我有這個資料框：
df <- structure(list(value = c(" ", " ", " ", "-", " ", "-", " ", " ",
" ", " ", "-", " ", " ", " ", " ", " ", " ", " ", " ", " ", " ",
" ", " ", " ", " ", " ", " ", " ", " ", " ", " ", " ", " ", " ",
" ", " ", " ", " ", " ")), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -39L))
value
<chr>
1
2
3
4 -
5
6 -
7
8
9
10
# ... with 29 more rows
我想為每 4 行添加一個分組 ID，例如：
df %>%
mutate(id = rep(row_number(), each=4, length.out = n()))
value id
1 1
2 1
3 1
4 - 1
5 2
6 - 2
7 2
8 2
9 3
10 3
... and so on
但我希望-省略這些行，例如：
df_expected
value id expected_id
1 1 1
2 1 1
3 1 1
4 - 1 1
5 2 1
6 - 2 2
7 2 2
8 2 2
9 3 2
10 3 2
11 - 3 3
12 3 3
13 4 3
14 4 3
15 4 3
16 4 4
17 5 4
18 5 4
19 5 4
20 5 5
21 6 5
22 6 5
23 6 5
24 6 6
25 7 6
26 7 6
27 7 6
28 7 7
29 8 7
30 8 7
31 8 7
32 8 8
33 9 8
34 9 8
35 9 8
36 9 9
37 10 9
38 10 9
39 10 9
uj5u.com熱心網友回復：
一種選擇是對“value”不是的行進行子集化，使用與 OP 帖子中-相同的代碼創建列“id”，然后fillNAs
library(data.table)
library(dplyr)
library(tidyr)
setDT(df)[value != "-", id := rep(seq_len(.N), each = 4, length.out = .N) ]
df %>%
fill(id, .direction = "updown")
-輸出
value id
<char> <int>
1: 1
2: 1
3: 1
4: - 1
5: 1
6: - 2
7: 2
8: 2
9: 2
10: 2
11: - 3
12: 3
13: 3
14: 3
15: 3
16: 4
17: 4
18: 4
19: 4
20: 5
21: 5
22: 5
23: 5
24: 6
25: 6
26: 6
27: 6
28: 7
29: 7
30: 7
31: 7
32: 8
33: 8
34: 8
35: 8
36: 9
37: 9
38: 9
39: 9
或者使用dplyrwith fillfrom tidyr- 而不是rep在整個序列上創建，子集row_number()'value'不等于'-'，并使用replace僅分配給具有rep輸出的那些元素
df %>%
mutate(id = replace(rep(NA_integer_, n()), value != "-",
rep(row_number()[value != "-"], each = 4,
length.out = sum(value != "-")))) %>%
fill(id, .direction = "updown")
uj5u.com熱心網友回復：
這是另一種有趣的方法：
setDT(df)[value!="-",id:=rep(1:(.N/4),each=4)][,id:=fifelse(is.na(id), ceiling(.I/4),id)]
輸出：
value id
1: 1
2: 1
3: 1
4: - 1
5: 1
6: - 2
7: 2
8: 2
9: 2
10: 2
11: - 3
12: 3
13: 3
14: 3
15: 3
16: 4
17: 4
18: 4
19: 4
20: 5
21: 5
22: 5
23: 5
24: 6
25: 6
26: 6
27: 6
28: 7
29: 7
30: 7
31: 7
32: 8
33: 8
34: 8
35: 8
36: 9
37: 9
38: 9
39: 9
uj5u.com熱心網友回復：
一個簡單的單線MESS::cumsumbinning：
df$id <- MESS:cumsumbinning(value == " ", 3, cutwhenpassed = T)
all.equal(df_expected$expected_id, df$id)
# [1] TRUE
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