按顺序分配正值和负值的位置

Question

假设我有以下数据框：

df = tibble(
        quarter = c(seq.Date(as.Date("2022-03-01"), as.Date("2023-12-01"), "quarter")),
        value = c(rnorm(3), 0, rnorm(4))
     )
    
# A tibble: 8 × 2
  quarter       value
  <date>        <dbl>
1 2022-03-01 -0.670  
2 2022-06-01 -0.00760
3 2022-09-01  1.78   
4 2022-12-01  0      
5 2023-03-01 -1.14   
6 2023-06-01  1.37   
7 2023-09-01  1.33   
8 2023-12-01  0.336 

我想找到当值的符号发生变化时开始和结束的序列中每个正值和负值的位置；对于

0

的特殊情况，应分配“零”。所以我最终想要的输出是这样的：

# A tibble: 8 × 3
  quarter       value position    
  <date>        <dbl> <chr>       
1 2022-03-01 -0.670   1st negative
2 2022-06-01 -0.00760 2nd negative
3 2022-09-01  1.78    1st positive
4 2022-12-01  0       zero        
5 2023-03-01 -1.14    1st negative
6 2023-06-01  1.37    1st positive
7 2023-09-01  1.33    2nd positive
8 2023-12-01  0.336   3nd positive

绞尽脑汁我想我找到了一个不太优雅的解决方案：

 df %>% 
   mutate(position = coalesce(accumulate(ifelse(sign(value) - lag(sign(value)) != 0 | row_number() == 1, 1, NA),
                                         ~ ifelse(is.na(.y), .x + 1, .y))),
          position = ifelse(sign(value) > 0, paste(position, "pos"), 
                            ifelse(sign(value) < 0, paste(position, "neg"), 'zero')))

# A tibble: 8 × 3
  quarter       value position
  <date>        <dbl> <chr>   
1 2022-03-01 -0.670   1 neg   
2 2022-06-01 -0.00760 2 neg   
3 2022-09-01  1.78    1 pos   
4 2022-12-01  0       zero    
5 2023-03-01 -1.14    1 neg   
6 2023-06-01  1.37    1 pos   
7 2023-09-01  1.33    2 pos   
8 2023-12-01  0.336   3 pos

我的问题：有更有效的方法来解决这个问题吗？提前致谢！

Answer 1

这里有一个更简洁的方法：

首先，我们识别 x 中的所有更改 +/-，然后按 x 分组并分配行号，最后将所有更改粘贴到

case_when

:

library(dplyr)

df %>%
  mutate(x = cumsum(c(1, diff(sign(value)) != 0))) %>% 
  mutate(y = row_number(), 
         position = case_when(
    value > 0 ~ paste(y, "positive"),
    value < 0 ~ paste(y, "negative"),
    .default = 'zero'), .by=x) %>% 
  select(-c(x,y))

 quarter     value position  
  <date>      <dbl> <chr>     
1 2022-03-01 -0.556 1 negative
2 2022-06-01  1.79  1 positive
3 2022-09-01  0.498 2 positive
4 2022-12-01  0     zero      
5 2023-03-01 -1.97  1 negative
6 2023-06-01  0.701 1 positive
7 2023-09-01 -0.473 1 negative
8 2023-12-01 -1.07  2 negative