问题描述
我有纵向网络数据（3 个时间点），其中并非每个时期都存在所有节点。我还为三个边列表中的每一个创建了属性列表，以确保在各自的属性数据中只有来自每个边列表的节点。由于我想通过 ID 匹配 edgelist 和 nodes 属性，我确保属性数据中的第一列是 ID，并且 edgelist 设置为 ego_id、alter_id、weight。
这是我的边缘列表示例：
edgelist_time1 <- tibble::tribble(
~ego_id,~alter_id,~weight,7L,8L,9L,113L,1L,114L,115L,4L,11L,6L,12L,13L,15L,4L
)
head(edgelist_time1)
#> # A tibble: 6 x 3
#> ego_id alter_id weight
#> <int> <int> <int>
#> 1 7 8 7
#> 2 7 9 7
#> 3 7 113 1
#> 4 7 114 1
#> 5 7 115 1
#> 6 8 7 7
我从有效的边缘列表创建了一个 igraph 对象。
contact_time1_net <- graph_from_data_frame(d=edgelist_time1,directed = T)
contact_time1_net
输出：
IGRAPH a75a5df DNW- 137 360 --
+ attr: name (v/c),weight (e/n)
+ edges from a75a5df (vertex names):
[1] 7 ->8 7 ->9 7 ->113 7 ->114 7 ->115 8 ->7 8 ->9 8 ->11 8 ->12 8 ->13 8 ->15 9 ->7 9 ->8
[14] 9 ->10 9 ->11 9 ->12 9 ->13 9 ->14 9 ->15 10->11 10->12 10->113 10->116 10->117 11->7 11->9
[27] 11->10 11->12 11->13 11->14 11->15 12->10 12->11 12->13 12->14 12->124 13->14 13->15 13->117
[40] 13->125 13->191 14->7 14->9 14->11 14->12 14->13 14->15 16->10 16->11 16->12 16->13 16->14
[53] 17->19 17->20 17->101 17->122 17->127 18->16 18->17 18->20 18->113 18->118 19->16 19->17 19->102
[66] 19->117 19->121 19->122 19->123 20->16 20->17 20->18 20->19 20->118 21->17 21->22 21->44 21->119
[79] 21->120 22->17 22->21 22->120 22->128 22->129 22->130 24->25 24->26 24->27 24->28 24->29 24->30
[92] 24->31 24->46 24->132 25->24 25->27 25->28 25->29 25->31 25->82 25->138 25->147 26->24 26->25
+ ... omitted several edges
但是，每当我尝试分配我的顶点/节点属性时，它都会给我错误：seq_len(no) 中的错误：参数必须可强制转换为非负整数。强>
contact_time1_net <- graph_from_data_frame(d=edgelist_time1,directed = T,vertices=attributes_time1)
contact_time1_net
这是我的属性数据示例：
attributes_time1 <- tibble::tribble(
~ID,~plant_t1,~name_t1,~level_t1,~participation_t1,~response_time_min_t1,"Daniel",35L,"Luca",21L,"Dejan",36L,10L,"Melissa",30L,"Markus",14L,"sven","christian",23L,"Milan",21L
)
head(attributes_time1)
#> # A tibble: 6 x 6
#> ID plant_t1 name_t1 level_t1 participation_t1 response_time_min_t1
#> <int> <int> <chr> <int> <int> <int>
#> 1 7 1 Daniel 4 1 35
#> 2 8 1 Luca 4 1 21
#> 3 9 1 Dejan 4 1 36
#> 4 10 1 Melissa 4 1 30
#> 5 11 1 Markus 4 1 14
#> 6 12 1 sven 4 1 36
输出：
IGRAPH 15f9f29 DNW- 187 360 -- + attr: name (v/c),plant_t1 (v/n),name_t1 (v/c),last_name_t1 (v/c),level_t1 (v/n),| participation_t1 (v/n),response_time_min_t1 (v/n),interest_tech_t1 (v/n),technological_skill_t1 | (v/n),training_t1 (v/n),training_app_t1 (v/n),awareness_t1 (v/n),availability_t1 (v/n),| availability_app_year_t1 (v/n),startusage_app_year_t1 (v/n),future_usage_t1 (v/n),| future_usage_frequency_t1 (v/n),current_usage_frequency_t1 (v/n),usage_machinestatus_t1 (v/n),| usage_measurementreport_t1 (v/n),usage_measuredvalues_t1 (v/n),push_machinestatus_t1 (v/n),| push_measurementreport_t1 (v/n),push_measuredvalues_t1 (v/n),ease_usage_t1 (v/n),| seed_usage_1_t1 (v/n),seed_usage_2_t1 (v/n),seed_usage_3_t1 (v/n),scaling_t1 (v/n),| sentimement_t1 (v/n),management_importance_t1 (v/n),gender_t1 (v/n),age_t1 (v/n),educ_t1 | (v/n),occup_t1 (v/n),management_2sition_t1 (v/n),language_1_t1 (v/n),language_2_t1 (v/n),| language_3_t1 (v/n),device_system_1_t1 (v/n),Covid_control_t1 (v/n),covid_changes_t1 (v/n),| covid_changes_2_t1 (v/n),weight (e/n) + edges from 15f9f29 (vertex names):
Traceback:
error in seq_len(no) : argument must be coercible to non-negative integer
17. eval(x$expr,data,x$env)
16. eval(x$expr,x$env)
15. FUN(X[[i]],...)
14. lapply(x,lazy_eval,data = data)
13. lazy_eval(args,data = c(attrs,.inc = .inc,inc = inc,adj = adj,.from = .from,from = from,.to = .to,to = to,.igraph.from = list(.Call(C_R_igraph_mybracket,graph,3L)[as.numeric(x)]),.igraph.to = list(.Call(C_R_igraph_mybracket,4L)[as.numeric(x)]),.igraph.graph = list(graph),...
12. `[.igraph.es`(edges,seq_len(no))
11. edges[seq_len(no)]
10. na.omit(es)
9. as.igraph.es(graph,na.omit(es))
8. ends(x,edges[seq_len(no)],names = names)
7. x("width",no = can_max)
6. head_print_callback(x,max_lines,header,footer,omitted_footer,...)
5. head_print(fun,max_lines = max.lines)
4. .print.edges.compressed.limit(x,edges,names,max.lines)
3. .print.edges.compressed(x,names = names,max.lines = max.lines - head_lines)
2. print.igraph(x)
1. (function (x,...) UseMethod("print"))(x)
也许，任何人都知道可能是什么问题。
非常感谢！
解决方法
您应该知道您的 attributes_time1 并未覆盖所有顶点。在这种情况下，您需要使用 merge 获得完整信息。
你可以试试这个代码
v_attr <- merge(data.frame(ID = unlist(edgelist_time1[1:2])),attributes_time1,all = TRUE)
contact_time1_net <- graph_from_data_frame(d = edgelist_time1,directed = T,vertices = unique(v_attr))
你会看到
> vertex.attributes(contact_time1_net)
$name
[1] "7" "8" "9" "10" "11" "12" "13" "14" "15" "113" "114" "115"
$plant_t1
[1] 1 1 1 1 1 1 1 1 NA NA NA NA
$name_t1
[1] "Daniel" "Luca" "Dejan" "Melissa" "Markus" "Sven"
[7] "christian" "Milan" NA NA NA NA
$level_t1
[1] 4 4 4 4 4 4 4 4 NA NA NA NA
$participation_t1
[1] 1 1 1 1 1 1 1 1 NA NA NA NA
$response_time_min_t1
[1] 35 21 36 30 14 36 23 21 NA NA NA NA