R语言 为什么我无法删除离群值？

wnavrhmk  于 2022-12-20  发布在  其他
关注(0)|答案(1)|浏览(166)
# The outlier quantity that should be removed
filter(HF2,
serum_creatinine >= quantile(serum_creatinine,probs=.75) + 
  ((quantile(serum_creatinine,probs=.75)-quantile(serum_creatinine,probs=.25))*1.5) |
creatinine_phosphokinase >= quantile(creatinine_phosphokinase,probs=.75) + 
  ((quantile(creatinine_phosphokinase,probs=.75)-quantile(creatinine_phosphokinase,probs=.25))*1.5)) %>% nrow

[1] 56
# The code being used to remove them
filter(HF2,
serum_creatinine < quantile(serum_creatinine,probs=.75) + 
  ((quantile(serum_creatinine,probs=.75)-quantile(serum_creatinine,probs=.25))*1.5) |
creatinine_phosphokinase < quantile(creatinine_phosphokinase,probs=.75) + 
  ((quantile(creatinine_phosphokinase,probs=.75)-quantile(creatinine_phosphokinase,probs=.25))*1.5)) %>% nrow

[1] 297

为什么我无法按预期过滤掉离群值？我的数据集有299个观测值。
我试过改变符号，但我不认为那是错误的地方。也许是我的括号？
高于[Q3+（1.5）IQR]的所有离群值是我试图在serum_creatinine和creatine_phosphokinase中删除的值

数据

structure(list(age = c(75, 55, 65, 50, 65, 90, 75, 60, 65, 80, 
75, 62, 45, 50, 49, 82, 87, 45, 70, 48, 65, 65, 68, 53, 75, 80, 
95, 70, 58, 82, 94, 85, 50, 50, 65, 69, 90, 82, 60, 60, 70, 50, 
70, 72, 60, 50, 51, 60, 80, 57, 68, 53, 60, 70, 60, 95, 70, 60, 
49, 72, 45, 50, 55, 45, 45, 60, 42, 72, 70, 65, 41, 58, 85, 65, 
69, 60, 70, 42, 75, 55, 70, 67, 60, 79, 59, 51, 55, 65, 44, 57, 
70, 60, 42, 60, 58, 58, 63, 70, 60, 63, 65, 75, 80, 42, 60, 72, 
55, 45, 63, 45, 85, 55, 50, 70, 60, 58, 60, 85, 65, 86, 60, 66, 
60, 60, 60, 43, 46, 58, 61, 53, 53, 60, 46, 63, 81, 75, 65, 68, 
62, 50, 80, 46, 50, 61, 72, 50, 52, 64, 75, 60, 72, 62, 50, 50, 
65, 60, 52, 50, 85, 59, 66, 45, 63, 50, 45, 80, 53, 59, 65, 70, 
51, 52, 70, 50, 65, 60, 69, 49, 63, 55, 40, 59, 65, 75, 58, 60.667, 
50, 60, 60.667, 40, 80, 64, 50, 73, 45, 77, 45, 65, 50, 60, 63, 
45, 70, 60, 78, 50, 40, 85, 60, 49, 70, 50, 78, 48, 65, 73, 70, 
54, 68, 55, 73, 65, 42, 47, 58, 75, 58, 55, 65, 72, 60, 70, 40, 
53, 53, 77, 75, 70, 65, 55, 70, 65, 40, 73, 54, 61, 55, 64, 40, 
53, 50, 55, 50, 70, 53, 52, 65, 58, 45, 53, 55, 62, 65, 68, 61, 
50, 55, 56, 45, 40, 44, 51, 67, 42, 60, 45, 70, 70, 50, 55, 70, 
70, 42, 65, 50, 55, 60, 45, 65, 90, 45, 60, 52, 63, 62, 55, 45, 
45, 50), anaemia = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 
1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 
1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 
1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 
1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 
2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 
1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 
2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 
2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 
1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 
1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 
1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 
1L, 1L, 1L), levels = c("0", "1"), class = "factor"), creatinine_phosphokinase = c(582L, 
7861L, 146L, 111L, 160L, 47L, 246L, 315L, 157L, 123L, 81L, 231L, 
981L, 168L, 80L, 379L, 149L, 582L, 125L, 582L, 52L, 128L, 220L, 
63L, 582L, 148L, 112L, 122L, 60L, 70L, 582L, 23L, 249L, 159L, 
94L, 582L, 60L, 855L, 2656L, 235L, 582L, 124L, 571L, 127L, 588L, 
582L, 1380L, 582L, 553L, 129L, 577L, 91L, 3964L, 69L, 260L, 371L, 
75L, 607L, 789L, 364L, 7702L, 318L, 109L, 582L, 582L, 68L, 250L, 
110L, 161L, 113L, 148L, 582L, 5882L, 224L, 582L, 47L, 92L, 102L, 
203L, 336L, 69L, 582L, 76L, 55L, 280L, 78L, 47L, 68L, 84L, 115L, 
66L, 897L, 582L, 154L, 144L, 133L, 514L, 59L, 156L, 61L, 305L, 
582L, 898L, 5209L, 53L, 328L, 748L, 1876L, 936L, 292L, 129L, 
60L, 369L, 143L, 754L, 400L, 96L, 102L, 113L, 582L, 737L, 68L, 
96L, 582L, 582L, 358L, 168L, 200L, 248L, 270L, 1808L, 1082L, 
719L, 193L, 4540L, 582L, 59L, 646L, 281L, 1548L, 805L, 291L, 
482L, 84L, 943L, 185L, 132L, 1610L, 582L, 2261L, 233L, 30L, 115L, 
1846L, 335L, 231L, 58L, 250L, 910L, 129L, 72L, 130L, 582L, 2334L, 
2442L, 776L, 196L, 66L, 582L, 835L, 582L, 3966L, 171L, 115L, 
198L, 95L, 1419L, 69L, 122L, 835L, 478L, 176L, 395L, 99L, 145L, 
104L, 582L, 1896L, 151L, 244L, 582L, 62L, 121L, 231L, 582L, 418L, 
582L, 167L, 582L, 1211L, 1767L, 308L, 97L, 59L, 64L, 167L, 101L, 
212L, 2281L, 972L, 212L, 582L, 224L, 131L, 135L, 582L, 1202L, 
427L, 1021L, 582L, 582L, 118L, 86L, 582L, 582L, 675L, 57L, 2794L, 
56L, 211L, 166L, 93L, 129L, 707L, 582L, 109L, 119L, 232L, 720L, 
180L, 81L, 582L, 90L, 1185L, 582L, 80L, 2017L, 143L, 624L, 207L, 
2522L, 572L, 245L, 88L, 446L, 191L, 326L, 132L, 66L, 56L, 66L, 
655L, 258L, 157L, 582L, 298L, 1199L, 135L, 582L, 582L, 582L, 
582L, 213L, 64L, 257L, 582L, 618L, 582L, 1051L, 84L, 2695L, 582L, 
64L, 1688L, 54L, 170L, 253L, 582L, 892L, 337L, 615L, 320L, 190L, 
103L, 61L, 1820L, 2060L, 2413L, 196L), diabetes = structure(c(1L, 
1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 
1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 
1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 
1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 
1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 
2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 
1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 
2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 
2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 
2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 
1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 
1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L), levels = c("Absent", 
"Present"), class = "factor"), ejection_fraction = c(20L, 38L, 
20L, 20L, 20L, 40L, 15L, 60L, 65L, 35L, 38L, 25L, 30L, 38L, 30L, 
50L, 38L, 14L, 25L, 55L, 25L, 30L, 35L, 60L, 30L, 38L, 40L, 45L, 
38L, 30L, 38L, 45L, 35L, 30L, 50L, 35L, 50L, 50L, 30L, 38L, 20L, 
30L, 45L, 50L, 60L, 38L, 25L, 38L, 20L, 30L, 25L, 20L, 62L, 50L, 
38L, 30L, 35L, 40L, 20L, 20L, 25L, 40L, 35L, 35L, 80L, 20L, 15L, 
25L, 25L, 25L, 40L, 35L, 35L, 50L, 20L, 20L, 60L, 40L, 38L, 45L, 
40L, 50L, 25L, 50L, 25L, 50L, 35L, 60L, 40L, 25L, 45L, 45L, 60L, 
25L, 38L, 60L, 25L, 60L, 25L, 40L, 25L, 45L, 25L, 30L, 50L, 30L, 
45L, 35L, 38L, 35L, 60L, 35L, 25L, 60L, 40L, 40L, 60L, 60L, 60L, 
38L, 60L, 38L, 38L, 30L, 40L, 50L, 17L, 60L, 30L, 35L, 60L, 45L, 
40L, 60L, 35L, 40L, 60L, 25L, 35L, 30L, 38L, 35L, 30L, 40L, 25L, 
30L, 30L, 60L, 30L, 35L, 45L, 60L, 45L, 35L, 35L, 25L, 35L, 25L, 
50L, 45L, 40L, 35L, 40L, 35L, 30L, 38L, 60L, 20L, 40L, 35L, 35L, 
40L, 60L, 20L, 35L, 60L, 40L, 50L, 60L, 40L, 30L, 25L, 25L, 38L, 
25L, 30L, 50L, 25L, 40L, 45L, 35L, 60L, 40L, 30L, 20L, 45L, 38L, 
30L, 20L, 35L, 45L, 60L, 60L, 25L, 40L, 45L, 40L, 38L, 40L, 35L, 
17L, 62L, 50L, 30L, 35L, 35L, 50L, 70L, 35L, 35L, 20L, 50L, 35L, 
25L, 25L, 60L, 25L, 35L, 25L, 25L, 30L, 35L, 35L, 38L, 45L, 50L, 
50L, 30L, 40L, 45L, 35L, 30L, 35L, 40L, 38L, 38L, 25L, 25L, 35L, 
40L, 30L, 35L, 45L, 35L, 60L, 30L, 38L, 38L, 25L, 50L, 40L, 40L, 
25L, 60L, 38L, 35L, 20L, 38L, 38L, 35L, 30L, 40L, 38L, 40L, 30L, 
38L, 35L, 38L, 30L, 38L, 40L, 40L, 30L, 38L, 40L, 40L, 35L, 55L, 
35L, 38L, 55L, 35L, 38L, 35L, 38L, 38L, 60L, 38L, 45L), platelets = c(265000, 
263358.03, 162000, 210000, 327000, 204000, 127000, 454000, 263358.03, 
388000, 368000, 253000, 136000, 276000, 427000, 47000, 262000, 
166000, 237000, 87000, 276000, 297000, 289000, 368000, 263358.03, 
149000, 196000, 284000, 153000, 2e+05, 263358.03, 360000, 319000, 
302000, 188000, 228000, 226000, 321000, 305000, 329000, 263358.03, 
153000, 185000, 218000, 194000, 310000, 271000, 451000, 140000, 
395000, 166000, 418000, 263358.03, 351000, 255000, 461000, 223000, 
216000, 319000, 254000, 390000, 216000, 254000, 385000, 263358.03, 
119000, 213000, 274000, 244000, 497000, 374000, 122000, 243000, 
149000, 266000, 204000, 317000, 237000, 283000, 324000, 293000, 
263358.03, 196000, 172000, 302000, 406000, 173000, 304000, 235000, 
181000, 249000, 297000, 263358.03, 210000, 327000, 219000, 254000, 
255000, 318000, 221000, 298000, 263358.03, 149000, 226000, 286000, 
621000, 263000, 226000, 304000, 850000, 306000, 228000, 252000, 
351000, 328000, 164000, 271000, 507000, 203000, 263358.03, 210000, 
162000, 228000, 127000, 217000, 237000, 271000, 3e+05, 267000, 
227000, 249000, 250000, 263358.03, 295000, 231000, 263358.03, 
172000, 305000, 221000, 211000, 263358.03, 348000, 329000, 229000, 
338000, 266000, 218000, 242000, 225000, 228000, 235000, 244000, 
184000, 263358.03, 235000, 194000, 277000, 262000, 235000, 362000, 
242000, 174000, 448000, 75000, 334000, 192000, 220000, 70000, 
270000, 305000, 263358.03, 325000, 176000, 189000, 281000, 337000, 
105000, 132000, 267000, 279000, 303000, 221000, 265000, 224000, 
219000, 389000, 153000, 365000, 201000, 275000, 350000, 309000, 
260000, 160000, 126000, 223000, 263358.03, 259000, 279000, 263358.03, 
73000, 377000, 220000, 212000, 277000, 362000, 226000, 186000, 
283000, 268000, 389000, 147000, 481000, 244000, 290000, 203000, 
358000, 151000, 271000, 371000, 263358.03, 194000, 365000, 130000, 
504000, 265000, 189000, 141000, 237000, 274000, 62000, 185000, 
255000, 330000, 305000, 406000, 248000, 173000, 257000, 263358.03, 
533000, 249000, 255000, 220000, 264000, 282000, 314000, 246000, 
301000, 223000, 404000, 231000, 274000, 236000, 263358.03, 334000, 
294000, 253000, 233000, 308000, 203000, 283000, 198000, 208000, 
147000, 362000, 263358.03, 133000, 302000, 222000, 263358.03, 
221000, 215000, 189000, 150000, 422000, 327000, 25100, 232000, 
451000, 241000, 51000, 215000, 263358.03, 279000, 336000, 279000, 
543000, 263358.03, 390000, 222000, 133000, 382000, 179000, 155000, 
270000, 742000, 140000, 395000), serum_creatinine = c(1.9, 1.1, 
1.3, 1.9, 2.7, 2.1, 1.2, 1.1, 1.5, 9.4, 4, 0.9, 1.1, 1.1, 1, 
1.3, 0.9, 0.8, 1, 1.9, 1.3, 1.6, 0.9, 0.8, 1.83, 1.9, 1, 1.3, 
5.8, 1.2, 1.83, 3, 1, 1.2, 1, 3.5, 1, 1, 2.3, 3, 1.83, 1.2, 1.2, 
1, 1.1, 1.9, 0.9, 0.6, 4.4, 1, 1, 1.4, 6.8, 1, 2.2, 2, 2.7, 0.6, 
1.1, 1.3, 1, 2.3, 1.1, 1, 1.18, 2.9, 1.3, 1, 1.2, 1.83, 0.8, 
0.9, 1, 1.3, 1.2, 0.7, 0.8, 1.2, 0.6, 0.9, 1.7, 1.18, 2.5, 1.8, 
1, 0.7, 1.1, 0.8, 0.7, 1.1, 0.8, 1, 1.18, 1.7, 0.7, 1, 1.3, 1.1, 
1.2, 1.1, 1.1, 1.18, 1.1, 1, 2.3, 1.7, 1.3, 0.9, 1.1, 1.3, 1.2, 
1.2, 1.6, 1.3, 1.2, 1, 0.7, 3.2, 0.9, 1.83, 1.5, 1, 0.75, 0.9, 
3.7, 1.3, 2.1, 0.8, 0.7, 3.4, 0.7, 6.1, 1.18, 1.3, 1.18, 1.18, 
0.9, 2.1, 1, 0.8, 1.1, 0.9, 0.9, 0.9, 1.7, 0.7, 0.7, 1, 1.83, 
0.9, 2.5, 0.9, 0.9, 1.18, 0.8, 1.7, 1.4, 1, 1.3, 1.1, 1.2, 0.8, 
0.9, 0.9, 1.1, 1.3, 0.7, 2.4, 1, 0.8, 1.5, 0.9, 1.1, 0.8, 0.9, 
1, 1, 1, 1.2, 0.7, 0.9, 1, 1.2, 2.5, 1.2, 1.5, 0.6, 2.1, 1, 0.9, 
2.1, 1.5, 0.7, 1.18, 1.6, 1.8, 1.18, 0.8, 1, 1.8, 0.7, 1, 0.9, 
3.5, 0.7, 1, 0.8, 0.9, 1, 0.8, 1, 0.8, 1.4, 1.6, 0.8, 1.3, 0.9, 
9, 1.1, 0.7, 1.83, 1.1, 1.1, 0.8, 1, 1.4, 1.3, 1, 5, 1.2, 1.7, 
1.1, 0.9, 1.4, 1.1, 1.1, 1.1, 1.2, 1, 1.18, 1.3, 1.3, 1.1, 0.9, 
1.8, 1.4, 1.1, 2.4, 1, 1.2, 0.5, 0.8, 1, 1.2, 1, 1, 1.7, 1, 0.8, 
0.7, 1, 0.7, 1.4, 1, 1.2, 0.9, 1.83, 1.7, 0.9, 1, 1.6, 0.9, 1.2, 
0.7, 1, 0.8, 1.1, 1.1, 0.7, 1.3, 1, 2.7, 3.8, 1.1, 0.8, 1.2, 
1.7, 1, 1.1, 0.9, 0.8, 1.4, 1, 0.9, 1.1, 1.2, 0.8, 1.4, 1.6), 
    serum_sodium = c(130L, 136L, 129L, 137L, 116L, 132L, 137L, 
    131L, 138L, 133L, 131L, 140L, 137L, 137L, 138L, 136L, 140L, 
    127L, 140L, 121L, 137L, 136L, 140L, 135L, 134L, 144L, 138L, 
    136L, 134L, 132L, 134L, 132L, 128L, 138L, 140L, 134L, 134L, 
    145L, 137L, 142L, 134L, 136L, 139L, 134L, 142L, 135L, 130L, 
    138L, 133L, 140L, 138L, 139L, 146L, 134L, 132L, 132L, 138L, 
    138L, 136L, 136L, 139L, 131L, 139L, 145L, 137L, 127L, 136L, 
    140L, 142L, 135L, 140L, 139L, 132L, 137L, 134L, 139L, 140L, 
    140L, 131L, 140L, 136L, 137L, 132L, 133L, 141L, 140L, 137L, 
    140L, 139L, 144L, 136L, 133L, 137L, 135L, 142L, 141L, 134L, 
    136L, 137L, 140L, 141L, 137L, 144L, 140L, 143L, 138L, 137L, 
    138L, 133L, 142L, 132L, 135L, 136L, 137L, 126L, 139L, 136L, 
    138L, 140L, 134L, 135L, 136L, 140L, 145L, 134L, 135L, 124L, 
    137L, 136L, 145L, 138L, 131L, 137L, 145L, 137L, 137L, 137L, 
    130L, 136L, 138L, 134L, 140L, 132L, 141L, 139L, 141L, 136L, 
    137L, 134L, 136L, 135L, 139L, 134L, 137L, 136L, 140L, 136L, 
    136L, 134L, 139L, 134L, 139L, 137L, 142L, 139L, 135L, 133L, 
    134L, 138L, 133L, 136L, 140L, 145L, 139L, 137L, 138L, 135L, 
    140L, 145L, 140L, 136L, 136L, 136L, 134L, 137L, 136L, 134L, 
    144L, 136L, 140L, 134L, 135L, 130L, 142L, 135L, 145L, 137L, 
    138L, 134L, 113L, 137L, 136L, 138L, 136L, 137L, 136L, 141L, 
    136L, 141L, 130L, 136L, 140L, 138L, 130L, 134L, 134L, 141L, 
    137L, 134L, 140L, 134L, 145L, 139L, 134L, 138L, 125L, 132L, 
    140L, 130L, 134L, 127L, 134L, 137L, 137L, 137L, 137L, 148L, 
    132L, 136L, 137L, 139L, 136L, 136L, 141L, 134L, 137L, 138L, 
    135L, 142L, 130L, 139L, 143L, 133L, 132L, 139L, 142L, 139L, 
    139L, 135L, 135L, 138L, 133L, 129L, 140L, 141L, 140L, 134L, 
    140L, 140L, 132L, 130L, 134L, 133L, 140L, 137L, 137L, 142L, 
    140L, 136L, 136L, 137L, 136L, 128L, 138L, 141L, 135L, 140L, 
    132L, 142L, 144L, 141L, 139L, 140L, 136L, 143L, 139L, 138L, 
    140L, 136L), sex = structure(c(2L, 2L, 2L, 2L, 1L, 2L, 2L, 
    2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 
    2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 
    1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 
    1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 
    2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 
    1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 
    1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 
    1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 
    1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 
    2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
    1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 
    2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 
    1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 
    2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 
    2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 
    2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 
    2L, 2L, 2L, 1L, 1L, 2L, 2L), levels = c("Female", "Male"), class = "factor"), 
    smoking = structure(c(1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 
    2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 
    1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
    2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 
    1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
    1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 
    2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 
    2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 
    2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 
    2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 
    1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 
    1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 
    1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
    2L, 1L, 1L, 2L, 2L), levels = c("No", "Yes"), class = "factor"), 
    time = c(4L, 6L, 7L, 7L, 8L, 8L, 10L, 10L, 10L, 10L, 10L, 
    10L, 11L, 11L, 12L, 13L, 14L, 14L, 15L, 15L, 16L, 20L, 20L, 
    22L, 23L, 23L, 24L, 26L, 26L, 26L, 27L, 28L, 28L, 29L, 29L, 
    30L, 30L, 30L, 30L, 30L, 31L, 32L, 33L, 33L, 33L, 35L, 38L, 
    40L, 41L, 42L, 43L, 43L, 43L, 44L, 45L, 50L, 54L, 54L, 55L, 
    59L, 60L, 60L, 60L, 61L, 63L, 64L, 65L, 65L, 66L, 67L, 68L, 
    71L, 72L, 72L, 73L, 73L, 74L, 74L, 74L, 74L, 75L, 76L, 77L, 
    78L, 78L, 79L, 79L, 79L, 79L, 79L, 80L, 80L, 82L, 82L, 83L, 
    83L, 83L, 85L, 85L, 86L, 87L, 87L, 87L, 87L, 87L, 88L, 88L, 
    88L, 88L, 88L, 90L, 90L, 90L, 90L, 91L, 91L, 94L, 94L, 94L, 
    95L, 95L, 95L, 95L, 95L, 96L, 97L, 100L, 104L, 104L, 105L, 
    106L, 107L, 107L, 107L, 107L, 107L, 107L, 108L, 108L, 108L, 
    109L, 109L, 109L, 110L, 111L, 112L, 112L, 113L, 113L, 115L, 
    115L, 117L, 118L, 119L, 120L, 120L, 120L, 120L, 121L, 121L, 
    121L, 121L, 123L, 126L, 129L, 130L, 134L, 135L, 140L, 145L, 
    145L, 146L, 146L, 146L, 146L, 146L, 147L, 147L, 147L, 147L, 
    148L, 150L, 154L, 162L, 170L, 171L, 172L, 172L, 172L, 174L, 
    174L, 174L, 175L, 180L, 180L, 180L, 185L, 186L, 186L, 186L, 
    186L, 186L, 186L, 187L, 187L, 187L, 187L, 187L, 187L, 187L, 
    188L, 192L, 192L, 193L, 194L, 195L, 196L, 196L, 197L, 197L, 
    198L, 200L, 201L, 201L, 205L, 205L, 205L, 206L, 207L, 207L, 
    207L, 208L, 209L, 209L, 209L, 209L, 209L, 210L, 210L, 211L, 
    212L, 212L, 212L, 213L, 213L, 213L, 214L, 214L, 214L, 214L, 
    214L, 215L, 215L, 215L, 215L, 216L, 220L, 230L, 230L, 231L, 
    233L, 233L, 235L, 237L, 237L, 240L, 241L, 244L, 244L, 244L, 
    244L, 244L, 245L, 245L, 245L, 245L, 245L, 246L, 246L, 246L, 
    247L, 250L, 250L, 250L, 250L, 250L, 250L, 250L, 256L, 256L, 
    257L, 258L, 258L, 270L, 270L, 271L, 278L, 280L, 285L), DEATH_EVENT = c(1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 
    0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 
    1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 
    1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 
    0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 
    0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 
    0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), hypertension = structure(c(2L, 
    1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 
    1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 
    1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 
    2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 
    2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 
    1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
    2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 
    1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 
    2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 
    1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 
    2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
    2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 
    1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 
    1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
    1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L), levels = c("Absent", 
    "Present"), class = "factor")), row.names = c(NA, -299L), class = "data.frame")
r

1条答案

按热度按时间
2q5ifsrm

2q5ifsrm1#

因此，要更正代码，需要对整个条件使用!运算符，如下所示：

HF2 %>% 
filter(!(serum_creatinine >= quantile(serum_creatinine,probs=.75) + 
         ((quantile(serum_creatinine,probs=.75)-quantile(serum_creatinine,probs=.25))*1.5) |
         creatinine_phosphokinase >= quantile(creatinine_phosphokinase,probs=.75) + 
         ((quantile(creatinine_phosphokinase,probs=.75)-quantile(creatinine_phosphokinase,probs=.25))*1.5))) %>%
         nrow()

[1] 243

现在，如果你想写更干净的代码，我建议你创建一个函数：

is_outlier <- function(x){

  condition <- quantile(x, 0.75, na.rm = TRUE) + 1.5 * IQR(x,na.rm = TRUE)

  output <- ifelse(x >= condition, TRUE, FALSE)
  
  return(output)
}

然后

HF2 %>% 
  filter(!(is_outlier(serum_creatinine) | is_outlier(creatinine_phosphokinase))) %>% 
  nrow()

[1] 243
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