整洁图和igraph - 如何从数据框构建图有所不同

9

我可以在igraph中从两个数据框构建一个图形对象,没有任何问题。但是当我尝试在tidygraph中做同样的事情时,出现了错误。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))

接下来,我的边缘列表(通过讨论主题连接在一起的人):

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,我可以创建以下这个图形对象:

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

这看起来很不错。现在我尝试用tidygraph做同样的事情:

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

哎呀!然而,当我将igraph对象导入tidygraph时:

tidy_net <- as_tbl_graph(test_net)
plot(tidy_net)

一切都正常!发生了什么事情?请帮忙。
1个回答

12

我认为由于你的节点id和边缘tofrom是数字,它会假定在min(node$id)(2)和max(node$id)(512)之间的每个整数都应该有节点。您可以通过将它们强制转换为字符来避免这种情况。此外,您的iterpc命令对我不起作用,因此我将其转换为扩展数据的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)

1
那个筛选器(从<到)是提取唯一组合的一个巧妙技巧!谢谢。 - aterhorst

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