Apply functions to all attributes on the neighborhood of a node in Tidygraph

Question

I have a graph, with multiple attributes, W1, W2, B1, B2, etc. I want to mutate the average attribute values of the neighbors.

I can write code for one attribute, but how could I use one operation to get all the averages of all the attributes and create separate variables for each neighbor's average attribute values?

create_ring(40) %>%
  mutate(id = row_number()) -> ring

ring %>%
      mutate(W1 = rnorm(40,75,15),
             W2 = rnorm(40,20,11),
             B1 = rnorm(40,2,11),
             B2 = rnorm(40,1,1)) %>%
      mutate(Neighs = local_members(order = 1,
                                    mindist = 1),
             k = local_size(order = 1,
                            mindist = 1)) -> ring


ring <- ring %>% mutate(W1_neighs = as.igraph(.) %>%
      ego(order = 1, mindist = 1) %>%
      sapply(function(v) mean(v$W1, na.rm=T)))```

Answer 1

I don't think need to use as.igraph + ego to compute the mean of neighbors' attribute values. You can directly use the attribute values from the vertex dataframe and compute the means like below

ring %>%
    mutate(
        across(W1:B2,
            ~ {
                x <- .x
                map_dbl(Neighs, \(k) mean(x[k], na.rm = TRUE))
            },
            .names = "{.col}_Neighs"
        )
    )

and you will obtain

# A tbl_graph: 40 nodes and 40 edges
#
# An undirected simple graph with 1 component
#
# A tibble: 40 × 11
     id    W1    W2    B1      B2 Neighs        k W1_Neighs W2_Neighs B1_Neighs
  <int> <dbl> <dbl> <dbl>   <dbl> <list>    <dbl>     <dbl>     <dbl>     <dbl>
1     1  50.8  39.5  6.44 -0.0558 <int [2]>     2      75.4      23.5     10.1
2     2  69.3  17.6  4.04  0.176  <int [2]>     2      64.7      34.0     -1.17
3     3  78.7  28.5 -8.78  0.654  <int [2]>     2      55.1      17.2      9.59
4     4  41.0  16.8 15.1   2.34   <int [2]>     2      78.5      27.8     -1.74
5     5  78.2  27.0  5.29  2.27   <int [2]>     2      64.7      30.8     17.0
6     6  88.5  44.9 18.8   1.43   <int [2]>     2      90.8      23.6     -3.62
# ℹ 34 more rows
# ℹ 1 more variable: B2_Neighs <dbl>
# ℹ Use `print(n = ...)` to see more rows
#
# A tibble: 40 × 2
   from    to
  <int> <int>
1     1     2
2     2     3
3     3     4
# ℹ 37 more rows
# ℹ Use `print(n = ...)` to see more rows