我有一个列表列表，其中包含植物-传粉者相互作用的边缘列表，用于许多二分网络。使用
igraph我无法将顶点（
igraph::Vgraph_listfor我的数据的可重现示例。
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)
我正在尝试将顶点序列添加到列表中的每个图形。但是下面的代码并没有像我期望的那样工作。
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.incidencegraph作为参考，我正在遵循适用于单个网络的代码。
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)$type #compute vertices ('type') and add as vector to graph
incidence_matrix <- get.incidence(net_graph) #produce incidence matrix
incidence_matrix[incidence_matrix > 1] <- 1 #force 0/1 edge, some rows are duplicated in random list resulting in values >1
我想要的列表中每个关联矩阵的格式是：
poll_z poll_g poll_h poll_d poll_r poll_i poll_l poll_x plnt_v 1 0 0 0 0 0 0 0 plnt_o 0 1 0 0 0 0 0 0 plnt_r 0 0 1 0 1 0 0 0 plnt_c 0 0 0 1 0 0 0 0 plnt_x 0 0 0 1 0 0 0 0 plnt_a 0 0 0 0 0 1 1 0 plnt_j 0 0 0 0 0 0 1 0 plnt_h 0 0 0 0 0 0 0 1
此外，将矩阵列表中所有可能的重复值（即值 >1）强制等于 1 也会有很大帮助。
你
mapplycfunction(g, type) { V(g)$type <- type; return(g) }mapplyvertex_attr<-我倾向于使用
MapmapplymapplyMap(...)mapply(..., SIMPLIFY = FALSE)最后，要转换矩阵列表，使 0 保留为 0，所有正数变为 1，我们可以这样做
lapply(incidence_list, sign)所以你的整个代码可能是这样的：
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)
你的结果是：
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
创建于 2023-02-19 与 reprex v2.0.2
使用 for 循环避免 igraph。
## from question
set.seed(75498353)
n <- 10 # size of plant, pollinator.
network_list <- replicate(2, expr = {
data.frame( plant = paste('plnt', '_', sample(x = letters, size = n, replace = TRUE), sep = ""),
pollinator = paste('pollinator', '_', sample(x = letters, size = n, replace = TRUE), sep = "")
)
}, simplify = FALSE)
## create list of incidence matrices
incidence_list <- list()
for (el in network_list){
incidence_list[length(incidence_list) + 1] <- list(table(el)) # append next incidence matrix
}
这取代了 for 循环。慢，包括复制。
incidence_list <- lapply(network_list, function(x) { rbind(incidence_list, table(x)) })