使用筛选器dplyr筛选df中的现有值失败

我过去也遇到过类似的问题，但以前行之有效的一切现在都不起作用了

使用dplyr，我试图从以下df中过滤值

structure(list(Sample = c(33L, 34L, 35L, 32L, 21L, 19L, 10L, 
17L, 43L, 44L, 16L, 11L, 18L, 20L, 45L, 42L, 39L, 8L, 37L, 31L, 
9L, 36L, 38L, 7L, 47L, 40L, 22L, 14L, 13L, 48L, 41L, 46L, 12L, 
15L, 23L, 24L, 33L, 34L, 35L, 32L, 21L, 19L, 10L, 17L, 43L, 44L, 
16L, 11L, 18L, 20L, 45L, 42L, 39L, 8L, 37L, 31L, 9L, 36L, 38L, 
7L, 47L, 40L, 22L, 14L, 13L, 48L, 41L, 46L, 12L, 15L, 23L, 24L, 
33L, 34L, 35L, 32L, 21L, 19L, 10L, 17L, 43L, 44L, 16L, 11L, 18L, 
20L, 45L, 42L, 39L, 8L, 37L, 31L, 9L, 36L, 38L, 7L, 47L, 40L, 
22L, 14L, 13L, 48L, 41L, 46L, 12L, 15L, 23L, 24L), day = c(6L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L), Roll = structure(c(1L, 
1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 
2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 
2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 
1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 
1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 
1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L), .Label = c("WT", 
"CNS"), class = "factor"), Movie = c("CB", "CSF", "JE", "CL", 
"CB", "BS", "CSF", "NT", "BS", "CL", "CB", "LN", "OB", "BS", 
"CB", "OB", "CL", "CL", "OB", "BS", "CB", "NT", "BS", "BS", "NT", 
"CB", "CSF", "BS", "OB", "OB", "NT", "CSF", "NT", "CL", "NT", 
"OB", "CB", "CSF", "JE", "CL", "CB", "BS", "CSF", "NT", "BS", 
"CL", "CB", "LN", "OB", "BS", "CB", "OB", "CL", "CL", "OB", "BS", 
"CB", "NT", "BS", "BS", "NT", "CB", "CSF", "BS", "OB", "OB", 
"NT", "CSF", "NT", "CL", "NT", "OB", "CB", "CSF", "JE", "CL", 
"CB", "BS", "CSF", "NT", "BS", "CL", "CB", "LN", "OB", "BS", 
"CB", "OB", "CL", "CL", "OB", "BS", "CB", "NT", "BS", "BS", "NT", 
"CB", "CSF", "BS", "OB", "OB", "NT", "CSF", "NT", "CL", "NT", 
"OB"), Number = c("1078", "1078", "1078", "1078", "1086", "1086", 
"1084", "1085", "1080", "1080", "1085", "1084", "1085", "1086", 
"1080", "1079", "1079", "1084", "1078", "1078", "1084", "1078", 
"1079", "1084", "1080", "1079", "1086", "1085", "1084", "1080", 
"1079", "1080", "1084", "1085", "1086", "1086", "1078", "1078", 
"1078", "1078", "1086", "1086", "1084", "1085", "1080", "1080", 
"1085", "1084", "1085", "1086", "1080", "1079", "1079", "1084", 
"1078", "1078", "1084", "1078", "1079", "1084", "1080", "1079", 
"1086", "1085", "1084", "1080", "1079", "1080", "1084", "1085", 
"1086", "1086", "1078", "1078", "1078", "1078", "1086", "1086", 
"1084", "1085", "1080", "1080", "1085", "1084", "1085", "1086", 
"1080", "1079", "1079", "1084", "1078", "1078", "1084", "1078", 
"1079", "1084", "1080", "1079", "1086", "1085", "1084", "1080", 
"1079", "1080", "1084", "1085", "1086", "1086"), PiType = c("Pi", 
"Pi", "Pi", "Pi", "Pi", "Pi", "Pi", "Pi", "Pi", "Pi", "Pi", "Pi", 
"Pi", "Pi", "Pi", "Pi", "Pi", "Pi", "Pi", "Pi", "Pi", "Pi", "Pi", 
"Pi", "Pi", "Pi", "Pi", "Pi", "Pi", "Pi", "Pi", "Pi", "Pi", "Pi", 
"Pi", "Pi", "PiN", "PiN", "PiN", "PiN", "PiN", "PiN", "PiN", 
"PiN", "PiN", "PiN", "PiN", "PiN", "PiN", "PiN", "PiN", "PiN", 
"PiN", "PiN", "PiN", "PiN", "PiN", "PiN", "PiN", "PiN", "PiN", 
"PiN", "PiN", "PiN", "PiN", "PiN", "PiN", "PiN", "PiN", "PiN", 
"PiN", "PiN", "PiS", "PiS", "PiS", "PiS", "PiS", "PiS", "PiS", 
"PiS", "PiS", "PiS", "PiS", "PiS", "PiS", "PiS", "PiS", "PiS", 
"PiS", "PiS", "PiS", "PiS", "PiS", "PiS", "PiS", "PiS", "PiS", 
"PiS", "PiS", "PiS", "PiS", "PiS", "PiS", "PiS", "PiS", "PiS", 
"PiS", "PiS"), PiValue = c(0.000118030874000432, 0.000347394403155171, 
0.000334837948379439, 5.30072253561085e-05, 2.24330352675195e-05, 
3.86268327796905e-05, 6.2507327248452e-05, 9.60416615469501e-05, 
0.000144334778337716, 6.67586823161674e-05, 1.78663898426022e-05, 
0.000316509713879677, 4.47521244052015e-05, 3.96867129962584e-05, 
0.000169993756345233, 0.000191198008647424, 2.26780120661099e-05, 
8.24479797741708e-05, 0.000192302896751376, 4.7619494686152e-05, 
7.37708010974279e-05, 4.48908253519562e-05, 2.73829490754403e-05, 
5.25177343097397e-05, 4.27185614032656e-05, 7.5006506956365e-05, 
0.000116609688359558, 6.68697691071145e-05, 6.76843888183842e-05, 
7.50792709794965e-05, 4.97003975900831e-05, 5.97682955162379e-05, 
3.17211538748376e-05, 0, 0.000124597626630711, 5.06964132868791e-05, 
6.55143240489323e-05, 0.000282236533520939, 0.000231673136666841, 
7.8541479377849e-06, 1.39078438106325e-05, 1.13160982950996e-05, 
4.41068224408621e-05, 0.000113555359247769, 0.000124644875822084, 
5.50994542363206e-05, 1.55034145915711e-05, 0.000267386418482701, 
5.18260236496838e-05, 4.05457367440565e-05, 0.000110040218632992, 
0.000115314276877192, 0, 3.79060738095635e-05, 0.000136298514781125, 
1.0191727648554e-05, 2.24866030942902e-05, 1.83074103814636e-05, 
6.03265473949701e-06, 4.25225247204989e-05, 1.92722508892339e-05, 
1.84357396441799e-05, 9.21240992824623e-05, 7.26769701372556e-05, 
6.35987336654841e-05, 3.95393972658535e-05, 2.09528494998807e-05, 
5.01526066480047e-05, 1.5637656638317e-05, 0, 0.000101461574436309, 
3.1444160517462e-05, 0.000373108512754004, 0.000577144245516474, 
0.000718783415458189, 0.000116646703711194, 5.7651629182113e-05, 
3.02814327941576e-05, 6.30072703727483e-05, 8.33284324327778e-05, 
0.000184726271317848, 6.59651530576878e-05, 2.75776588339286e-05, 
0.000510545905213385, 7.45382607589015e-06, 2.94165975027625e-05, 
0.000349308266764161, 0.000186656895351811, 0, 0.000157093160232072, 
0.000324422950785617, 0.000100785379847999, 0.000217514231635164, 
3.22722832241936e-05, 4.41407033282829e-05, 2.89288759086921e-05, 
3.19081372774688e-05, 9.55622182991179e-05, 0.00015844663293416, 
5.23569281384674e-05, 5.28410056207709e-05, 9.8060534699267e-05, 
6.92202005431013e-05, 3.67945212590064e-05, 3.52367433560858e-05, 
0, 0.000242180735270571, 7.63615212494576e-05)), row.names = c(NA, 
-108L), class = "data.frame")

执行以下操作不包括1084号电影OB，即使它以百分之一的Meta_long_6dpi形式出现

OnePercentMeta_long_6dpi_OB.NT<-
  OnePercentMeta_long_6dpi %>% 
 filter(Movie == c("NT","OB")) 

OnePercentMeta\u long\u 6dpi\u OB.NT%
过滤器（电影==c（“NT”，“OB”））

---

我已经删除了可能的尾随空格和前导空格（从dput（）中可以看到），但仍然没有。我们需要在%中使用

%而不是
=
，因为
=
是元素比较，它会循环使用“电影”的每2个值中的“NT”，“OB”，从而产生不同于预期的结果

library(dplyr)
OnePercentMeta_long_6dpi_OB.NT <- OnePercentMeta_long_6dpi %>% 
   filter(Movie %in% c("NT","OB")) 

库（dplyr）
百分之一元长6dpi OB.NT%
筛选器（电影%c（“NT”、“OB”））
非常感谢您！这就解决了问题！