r - ifelse 在 R 中有两个条件数字和分类

Question

我有以下数据集，其中一个子集是:

structure(list(First.Name = c(5006L, 5006L, 5006L, 5006L, 5006L, 
5006L, 5006L, 5006L, 5006L, 5006L, 5006L, 5006L), TimePoint = c(NA, 
NA, NA, NA, NA, "PRE", NA, NA, NA, NA, NA, NA), Year_Day = c(125, 
126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136), Week_Year = c(18, 
18, 19, 19, 19, 19, 19, 19, 19, 20, 20, 20), Session = c("Pre", 
"Pre", "Pre", "Pre", "Pre", "Pre", "Pre", "Pre", "Pre", "Post", 
"Post", "Post")), row.names = c(NA, -12L), class = c("tbl_df", 
"tbl", "data.frame"))

看起来像(用星号表示错误):

# A tibble: 12 x 5
   First.Name TimePoint Year_Day Week_Year Session
        <int> <chr>        <dbl>     <dbl> <chr>  
 1       5006 NA             125        18 Pre    
 2       5006 NA             126        18 Pre    
 3       5006 NA             127        19 Pre    
 4       5006 NA             128        19 Pre    
 5       5006 NA             129        19 Pre    
 6       5006 PRE            130        19 Pre    
 7       5006 NA             131        19 **Pre**    
 8       5006 NA             132        19 **Pre**    
 9       5006 NA             133        19 **Pre**    
10       5006 NA             134        20 Post   
11       5006 NA             135        20 Post   
12       5006 NA             136        20 Post   

如果 Week_Year 是主题数据的开始，我正在尝试为每个主题创建一个名为 Session 的新列，其中包含单词“Pre”直到(包括) TimePoint 列包含单词“PRE”，所有其他行应为“Post”

上述子集的理想输出应该是:

# A tibble: 12 x 5
   First.Name TimePoint Year_Day Week_Year Session
        <int> <chr>        <dbl>     <dbl> <chr>  
 1       5006 NA             125        18 Pre    
 2       5006 NA             126        18 Pre    
 3       5006 NA             127        19 Pre    
 4       5006 NA             128        19 Pre    
 5       5006 NA             129        19 Pre    
 6       5006 PRE            130        19 Pre    
 7       5006 NA             131        19 **Post**   
 8       5006 NA             132        19 **Post**   
 9       5006 NA             133        19 **Post**   
10       5006 NA             134        20 Post   
11       5006 NA             135        20 Post   
12       5006 NA             136        20 Post   

我正在尝试各种变体

df %>%
  group_by(First.Name) %>%
  mutate(Session = ifelse(TimePoint == "PRE" & Week_Year <= first(Week_Year) + 1, "Pre", "Post")) %>%
  ungroup()

但它没有正确输出。感谢帮助。

Answer 1

您可以使用 dplyr 包中的 case_when 以及 lag 和 fill 来完成此操作。

df <- structure(list(First.Name = c(5006L, 5006L, 5006L, 5006L, 5006L, 
5006L, 5006L, 5006L, 5006L, 5006L, 5006L, 5006L), TimePoint = c(NA, 
NA, NA, NA, NA, "PRE", NA, NA, NA, NA, NA, NA), Year_Day = c(125, 
126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136), Week_Year = c(18, 
18, 19, 19, 19, 19, 19, 19, 19, 20, 20, 20), Session = c("Pre", 
"Pre", "Pre", "Pre", "Pre", "Pre", "Pre", "Pre", "Pre", "Post", 
"Post", "Post")), row.names = c(NA, -12L), class = c("tbl_df", 
"tbl", "data.frame"))

library(dplyr)
library(tidyr)

df %>% 
  mutate(
    Session = case_when(
      TimePoint == "PRE" ~ "Pre",
      lag(TimePoint) == "PRE" ~ "Post")
    ) %>% 
    fill(Session, .direction = "updown") %>% 
  ungroup()

#> # A tibble: 12 x 5
#>    First.Name TimePoint Year_Day Week_Year Session
#>         <int> <chr>        <dbl>     <dbl> <chr>  
#>  1       5006 <NA>           125        18 Pre    
#>  2       5006 <NA>           126        18 Pre    
#>  3       5006 <NA>           127        19 Pre    
#>  4       5006 <NA>           128        19 Pre    
#>  5       5006 <NA>           129        19 Pre    
#>  6       5006 PRE            130        19 Pre    
#>  7       5006 <NA>           131        19 Post   
#>  8       5006 <NA>           132        19 Post   
#>  9       5006 <NA>           133        19 Post   
#> 10       5006 <NA>           134        20 Post   
#> 11       5006 <NA>           135        20 Post   
#> 12       5006 <NA>           136        20 Post

^{由 reprex package 创建于 2021-02-01 (v0.3.0)}