Storing node attributes by name in igraph for subsetting plots - r

Question

I am working with a large social network that spans 5 years of data collection. My intention is to subset the data by month/year to analyze the change in various network metrics.
I want to store node attributes into the data frame such that they can be called back after subsetting. For the reproducible example, I want to subset my global network by weight (e.g. all nodes with edges >2).
Is there a way to store node attributes (i.e. Male/Female) into the nodes on the original (larger data frame) that can be recalled after filtering out various nodes (and ignored if the node is missing)? Meaning, if a node's order was changed in the df it will still be associated with the correct sex (i.e. node 1 is always a male regardless if other nodes have been removed or reordered).
I've found answers to creating node attributes for each individual sub-network but I need to generate ~50 subnetworks and being forced to label each individual networks node, in the correct order would be hours of extra work. (e.g. V(any.given.subnetwork)$Sex <- c("male","female","male","male","female","male")).
Very small reproducible example below to illustrate my code:
library(igraph)
library(ggraph)
library(dplyr)
#Load Global Data
set.seed(43)
df <- data.frame(id=1:6,id2=c(2,3,4,5,6,1), weight= c(4,3,1,8,2,7))
#make an igraph graph from the df dataframe
df.df <- graph.data.frame(df, directed = TRUE)
#make df.df and adjacency matrix
df.mat <- as_adjacency_matrix(df.df, type = "both", names = TRUE,
sparse = FALSE, attr= "weight")
#make it an igraph object
df.mat <- graph.adjacency(df.mat, mode= "directed", weighted=TRUE, diag = FALSE)
summary(df.mat)
#Applying "sex" attribute to the nodes
V(df.mat)$Sex <- c("male","female","male","male","female","male")
#Plot
ggraph(df.mat, layout = "nicely") +
geom_edge_link(aes(alpha= weight)) + geom_node_point(aes(color = factor(Sex)))
#filtering out any nodes from Global "df" that have a weight less than 2
df.mat.01 <- df %>%
filter(weight > 2)
#make an igraph graph from the df.01 dataframe
df.df.01 <- graph.data.frame(df.mat.01, directed = TRUE)
#make df.df.01 and adjacency matrix
df.mat01 <- as_adjacency_matrix(df.df.01, type = "both", names = TRUE,
sparse = FALSE, attr= "weight")
#make it an igraph object
df.mat01 <- graph.adjacency(df.mat01, mode= "directed", weighted=TRUE, diag = FALSE)
ggraph(df.mat01, layout = "nicely") +
geom_edge_link(aes(alpha= weight)) + geom_node_point(aes(color = factor(Sex)))
#Error in factor(Sex) : object 'Sex' not found

Answer 1

The package tidygraph makes such manipulations straightforward. You can convert your graph into a tbl_graph which is easier to manipulate, and can be plotted directly with ggraph or converted back to an igraph:
library(tidygraph)
df_mat <- as_tbl_graph(df.mat)
df_mat
#> # A tbl_graph: 6 nodes and 6 edges
#> #
#> # A directed simple graph with 1 component
#> #
#> # Node Data: 6 x 2 (active)
#> name Sex
#> <chr> <chr>
#> 1 1 male
#> 2 2 female
#> 3 3 male
#> 4 4 male
#> 5 5 female
#> 6 6 male
#> #
#> # Edge Data: 6 x 3
#> from to weight
#> <int> <int> <dbl>
#> 1 1 2 4
#> 2 2 3 3
#> 3 3 4 1
#> # ... with 3 more rows
For example, to filter out all edges with weight <= 2 and plot with ggraph, we can do:
df_mat %>%
activate(edges) %>%
filter(weight > 2) %>%
ggraph(layout = "nicely") +
geom_edge_link(aes(alpha= weight)) +
geom_node_point(aes(color = factor(Sex)))