循环输出存储为列表

Question

我有广泛的监督数据，其中单个观察由 1 级员工及其部门一直到 8 级组成。我使用带有其他命令的循环来生成所有员工及其下属部门的长格式列表这样我就可以看到各级员工负责哪些部门。可能有比循环更优雅的方法来做到这一点，但它工作正常。样本数据（为简洁起见，通过 3 级）：

data <- tibble(LV1_Employee_Name = "Chuck", LV1_Employee_Nbr = "1", LV1_Department = "Tha Boss", LV1_Department_Nbr = "90",
               LV2_Employee_Name = c("Alex", "Alex", "Paul", "Paul", "Jennifer", "Jennifer"), LV2_Employee_Nbr = c("2", "2", "3", "3", "4", "4"), LV2_Department = c("Leadership", "Leadership", "Finance", "Finance", "Philanthropy", "Philanthropy"), LV2_Department_Nbr = c("91", "91", "92", "92",  "93", "93"),
               LV3_Employee_Name = c("Dan", "Wendy", "Sarah", "Monique", "Miguel", "Brandon"), LV3_Employee_Nbr = c("2", "2", "3", "3", "4", "4"), LV3_Department = c("Analytics", "Pop Health", "Acounting", "Investments", "Yacht Aquisitions", "Golf Junkets"), LV3_Department_Nbr = c("94", "95", "96", "97",  "98", "99"))

下面的循环首先产生六个小标题，分别命名为 level1_1、level1_2、level1_3、level2_2、level2_3、level3_3。每个 tibble 包含员工姓名、编号和同一部门级别或以下的部门。然后代码列出并使用ls() 和bind_rows() 绑定这些小标题的行，然后应用distinct() 命令，我就得到了我需要的东西。

first_department <- 1

data_colnames <- c("Employee", "Employee_Id", "Department", "Department_Number")

for(i in 1:3){
  for(k in first_department:3){
    
    assign(paste0("level", i, "_", k), setNames(distinct(as_tibble(c(data[ ,paste0("LV", i, "_", "Employee_Name")], data[ ,paste0("LV", i, "_", "Employee_Nbr")],
                                                                     data[ ,paste0("LV", k, "_", "Department")],  data[ ,paste0("LV", k, "_", "Department_Nbr")]))), 
                                                data_colnames))
    
  }
  first_department = first_department + 1
}

employees_departments <- distinct(bind_rows(mget(ls(pattern = "^level")))) %>%
  filter(is.na(Department) == FALSE)

rm(list = ls(pattern = "^level"))

我想做的是，让循环本身输出列表，而不是产生六个小标题的初始输出。这将使我免于在输出中出现大量 tibbles，据我所知，这些 tibbles 不是很“R-like”。

Answer 1

这是一个修订版本，它将结果存储在循环中的列表中。这将包括一个索引idx，每次循环都会增加。之后，您可以使用此列表中的bind_rows 来获得完整的结果。

library(tidyverse)

idx <- 1
first_department <- 1
data_colnames <- c("Employee", "Employee_Id", "Department", "Department_Number")
data_lst <- list()

for(i in 1:3){
  for(k in first_department:3){
    data_lst[[idx]] <- setNames(
      distinct(as_tibble(
        c(data[ ,paste0("LV", i, "_", "Employee_Name")], 
          data[ ,paste0("LV", i, "_", "Employee_Nbr")],
          data[ ,paste0("LV", k, "_", "Department")],  
          data[ ,paste0("LV", k, "_", "Department_Nbr")]))),
      data_colnames)
    idx <- idx + 1
  }
  first_department = first_department + 1
}

distinct(bind_rows(data_lst)) %>%
  filter(!is.na(Department))

输出

   Employee Employee_Id Department        Department_Number
   <chr>    <chr>       <chr>             <chr>            
 1 Chuck    1           Tha Boss          90               
 2 Chuck    1           Leadership        91               
 3 Chuck    1           Finance           92               
 4 Chuck    1           Philanthropy      93               
 5 Chuck    1           Analytics         94               
 6 Chuck    1           Pop Health        95               
 7 Chuck    1           Acounting         96               
 8 Chuck    1           Investments       97               
 9 Chuck    1           Yacht Aquisitions 98               
10 Chuck    1           Golf Junkets      99               
11 Alex     2           Leadership        91               
12 Paul     3           Finance           92               
13 Jennifer 4           Philanthropy      93               
14 Alex     2           Analytics         94               
15 Alex     2           Pop Health        95               
16 Paul     3           Acounting         96               
17 Paul     3           Investments       97               
18 Jennifer 4           Yacht Aquisitions 98               
19 Jennifer 4           Golf Junkets      99               
20 Dan      2           Analytics         94               
21 Wendy    2           Pop Health        95               
22 Sarah    3           Acounting         96               
23 Monique  3           Investments       97               
24 Miguel   4           Yacht Aquisitions 98               
25 Brandon  4           Golf Junkets      99