R 将列转换为行重复公共值

好朋友，我有一个数据帧，我想如下所示从dataframe1转换为dataframe2：

dataframe1 <- data.frame(Company_Name = c("KFC", "McD"), 
                 Company_ID = c(1,  2),
                 Company_Phone = c("237389", "237002"),
                 Employee = c("John", "Joshua" ),
                 ID = c(1001, 2001),
                 Employee = c("Mary", "Anne"),
                 ID = c(1002, 2002),
                 Employee = c("Jane", ""),
                ID = c(1003, ""))

dataframe2 <- data.frame(Company_Name = c("KFC", "KFC", "KFC", "McD", "McD"), 
                      Company_ID = c(1, 1, 1, 2, 2),
                      Company_Phone = c("237389", "237389", "237389", "237002", "237002"),
                      Employee = c("John", "Mary", "Jane", "Joshua", 
                                        "Anne"),
                     ID = c(1001, 1002, 1003, 2001, 2002))

及

---

没有得到所需的输出。任何帮助或见解都将不胜感激

使用

data.table

中的

melt

，带有模式功能

library(data.table)
melt(setDT(dataframe1),measure = patterns("^Employee","^ID"),value.name = c("Employee","Employee_ID"))[Employee != ""]


   Company_Name Company_ID Company_Phone variable Employee Employee_ID
1:          KFC          1        237389        1     John        1001
2:          McD          2        237002        1   Joshua        2001
3:          KFC          1        237389        2     Mary        1002
4:          McD          2        237002        2     Anne        2002
5:          KFC          1        237389        3     Jane        1003

library（tidyverse）；dataframe1%%>%gather（var，val，-contains（“公司”））%%>%separate（var，c（'var'，'i'），fill='right'）%%>%spread（var，val）%%>%select（-i）%%>%filter（Employee！=“”）

  stack(dataframe2) 

library(data.table)
melt(setDT(dataframe1),measure = patterns("^Employee","^ID"),value.name = c("Employee","Employee_ID"))[Employee != ""]


   Company_Name Company_ID Company_Phone variable Employee Employee_ID
1:          KFC          1        237389        1     John        1001
2:          McD          2        237002        1   Joshua        2001
3:          KFC          1        237389        2     Mary        1002
4:          McD          2        237002        2     Anne        2002
5:          KFC          1        237389        3     Jane        1003