基于数据差异的时序图和igraph图

Question

我可以从两个数据文件中构建一个图形对象，没有问题。当我尝试做同样的计时器时，我会出错。让我演示一下。首先，我加载我的源数据(来自留言板的数据)：

library(dplyr)
library(tidyr)
library(tidygraph)
library(lubridate)
library(iterpc)
library(igraph)

df <- data.frame(author_id = c(2,4,8,16,4,8,2,256,512,8),
             topic_id = c(101,101,101,101,301,301,501,501,501,501),
             time = as.POSIXct(c("2011-08-16 20:20:11", "2011-08-16 21:10:00", "2011-08-17 06:30:10",
                                 "2011-08-17 10:08:32", "2011-08-20 22:23:01","2011-08-20 23:03:03",
                                 "2011-08-25 17:05:01", "2011-08-25 19:15:10",  "2011-08-25 20:07:11",
                                 "2011-08-25 23:59:59")),
             vendor = as.logical(c("FALSE", "FALSE", "TRUE", "FALSE", "FALSE",
                                   "TRUE", "FALSE", "FALSE", "FALSE", "TRUE"))) 

接下来，我创建了一个唯一的节点列表(在留言板上发布信息的人)：

node <- df %>% distinct(author_id, vendor) %>% rename(id = author_id) %>% mutate(vendor = as.numeric(vendor))

然后，我的边缘列表(通过讨论线程(主题)连接的人)：

edge <- df %>% 
  group_by(topic_id) %>% 
  do(data.frame(getall(iterpc(table(.$author_id), 2, replace =TRUE)))) %>%
  filter(X1 != X2) %>% rename(from = X1, to = X2) %>% select(to, from, topic_id)

使用igraph可以创建这个图形对象：

test_net <- graph_from_data_frame(d = edge, directed = F, vertices = node)
plot(test_net)

这个看起来不错。现在，我试着做同样的计时器：

tidy_net <- tbl_graph(nodes = node, edges = edge, directed = F)
Error in add_vertices(gr, nrow(nodes) - gorder(gr)) : At type_indexededgelist.c:369 : cannot add negative number of vertices, Invalid value

呀！但是，当我将i图形对象导入TidyGraph时：

tidy_net <- as_tbl_graph(test_net)
plot(tidy_net)

一切都成功了！怎么一回事？请帮帮忙。

Answer 1

我认为，因为您的节点id、边to和from都是数字的，所以假定min(node$id) (2)和max(node$id) (512)之间的每个整数都应该有节点。你可以通过强迫他们扮演角色来避开这个问题。而且，您的iterpc命令对我来说不能正常工作，所以我将它转换为扩展数据的tidyr版本。

node <- 
  df %>% 
  distinct(author_id, vendor) %>% 
  rename(id = author_id) %>% 
  mutate(vendor = as.numeric(vendor)) %>% 
  mutate(id = as.character(id))

edge <- 
  df %>% 
  group_by(topic_id) %>% 
  expand(topic_id, from = author_id, to = author_id) %>% 
  filter(from < to) %>% 
  select(to, from, topic_id) %>% 
  mutate_at(vars(to, from), as.character)

tidy_net <- tbl_graph(nodes = node, edges = edge, directed = F)
plot(tidy_net)