问题描述
你好，希望一切顺利。 我对我之前的问题进行了编辑，希望它能更清楚。
我创建了一个 igraph 对象，并希望多次运行相同的分析并在每次迭代中提取一些信息。
我无法分享全部数据，所以我只分享了一小部分。
df_edge 如下：
library(dplyr)
job_1 <-c(1,2,6,5,7,8,8)
job_2 <- c(2,4,3,1,5)
weight <- c(1,1)
df_edge <- tibble(job_1,job_2,weight)
df_edge %>% glimpse()
Rows: 14
Columns: 3
$ job_1 <dbl> 1,8
$ job_2 <dbl> 2,5
$ weight <dbl> 1,1
df_node 如下：
job_id <- c(1,8)
job_type <- c(1,1)
df_node <- tibble(job_id,job_type)
df_node %>% glimpse()
Rows: 8
Columns: 2
$ job_id <dbl> 1,8
$ job_type <dbl> 1,1
创建 igraph 对象：
library(igraph)
library(tidygraph)
tp_network_subset <- graph.data.frame(df_edge,vertices = df_node,directed = F)
job_type 列中的 df_node 摘要
df_node %>%
count(job_type)
A tibble: 4 x 2
job_type n
<dbl> <int>
1 0 2
2 1 4
3 2 1
4 3 1
我手动执行的操作如下：
### finding a job_id that belongs to job_type==1 category
df_node %>% filter(job_type==1) %>%
select(job_id)
A tibble: 4 x 1
job_id
<dbl>
1 1
2 6
3 7
4 8
# for instance,I picked one of them and it is job_id = 6
### using the job_id to create a subgraph by selecting order 1 neighbors of this job_id (6)
node_test <- make_ego_graph(tp_network_subset,order = 1,nodes="6")
### creating a dataframe of this subgrapgh where there is no isolated nodes
df_test <- as_tbl_graph(node_test[[1]]) %>%
activate(nodes) %>%
filter(!node_is_isolated()) %>%
as_tibble()
df_test %>% glimpse()
Rows: 6
Columns: 2
$ name <chr> "1","2","4","5","6","8"
$ job_type <dbl> 1,1
## subgraph size is 6 which will be an outcome of interest
### if the graph is zero length,I should stop here and pick another job_id that belongs to job_type==1 category
在这个例子中，not zero length 中的图形所以我继续next step
### calculating the measure of interest in respect to job_type==1 category
df_test %>%
summarise(job_rate= (nrow(df_test %>% filter(job_type==1)))/(nrow(df_test %>%
filter(job_type %in% c(1,3)))))
# 0.6
if job_rate > 0.5 ，我想保留子图的 job_rate 类别的 job_type=4 和行（对应节点）。在这种情况下，job_rate was 0.6 所以我保留以下内容
df_final <- as_tbl_graph(node_test[[1]]) %>%
activate(nodes) %>%
filter(!node_is_isolated()) %>%
as_tibble() %>% filter(job_type==0)
# A tibble: 1 x 2
name job_type
<chr> <dbl>
1 4 0
但是，我需要分配它们对应的 job__rate 和一些 other related columns。所以，我最喜欢的结果是
name job_type subgraph_origin_id job_rate subgraph_size no_(job_type==0)_in_subgrapgh no_(job_type==1)_in_subgrapgh no_(job_type==2)_in_subgrapgh no_(job_type==3)_in_subgrapgh
<chr> <dbl>
1 4 0 6 0.6 6
因此，我需要执行此过程并为所有 job_type==1 节点创建子图。如果图形不是 zero length 及其 job_rate > 0.5，则提取 那个子图形 中的所有 对应节点以及 job_rate 和其他最喜欢的结果中显示的列。
解决方法
这对你有用吗？
aggregate给出
dflst <- split(df_node,job_type)
tpe <- as.numeric(names(dflst))
out <- tibble()
for (i in seq_along(dflst)) {
df <- dflst[[i]]
node_test_lst <- make_ego_graph(tp_network_subset,order = 1,nodes = df$job_id)
origin_id <- df$job_id
jtpe <- tpe[i]
for (j in seq_along(node_test_lst)) {
node_test <- node_test_lst[[j]]
df_test <- as_tbl_graph(node_test) %>%
activate(nodes) %>%
filter(!node_is_isolated()) %>%
as_tibble()
if (nrow(df_test %>% filter(job_type == 0)) > 0 & any(df_test$job_type %in% 1:3)) {
job_rate <- with(df_test,sum(job_type == jtpe) / sum(job_type %in% 1:3))
if (job_rate > 0.5) {
df_final <- df_test %>%
filter(job_type == 0) %>%
mutate(
subgraph_origin_id = origin_id[j],job_rate = job_rate,subgraph_size = nrow(df_test)
) %>%
cbind(
setNames(
as.list(table(factor(df_test$job_type,levels = 0:3))),sprintf("no_(job_type==%s)_in_subgrapgh",0:3)
)
)
out <- out %>% rbind(df_final)
}
}
}
}