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This preserves attributes (column) types. By contrast, the (smarter) code in fortify.igraph uses sapply, which erroneously converts all vertex/edge attributes to character if a single attribute is of that class, resulting in numeric attributes being converted to characters (and then to factors, unless stringsAsFactors has been set to FALSE: see #53):
if (length(igraph::list.vertex.attributes(model))) {
nodes<- cbind(
nodes,
sapply(
igraph::list.vertex.attributes(model),
FUN=igraph::get.vertex.attribute,
graph=model,
USE.NAMES=TRUE
),
stringsAsFactors=stringsAsFactors
)
}
Solution (1) would be to use purrr::map_dfc to get the sapply method to preserve column types, but that would end up being more complex than a for at the cost of additional dependencies (including dplyr, since purrr::map_dfc relies on it).
Solution (2), which is dumber but better in this context, is to use for loops in fortify.igraph, as in fortify.network.
Solution (3), the best one in my view, is to use igraph::as_data_frame(x, what = "vertices") and igraph::as_data_frame(x, what = "edges") to import vertex and edge attributes, which is what I'll do, unless @jcfisher has a better fix.
library(ggnetwork)
#> Loading required package: ggplot2
library(network)
#> network: Classes for Relational Data#> Version 1.15 created on 2019-04-01.#> copyright (c) 2005, Carter T. Butts, University of California-Irvine#> Mark S. Handcock, University of California -- Los Angeles#> David R. Hunter, Penn State University#> Martina Morris, University of Washington#> Skye Bender-deMoll, University of Washington#> For citation information, type citation("network").#> Type help("network-package") to get started.
library(igraph)
#> #> Attaching package: 'igraph'#> The following objects are masked from 'package:network':#> #> %c%, %s%, add.edges, add.vertices, delete.edges,#> delete.vertices, get.edge.attribute, get.edges,#> get.vertex.attribute, is.bipartite, is.directed,#> list.edge.attributes, list.vertex.attributes,#> set.edge.attribute, set.vertex.attribute#> The following objects are masked from 'package:stats':#> #> decompose, spectrum#> The following object is masked from 'package:base':#> #> union
library(intergraph)
# network with numeric and character edges
data(emon, package="network")
emon[[1]]
#> Network attributes:#> vertices = 14 #> directed = TRUE #> hyper = FALSE #> loops = FALSE #> multiple = FALSE #> total edges= 83 #> missing edges= 0 #> non-missing edges= 83 #> #> Vertex attribute names: #> Command.Rank.Score Decision.Rank.Score Formalization Location Paid.Staff Sponsorship vertex.names Volunteer.Staff #> #> Edge attribute names: #> Frequencyintergraph::asIgraph(emon[[1]])
#> IGRAPH 031e6f4 D--- 14 83 -- #> + attr: Command.Rank.Score (v/n), Decision.Rank.Score (v/n),#> | Formalization (v/n), Location (v/c), na (v/l), Paid.Staff (v/n),#> | Sponsorship (v/c), vertex.names (v/c), Volunteer.Staff (v/n),#> | Frequency (e/n), na (e/l)#> + edges from 031e6f4:#> [1] 2->1 3->1 8->1 9->1 14->1 1->2 3->2 4->2 8->2 1->3 2->3#> [12] 4->3 7->3 12->3 13->3 1->4 3->4 8->4 1->5 3->5 8->5 14->5#> [23] 3->6 8->6 9->6 1->7 2->7 3->7 4->7 5->7 8->7 9->7 10->7#> [34] 11->7 12->7 13->7 1->8 2->8 3->8 5->8 7->8 9->8 12->8 13->8#> [45] 14->8 1->9 2->9 3->9 4->9 8->9 10->9 11->9 12->9 13->9#> + ... omitted several edges# all goes well with fortify.network, characters as factors (as per #53)
str(ggnetwork(emon[[1]]))
#> 'data.frame': 97 obs. of 15 variables:#> $ x : num 0.134 0 0.628 0.111 0.561 ...#> $ y : num 0.68 0.485 0.447 0.246 1 ...#> $ Command.Rank.Score : num 0 10 3 5 0 0 20 40 10 30 ...#> $ Decision.Rank.Score: num 20 7 0 5 0 0 20 50 10 20 ...#> $ Formalization : num 2 1 1 1 1 1 1 2 1 3 ...#> $ Location : Factor w/ 1 level "L": 1 1 1 1 1 1 1 1 1 1 ...#> $ na.x : logi FALSE FALSE FALSE FALSE FALSE FALSE ...#> $ Paid.Staff : num 10 400 200 60 1 7 60 7 70 100 ...#> $ Sponsorship : Factor w/ 6 levels "City","County",..: 6 6 6 4 5 5 2 3 1 1 ...#> $ vertex.names : Factor w/ 14 levels "A.1.Ambulance.Service",..: 12 14 13 4 9 10 8 5 2 3 ...#> $ Volunteer.Staff : num 50 2000 0 0 20 80 20 100 0 0 ...#> $ xend : num 0.134 0 0.628 0.111 0.561 ...#> $ yend : num 0.68 0.485 0.447 0.246 1 ...#> $ Frequency : num NA NA NA NA NA NA NA NA NA NA ...#> $ na.y : logi NA NA NA NA NA NA ...# however, fortify.igraph messes with the (vertex) attribute types
str(ggnetwork(intergraph::asIgraph(emon[[1]])))
#> 'data.frame': 97 obs. of 15 variables:#> $ x : num 0.662 0.724 0.392 0.537 0.424 ...#> $ y : num 0.414 0.69 0.664 1 0 ...#> $ Command.Rank.Score : Factor w/ 8 levels "0","10","2","20",..: 1 2 5 8 1 1 4 7 2 6 ...#> $ Decision.Rank.Score: Factor w/ 8 levels "0","10","2","20",..: 4 8 1 6 1 1 4 7 2 4 ...#> $ Formalization : Factor w/ 3 levels "1","2","3": 2 1 1 1 1 1 1 2 1 3 ...#> $ Location : Factor w/ 1 level "L": 1 1 1 1 1 1 1 1 1 1 ...#> $ na.x : Factor w/ 1 level "FALSE": 1 1 1 1 1 1 1 1 1 1 ...#> $ Paid.Staff : Factor w/ 9 levels "0","1","10","100",..: 3 6 5 7 2 8 7 8 9 4 ...#> $ Sponsorship : Factor w/ 6 levels "City","County",..: 6 6 6 4 5 5 2 3 1 1 ...#> $ vertex.names : Factor w/ 14 levels "A.1.Ambulance.Service",..: 12 14 13 4 9 10 8 5 2 3 ...#> $ Volunteer.Staff : Factor w/ 7 levels "0","100","20",..: 5 4 1 1 3 7 3 2 1 1 ...#> $ xend : num 0.662 0.724 0.392 0.537 0.424 ...#> $ yend : num 0.414 0.69 0.664 1 0 ...#> $ Frequency : num NA NA NA NA NA NA NA NA NA NA ...#> $ na.y : num NA NA NA NA NA NA NA NA NA NA ...
See reprex at end of issue.
This is because
fortify.network
uses dumbfor
loops to import vertex/edge attributes (show vertex attributes only below):ggnetwork/R/fortify-network.R
Lines 143 to 147 in 97e6b56
This preserves attributes (column) types. By contrast, the (smarter) code in
fortify.igraph
usessapply
, which erroneously converts all vertex/edge attributes tocharacter
if a single attribute is of that class, resulting in numeric attributes being converted to characters (and then to factors, unlessstringsAsFactors
has been set toFALSE
: see #53):ggnetwork/R/fortify-igraph.R
Lines 55 to 67 in 97e6b56
Solution (1) would be to use
purrr::map_dfc
to get thesapply
method to preserve column types, but that would end up being more complex than afor
at the cost of additional dependencies (includingdplyr
, sincepurrr::map_dfc
relies on it).Solution (2), which is dumber but better in this context, is to use
for
loops infortify.igraph
, as infortify.network
.Solution (3), the best one in my view, is to use
igraph::as_data_frame(x, what = "vertices")
andigraph::as_data_frame(x, what = "edges")
to import vertex and edge attributes, which is what I'll do, unless @jcfisher has a better fix.Created on 2019-07-30 by the reprex package (v0.3.0)
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