按顺序标记R中一系列值中的特定值

Question

我有一个如下所示的表：

Date Season
1  2022-01-01  Val_1
2  2022-01-02  Val_1
3  2022-01-03  Val_1
4  2022-01-04  Val_2
5  2022-01-05  Val_2
6  2022-01-06  Val_2
7  2022-01-07  Val_1
8  2022-01-08  Val_1
9  2022-01-09  Val_1
10 2022-01-10  Val_2
11 2022-01-11  Val_2
12 2022-01-12  Val_2
13 2022-01-13  Val_1
14 2022-01-14  Val_1
15 2022-01-15  Val_1

我想做的是为列中的每个值标记每个连续Season值序列，从1到列中存在的连续序列总数。我见过类似的解决方案用rle这样的函数来解决，但我目前还不知道如何将其转化为这个问题。以下是我想要的输出示例：

Date Season Season_Num
1  2022-01-01  Val_1          1
2  2022-01-02  Val_1          1
3  2022-01-03  Val_1          1
4  2022-01-04  Val_2          1
5  2022-01-05  Val_2          1
6  2022-01-06  Val_2          1
7  2022-01-07  Val_1          2
8  2022-01-08  Val_1          2
9  2022-01-09  Val_1          2
10 2022-01-10  Val_2          2
11 2022-01-11  Val_2          2
12 2022-01-12  Val_2          2
13 2022-01-13  Val_1          3
14 2022-01-14  Val_1          3
15 2022-01-15  Val_1          3

Answer 1

使用单个mutate调用，使用cumsum和lag:

library(dplyr)
df %>% 
mutate(Season_num = cumsum(Season == "Val_1" & lag(Season, default = "Val_2") != Season))
#          Date Season Season_num
# 1  2022-01-01  Val_1          1
# 2  2022-01-02  Val_1          1
# 3  2022-01-03  Val_1          1
# 4  2022-01-04  Val_2          1
# 5  2022-01-05  Val_2          1
# 6  2022-01-06  Val_2          1
# 7  2022-01-07  Val_1          2
# 8  2022-01-08  Val_1          2
# 9  2022-01-09  Val_1          2
# 10 2022-01-10  Val_2          2
# 11 2022-01-11  Val_2          2
# 12 2022-01-12  Val_2          2
# 13 2022-01-13  Val_1          3
# 14 2022-01-14  Val_1          3
# 15 2022-01-15  Val_1          3

Answer 2

我们可以在按"季节"分组后获得日期对象之间的diff，并进行累积和

library(dplyr)
df1 %>%
group_by(Season) %>%
mutate(Season_Num = cumsum(c(TRUE, diff(Date) != 1))) %>%
ungroup

-输出

# A tibble: 15 × 3
Date       Season Season_Num
<date>     <chr>       <int>
1 2022-01-01 Val_1           1
2 2022-01-02 Val_1           1
3 2022-01-03 Val_1           1
4 2022-01-04 Val_2           1
5 2022-01-05 Val_2           1
6 2022-01-06 Val_2           1
7 2022-01-07 Val_1           2
8 2022-01-08 Val_1           2
9 2022-01-09 Val_1           2
10 2022-01-10 Val_2           2
11 2022-01-11 Val_2           2
12 2022-01-12 Val_2           2
13 2022-01-13 Val_1           3
14 2022-01-14 Val_1           3
15 2022-01-15 Val_1           3

数据

df1 <- structure(list(Date = structure(c(18993, 18994, 18995, 18996, 
18997, 18998, 18999, 19000, 19001, 19002, 19003, 19004, 19005, 
19006, 19007), class = "Date"), Season = c("Val_1", "Val_1", 
"Val_1", "Val_2", "Val_2", "Val_2", "Val_1", "Val_1", "Val_1", 
"Val_2", "Val_2", "Val_2", "Val_1", "Val_1", "Val_1")), row.names = c("1", 
"2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", 
"14", "15"), class = "data.frame")