我有以下数据框：
df <- structure(list(united_sample_names = structure(92:101, .Label = c("1-XX_iv.161202_NB501621_0042_AHV7NWBGXY.KK_208",
"1-XX_iv.161202_NB501621_0042_AHV7NWBGXY.KK_209", "1-XX_iv.161202_NB501621_0042_AHV7NWBGXY.KK_210",
"1-XX_iv.161202_NB501621_0042_AHV7NWBGXY.KK_211", "1-XX_iv.161202_NB501621_0042_AHV7NWBGXY.KK_212",
"1-XX_iv.161202_NB501621_0042_AHV7NWBGXY.KK_213", "1-XX_iv.161202_NB501621_0042_AHV7NWBGXY.KK_214",
"1-XX_iv.161202_NB501621_0042_AHV7NWBGXY.KK_215", "1-XX_iv.161202_NB501621_0042_AHV7NWBGXY.KK_216",
"1-XX_iv.161202_NB501621_0042_AHV7NWBGXY.KK_218", "1-XX_iv.161205_NB501621_0043_AHVFM5BGXY.KK_172",
"1-XX_iv.161205_NB501621_0043_AHVFM5BGXY.KK_173", "1-XX_iv.161205_NB501621_0043_AHVFM5BGXY.KK_174",
"1-XX_iv.161205_NB501621_0043_AHVFM5BGXY.KK_175", "1-XX_iv.161205_NB501621_0043_AHVFM5BGXY.KK_176",
"1-XX_iv.161205_NB501621_0043_AHVFM5BGXY.KK_177", "1-XX_iv.161205_NB501621_0043_AHVFM5BGXY.KK_178",
"1-XX_iv.161205_NB501621_0043_AHVFM5BGXY.KK_179", "1-XX_iv.161205_NB501621_0043_AHVFM5BGXY.KK_180",
"1-XX_iv.161205_NB501621_0043_AHVFM5BGXY.KK_181", "1-XX_iv.161205_NB501621_0043_AHVFM5BGXY.KK_182",
"1-XX_iv.161205_NB501621_0043_AHVFM5BGXY.KK_183", "1-XX_iv.161205_NB501621_0043_AHVFM5BGXY.KK_184",
"1-XX_iv.161205_NB501621_0043_AHVFM5BGXY.KK_185", "1-XX_iv.161205_NB501621_0043_AHVFM5BGXY.KK_186",
"1-XX_iv.161205_NB501621_0043_AHVFM5BGXY.KK_187", "1-XX_iv.161205_NB501621_0043_AHVFM5BGXY.KK_188",
"1-XX_iv.161205_NB501621_0043_AHVFM5BGXY.KK_189", "1-XX_iv.161205_NB501621_0043_AHVFM5BGXY.KK_190",
"1-XX_iv.161205_NB501621_0043_AHVFM5BGXY.KK_191", "1-XX_iv.161205_NB501621_0043_AHVFM5BGXY.KK_192",
"1-XX_iv.161205_NB501621_0043_AHVFM5BGXY.KK_193", "1-XX_iv.161205_NB501621_0043_AHVFM5BGXY.KK_194",
"1-XX_iv.161205_NB501621_0043_AHVFM5BGXY.KK_195", "1-XX_iv.161205_NB501621_0043_AHVFM5BGXY.KK_196",
"1-XX_iv.161205_NB501621_0043_AHVFM5BGXY.KK_197", "1-XX_iv.161205_NB501621_0043_AHVFM5BGXY.KK_198",
"1-XX_iv.161205_NB501621_0043_AHVFM5BGXY.KK_199", "1-XX_iv.161205_NB501621_0043_AHVFM5BGXY.KK_200",
"1-XX_iv.161205_NB501621_0043_AHVFM5BGXY.KK_201", "1-XX_iv.161205_NB501621_0043_AHVFM5BGXY.KK_202",
"1-XX_iv.161205_NB501621_0043_AHVFM5BGXY.KK_203", "1-XX_iv.170125_NB501621_0067_AHW3MGBGXY.KK_359",
"1-XX_iv.170125_NB501621_0067_AHW3MGBGXY.KK_360", "1-XX_iv.170125_NB501621_0067_AHW3MGBGXY.KK_361",
"1-XX_iv.170125_NB501621_0067_AHW3MGBGXY.KK_362", "1-XX_iv.170125_NB501621_0067_AHW3MGBGXY.KK_363",
"1-XX_iv.170125_NB501621_0067_AHW3MGBGXY.KK_364", "1-XX_iv.170125_NB501621_0067_AHW3MGBGXY.KK_365",
"1-XX_iv.170125_NB501621_0067_AHW3MGBGXY.KK_366", "1-XX_iv.170125_NB501621_0067_AHW3MGBGXY.KK_367",
"1-XX_iv.170125_NB501621_0067_AHW3MGBGXY.KK_368", "1-XX_iv.170125_NB501621_0067_AHW3MGBGXY.KK_369",
"1-XX_iv.170125_NB501621_0067_AHW3MGBGXY.KK_370", "1-XX_iv.170125_NB501621_0067_AHW3MGBGXY.KK_371",
"1-XX_iv.170125_NB501621_0067_AHW3MGBGXY.KK_372", "1-XX_iv.170125_NB501621_0067_AHW3MGBGXY.KK_373",
"1-XX_iv.170125_NB501621_0067_AHW3MGBGXY.KK_374", "1-XX_iv.170125_NB501621_0067_AHW3MGBGXY.KK_375",
"1-XX_iv.170125_NB501621_0067_AHW3MGBGXY.KK_376", "1-XX_iv.170125_NB501621_0067_AHW3MGBGXY.KK_377",
"1-XX_iv.170125_NB501621_0067_AHW3MGBGXY.KK_378", "1-XX_iv.170125_NB501621_0067_AHW3MGBGXY.KK_379",
"1-XX_iv.170125_NB501621_0067_AHW3MGBGXY.KK_381", "1-XX_iv.170125_NB501621_0067_AHW3MGBGXY.KK_382",
"1-XX_iv.170125_NB501621_0067_AHW3MGBGXY.KK_383", "Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_100",
"Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_101", "Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_102",
"Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_103", "Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_104",
"Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_106", "Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_107",
"Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_109", "Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_110",
"Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_111", "Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_112",
"Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_113", "Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_114",
"Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_115", "Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_116",
"Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_117", "Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_118",
"Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_119", "Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_120",
"Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_122", "Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_124",
"Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_125", "Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_126",
"Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_127", "Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_128",
"Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_86", "Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_87",
"Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_88", "Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_89",
"Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_90", "Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_92",
"Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_93", "Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_95",
"Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_97", "Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_99"
), class = "factor")), .Names = "united_sample_names", row.names = c(NA,
10L), class = "data.frame")
df
#> united_sample_names
#> 1 Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_86
#> 2 Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_87
#> 3 Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_88
#> 4 Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_89
#> 5 Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_90
#> 6 Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_92
#> 7 Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_93
#> 8 Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_95
#> 9 Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_97
#> 10 Vehicle_iv.170414_NB501621_0118_AHLTYHBGX2.KK_99
我想要做的是将其拆分为：
header1 header2 header3
Vehicle_iv 170414_NB501621_0118_AHLTYHBGX2 KK_86
Vehicle_iv 170414_NB501621_0118_AHLTYHBGX2 KK_87
Vehicle_iv 170414_NB501621_0118_AHLTYHBGX2 KK_88
Vehicle_iv 170414_NB501621_0118_AHLTYHBGX2 KK_89
Vehicle_iv 170414_NB501621_0118_AHLTYHBGX2 KK_90
Vehicle_iv 170414_NB501621_0118_AHLTYHBGX2 KK_92
Vehicle_iv 170414_NB501621_0118_AHLTYHBGX2 KK_93
Vehicle_iv 170414_NB501621_0118_AHLTYHBGX2 KK_95
Vehicle_iv 170414_NB501621_0118_AHLTYHBGX2 KK_97
Vehicle_iv 170414_NB501621_0118_AHLTYHBGX2 KK_99
我试了这个并且失败了：
> df %>% separate(united_sample_names, c("header1","header2","header3"))
header1 header2 header3
1 Vehicle iv 170414
2 Vehicle iv 170414
3 Vehicle iv 170414
4 Vehicle iv 170414
5 Vehicle iv 170414
6 Vehicle iv 170414
7 Vehicle iv 170414
8 Vehicle iv 170414
9 Vehicle iv 170414
10 Vehicle iv 170414
Warning message:
Too many values at 10 locations: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
做正确的方法是什么？
答案 0 :(得分：1)
我们需要指定sep。否则，它会自动检测某些其他字符，例如_，并将列分开。
library(tidyr)
df %>%
separate(united_sample_names, c("header1","header2","header3"), sep="[.]")
# header1 header2 header3
#1 Vehicle_iv 170414_NB501621_0118_AHLTYHBGX2 KK_86
#2 Vehicle_iv 170414_NB501621_0118_AHLTYHBGX2 KK_87
#3 Vehicle_iv 170414_NB501621_0118_AHLTYHBGX2 KK_88
#4 Vehicle_iv 170414_NB501621_0118_AHLTYHBGX2 KK_89
#5 Vehicle_iv 170414_NB501621_0118_AHLTYHBGX2 KK_90
#6 Vehicle_iv 170414_NB501621_0118_AHLTYHBGX2 KK_92
#7 Vehicle_iv 170414_NB501621_0118_AHLTYHBGX2 KK_93
#8 Vehicle_iv 170414_NB501621_0118_AHLTYHBGX2 KK_95
#9 Vehicle_iv 170414_NB501621_0118_AHLTYHBGX2 KK_97
#10 Vehicle_iv 170414_NB501621_0118_AHLTYHBGX2 KK_99