Creating classes on vertices data frame from components$membership - networking

Question

I am looking to add a 'description' variable to the vertices data frame which describes the cluster in which a node is found. My network is family relationships so clusters could be a family of two adults and two children, single parent with three children, couple etc.
My data looks like
Vertices data frame
ID Date.Of.B Nationality
X1 02/05/1995 Ugandan
X2 10/10/2010 Ugandan
X3 15/12/1975 Irish
: : :
Edgelist
ID1 ID2
X1 X2
X1 X3
X2 X3
X3 X1
: :
I plan to create factor levels to describe clusters i.e
2 adults = 2A
2 adults 2 children = 2A2C
5 adults 0 children = 5A
After creating the graph using graph_from_data_frame() I can extract the components using componets() with components$membership giving each cluster a membership number with the IDs an attribute of components$membership. I can apply a label to each vertex to determine their status as an adult or child.
Basically I am looking to add another variable which classes each ID given the cluster it is in:
New vertices data frame
ID Date.Of.B Nationality Class
X1 02/05/1995 Ugandan 2A1C
X2 10/10/2010 Ugandan 2A1C
X3 15/12/1975 Irish 2A1C
: : :
I am thinking I am going to have to use some sort of loop to go through each cluster and apply a level to each vertex by component$membership
This is one option I thought of and am currently working on.
Please let me know if you have any other ideas or better ways to do it.
Thanks

Answer 1

Maybe this helps:
library(igraph)
library(dplyr)
library(tidyr)
Generate example data:
set.seed(1)
vertices <- data.frame(ID = 1:20,
date = as.character(rnorm(20, -5000, 3000) + Sys.Date()),
Nationality = letters[1:20])
edgelist <- data.frame(from = sample(1:20, 15, replace = T),
to = sample(1:20, 15, replace = T))
g <- graph_from_data_frame(edgelist,
directed = F,
vertices = vertices)
cp <- components(g)
Save component-membership as new vertex attribute:
V(g)$components <- membership(cp)
Extract vertices plus additional attributes:
df <- get.data.frame(g, "vertices")
Work with the dataframe:
First generate a new coding variable based on age (in days), count the occurence and paste the result into a new variable.
df <- df %>%
mutate(coding = ifelse(Sys.Date() - as.Date(df$date) > 6570, "A", "C")) %>%
group_by(components, coding) %>%
mutate(n = n()) %>%
ungroup() %>%
mutate(new = paste(n, coding, sep = "")) %>%
select(-coding, -n)
Then nest the dataframe based on components into a new dataframe and delete duplicates.
df2 <- df %>%
select(new, components) %>%
distinct(.keep_all = T) %>%
nest(-components)
After that you can merge the two dataframes and loop through (sapply) to unlist your new class variable (in this case called data), which is also your final result.
df3 <- left_join(df, df2) %>%
select(-new)
df3$data <- sapply(df3$data, function(x) paste(unname(unlist(x)), collapse = ""))