pam yes
#> 4 Tina yes
#> # A tibble: 1 × 2
#> from to
#> <int> <int>
#> 1 1 2
#>
to_components(ison_marvel_relationships)
#> [[1]]
#> # A labelled, complex, multiplex, signed, undirected network with 50 nodes and 558 ties
#> # A tibble: 50 × 10
#> name Gender Appearances Attractive Rich Intellect Omnilingual PowerOrigin
#> <chr> <chr> <int> <int> <int> <int> <int> <chr>
#> 1 Abomina… Male 427 0 0 1 1 Radiation
#> 2 Ant-Man Male 589 1 0 1 0 Human
#> 3 Apocaly… Male 1207 0 0 1 1 Mutant
#> 4 Beast Male 7609 1 0 1 0 Mutant
#> 5 Black P… Male 2189 1 1 1 0 Human
#> 6 Black W… Female 2907 1 0 1 0 Human
#> # ℹ 44 more rows
#> # ℹ 2 more variables: UnarmedCombat <int>, ArmedCombat <int>
#> # A tibble: 558 × 3
#> from to sign
#> <int> <int> <dbl>
#> 1 1 4 -1
#> 2 1 10 -1
#> 3 1 11 -1
#> 4 1 22 -1
#> 5 1 23 -1
#> 6 1 24 -1
#> # ℹ 552 more rows
#>
#> [[2]]
#> # A labelled, signed, undirected network with 1 nodes and 0 ties
#> # A tibble: 1 × 10
#> name Gender Appearances Attractive Rich Intellect Omnilingual PowerOrigin
#> <chr> <chr> <int> <int> <int> <int> <int> <chr>
#> 1 Cable Male 2734 0 0 1 0 Mutant
#> # ℹ 2 more variables: UnarmedCombat <int>, ArmedCombat <int>
#> # A tibble: 0 × 3
#> # ℹ 3 variables: from <int>, to <int>, sign <dbl>
#>
#> [[3]]
#> # A labelled, signed, undirected network with 1 nodes and 0 ties
#> # A tibble: 1 × 10
#> name Gender Appearances Attractive Rich Intellect Omnilingual PowerOrigin
#> <chr> <chr> <int> <int> <int> <int> <int> <chr>
#> 1 Iron Fi… Male 1789 0 1 1 0 Human
#> # ℹ 2 more variables: UnarmedCombat <int>, ArmedCombat <int>
#> # A tibble: 0 × 3
#> # ℹ 3 variables: from <int>, to <int>, sign <dbl>
#>
#> [[4]]
#> # A labelled, signed, undirected network with 1 nodes and 0 ties
#> # A tibble: 1 × 10
#> name Gender Appearances Attractive Rich Intellect Omnilingual PowerOrigin
#> <chr> <chr> <int> <int> <int> <int> <int> <chr>
#> 1 Luke Ca… Male 2466 0 0 0 0 Human
#> # ℹ 2 more variables: UnarmedCombat <int>, ArmedCombat <int>
#> # A tibble: 0 × 3
#> # ℹ 3 variables: from <int>, to <int>, sign <dbl>
#>
ison_adolescents %>%
mutate_ties(wave = sample(1995:1998, 10, replace = TRUE)) %>%
to_waves(attribute = "wave")
#> $`1995`
#> # A longitudinal, labelled, undirected network with 8 nodes and 2 ties
#> # A tibble: 8 × 1
#> name
#> <chr>
#> 1 Betty
#> 2 Sue
#> 3 Alice
#> 4 Jane
#> 5 Dale
#> 6 Pam
#> # ℹ 2 more rows
#> # A tibble: 2 × 3
#> from to wave
#> <int> <int> <int>
#> 1 1 2 1995
#> 2 2 3 1995
#>
#> $`1998`
#> # A longitudinal, labelled, undirected network with 8 nodes and 5 ties
#> # A tibble: 8 × 1
#> name
#> <chr>
#> 1 Betty
#> 2 Sue
#> 3 Alice
#> 4 Jane
#> 5 Dale
#> 6 Pam
#> # ℹ 2 more rows
#> # A tibble: 5 × 3
#> from to wave
#> <int> <int> <int>
#> 1 3 4 1998
#> 2 2 5 1998
#> 3 4 5 1998
#> 4 2 6 1998
#> 5 7 8 1998
#>
#> $`1996`
#> # A longitudinal, labelled, undirected network with 8 nodes and 2 ties
#> # A tibble: 8 × 1
#> name
#> <chr>
#> 1 Betty
#> 2 Sue
#> 3 Alice
#> 4 Jane
#> 5 Dale
#> 6 Pam
#> # ℹ 2 more rows
#> # A tibble: 2 × 3
#> from to wave
#> <int> <int> <int>
#> 1 3 5 1996
#> 2 6 7 1996
#>
#> $`1997`
#> # A longitudinal, labelled, undirected network with 8 nodes and 1 ties
#> # A tibble: 8 × 1
#> name
#> <chr>
#> 1 Betty
#> 2 Sue
#> 3 Alice
#> 4 Jane
#> 5 Dale
#> 6 Pam
#> # ℹ 2 more rows
#> # A tibble: 1 × 3
#> from to wave
#> <int> <int> <int>
#> 1 3 6 1997
#>
ison_adolescents %>%
mutate_ties(time = 1:10, increment = 1) %>%
add_ties(c(1,2), list(time = 3, increment = -1)) %>%
to_slices(slice = 7)
#> # A labelled, weighted, undirected network with 8 nodes and 6 ties
#> # A tibble: 8 × 1
#> name
#> <chr>
#> 1 Betty
#> 2 Sue
#> 3 Alice
#> 4 Jane
#> 5 Dale
#> 6 Pam
#> # ℹ 2 more rows
#> # A tibble: 6 × 3
#> from to weight
#> <int> <int> <dbl>
#> 1 2 3 1
#> 2 3 4 1
#> 3 2 5 1
#> 4 3 5 1
#> 5 4 5 1
#> 6 2 6 1