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RKeithL
Asked: 2024-07-15 08:59:18 +0800 CST

dplyr 添加行（基于条件），重现剩余列中的值（字符串）

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dplyr我查看了许多“根据条件执行 X ”的 SO 问题，我相信只有这个问题接近我正在做的事情。我尝试过但无法适应 @divibisan 的答案。

我的数据如下：

df <- tibble(
  Group_ID = c(1,2,3,4,5,6),
  statusA = c("NEW", "NEW", "OLD","NEW", "OLD","OLD"),
  statusB = c("BONUS", NA, "BONUS", NA, NA,"ROLLBACK"),
  someVar = c("Thanks", "for", "your", "help","!","!"))

当statusB是任何非 NA 值时，我想“拆分”（就像拆分）该行，使得和Group_ID存在不同的行。当行被“拆分”时， 的非 NA 值的行应显示 的 NA ， 的非 NA 值的行应显示 的 NA 。所有剩余变量（即和以外的变量）应按每行显示。statusAstatusBstatusAstatusBstatusBstatusAstatusAstatusBGroup_ID

desired_df <- tibble(
  Group_ID = c(1,1,2,3,3,4,5,6,6),
  statusA = c("NEW",NA,"NEW","OLD",NA,"NEW","OLD","OLD",NA),
  statusB = c(NA,"BONUS",NA,NA,"BONUS",NA,NA,NA,"ROLLBACK"),
  someVar = c("Thanks","Thanks", "for", "your","your","help","!","!","!"))

我强烈希望避免使用循环。可以使用 dplyr 来实现吗？

2 个回答

  1. Best Answer

    你可以试试：

    library(dplyr)
library(tidyr)

df |> 
  reframe(statusA = c(statusA, NA[!is.na(statusB)]),
          statusB = c(NA[!is.na(statusB)], statusB), .by = -c(statusA, statusB)) |> 
  relocate(c(statusA, statusB), .after = Group_ID)


# A tibble: 9 × 4
  Group_ID statusA statusB  someVar
     <dbl> <chr>   <chr>    <chr>  
1        1 NEW     NA       Thanks 
2        1 NA      BONUS    Thanks 
3        2 NEW     NA       for    
4        3 OLD     NA       your   
5        3 NA      BONUS    your   
6        4 NEW     NA       help   
7        5 OLD     NA       !      
8        6 OLD     NA       !      
9        6 NA      ROLLBACK !
    • 2

  2. 将各行绑定在一起，同时将其他各值清空为NA：

    df %>% mutate(statusB = NA) %>%
    bind_rows(df %>% filter(!is.na(statusB)) %>% mutate(statusA = NA)) %>%
    arrange(Group_ID)

##  Group_ID statusA  statusB someVar
##1        1     NEW     <NA>  Thanks
##2        1    <NA>    BONUS  Thanks
##3        2     NEW     <NA>     for
##4        3     OLD     <NA>    your
##5        3    <NA>    BONUS    your
##6        4     NEW     <NA>    help
##7        5     OLD     <NA>       !
##8        6     OLD     <NA>       !
##9        6    <NA> ROLLBACK       !
    • 2

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