Count edges of a given type for each node with tidygraph

Question

Objective: I want to count the number of incoming edges of a partial type for each node. How can I do that?

Criteria:

• I need to use the R package tidygraph
• Answers that allow me to mutate an existing graph via a magrittr pipeline are better
• Fewer lines of code are better

The following code will generate an example graph

g <- play_erdos_renyi(n = 20, p = .10) %>% 
  activate(edges) %>% 
  mutate(type = sample(c('a', 'b', 'c'), size = n(), replace = T))

Ideal output, when searching (for example) for incoming g edges if type "a" would look like:

Node   type_a_edges
X           3
Y           1
Z           4
...

EDIT: Added a figure to make the problem more concrete.

Answer 1

Here is an tidygraph + dplyr option

g %>%
  activate(edges) %>%
  filter(type == "a") %>%
  as_tibble() %>%
  group_by(to) %>%
  summarise(indegree_a = n())

which gives counts of all type "A" of inwards edges

# A tibble: 8 x 2
     to indegree_a
  <int>      <int>
1     3          1
2     5          1
3     8          2
4    11          1
5    12          1
6    15          2
7    17          2
8    18          2

---

If you want to have full information of all nodes, you can try the code below

g %>%
  activate(edges) %>%
  as_tibble() %>%
  select(-from) %>%
  mutate(counts = 1) %>%
  arrange(type) %>%
  pivot_wider(
    names_from = type,
    values_from = counts,
    values_fill = 0, values_fn = sum, names_glue = "indegree_{.name}"
  ) %>%
  arrange(to)

which gives

# A tibble: 18 x 4
      to indegree_a indegree_b indegree_c
   <int>      <dbl>      <dbl>      <dbl>
 1     1          0          1          2
 2     2          0          0          1
 3     3          1          0          0
 4     4          0          2          0
 5     5          1          0          2
 6     6          0          1          1
 7     8          2          1          0
 8     9          0          0          1
 9    10          0          0          1
10    11          1          1          0
11    12          1          0          1
12    13          0          1          2
13    15          2          0          0
14    16          0          3          0
15    17          2          0          0
16    18          2          1          0
17    19          0          0          1
18    20          0          1          0

Answer 2

This will produce output consistent with the request

g %>%  
  activate(nodes) %>% 
  mutate(
    indegree_type_a = centrality_degree(
      weights = as.numeric(.E()$type == 'a'),
      mode = 'in'))