删除组中的第一个和最后一个观测值

nt

我有一个数据框,我想删除每个组中的第一个和最后一个观察值。我已经测试了以下内容,并且可以实现我的期望:

df <- data.frame(v1 = c(1,1,1,1,1,2,2,2,2,2,3,3,3,3,3),
v2 = c(1,2,3,4,5,1,2,3,4,5,1,2,3,4,5))

df %>%
group_by(v1) %>%
slice(-c(1,n())

# A tibble: 9 x 2
# Groups:   v1 [3]
     v1    v2
  <dbl> <dbl>
1     1     2
2     1     3
3     1     4
4     2     2
5     2     3
6     2     4
7     3     2
8     3     3
9     3     4

但是,当我使用实际的df运行它时,它将删除所有观察结果。我怎样才能解决这个问题?

以下是我的实际数据以及数据框的子集的代码。

df.detTot2 <- df.detTot %>% 
  ungroup() %>% #added in incase there was additional grouping from previous
  group_by(ID, recvDeployName2, doy.local, ts.h.local) %>%
  slice(-c(1, n()))

dim(df.detTot2)
[1] 0 8

dput(df.detTot[1:100,])
structure(list(ID = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("NB2014.12", 
"NB2014.13", "NB2014.14", "NB2014.15", "NB2014.16", "NB2014.42", 
"NB2014.43", "NB2014.44", "NB2014.45", "NB2014.47", "NB2014.48", 
"NB2014.49", "NB2014.70", "NB2014.71", "NB2014.72", "NB2014.73", 
"NB2014.74", "NB2014.75", "NB2014.76", "NB2014.77", "NB2014.78", 
"NB2014.79", "NB2014.80", "NB2014.81", "NB2015.156", "NB2015.157", 
"NB2015.158", "NB2015.159", "NB2015.160", "NB2015.312", "NB2015.313", 
"NB2015.314", "NB2015.315", "NB2015.316", "NB2015.317", "NB2015.318", 
"NB2015.320", "NB2015.321", "NB2015.322", "NB2015.323", "NB2015.324", 
"NB2015.325", "NB2015.326", "NB2015.327", "NB2015.328", "NB2015.329", 
"NB2015.330", "NB2015.331", "NB2015.332", "NB2015.333", "NB2015.334", 
"NB2015.335", "NB2015.336", "NB2015.337", "NB2015.338", "NB2015.339", 
"NB2015.340", "NB2015.341", "NB2015.342", "NB2015.343", "NB2015.344", 
"NB2015.345", "NB2015.346", "NB2015.347", "NB2015.348", "NB2015.349", 
"NB2015.350", "NB2015.351", "NB2018.10", "NB2018.11", "NB2018.12", 
"NB2018.13", "NB2018.14", "NB2018.15", "NB2018.16", "NB2018.17", 
"NB2018.18", "NB2018.19", "NB2018.20", "NB2018.21", "NB2018.22", 
"NB2018.23", "NB2018.24", "NB2018.25", "NB2018.26", "NB2018.27", 
"NB2018.28", "NB2018.29", "NB2018.30", "NB2018.31", "NB2018.32", 
"NB2018.33", "NB2018.34", "NB2018.35", "NB2018.37", "NB2018.38", 
"NB2018.39", "NB2018.40", "NB2018.41", "NB2018.42", "NB2018.43", 
"NB2018.44", "NB2018.45", "NB2018.46", "NB2018.47", "NB2018.48", 
"NB2018.49", "NB2018.5", "NB2018.50", "NB2018.51", "NB2018.52", 
"NB2018.53", "NB2018.54", "NB2018.55", "NB2018.56", "NB2018.57", 
"NB2018.58", "NB2018.59", "NB2018.6", "NB2018.60", "NB2018.61", 
"NB2018.62", "NB2018.63", "NB2018.64", "NB2018.7", "NB2018.8", 
"NB2018.9"), class = "factor"), speciesEN = c("Bank Swallow", 
"Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
"Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
"Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
"Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
"Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
"Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
"Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
"Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
"Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
"Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
"Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
"Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
"Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
"Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
"Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
"Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
"Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
"Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
"Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
"Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
"Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
"Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
"Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
"Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
"Bank Swallow", "Bank Swallow", "Bank Swallow"), recvDeployName2 = c("Tantramar", 
"Tantramar", "Tantramar", "Tantramar", "Tantramar", "Tantramar", 
"Tantramar", "Tantramar", "Tantramar", "Tantramar", "Tantramar", 
"Tantramar", "Tantramar", "Tantramar", "Tantramar", "Tantramar", 
"Tantramar", "Tantramar", "Tantramar", "Tantramar", "Tantramar", 
"Tantramar", "Tantramar", "Tantramar", "Tantramar", "Tantramar", 
"Tantramar", "Tantramar", "Tantramar", "Tantramar", "Tantramar", 
"Tantramar", "Tantramar", "Tantramar", "Tantramar", "Tantramar", 
"Tantramar", "Tantramar", "Tantramar", "Tantramar", "Tantramar", 
"Tantramar", "Tantramar", "Tantramar", "Tantramar", "Tantramar", 
"Tantramar", "Tantramar", "Tantramar", "Tantramar", "Tantramar", 
"Tantramar", "Tantramar", "Tantramar", "Tantramar", "Tantramar", 
"Tantramar", "Tantramar", "Tantramar", "Tantramar", "Tantramar", 
"Tantramar", "Tantramar", "Tantramar", "Tantramar", "Tantramar", 
"Tantramar", "Tantramar", "Tantramar", "Tantramar", "Tantramar", 
"Tantramar", "Tantramar", "Tantramar", "Tantramar", "Tantramar", 
"Tantramar", "Tantramar", "Tantramar", "Tantramar", "Tantramar", 
"Tantramar", "Tantramar", "Tantramar", "Tantramar", "Tantramar", 
"Tantramar", "Tantramar", "Tantramar", "Tantramar", "Tantramar", 
"Tantramar", "Tantramar", "Tantramar", "Tantramar", "Tantramar", 
"Tantramar", "Tantramar", "Tantramar", "Tantramar"), year = c(2014, 
2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 
2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 
2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 
2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 
2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 
2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 
2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 
2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 
2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014
), ts.h.local = c(5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 
15L, 16L, 17L, 18L, 19L, 20L, 21L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 5L, 6L, 7L, 
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 
21L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 
18L, 19L, 20L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 
16L, 17L, 18L, 19L, 20L, 21L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 
13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L), doy.local = c(183, 183, 
183, 183, 183, 183, 183, 183, 183, 183, 183, 183, 183, 183, 183, 
183, 183, 184, 184, 184, 184, 184, 184, 184, 184, 184, 184, 184, 
184, 184, 184, 184, 184, 184, 185, 185, 185, 185, 185, 185, 185, 
185, 185, 185, 185, 185, 185, 185, 185, 185, 185, 186, 186, 186, 
186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 
187, 187, 187, 187, 187, 187, 187, 187, 187, 187, 187, 187, 187, 
187, 187, 187, 187, 188, 188, 188, 188, 188, 188, 188, 188, 188, 
188, 188, 188, 188, 188, 188, 188), nDet = c(0, 0, 0, 0, 10, 
23, 7, 41, 0, 0, 28, 3, 35, 39, 29, 40, 0, 0, 0, 13, 35, 43, 
106, 136, 116, 77, 43, 149, 130, 60, 44, 169, 26, 2, 6, 48, 38, 
38, 127, 50, 28, 74, 162, 211, 138, 85, 63, 63, 67, 30, 2, 0, 
0, 71, 2, 53, 161, 143, 63, 107, 26, 0, 0, 260, 168, 54, 46, 
132, 291, 171, 204, 154, 75, 198, 80, 155, 205, 158, 203, 137, 
59, 47, 170, 36, 95, 131, 167, 124, 100, 130, 131, 247, 247, 
102, 177, 191, 93, 171, 180, 127), dayNight = c("day", "day", 
"day", "day", "day", "day", "day", "day", "day", "day", "day", 
"day", "day", "day", "day", "day", "day", "day", "day", "day", 
"day", "day", "day", "day", "day", "day", "day", "day", "day", 
"day", "day", "day", "day", "day", "day", "day", "day", "day", 
"day", "day", "day", "day", "day", "day", "day", "day", "day", 
"day", "day", "day", "day", "day", "day", "day", "day", "day", 
"day", "day", "day", "day", "day", "day", "day", "day", "day", 
"day", "day", "day", "day", "day", "day", "day", "day", "day", 
"day", "day", "day", "day", "day", "day", "day", "day", "day", 
"day", "day", "day", "day", "day", "day", "day", "day", "day", 
"day", "day", "day", "day", "day", "day", "day", "day")), row.names = c(NA, 
-100L), class = c("grouped_df", "tbl_df", "tbl", "data.frame"
), vars = c("ID", "speciesEN", "recvDeployName2", "year", "doy.local", 
"ts.h.local"), drop = TRUE, indices = list(0L, 1L, 2L, 3L, 4L, 
    5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 
    18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 
    30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 
    42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 
    54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 
    66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 
    78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 
    90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L), group_sizes = c(1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L), biggest_group_size = 1L, labels = structure(list(
    ID = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L
    ), .Label = c("NB2014.12", "NB2014.13", "NB2014.14", "NB2014.15", 
    "NB2014.16", "NB2014.42", "NB2014.43", "NB2014.44", "NB2014.45", 
    "NB2014.47", "NB2014.48", "NB2014.49", "NB2014.70", "NB2014.71", 
    "NB2014.72", "NB2014.73", "NB2014.74", "NB2014.75", "NB2014.76", 
    "NB2014.77", "NB2014.78", "NB2014.79", "NB2014.80", "NB2014.81", 
    "NB2015.156", "NB2015.157", "NB2015.158", "NB2015.159", "NB2015.160", 
    "NB2015.312", "NB2015.313", "NB2015.314", "NB2015.315", "NB2015.316", 
    "NB2015.317", "NB2015.318", "NB2015.320", "NB2015.321", "NB2015.322", 
    "NB2015.323", "NB2015.324", "NB2015.325", "NB2015.326", "NB2015.327", 
    "NB2015.328", "NB2015.329", "NB2015.330", "NB2015.331", "NB2015.332", 
    "NB2015.333", "NB2015.334", "NB2015.335", "NB2015.336", "NB2015.337", 
    "NB2015.338", "NB2015.339", "NB2015.340", "NB2015.341", "NB2015.342", 
    "NB2015.343", "NB2015.344", "NB2015.345", "NB2015.346", "NB2015.347", 
    "NB2015.348", "NB2015.349", "NB2015.350", "NB2015.351", "NB2018.10", 
    "NB2018.11", "NB2018.12", "NB2018.13", "NB2018.14", "NB2018.15", 
    "NB2018.16", "NB2018.17", "NB2018.18", "NB2018.19", "NB2018.20", 
    "NB2018.21", "NB2018.22", "NB2018.23", "NB2018.24", "NB2018.25", 
    "NB2018.26", "NB2018.27", "NB2018.28", "NB2018.29", "NB2018.30", 
    "NB2018.31", "NB2018.32", "NB2018.33", "NB2018.34", "NB2018.35", 
    "NB2018.37", "NB2018.38", "NB2018.39", "NB2018.40", "NB2018.41", 
    "NB2018.42", "NB2018.43", "NB2018.44", "NB2018.45", "NB2018.46", 
    "NB2018.47", "NB2018.48", "NB2018.49", "NB2018.5", "NB2018.50", 
    "NB2018.51", "NB2018.52", "NB2018.53", "NB2018.54", "NB2018.55", 
    "NB2018.56", "NB2018.57", "NB2018.58", "NB2018.59", "NB2018.6", 
    "NB2018.60", "NB2018.61", "NB2018.62", "NB2018.63", "NB2018.64", 
    "NB2018.7", "NB2018.8", "NB2018.9"), class = "factor"), speciesEN = c("Bank Swallow", 
    "Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
    "Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
    "Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
    "Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
    "Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
    "Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
    "Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
    "Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
    "Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
    "Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
    "Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
    "Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
    "Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
    "Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
    "Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
    "Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
    "Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
    "Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
    "Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
    "Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
    "Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
    "Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
    "Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
    "Bank Swallow", "Bank Swallow", "Bank Swallow", "Bank Swallow", 
    "Bank Swallow", "Bank Swallow", "Bank Swallow"), recvDeployName2 = c("Tantramar", 
    "Tantramar", "Tantramar", "Tantramar", "Tantramar", "Tantramar", 
    "Tantramar", "Tantramar", "Tantramar", "Tantramar", "Tantramar", 
    "Tantramar", "Tantramar", "Tantramar", "Tantramar", "Tantramar", 
    "Tantramar", "Tantramar", "Tantramar", "Tantramar", "Tantramar", 
    "Tantramar", "Tantramar", "Tantramar", "Tantramar", "Tantramar", 
    "Tantramar", "Tantramar", "Tantramar", "Tantramar", "Tantramar", 
    "Tantramar", "Tantramar", "Tantramar", "Tantramar", "Tantramar", 
    "Tantramar", "Tantramar", "Tantramar", "Tantramar", "Tantramar", 
    "Tantramar", "Tantramar", "Tantramar", "Tantramar", "Tantramar", 
    "Tantramar", "Tantramar", "Tantramar", "Tantramar", "Tantramar", 
    "Tantramar", "Tantramar", "Tantramar", "Tantramar", "Tantramar", 
    "Tantramar", "Tantramar", "Tantramar", "Tantramar", "Tantramar", 
    "Tantramar", "Tantramar", "Tantramar", "Tantramar", "Tantramar", 
    "Tantramar", "Tantramar", "Tantramar", "Tantramar", "Tantramar", 
    "Tantramar", "Tantramar", "Tantramar", "Tantramar", "Tantramar", 
    "Tantramar", "Tantramar", "Tantramar", "Tantramar", "Tantramar", 
    "Tantramar", "Tantramar", "Tantramar", "Tantramar", "Tantramar", 
    "Tantramar", "Tantramar", "Tantramar", "Tantramar", "Tantramar", 
    "Tantramar", "Tantramar", "Tantramar", "Tantramar", "Tantramar", 
    "Tantramar", "Tantramar", "Tantramar", "Tantramar"), year = c(2014, 
    2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 
    2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 
    2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 
    2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 
    2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 
    2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 
    2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 
    2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 
    2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 
    2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014), doy.local = c(183, 
    183, 183, 183, 183, 183, 183, 183, 183, 183, 183, 183, 183, 
    183, 183, 183, 183, 184, 184, 184, 184, 184, 184, 184, 184, 
    184, 184, 184, 184, 184, 184, 184, 184, 184, 185, 185, 185, 
    185, 185, 185, 185, 185, 185, 185, 185, 185, 185, 185, 185, 
    185, 185, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 
    186, 186, 186, 186, 186, 186, 187, 187, 187, 187, 187, 187, 
    187, 187, 187, 187, 187, 187, 187, 187, 187, 187, 187, 188, 
    188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 
    188, 188, 188), ts.h.local = c(5L, 6L, 7L, 8L, 9L, 10L, 11L, 
    12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 5L, 6L, 
    7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 
    19L, 20L, 21L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 
    15L, 16L, 17L, 18L, 19L, 20L, 21L, 5L, 6L, 7L, 8L, 9L, 10L, 
    11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 5L, 6L, 
    7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 
    19L, 20L, 21L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 
    15L, 16L, 17L, 18L, 19L, 20L)), row.names = c(NA, -100L), class = "data.frame", vars = c("ID", 
"speciesEN", "recvDeployName2", "year", "doy.local", "ts.h.local"
), drop = TRUE))
BEN_YO

你不应该 group_by ts.h.local

df %>% 
     ungroup() %>% 
     group_by(ID, recvDeployName2, doy.local) %>%
     slice(-c(1, n()))

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