I have a list of lists containing the edgelists of plant-pollinator interactions, for a number bipartite networks. Using the igraph r-package I am attempting to convert these lists into bipartite incidence matrices for analysis.
I am having trouble adding vertices (igraph::V) to the igraph objects in the list graph_list. Note: I would prefer to avoid for loop solutions as my complete dataset is a list of 238 networks comprising >80k edges.
Reproducable example of my data.
network_list <- replicate(10, expr = {data.frame(plant = paste('plnt', '_', sample(x = letters, size = 10, replace = T), sep = ""),pollinator = paste('pollinator', '_', sample(x = letters, size = 10, replace = T), sep = ""))}, simplify = F)
library(tidyverse)
library(dplyr)
library(igraph)
I am trying to add the vertex sequence to each graph in the list. But the following code is not working as I am expecting.
graph_list <- lapply(network_list, graph_from_data_frame) #creates list of igraph objects from list of edgelists
list_mapping <- lapply(graph_list, bipartite.mapping) #map networks as bipartit
list_type<- lapply(list_mapping, with, type) #extract list of vertices for each network
graph_list <- mapply(c, graph_list, list_type) #ATTEMPT to add vertices to graph_list
incidence_list <- lapply(graph_list, get.incidence) #breaks
`Error in FUN(X[[i]], ...) : Not a graph object
get.incidence is expecting bipartite graph objects with types argument supplied.
For reference, I am following code which works for a single network.
example_network <- network_list[[1]] #select one network for example
net_graph <- graph_from_data_frame(example_network) #take the edge list and make it into a graph
bipartite.mapping(net_graph) #make bipartite graph
OUTPUT:
$res
[1] TRUE
$type
[1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
V(net_graph)$type <- bipartite.mapping(net_graph)$type #compute vertices ('type') and add as vector to graph
incidence_matrix <- get.incidence(net_graph) #produce incidence matrix
incidence_matrix[incidence_matrix > 1] <- 1 #force 0/1 edge, some rows are duplicated in random list resulting in values >1
where my desired format of each incidence matrix in the list is:
poll_z poll_g poll_h poll_d poll_r poll_i poll_l poll_x plnt_v 1 0 0 0 0 0 0 0 plnt_o 0 1 0 0 0 0 0 0 plnt_r 0 0 1 0 1 0 0 0 plnt_c 0 0 0 1 0 0 0 0 plnt_x 0 0 0 1 0 0 0 0 plnt_a 0 0 0 0 0 1 1 0 plnt_j 0 0 0 0 0 0 1 0 plnt_h 0 0 0 0 0 0 0 1
Also it would be a great help to have all possible duplicate values (i.e. values >1) in the list of matrices forced to equal 1.
You mapply call using c isn't correct. You could write your own little lambda function such as function(g, type) { V(g)$type <- type; return(g) } directly inside mapply, though there is also the igraph function vertex_attr<- which can do the same thing directly.
I tend to use Map rather than mapply, because it is essentially the same function but never attempts to "simplify" the output the way mapply does, which can lead to unexpected results. (Map(...) is identical to mapply(..., SIMPLIFY = FALSE) but requires fewer keystrokes).
At the end, to convert the list of matrices such that 0s remain 0s and all positive numbers become 1, we can do lapply(incidence_list, sign)
So your whole code might be something like this:
library(tidyverse)
library(igraph)
graph_list <- lapply(network_list, graph_from_data_frame)
list_mapping <- lapply(graph_list, bipartite.mapping)
list_type <- lapply(list_mapping, with, type)
graph_list <- Map(`vertex_attr<-`, graph_list, "type", value = list_type)
incidence_list <- lapply(graph_list, get.incidence)
incidence_list <- lapply(incidence_list, sign)
And your result would be:
incidence_list
#> [[1]]
#> pollinator_z pollinator_h pollinator_a pollinator_c pollinator_x
#> plnt_m 1 0 0 0 0
#> plnt_k 0 1 1 0 1
#> plnt_l 0 0 0 1 0
#> plnt_c 0 0 0 0 0
#> plnt_y 0 0 0 0 0
#> plnt_u 1 0 0 0 0
#> pollinator_b pollinator_n pollinator_l pollinator_i
#> plnt_m 0 0 1 1
#> plnt_k 0 0 0 0
#> plnt_l 0 0 0 0
#> plnt_c 1 0 0 0
#> plnt_y 0 1 0 0
#> plnt_u 0 0 0 0
#>
#> [[2]]
#> pollinator_j pollinator_t pollinator_o pollinator_s pollinator_l
#> plnt_e 1 0 0 0 0
#> plnt_t 0 1 0 0 0
#> plnt_j 0 0 1 0 0
#> plnt_v 0 0 0 1 0
#> plnt_o 0 0 1 0 1
#> plnt_p 0 0 0 0 0
#> plnt_b 0 0 0 0 0
#> plnt_u 0 0 0 0 0
#> pollinator_c pollinator_r pollinator_f
#> plnt_e 0 1 0
#> plnt_t 0 0 0
#> plnt_j 0 0 0
#> plnt_v 0 0 0
#> plnt_o 0 0 0
#> plnt_p 1 0 0
#> plnt_b 0 1 0
#> plnt_u 0 0 1
#>
#> [[3]]
#> pollinator_k pollinator_u pollinator_x pollinator_g pollinator_o
#> plnt_a 1 0 0 0 0
#> plnt_e 0 1 0 0 0
#> plnt_u 0 0 1 0 0
#> plnt_w 0 0 0 1 0
#> plnt_i 0 0 0 0 1
#> plnt_d 0 0 0 0 0
#> plnt_r 0 0 0 0 0
#> plnt_h 0 0 0 0 0
#> pollinator_s pollinator_e pollinator_h
#> plnt_a 0 0 1
#> plnt_e 0 0 0
#> plnt_u 0 0 0
#> plnt_w 0 0 0
#> plnt_i 0 0 0
#> plnt_d 1 0 0
#> plnt_r 0 1 0
#> plnt_h 1 0 0
#>
#> [[4]]
#> pollinator_r pollinator_i pollinator_l pollinator_m pollinator_z
#> plnt_n 1 0 0 0 0
#> plnt_d 0 1 0 0 0
#> plnt_g 0 0 1 0 0
#> plnt_v 0 0 0 1 0
#> plnt_y 0 0 0 0 1
#> plnt_l 0 0 0 0 0
#> plnt_r 0 0 0 0 0
#> plnt_j 0 0 0 0 0
#> pollinator_k pollinator_o pollinator_c pollinator_j pollinator_x
#> plnt_n 0 0 0 0 0
#> plnt_d 0 0 0 0 0
#> plnt_g 0 0 0 0 0
#> plnt_v 0 0 1 0 0
#> plnt_y 0 0 0 0 0
#> plnt_l 1 0 0 0 1
#> plnt_r 0 1 0 0 0
#> plnt_j 0 0 0 1 0
#>
#> [[5]]
#> pollinator_l pollinator_p pollinator_f pollinator_x pollinator_m
#> plnt_m 1 0 0 1 0
#> plnt_i 0 1 0 0 0
#> plnt_x 0 0 1 0 0
#> plnt_k 0 0 0 0 1
#> plnt_l 0 0 0 0 0
#> plnt_z 0 0 0 0 0
#> pollinator_u pollinator_e pollinator_r pollinator_b pollinator_a
#> plnt_m 0 0 0 0 0
#> plnt_i 0 0 1 0 1
#> plnt_x 1 0 0 0 0
#> plnt_k 0 0 0 0 0
#> plnt_l 0 1 0 0 0
#> plnt_z 0 0 0 1 0
#>
#> [[6]]
#> pollinator_d pollinator_x pollinator_n pollinator_y pollinator_s
#> plnt_k 1 0 0 0 0
#> plnt_b 0 1 0 0 0
#> plnt_m 0 0 1 0 0
#> plnt_g 0 0 0 1 0
#> plnt_z 0 0 0 0 1
#> plnt_j 0 0 0 0 0
#> plnt_x 0 0 0 0 0
#> plnt_v 0 0 0 0 0
#> pollinator_k pollinator_i pollinator_c
#> plnt_k 0 0 0
#> plnt_b 0 0 0
#> plnt_m 1 0 0
#> plnt_g 0 0 0
#> plnt_z 0 0 0
#> plnt_j 1 0 0
#> plnt_x 0 1 0
#> plnt_v 0 0 1
#>
#> [[7]]
#> pollinator_r pollinator_x pollinator_k pollinator_j pollinator_u
#> plnt_q 1 0 0 0 0
#> plnt_o 0 1 0 0 0
#> plnt_z 0 0 1 0 0
#> plnt_t 0 1 0 0 0
#> plnt_g 0 0 0 1 0
#> plnt_e 0 0 0 0 1
#> plnt_p 0 0 0 0 1
#> plnt_r 0 0 0 0 0
#> plnt_n 0 0 0 0 0
#> pollinator_a pollinator_s pollinator_d
#> plnt_q 0 0 0
#> plnt_o 0 0 1
#> plnt_z 0 0 0
#> plnt_t 0 0 0
#> plnt_g 0 0 0
#> plnt_e 0 0 0
#> plnt_p 0 0 0
#> plnt_r 1 0 0
#> plnt_n 0 1 0
#>
#> [[8]]
#> pollinator_g pollinator_b pollinator_m pollinator_v pollinator_c
#> plnt_w 1 0 0 1 0
#> plnt_l 0 1 0 0 0
#> plnt_s 0 0 1 0 0
#> plnt_c 0 0 0 1 0
#> plnt_p 0 0 0 0 1
#> plnt_x 0 0 0 0 0
#> plnt_d 0 0 0 0 0
#> plnt_r 0 0 0 0 0
#> pollinator_o pollinator_z pollinator_t pollinator_j
#> plnt_w 0 0 0 0
#> plnt_l 0 0 0 0
#> plnt_s 0 0 0 0
#> plnt_c 0 0 0 1
#> plnt_p 0 0 0 0
#> plnt_x 1 0 0 0
#> plnt_d 0 1 0 0
#> plnt_r 0 0 1 0
#>
#> [[9]]
#> pollinator_g pollinator_x pollinator_f pollinator_v pollinator_q
#> plnt_t 1 0 0 0 0
#> plnt_k 0 1 0 0 0
#> plnt_l 0 0 1 0 0
#> plnt_r 0 0 0 1 0
#> plnt_b 0 0 0 0 1
#> plnt_s 0 0 0 0 0
#> plnt_m 0 0 0 0 0
#> plnt_n 0 0 1 0 0
#> plnt_g 0 0 0 0 0
#> pollinator_z pollinator_e pollinator_n pollinator_w
#> plnt_t 0 0 0 0
#> plnt_k 0 0 0 0
#> plnt_l 1 0 0 0
#> plnt_r 0 0 0 0
#> plnt_b 0 0 0 0
#> plnt_s 0 1 0 0
#> plnt_m 0 0 1 0
#> plnt_n 0 0 0 0
#> plnt_g 0 0 0 1
#>
#> [[10]]
#> pollinator_i pollinator_q pollinator_g pollinator_b pollinator_p
#> plnt_p 1 0 0 0 0
#> plnt_h 0 1 0 0 0
#> plnt_y 0 0 1 0 0
#> plnt_s 0 0 0 1 0
#> plnt_z 0 0 0 0 1
#> plnt_j 0 0 0 0 0
#> plnt_e 0 0 0 0 0
#> plnt_m 0 0 0 0 0
#> plnt_x 0 0 0 0 0
#> pollinator_o pollinator_z pollinator_a pollinator_y pollinator_h
#> plnt_p 0 0 0 0 0
#> plnt_h 0 0 0 0 0
#> plnt_y 0 0 0 0 0
#> plnt_s 0 0 0 0 0
#> plnt_z 0 0 0 0 0
#> plnt_j 1 1 0 0 0
#> plnt_e 0 0 1 0 0
#> plnt_m 0 0 0 1 0
#> plnt_x 0 0 0 0 1
Created on 2023-02-19 with reprex v2.0.2