удалить 1-е и последнее наблюдение в группе - PullRequest
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удалить 1-е и последнее наблюдение в группе

0 голосов
/ 31 января 2019

У меня есть фрейм данных, где я хотел бы удалить первое и последнее наблюдения в каждой группе.Я проверил следующее, и он делает то, на что я надеюсь: 

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

Но когда я запускаю его с моим фактическим значением df, он удаляет все мои наблюдения.Как я могу это исправить?

Ниже приведен код для моих фактических данных, а также подмножество моего фрейма данных. 

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", 
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    "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", 
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    "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", 
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    "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", 
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    "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))

1 Ответ

0 голосов
/ 31 января 2019

Вы не должны group_by ts.h.local

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