R 函数中包含%in%的子集

我有以下数据帧

head(graph_data, n = 15)
   source target
1   Ohrid СКОПЈЕ
2   Ohrid СКОПЈЕ
3   Ohrid СКОПЈЕ
4   Ohrid СКОПЈЕ
5   Ohrid СКОПЈЕ
6   Ohrid СКОПЈЕ
7   Ohrid СКОПЈЕ
8   Ohrid СКОПЈЕ
9   Ohrid СКОПЈЕ
10  Ohrid СКОПЈЕ
11  Ohrid СКОПЈЕ
12  Ohrid СКОПЈЕ
13  Ohrid СКОПЈЕ
14  Ohrid СКОПЈЕ
15  Ohrid СКОПЈЕ

我编写了以下函数来自动过滤与源代码匹配的最大数量的过程

top_connections <- function(data, city, top_n) {
  temp <- filter(data, source == city)
  temp2 <- as.data.frame(table(temp$target))  
  temp2 <- arrange(temp2, desc(Freq))
  temp2 <- temp2[1:top_n, ]
  temp3 <- as.data.frame(unique(temp2$Var1))
  colnames(temp3)[1] <- "top_connecitons" 

  #works fine until here

  temp4 <- subset(temp, source %in% temp3[, "top_connecitons"])

   return(temp4)
}

---

您正在使用其他软件包吗？看起来您正在使用

dplyr

中的函数。不要忘记

library（）

调用，这一点很清楚。在您的示例中，“temp3”包含

data$target

中的变量。然后将其与

数据$source

进行比较。是否可能

唯一（数据%目标）！=唯一（数据$source）

？请查看@Istrel yes-唯一（数据%target）！=唯一（数据$source）。但我们并不是在寻求平等。我们计算数据$target中每个唯一模态的频率。对于前n个最常见的模式，我们从数据中对它们进行子集。我仍然得到相同的结果。零行。在任何情况下，有问题的部分是temp4。。子集行。这行之前的所有结果对我来说都很好。你能帮我发布/重定向到你的数据吗？如何调用函数？isI更新了哪些参数。原始帖子中包含的数据/环境/函数调用。谢谢你的帮助。编码混乱：“1949”，“斯科普里”���ϣ�"我发现了我的错误，应该是temp4

test1 <- top_connections(graph_data, "Skopje", top_n = 15)

search()
 [1] ".GlobalEnv"         "package:networkD3" 
 [3] "package:data.table" "package:DT"        
 [5] "package:corrplot"   "package:scales"    
 [7] "package:dplyr"      "package:purrr"     
 [9] "package:readr"      "package:tidyr"     
[11] "package:tibble"     "package:tidyverse" 
[13] "package:ggthemes"   "package:ggplot2"   
[15] "package:readxl"     "package:lubridate" 
[17] "tools:rstudio"      "package:stats"     
[19] "package:graphics"   "package:grDevices" 
[21] "package:utils"      "package:datasets"  
[23] "package:methods"    "Autoloads"         
[25] "package:base"    

graph_data < data.frame(source=c("Paris","Berlin","Paris","London","Munich"),target=c("Amsterdam","Paris","Paris","Brighton","Paris"),stringsAsFactors = F)

top_connections <- function(data, city, top_n) {
  temp <- dplyr::filter(data,source==city)

  temp2 <- as.data.frame(table(temp$target))  
  temp2 <- dplyr::arrange(temp2, desc(Freq))
  temp2 <- temp2[1:top_n, ]
  temp3 <- as.data.frame(unique(temp2$Var1))
  colnames(temp3)[1] <- "top_connecitons"   
  temp4 <- subset(temp, source %in% temp3[, "top_connecitons"]) 
  return(temp4)
}

  top_connections(graph_data,"Paris",2)
  source    target
1  Paris Amsterdam
2  Paris     Paris