Joining lists of networks, graphs, and matrices

These functions offer tools for joining lists of manynet-consistent objects (matrices, igraph, tidygraph, or network objects) into a single object.

• from_subgraphs() modifies a list of subgraphs into a single tidygraph.

• from_egos() modifies a list of ego networks into a whole tidygraph

• from_waves() modifies a list of network waves into a longitudinal tidygraph.

• from_slices() modifies a list of time slices of a network into a dynamic tidygraph.

• from_ties() modifies a list of different ties into a multiplex tidygraph

from_subgraphs(netlist)

from_egos(netlist)

from_waves(netlist)

from_slices(netlist, remove.duplicates = FALSE)

from_ties(netlist, netnames)

Arguments

netlist

A list of network, igraph, tidygraph, matrix, or edgelist objects.

remove.duplicates

Should duplicates be removed? By default FALSE. If TRUE, duplicated edges are removed.

netnames

A character vector of names for the different network objects, if not already named within the list.

Value

A tidygraph object modified as explained in the function description, details, or section.

See also

Other modifications: modif_correlation, modif_direction, modif_labels, modif_levels, modif_miss, modif_paths, modif_permutation, modif_plexity, modif_project, modif_scope, modif_split, modif_weight

Examples

ison_adolescents |>
  mutate(unicorn = sample(c("yes", "no"), 8, replace = TRUE)) |>
  to_subgraphs(attribute = "unicorn") |>
  from_subgraphs()
#> # A labelled, undirected network of 8 nodes and 2 ties
#> 
#> ── Nodes 
#> # A tibble: 8 × 3
#>   name  unicorn.x unicorn.y
#>   <chr> <chr>     <chr>    
#> 1 Betty no        NA       
#> 2 Alice no        NA       
#> 3 Dale  no        NA       
#> 4 Carol no        NA       
#> 5 Sue   NA        yes      
#> 6 Jane  NA        yes      
#> # ℹ 2 more rows
#> 
#> ── Ties 
#> # A tibble: 2 × 2
#>    from    to
#>   <int> <int>
#> 1     2     3
#> 2     5     7
#> 
ison_adolescents |>
  to_egos() |>
  from_egos()
#> # A labelled, directed network of 8 nodes and 10 arcs
#> 
#> ── Nodes 
#> # A tibble: 8 × 1
#>   name 
#>   <chr>
#> 1 Betty
#> 2 Sue  
#> 3 Alice
#> 4 Jane 
#> 5 Pam  
#> 6 Carol
#> # ℹ 2 more rows
#> 
#> ── Ties 
#> # A tibble: 10 × 2
#>    from    to
#>   <int> <int>
#> 1     1     2
#> 2     2     3
#> 3     2     7
#> 4     3     7
#> 5     2     5
#> 6     3     5
#> # ℹ 4 more rows
#> 
ison_adolescents |>
  mutate_ties(wave = sample(1:4, 10, replace = TRUE)) |>
  to_waves(attribute = "wave") |>
  from_waves()
#> # A longitudinal, labelled, directed network of 8 nodes and 10 arcs over 4
#> waves
#> 
#> ── Nodes 
#> # A tibble: 8 × 1
#>   name 
#>   <chr>
#> 1 Sue  
#> 2 Alice
#> 3 Betty
#> 4 Pam  
#> 5 Jane 
#> 6 Carol
#> # ℹ 2 more rows
#> 
#> ── Ties 
#> # A tibble: 10 × 3
#>    from    to  wave
#>   <int> <int> <int>
#> 1     1     2     1
#> 2     2     7     1
#> 3     3     1     2
#> 4     2     5     2
#> 5     1     7     2
#> 6     1     4     2
#> # ℹ 4 more rows
#> 
ison_adolescents |>
  mutate_ties(time = 1:10, increment = 1) |> 
  add_ties(c(1,2), list(time = 3, increment = -1)) |> 
  to_slices(slice = c(5,7)) |>
  from_slices()
#> # A labelled, weighted, directed network of 5 nodes and 10 arcs
#> 
#> ── Nodes 
#> # A tibble: 5 × 1
#>   name 
#>   <chr>
#> 1 Sue  
#> 2 Alice
#> 3 Jane 
#> 4 Dale 
#> 5 Pam  
#> 
#> ── Ties 
#> # A tibble: 10 × 3
#>    from    to weight
#>   <int> <int>  <dbl>
#> 1     1     2      1
#> 2     2     3      1
#> 3     1     4      1
#> 4     2     4      1
#> 5     1     2      1
#> 6     2     3      1
#> # ℹ 4 more rows
#>