Digraph6

The ‘digraph6’ format is designed for directed graphs. Encoding g_directed will give:

library(rgraph6)
as_digraph6(g_directed)
## [1] "&KG?E@?????GA_C?E??A????_?"

Graph6

The ‘graph6’ format is designed for undirected graphs. It should be more efficient for dense graphs. Encoding g_undirected will give:

as_graph6(g_undirected)
## [1] "KQOGgoG??@W?"

Sparse6

The ‘sparse6’ format is designed for undirected graphs. It should be more efficient for sparse graphs. Encoding g_undirected will give:

as_sparse6(g_undirected)
## [1] ":KcAKYRJKdLG_F"

Encode a list of ‘igraph’ objects

Let’s generate a list of igraph objects:

set.seed(666)
igraph_list <- replicate(5, igraph::sample_gnp(10, 0.1, directed=FALSE),
simplify = FALSE)

Encode as a vector of ‘graph6’ symbols:

as_graph6(igraph_list)
## [1] "ICG_@?W??" "I????@B?G" "I?@O????W" "I@@A?E???" "I?_?_@_??"

Encode as a vector of ‘sparse6’ symbols:

as_sparse6(igraph_list)
## [1] ":IeASjaeR" ":IoCp{^" ":IiC]Rg" ":IeIgWu`" ":IgAo{@D"

Decode a vector of different types of symbols

The package provides example data g6, d6, and s6. Let’s create a short vector with a mixture of ‘graph6’, ‘digraph6’ and ‘sparse6’ symbols:

x <- c(g6[1], s6[2], d6[3])
x
## [1] "N??E??G?e?G?????GGO"
## [2] ":NkF?XduSqiDRwYU~"
## [3] "&N?R_?E?C?D??U_A????????O???????????????"

Decode it to a list of igraph objects:

igraph_from_text(x)
## [[1]]
## IGRAPH 59d2f61 U--- 15 10 --
## + edges from 59d2f61:
## [1] 1-- 7 1--11 2-- 7 2--11 2--12 2--15 5-- 9 7--10 8--15 13--15
##
## [[2]]
## IGRAPH 59d2a80 U--- 15 13 --
## + edges from 59d2a80:
## [1] 2-- 7 2-- 9 4--10 6--10 6--12 7--12 11--12 5--13 6--13 10--13
## [11] 4--15 10--15 14--15
##
## [[3]]
## IGRAPH 59d31d4 D--- 15 15 --
## + edges from 59d31d4:
## [1] 1-> 8 1->11 1->12 1->13 2->13 2->14 3->10 4-> 7 4-> 9 5-> 8 5->10 5->11
## [13] 5->13 6-> 8 9->14

Decode it to a list of network objects:

network_from_text(x)
## Loading required namespace: network
## [[1]]
## Network attributes:
## vertices = 15
## directed = FALSE
## hyper = FALSE
## loops = FALSE
## multiple = FALSE
## bipartite = FALSE
## total edges= 10
## missing edges= 0
## non-missing edges= 10
##
## Vertex attribute names:
## vertex.names
##
## No edge attributes
##
## [[2]]
## Network attributes:
## vertices = 15
## directed = FALSE
## hyper = FALSE
## loops = FALSE
## multiple = FALSE
## bipartite = FALSE
## total edges= 13
## missing edges= 0
## non-missing edges= 13
##
## Vertex attribute names:
## vertex.names
##
## No edge attributes
##
## [[3]]
## Network attributes:
## vertices = 15
## directed = TRUE
## hyper = FALSE
## loops = FALSE
## multiple = FALSE
## bipartite = FALSE
## total edges= 15
## missing edges= 0
## non-missing edges= 15
##
## Vertex attribute names:
## vertex.names
##
## No edge attributes

Tidy graph databases (base R)

The formats shine if we need to store large number of graphs in a data frame / tibble. Let’s generate a list of random graphs as igraph objects and store them in a data frame column of ‘graph6’ symbols:

set.seed(666)
d <- data.frame(
gr6 = as_graph6(replicate(
10,
random.graph.game(sample(3:12, replace=TRUE), p=.5, directed=FALSE),
simplify=FALSE
))
)
d
## gr6
## 1 FblF_
## 2 DFc
## 3 HfTaMwk
## 4 KefToktrftZ~
## 5 JPraDzZQ?M?
## 6 Bo
## 7 Ed`w
## 8 Gpuq|{
## 9 EbSG
## 10 ICNa@Gg\\o

Nice and compact. We can go further by doing some computations and saving the results together with the graph data. Let’s add number of edges, vertices and the density:

d2 <- within(
d, {
igraphs <- igraph_from_text(gr6)
vc <- vapply(igraphs, vcount, integer(1))
ec <- vapply(igraphs, ecount, numeric(1))
density <- vapply(igraphs, edge_density, numeric(1))
})
d2$igraphs <- NULL # drop the list column with igraph objects
d2
## gr6 density ec vc
## 1 FblF_ 0.5238095 11 7
## 2 DFc 0.5000000 5 5
## 3 HfTaMwk 0.5000000 18 9
## 4 KefToktrftZ~ 0.6212121 41 12
## 5 JPraDzZQ?M? 0.4363636 24 11
## 6 Bo 0.6666667 2 3
## 7 Ed`w 0.5333333 8 6
## 8 Gpuq|{ 0.6785714 19 8
## 9 EbSG 0.4000000 6 6
## 10 ICNa@Gg\\o 0.3777778 17 10

This can be to a simple CSV file

write.csv(d2, row.names = FALSE)
## "gr6","density","ec","vc"
## "FblF_",0.523809523809524,11,7
## "DFc",0.5,5,5
## "HfTaMwk",0.5,18,9
## "KefToktrftZ~",0.621212121212121,41,12
## "JPraDzZQ?M?",0.436363636363636,24,11
## "Bo",0.666666666666667,2,3
## "Ed`w",0.533333333333333,8,6
## "Gpuq|{",0.678571428571429,19,8
## "EbSG",0.4,6,6
## "ICNa@Gg\o",0.377777777777778,17,10

Detaching igraph to avoid conflicts:

detach(package:igraph)

Tidy graph databases (tidyverse)

The same operations as above, but using tidyverse (Wickham et al. 2019) functions and tibbles:

library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
set.seed(666)
d <- tibble(
gr6 = as_graph6(replicate(
10,
igraph::random.graph.game(sample(3:12, replace=TRUE), p=.5, directed=FALSE),
simplify=FALSE
))
)
d
## # A tibble: 10 x 1
## gr6
## <chr>
## 1 "FblF_"
## 2 "DFc"
## 3 "HfTaMwk"
## 4 "KefToktrftZ~"
## 5 "JPraDzZQ?M?"
## 6 "Bo"
## 7 "Ed`w"
## 8 "Gpuq|{"
## 9 "EbSG"
## 10 "ICNa@Gg\\o"
d2 <- d %>%
mutate(
igraphs = igraph_from_text(gr6),
vc = vapply(igraphs, igraph::vcount, integer(1)),
ec = vapply(igraphs, igraph::ecount, numeric(1)),
density = vapply(igraphs, igraph::edge_density, numeric(1))
)
d2
## # A tibble: 10 x 5
## gr6 igraphs vc ec density
## <chr> <list> <int> <dbl> <dbl>
## 1 "FblF_" <igraph> 7 11 0.524
## 2 "DFc" <igraph> 5 5 0.5
## 3 "HfTaMwk" <igraph> 9 18 0.5
## 4 "KefToktrftZ~" <igraph> 12 41 0.621
## 5 "JPraDzZQ?M?" <igraph> 11 24 0.436
## 6 "Bo" <igraph> 3 2 0.667
## 7 "Ed`w" <igraph> 6 8 0.533
## 8 "Gpuq|{" <igraph> 8 19 0.679
## 9 "EbSG" <igraph> 6 6 0.4
## 10 "ICNa@Gg\\o" <igraph> 10 17 0.378
# Show-off the CSV
d2 %>%
select(- igraphs) %>%
readr::write_csv(file=stdout())
## gr6,vc,ec,density
## FblF_,7,11,0.5238095238095238
## DFc,5,5,0.5
## HfTaMwk,9,18,0.5
## KefToktrftZ~,12,41,0.6212121212121212
## JPraDzZQ?M?,11,24,0.43636363636363634
## Bo,3,2,0.6666666666666666
## Ed`w,6,8,0.5333333333333333
## Gpuq|{,8,19,0.6785714285714286
## EbSG,6,6,0.4
## ICNa@Gg\o,10,17,0.37777777777777777