dplyr::left_join() 产生意外错误

米里安

我有

> head(p)
  studie    sex n_fjernet n_sygdom
1 Group1   Male        22        1
2 Group1   Male        61        2
3 Group1 Female        50        1
4 Group1 Female        47        3
5 Group1 Female        30        1
6 Group1 Female        60        0

和

> head(u)
  studie alder    sex n_fjernet n_sygdom n_otte
1 Group4    59 Female        26        0      0
2 Group4    85   Male         7        1      1
3 Group4    74 Female        17        9      6
4 Group4    78   Male        13        0      0
5 Group4    41   Male        11        0      0
6 Group4    62   Male        12        0      0

我想添加u$n_otte到p全部案件p$studie==u$studie和p$sex==u$sex和p$n_fjernet==u$n_fjernet和p$n_sygdom==u$n_sygdom，这是895在案件u出总的1485案件p。所有p不匹配的情况下，都u$n_otte left_joined()应该被列为NA

所以我写了

left_join(p, u %>% distinct(studie, sex, n_fjernet, n_sygdom, .keep_all = TRUE), by = "n_otte")

哪个返回错误

Error: `by` can't contain join column `n_otte` which is missing from LHS

我尝试了不同的left_join()方法，但都返回了错误。我究竟做错了什么？

u <- structure(list(studie = 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, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Group4", 
"Group3"), class = "factor"), sex = structure(c(1L, 2L, 1L, 2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 
1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 
2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 
2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 
1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 
2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 
2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 
2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 
2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 
1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 
1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 
2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 
2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 
1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 
1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 
2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 
2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 
1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 
1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 
1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 
1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L
), .Label = c("Female", "Male"), class = "factor"), n_fjernet = c(26L, 
7L, 17L, 13L, 11L, 12L, 8L, 2L, 14L, 8L, 35L, 23L, 5L, 20L, 11L, 
5L, 30L, 12L, 23L, 37L, 13L, 26L, 9L, 9L, 9L, 15L, 39L, 13L, 
5L, 9L, 19L, 32L, 18L, 16L, 45L, 35L, 25L, 20L, 27L, 34L, 11L, 
44L, 20L, 48L, 92L, 6L, 29L, 12L, 26L, 37L, 30L, 54L, 32L, 39L, 
15L, 21L, 22L, 34L, 39L, 30L, 36L, 19L, 26L, 43L, 26L, 42L, 18L, 
15L, 32L, 29L, 36L, 28L, 38L, 35L, 66L, 11L, 49L, 32L, 61L, 49L, 
36L, 51L, 42L, 13L, 10L, 36L, 45L, 49L, 52L, 21L, 42L, 29L, 38L, 
28L, 37L, 47L, 33L, 50L, 19L, 45L, 23L, 29L, 31L, 59L, 60L, 32L, 
32L, 30L, 50L, 29L, 32L, 42L, 24L, 22L, 47L, 24L, 22L, 8L, 38L, 
25L, 34L, 45L, 50L, 51L, 28L, 8L, 21L, 17L, 30L, 36L, 20L, 56L, 
23L, 77L, 23L, 76L, 58L, 35L, 33L, 52L, 34L, 17L, 66L, 38L, 58L, 
16L, 58L, 44L, 22L, 42L, 17L, 33L, 9L, 31L, 15L, 46L, 31L, 32L, 
25L, 17L, 31L, 35L, 29L, 18L, 69L, 28L, 25L, 35L, 19L, 18L, 15L, 
51L, 41L, 55L, 35L, 19L, 45L, 24L, 39L, 57L, 45L, 37L, 30L, 33L, 
34L, 47L, 21L, 16L, 22L, 26L, 36L, 32L, 17L, 28L, 32L, 35L, 37L, 
30L, 32L, 29L, 41L, 18L, 26L, 32L, 30L, 17L, 35L, 17L, 27L, 27L, 
10L, 30L, 50L, 28L, 22L, 13L, 32L, 35L, 51L, 44L, 16L, 17L, 43L, 
27L, 21L, 34L, 13L, 18L, 37L, 20L, 8L, 19L, 43L, 24L, 48L, 15L, 
11L, 22L, 20L, 19L, 20L, 23L, 12L, 31L, 28L, 34L, 25L, 22L, 38L, 
28L, 26L, 30L, 45L, 50L, 39L, 22L, 41L, 14L, 60L, 35L, 10L, 29L, 
24L, 25L, 31L, 32L, 33L, 10L, 16L, 10L, 10L, 32L, 30L, 34L, 31L, 
24L, 15L, 20L, 20L, 31L, 33L, 15L, 27L, 19L, 40L, 17L, 48L, 35L, 
25L, 25L, 22L, 19L, 24L, 20L, 30L, 13L, 28L, 19L, 7L, 29L, 18L, 
41L, 11L, 42L, 35L, 24L, 16L, 29L, 39L, 28L, 32L, 16L, 31L, 30L, 
27L, 17L, 28L, 29L, 12L, 25L, 30L, 14L, 19L, 13L, 32L, 16L, 12L, 
24L, 10L, 34L, 49L, 17L, 11L, 37L, 38L, 36L, 18L, 42L, 14L, 33L, 
41L, 21L, 10L, 16L, 16L, 14L, 32L, 25L, 22L, 19L, 28L, 16L, 24L, 
28L, 29L, 34L, 27L, 23L, 33L, 23L, 57L, 30L, 16L, 13L, 20L, 42L, 
14L, 18L, 31L, 19L, 22L, 27L, 11L, 12L, 7L, 25L, 29L, 35L, 21L, 
64L, 39L, 51L, 21L, 16L, 36L, 22L, 15L, 29L, 38L, 20L, 23L, 5L, 
33L, 15L, 20L, 52L, 31L, 16L, 10L, 12L, 47L, 23L, 28L, 27L, 18L, 
24L, 34L, 45L, 24L, 43L, 28L, 34L, 20L, 26L, 17L, 41L, 25L, 38L, 
35L, 25L, 21L, 24L, 21L, 24L, 14L, 40L, 19L, 11L, 21L, 38L, 43L, 
23L, 28L, 17L, 78L, 12L, 27L, 16L, 24L, 16L, 21L, 43L, 25L, 50L, 
44L, 30L, 33L, 31L, 20L, 47L, 47L, 34L, 22L, 31L, 28L, 51L, 23L, 
45L, 30L, 34L, 32L, 39L, 41L, 25L, 15L, 19L, 14L, 41L, 40L, 49L, 
27L, 35L, 26L, 22L, 59L, 10L, 29L, 38L, 64L, 16L, 36L, 56L, 31L, 
50L, 23L, 27L, 49L, 30L, 28L, 25L, 38L, 37L, 25L, 30L, 23L, 18L, 
31L, 48L, 47L, 49L), n_sygdom = c(0L, 1L, 9L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 4L, 0L, 0L, 21L, 0L, 2L, 
0L, 0L, 0L, 2L, 1L, 1L, 0L, 0L, 2L, 2L, 0L, 0L, 7L, 1L, 0L, 0L, 
0L, 0L, 0L, 0L, 11L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 7L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 
2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 5L, 6L, 0L, 1L, 
0L, 1L, 0L, 0L, 1L, 0L, 3L, 0L, 0L, 19L, 2L, 0L, 0L, 0L, 0L, 
0L, 1L, 0L, 4L, 0L, 0L, 0L, 0L, 0L, 3L, 1L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 5L, 0L, 2L, 6L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 
0L, 16L, 1L, 6L, 0L, 2L, 5L, 0L, 0L, 0L, 0L, 3L, 0L, 2L, 3L, 
4L, 0L, 1L, 0L, 0L, 0L, 4L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 
1L, 0L, 0L, 4L, 0L, 9L, 0L, 0L, 0L, 1L, 0L, 2L, 0L, 0L, 0L, 2L, 
2L, 3L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 
0L, 0L, 0L, 5L, 1L, 5L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 
2L, 5L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 8L, 0L, 0L, 0L, 0L, 
0L, 0L, 1L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 
0L, 2L, 0L, 14L, 3L, 0L, 0L, 0L, 0L, 4L, 1L, 0L, 0L, 2L, 0L, 
1L, 0L, 0L, 1L, 0L, 2L, 0L, 5L, 0L, 0L, 0L, 1L, 0L, 0L, 4L, 0L, 
1L, 1L, 3L, 0L, 2L, 0L, 0L, 0L, 2L, 7L, 18L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 1L, 0L, 3L, 1L, 0L, 0L, 6L, 1L, 0L, 0L, 7L, 2L, 
0L, 0L, 0L, 1L, 0L, 8L, 0L, 0L, 3L, 3L, 1L, 3L, 2L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 3L, 0L, 4L, 0L, 0L, 
1L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 2L, 0L, 0L, 9L, 0L, 0L, 
6L, 0L, 1L, 0L, 1L, 1L, 2L, 0L, 5L, 4L, 0L, 4L, 0L, 0L, 0L, 2L, 
0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 8L, 0L, 0L, 3L, 
0L, 3L, 0L, 0L, 0L, 0L, 0L, 5L, 0L, 3L, 1L, 7L, 3L, 0L, 0L, 2L, 
0L, 1L, 0L, 0L, 0L, 2L, 0L, 2L, 0L, 3L, 1L, 0L, 3L, 0L, 0L, 4L, 
0L, 1L, 5L, 4L, 16L, 0L, 1L, 5L, 1L, 0L, 1L, 0L, 0L, 0L, 3L, 
0L, 4L, 2L, 4L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L), n_otte = c(0L, 1L, 6L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 3L, 0L, 0L, 0L, 0L, 3L, 0L, 0L, 6L, 0L, 3L, 0L, 0L, 0L, 
2L, 6L, 6L, 0L, 0L, 4L, 6L, 0L, 0L, 6L, 1L, 0L, 0L, 0L, 0L, 0L, 
0L, 6L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 
0L, 0L, 1L, 0L, 6L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 2L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 6L, 0L, 4L, 3L, 0L, 1L, 0L, 1L, 0L, 0L, 
1L, 0L, 6L, 0L, 0L, 6L, 6L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 3L, 0L, 
0L, 0L, 0L, 0L, 4L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 6L, 
0L, 3L, 4L, 0L, 0L, 6L, 0L, 6L, 0L, 1L, 0L, 0L, 6L, 6L, 6L, 0L, 
3L, 6L, 0L, 0L, 0L, 0L, 4L, 0L, 3L, 3L, 6L, 0L, 1L, 0L, 0L, 0L, 
3L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 4L, 0L, 3L, 
0L, 0L, 0L, 1L, 0L, 4L, 0L, 0L, 0L, 4L, 6L, 4L, 0L, 0L, 0L, 0L, 
1L, 0L, 0L, 0L, 0L, 0L, 0L, 6L, 0L, 0L, 0L, 0L, 0L, 4L, 1L, 6L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 6L, 4L, 6L, 0L, 1L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 6L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 
0L, 3L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 3L, 0L, 6L, 3L, 0L, 
0L, 0L, 0L, 6L, 1L, 0L, 0L, 6L, 0L, 1L, 0L, 0L, 1L, 6L, 6L, 0L, 
3L, 6L, 0L, 0L, 1L, 0L, 0L, 3L, 0L, 1L, 1L, 3L, 6L, 3L, 0L, 0L, 
0L, 3L, 3L, 6L, 6L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 3L, 6L, 
0L, 0L, 6L, 1L, 0L, 0L, 6L, 2L, 0L, 0L, 0L, 1L, 0L, 6L, 0L, 0L, 
6L, 4L, 1L, 3L, 4L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 6L, 
0L, 0L, 0L, 6L, 0L, 4L, 0L, 0L, 4L, 0L, 6L, 0L, 0L, 0L, 0L, 0L, 
1L, 0L, 4L, 0L, 0L, 4L, 0L, 0L, 4L, 0L, 6L, 0L, 1L, 1L, 6L, 0L, 
6L, 6L, 0L, 3L, 0L, 0L, 0L, 3L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 6L, 0L, 0L, 3L, 0L, 6L, 0L, 0L, 0L, 0L, 6L, 3L, 
0L, 6L, 1L, 6L, 6L, 0L, 0L, 3L, 0L, 1L, 0L, 0L, 0L, 3L, 0L, 6L, 
0L, 6L, 1L, 0L, 6L, 0L, 0L, 6L, 0L, 1L, 3L, 6L, 6L, 0L, 1L, 6L, 
1L, 0L, 1L, 0L, 0L, 0L, 6L, 0L, 4L, 6L, 3L, 6L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L)), row.names = c(NA, 
500L), class = "data.frame")

和

p <- structure(list(studie = 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Group2", 
"Group3", "Group4"), class = "factor"), sex = structure(c(2L, 
1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 
2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 
2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 
1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 
1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 
2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 
1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 
2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 
2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 
2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 
1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 
1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 
2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 
2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 
1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 
2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 
2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 
1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 
2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 
1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 
1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 
2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 
2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 
2L, 2L, 1L), .Label = c("Female", "Male"), class = "factor"), 
    n_fjernet = c(18L, 26L, 24L, 20L, 41L, 31L, 13L, 41L, 25L, 
    16L, 18L, 26L, 35L, 36L, 22L, 20L, 16L, 10L, 19L, 46L, 6L, 
    49L, 70L, 46L, 55L, 25L, 22L, 37L, 28L, 52L, 27L, 15L, 11L, 
    7L, 24L, 11L, 56L, 47L, 27L, 14L, 16L, 21L, 43L, 25L, 50L, 
    44L, 30L, 33L, 31L, 20L, 47L, 47L, 34L, 22L, 31L, 28L, 51L, 
    23L, 45L, 30L, 34L, 32L, 39L, 41L, 25L, 15L, 19L, 14L, 41L, 
    40L, 49L, 27L, 35L, 26L, 22L, 59L, 10L, 29L, 38L, 64L, 16L, 
    36L, 56L, 31L, 50L, 23L, 27L, 49L, 30L, 28L, 25L, 38L, 37L, 
    25L, 30L, 23L, 18L, 31L, 48L, 47L, 49L, 38L, 19L, 3L, 69L, 
    26L, 30L, 57L, 52L, 40L, 32L, 17L, 42L, 32L, 15L, 63L, 25L, 
    29L, 45L, 49L, 27L, 21L, 43L, 31L, 13L, 22L, 28L, 45L, 24L, 
    17L, 49L, 34L, 61L, 51L, 51L, 29L, 32L, 23L, 9L, 14L, 28L, 
    35L, 43L, 46L, 32L, 52L, 22L, 34L, 66L, 27L, 59L, 31L, 27L, 
    34L, 38L, 69L, 50L, 63L, 48L, 37L, 41L, 31L, 48L, 35L, 36L, 
    30L, 38L, 39L, 22L, 97L, 19L, 29L, 72L, 25L, 113L, 17L, 62L, 
    29L, 44L, 24L, 20L, 48L, 66L, 30L, 24L, 19L, 42L, 27L, 87L, 
    24L, 19L, 45L, 30L, 34L, 57L, 51L, 28L, 26L, 40L, 102L, 23L, 
    54L, 32L, 18L, 22L, 4L, 40L, 56L, 3L, 34L, 46L, 29L, 14L, 
    33L, 52L, 15L, 33L, 44L, 25L, 35L, 33L, 45L, 50L, 38L, 33L, 
    24L, 45L, 61L, 17L, 38L, 18L, 65L, 61L, 19L, 19L, 25L, 68L, 
    39L, 21L, 18L, 39L, 36L, 46L, 35L, 68L, 18L, 14L, 18L, 28L, 
    55L, 30L, 40L, 57L, 52L, 91L, 60L, 84L, 92L, 26L, 65L, 39L, 
    73L, 36L, 33L, 51L, 133L, 66L, 62L, 38L, 53L, 70L, 33L, 20L, 
    52L, 45L, 64L, 106L, 70L, 24L, 23L, 44L, 35L, 31L, 52L, 46L, 
    33L, 15L, 42L, 35L, 33L, 19L, 54L, 64L, 37L, 27L, 51L, 27L, 
    52L, 61L, 38L, 31L, 46L, 86L, 44L, 58L, 32L, 27L, 13L, 12L, 
    38L, 72L, 20L, 59L, 37L, 27L, 23L, 59L, 36L, 28L, 38L, 26L, 
    64L, 34L, 38L, 21L, 34L, 44L, 33L, 55L, 38L, 51L, 49L, 45L, 
    44L, 40L, 33L, 19L, 18L, 45L, 52L, 63L, 16L, 24L, 50L, 59L, 
    98L, 60L, 63L, 49L, 59L, 35L, 35L, 38L, 56L, 78L, 68L, 56L, 
    42L, 80L, 58L, 39L, 50L, 17L, 37L, 40L, 22L, 51L, 32L, 34L, 
    17L, 33L, 18L, 33L, 25L, 4L, 57L, 47L, 27L, 33L, 20L, 42L, 
    29L, 41L, 22L, 17L, 9L, 17L, 39L, 78L, 19L, 37L, 50L, 34L, 
    14L, 29L, 49L, 25L, 33L, 54L, 47L, 12L, 18L, 30L, 22L, 33L, 
    52L, 80L, 20L, 33L, 61L, 34L, 36L, 67L, 35L, 36L, 24L, 12L, 
    47L, 29L, 38L, 30L, 25L, 19L, 28L, 37L, 72L, 31L, 39L, 36L, 
    30L, 60L, 45L, 29L, 56L, 44L, 124L, 42L, 39L, 26L, 74L, 25L, 
    25L, 124L, 32L, 28L, 32L, 9L, 21L, 25L, 24L, 40L, 14L, 42L, 
    49L, 21L, 28L, 44L, 38L, 24L, 28L, 34L, 26L, 46L, 36L, 31L, 
    39L, 22L, 80L, 37L, 54L, 19L, 14L, 55L, 42L, 45L, 23L, 31L, 
    21L, 33L, 25L, 18L, 46L, 22L, 54L, 32L, 28L, 28L, 31L, 28L, 
    29L, 41L, 34L, 24L, 41L, 32L, 39L, 14L, 32L, 46L, 32L), n_sygdom = c(0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 5L, 3L, 8L, 5L, 8L, 
    3L, 6L, 3L, 3L, 3L, 6L, 13L, 7L, 16L, 12L, 5L, 4L, 6L, 10L, 
    8L, 3L, 7L, 6L, 6L, 10L, 5L, 7L, 8L, 5L, 3L, 2L, 3L, 4L, 
    4L, 2L, 4L, 5L, 2L, 2L, 5L, 2L, 2L, 12L, 7L, 3L, 7L, 4L, 
    9L, 6L, 3L, 3L, 4L, 1L, 12L, 3L, 3L, 4L, 3L, 2L, 2L, 3L, 
    2L, 3L, 2L, 4L, 8L, 2L, 2L, 3L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 5L, 1L, 9L, 2L, 22L, 3L, 2L, 6L, 4L, 2L, 3L, 3L, 
    2L, 4L, 4L, 4L, 4L, 3L, 17L, 2L, 7L, 2L, 1L, 4L, 6L, 6L, 
    8L, 8L, 5L, 2L, 3L, 3L, 3L, 3L, 5L, 2L, 2L, 2L, 2L, 2L, 4L, 
    4L, 6L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 
    1L, 2L, 2L, 3L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 3L, 
    2L, 2L, 2L, 3L, 3L, 4L, 3L, 2L, 3L, 2L, 2L, 8L, 2L, 3L, 3L, 
    2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 4L, 3L, 1L, 3L, 13L, 4L, 9L, 4L, 3L, 2L, 3L, 4L, 
    3L, 2L, 8L, 4L, 10L, 10L, 2L, 3L, 6L, 8L, 6L, 3L, 3L, 2L, 
    7L, 5L, 3L, 12L, 2L, 2L, 1L, 2L, 3L, 1L, 2L, 5L, 2L, 7L, 
    3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L)), row.names = c(NA, 
500L), class = "data.frame")

呼比

merge(p, u, by = c('studie', 'sex', 'n_fjernet', 'n_sygdom'), all.x = T)

要么

p %>% 
    left_join(., u, by = c('studie', 'sex', 'n_fjernet', 'n_sygdom'))

本文收集自互联网，转载请注明来源。

如有侵权，请联系 [email protected] 删除。

编辑于 2021-08-7

我来说两句

0 条评论

登录后参与评论

使用 by=c(x=y) 错误在函数内执行 dplyr::left_join

覆盖left_join dplyr以更新数据

用dplyr的left_join编写函数

dplyr left_join小于，大于条件

dplyr left_join（）按行名

dplyr left_join匹配不适用

left_join (dplyr) 使用函数

在R中看到left_join发生意外错误

合并和dplyr的left_join的异常行为

dplyr的left_join行为不正确？

在自定义函数中使用 dplyr::left_join

MySQL-使用LEFT JOIN会产生意外结果

复杂的绑定列（使用 dplyr 使用 left_join 和 bind cols 重新组织数据）

R - dplyr left_join() - 多重匹配 - 如何重新组合...？

dplyr left_join 函数不完全加入，看似随机的 NA

将dplyr中的left_join与指定的合并变量一起使用

使用 data.table x[y] 语法复制 dplyr::left_join() 列顺序

匹配两个数据表（Vlookup、dplyr、match()、left_join）保持行数

left_join（dplyr）下一个可用日期

r dplyr::left_join 使用日期时间列不能正确连接

dplyr`left_join（）`不适用于字符对象作为LHS变量

LEFT JOIN 与具有布尔数据的表产生意外结果集

R，使用dplyr :: mutate和ifelse包含grepl（）会产生意外结果

在dplyr的left_join中使用as_label / as_name而不是quo_name连接两个数据集

将由left_join（）产生的所有列前缀为原始表名

Left_join：错误：无法分配大小为“ small” Mb的向量

为什么在 R 中使用 left_join() 时会出现“无效的‘时间’参数”错误？

C FILE读取产生意外错误

将两个数据框与left_join合并会在“ right”列中产生NA

TOP 榜单

文章

dplyr::left_join() 产生意外错误

dplyr::left_join() 产生意外错误

Linux的官方Adobe Flash存储库是否已过时？

用日期数据透视表和日期顺序查询

应用发明者仅从列表中选择一个随机项一次

Java Eclipse中的错误13，如何解决？

在Windows 7中无法删除文件（2）

在 Python 2.7 中。如何从文件中读取特定文本并分配给变量

套接字无法检测到断开连接

带有错误“ where”条件的查询如何返回结果？

有什么解决方案可以将android设备用作Cast Receiver？

Mac OS X更新后的GRUB 2问题

ggplot：对齐多个分面图-所有大小不同的分面

验证REST API参数

如何从视图一次更新多行（ASP.NET - Core）

尝试反复更改屏幕上按钮的位置 - kotlin android studio

计算数据帧中每行的NA

检索角度选择div的当前值

离子动态工具栏背景色

UITableView的项目向下滚动后更改颜色，然后快速备份

VB.net将2条特定行导出到DataGridView

蓝屏死机没有修复解决方案

通过 Git 在运行 Jenkins 作业时获取 ClassNotFoundException