Obtain overview of available network data

Source: R/manynet-tutorials.R

This function makes it easy to get an overview of available data:

  • table_data() returns a tibble with details of the network datasets included in the packages.

table_data(..., pkg = c("manynet", "migraph"))

Arguments

...

Network marks, e.g. directed, twomode, or signed, that are used to filter the results.

pkg

String, name of the package.

Examples

table_data()
#> # A tibble: 25 × 17
#>    dataset             components nodes  ties nattr tattr directed weighted
#>    <char>                   <dbl> <dbl> <dbl> <dbl> <dbl> <logi>   <logi>  
#>  1 ison_koenigsberg             1     4     7     3     1 FALSE    FALSE   
#>  2 irps_wwi                     1     6    20     1     3 FALSE    FALSE   
#>  3 ison_adolescents             1     8    10     1     0 FALSE    FALSE   
#>  4 ison_brandes                 1    11    12     1     0 FALSE    FALSE   
#>  5 ison_algebra                 1    16   279     0     2 TRUE     TRUE    
#>  6 ison_monks                   1    18   463     3     4 TRUE     TRUE    
#>  7 ison_hightech                1    21   312     4     1 TRUE     FALSE   
#>  8 ison_networkers              1    32   440     3     1 TRUE     TRUE    
#>  9 ison_southern_women          1    32    89     4     1 FALSE    FALSE   
#> 10 ison_karateka                1    34    78     2     1 FALSE    TRUE    
#> # ℹ 15 more rows
#> # ℹ 9 more variables: twomode <logi>, labelled <logi>, signed <logi>,
#> #   multiplex <logi>, longitudinal <logi>, dynamic <logi>, changing <logi>,
#> #   acyclic <logi>, attributed <logi>
# to obtain list of all e.g. directed networks:
table_data(pkg = "manynet", directed)
#> # A tibble: 9 × 17
#>   dataset         components nodes  ties nattr tattr directed weighted twomode
#>   <char>               <dbl> <dbl> <dbl> <dbl> <dbl> <logi>   <logi>   <logi> 
#> 1 ison_algebra             1    16   279     0     2 TRUE     TRUE     FALSE  
#> 2 ison_monks               1    18   463     3     4 TRUE     TRUE     FALSE  
#> 3 ison_hightech            1    21   312     4     1 TRUE     FALSE    FALSE  
#> 4 ison_networkers          1    32   440     3     1 TRUE     TRUE     FALSE  
#> 5 fict_potter             34    64   544     5     1 TRUE     FALSE    FALSE  
#> 6 ison_lawfirm             1    71  2571     7     1 TRUE     FALSE    FALSE  
#> 7 fict_starwars           64   110   563    12     2 TRUE     TRUE     FALSE  
#> 8 fict_thrones           130   208   404    10     1 TRUE     FALSE    FALSE  
#> 9 irps_blogs             688  1490 19090     3     0 TRUE     FALSE    FALSE  
#> # ℹ 8 more variables: labelled <logi>, signed <logi>, multiplex <logi>,
#> #   longitudinal <logi>, dynamic <logi>, changing <logi>, acyclic <logi>,
#> #   attributed <logi>
# to obtain overview of unique datasets:
table_data() %>% 
  dplyr::distinct(directed, weighted, twomode, signed, 
                 .keep_all = TRUE)
#> # A tibble: 7 × 17
#>   dataset             components nodes  ties nattr tattr directed weighted
#>   <char>                   <dbl> <dbl> <dbl> <dbl> <dbl> <logi>   <logi>  
#> 1 ison_koenigsberg             1     4     7     3     1 FALSE    FALSE   
#> 2 irps_wwi                     1     6    20     1     3 FALSE    FALSE   
#> 3 ison_algebra                 1    16   279     0     2 TRUE     TRUE    
#> 4 ison_monks                   1    18   463     3     4 TRUE     TRUE    
#> 5 ison_hightech                1    21   312     4     1 TRUE     FALSE   
#> 6 ison_southern_women          1    32    89     4     1 FALSE    FALSE   
#> 7 ison_karateka                1    34    78     2     1 FALSE    TRUE    
#> # ℹ 9 more variables: twomode <logi>, labelled <logi>, signed <logi>,
#> #   multiplex <logi>, longitudinal <logi>, dynamic <logi>, changing <logi>,
#> #   acyclic <logi>, attributed <logi>