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 combining the list of network data.

See also

Other modifications: manip_as, manip_correlation, manip_deformat, manip_levels, manip_miss, manip_nodes, manip_paths, manip_permutation, manip_preformat, manip_project, manip_reformat, manip_scope, manip_split, manip_ties

Examples

ison_adolescents %>%
  mutate(unicorn = sample(c("yes", "no"), 8, replace = TRUE)) %>%
  to_subgraphs(attribute = "unicorn") %>%
  from_subgraphs()
#> Joining with `by = join_by(name, unicorn)`
#> # A labelled, undirected network of 8 nodes and 5 ties
#> # A tibble: 8 × 2
#>   name  unicorn
#>   <chr> <chr>  
#> 1 Betty no     
#> 2 Sue   no     
#> 3 Jane  no     
#> 4 Dale  no     
#> 5 Tina  no     
#> 6 Alice yes    
#> # ℹ 2 more rows
#> # A tibble: 5 × 2
#>    from    to
#>   <int> <int>
#> 1     1     2
#> 2     2     4
#> 3     3     4
#> 4     6     7
#> 5     7     8
ison_adolescents %>%
  to_egos() %>%
  from_egos()
#> # A labelled, directed network of 8 nodes and 10 arcs
#> # A tibble: 8 × 1
#>   name 
#>   <chr>
#> 1 Betty
#> 2 Sue  
#> 3 Alice
#> 4 Jane 
#> 5 Pam  
#> 6 Carol
#> # ℹ 2 more rows
#> # 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
#> # A tibble: 8 × 1
#>   name 
#>   <chr>
#> 1 Betty
#> 2 Alice
#> 3 Jane 
#> 4 Sue  
#> 5 Pam  
#> 6 Carol
#> # ℹ 2 more rows
#> # A tibble: 10 × 3
#>    from    to  wave
#>   <int> <int> <int>
#> 1     1     4     2
#> 2     2     3     2
#> 3     3     7     2
#> 4     4     5     2
#> 5     2     7     3
#> 6     2     5     3
#> # 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
#> # A tibble: 5 × 1
#>   name 
#>   <chr>
#> 1 Sue  
#> 2 Alice
#> 3 Jane 
#> 4 Dale 
#> 5 Pam  
#> # 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