字符串拆分，合并和堆叠多列

如何解决字符串拆分，合并和堆叠多列

我有以下原始数据，如下所示：

rawData <- data.frame(ID = c(1,2,3),Name = c("Company B; Company A; Company C","Company A; Company D","Company E"),Name_location = c("Company A (USA (Primary)); Company B (Japan(Primary)); Company C (Korea,South (Primary))","Company A (USA (Primary)); Company D (USA (Primary))","European (Primary)" ))


 ID    Name                              Name_location
 1     Company B; Company A;Company C    Company A (USA (Primary)); Company B (Japan(Primary)); Company C (Korea,South (Primary))
 2     Company A; Company D              Company A (USA (Primary)); Company D (USA (Primary))
 3     Company E                         European (Primary)

我需要将数据转换为如下所示：

“名称_位置”字段在“名称”字段中具有每个公司的位置数据，但是可能会乱序。另外，如果“名称”字段中只有1家公司，则“名称_位置”字段将仅具有该位置，而如果“名称”字段中有多个公司，则“名称_位置”字段将遵循语法“公司（位置（主要））；公司（位置） （主要））”

我需要一种将公司及其位置分隔为可通过ID识别的单独行的方法。

 IdealData <- data.frame(ID = c(1,1,Name = c("Company B","Company A","Company C","Company D",Location = c("Japan","USA","Korea,South","European"))


     ID      Name            Location
     1       Company B       Japan
     1       Company A       USA
     1       Company C       Korea,South
     2       Company A       USA
     2       Company D       USA
     3       Company E       European

希望在R中做到这一点

解决方法

使用separate_rows之后，我们可以使用str_extract

提取特定组件

library(stringr)
library(dplyr)
library(tidyr)
rawData %>% 
   separate_rows(c(Name,Name_location),sep=";\\s*") %>%
   separate(Name_location,into = c('Name1','Location'),sep= "\\s+(?=\\()",extra = "merge") %>%
   mutate(Location = case_when(Name1 == 'European' ~ Name1,TRUE ~ trimws(str_extract(Location,"(?<=\\()[^(]+"))[match(Name,Name1)])) %>%
   select(-Name1)
# A tibble: 6 x 3
#     ID Name      Location   
#  <dbl> <chr>     <chr>      
#1     1 Company B Japan      
#2     1 Company A USA        
#3     1 Company C Korea,South
#4     2 Company A USA        
#5     2 Company D USA        
#6     3 Company E European   

,

如果要在没有包和库的情况下进行操作，则可以遍历条目并创建一个新的data.frame：

rawData <- data.frame("ID" = c(1,2,3),"Name" = c("Company B; Company A; Company C","Company A; Company D","Company E"),"Name_location" = c("Company A (USA (Primary)); Company B (Japan(Primary)); Company C (Korea,South (Primary))","Company A (USA (Primary)); Company D (USA (Primary))","European (Primary)" ))
rawData$Name = as.character(rawData$Name)
rawData$Name_location = as.character(rawData$Name_location)

idealData = list("ID"=c(),"Company"=c(),"Location"=c())
for(i in 1:length(rawData$ID)){
  print(strsplit(rawData$Name[i],";"))
  ncomp = length(strsplit(rawData$Name[i],";")[[1]])
  print(ncomp)
  if(ncomp==1){
    idealData[["ID"]]=c(idealData[["ID"]],rawData$ID[i])
    idealData[["Company"]]=c(idealData[["Company"]],rawData$Name[i])
    idealData[["Location"]]=c(idealData[["Location"]],strsplit(rawData$Name_location[i]," \\(")[[1]][1])
  }else{
    vcomp = strsplit(rawData$Name[i],"; ")[[1]]
    for(compi in 1:ncomp){
      idealData[["ID"]]=c(idealData[["ID"]],rawData$ID[i])
      idealData[["Company"]]=c(idealData[["Company"]],vcomp[compi])
      loc = strsplit(rawData$Name_location[i],";")[[1]]
      print(loc)
      loc = loc[grep(vcomp[compi],loc)][1]
      idealData[["Location"]]=c(idealData[["Location"]],strsplit(loc,"\\(")[[1]][2])
    } 
  }
}

idealData = as.data.frame(idealData)

哪个给出输出：

> idealData
  ID   Company     Location
1  1 Company B        Japan
2  1 Company A         USA 
3  1 Company C Korea,South 
4  2 Company A         USA 
5  2 Company D         USA 
6  3 Company E     European