R - Соединить два кадра данных в столбце, содержащем часть строки - PullRequest
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R - Соединить два кадра данных в столбце, содержащем часть строки

0 голосов
/ 22 января 2019

У меня есть два кадра данных

df1 

  Gate  Set
1  1  PIP D04 LMI1 975
2  6  PIP D06 LMI1 363
3 Time  PIP d08 LMI1 wk7 539

и

df2 

    ID     Weeks
1  d01       6
2  d04       8
3  d06       9
4  d08       19

, которые я хотел бы объединить.Как видите, строки в df2 $ ID возвращаются как часть строки в df1 $ Set.Я хотел бы объединить два кадра данных таким образом, чтобы строка соответствовала, и соответствующее значение в df2 $ Weeks было добавлено в новый столбец.Примерно так: 

  Gate       Set             Weeks
1  1    PIP D04 LMI1 975      8
2  6    PIP D06 LMI1 363      9
3 Time  PIP d08 LMI1 wk7 539  19

Я попробовал подход, где я зациклился с функцией, которая разбивает строку, которая не работала. 

test_day <- unlist(strsplit(df[,2][1], ""))
test_day <- paste(test_day[c(5:7)], collapse = "")

Редактировать: Здесь вывод dput дляоба кадра данных:

df1: 

structure(list(Gate = structure(c(1L, 24L, 1L, 23L, 1L, 13L, 
1L, 21L, 1L, 20L, 1L, 3L, 4L, 5L, 1L, 19L, 1L, 9L, 1L, 2L, 1L, 
10L, 14L, 15L, 22L, 1L, 12L, 16L, 17L, 18L, 1L, 6L, 7L, 8L, 1L, 
11L, 1L, 24L, 1L, 23L, 1L, 13L, 1L, 21L, 1L, 20L, 1L, 3L, 4L, 
5L, 1L, 19L, 1L, 9L, 1L, 2L, 1L, 10L, 14L, 15L, 22L, 1L, 12L, 
16L, 17L, 18L, 1L, 6L, 7L, 8L, 1L, 11L, 1L, 24L, 1L, 23L, 1L, 
13L, 1L, 21L, 1L, 20L, 1L, 3L, 4L, 5L, 1L, 19L, 1L, 9L, 1L, 2L, 
1L, 10L, 14L, 15L, 22L, 1L, 12L, 16L, 17L, 18L, 1L, 6L, 7L, 8L, 
1L, 11L, 1L, 24L, 1L, 23L, 1L, 13L, 1L, 21L, 1L, 20L, 1L, 3L, 
4L, 5L, 1L, 19L, 1L, 9L, 1L, 2L, 1L, 10L, 14L, 15L, 22L, 1L, 
12L, 16L, 17L, 18L, 1L, 6L, 7L, 8L, 1L, 11L, 1L, 24L, 1L, 23L, 
1L, 13L, 1L, 21L, 1L, 20L, 1L, 3L, 4L, 5L, 1L, 19L, 1L, 9L, 1L, 
2L, 1L, 10L, 14L, 15L, 22L, 1L, 12L, 16L, 17L, 18L, 1L, 6L, 7L, 
8L, 1L, 11L, 1L, 24L, 1L, 23L, 1L, 13L, 1L, 21L, 1L, 20L, 1L, 
3L, 4L, 5L, 1L, 19L, 1L, 9L, 1L, 2L, 1L, 10L, 14L, 15L, 22L, 
1L, 12L, 16L, 17L, 18L, 1L, 6L, 7L, 8L, 1L, 11L, 1L, 24L, 1L, 
23L, 1L, 13L, 1L, 21L, 1L, 20L, 1L, 3L, 4L, 5L, 1L, 19L, 1L, 
9L, 1L, 2L, 1L, 10L, 14L, 15L, 22L, 1L, 12L, 16L, 17L, 18L, 1L, 
6L, 7L, 8L, 1L, 11L, 1L, 24L, 1L, 23L, 1L, 13L, 1L, 21L, 1L, 
20L, 1L, 3L, 4L, 5L, 1L, 19L, 1L, 9L, 1L, 2L, 1L, 10L, 14L, 15L, 
22L, 1L, 12L, 16L, 17L, 18L, 1L, 6L, 7L, 8L, 1L, 11L, 1L, 24L, 
1L, 23L, 1L, 13L, 1L, 21L, 1L, 20L, 1L, 3L, 4L, 5L, 1L, 19L, 
1L, 9L, 1L, 2L, 1L, 10L, 14L, 15L, 22L, 1L, 12L, 16L, 17L, 18L, 
1L, 6L, 7L, 8L, 1L, 11L, 1L, 24L, 1L, 23L, 1L, 13L, 1L, 21L, 
1L, 20L, 1L, 3L, 4L, 5L, 1L, 19L, 1L, 9L, 1L, 2L, 1L, 10L, 14L, 
15L, 22L, 1L, 12L, 16L, 17L, 18L, 1L, 6L, 7L, 8L, 1L, 11L, 1L, 
24L, 1L, 23L, 1L, 13L, 1L, 21L, 1L, 20L, 1L, 3L, 4L, 5L, 1L, 
19L, 1L, 9L, 1L, 2L, 1L, 10L, 14L, 15L, 22L, 1L, 12L, 16L, 17L, 
18L, 1L, 6L, 7L, 8L, 1L, 11L, 1L, 24L, 1L, 23L, 1L, 13L, 1L, 
21L, 1L, 20L, 1L, 3L, 4L, 5L, 1L, 19L, 1L, 9L, 1L, 2L, 1L, 10L, 
14L, 15L, 22L, 1L, 12L, 16L, 17L, 18L, 1L, 6L, 7L, 8L, 1L, 11L, 
1L, 24L, 1L, 23L, 1L, 13L, 1L, 21L, 1L, 20L, 1L, 3L, 4L, 5L, 
1L, 19L, 1L, 9L, 1L, 2L, 1L, 10L, 14L, 15L, 22L, 1L, 12L, 16L, 
17L, 18L, 1L, 6L, 7L, 8L, 1L, 11L, 1L, 24L, 1L, 23L, 1L, 13L, 
1L, 21L, 1L, 20L, 1L, 3L, 4L, 5L, 1L, 19L, 1L, 9L, 1L, 2L, 1L, 
10L, 14L, 15L, 22L, 1L, 12L, 16L, 17L, 18L, 1L, 6L, 7L, 8L, 1L, 
11L, 1L, 24L, 1L, 23L, 1L, 13L, 1L, 21L, 1L, 20L, 1L, 3L, 4L, 
5L, 1L, 19L, 1L, 9L, 1L, 2L, 1L, 10L, 14L, 15L, 22L, 1L, 12L, 
16L, 17L, 18L, 1L, 6L, 7L, 8L, 1L, 11L, 1L, 24L, 1L, 23L, 1L, 
13L, 1L, 21L, 1L, 20L, 1L, 3L, 4L, 5L, 1L, 19L, 1L, 9L, 1L, 2L, 
1L, 10L, 14L, 15L, 22L, 1L, 12L, 16L, 17L, 18L, 1L, 6L, 7L, 8L, 
1L, 11L, 1L, 24L, 1L, 23L, 1L, 13L, 1L, 21L, 1L, 20L, 1L, 3L, 
4L, 5L, 1L, 19L, 1L, 9L, 1L, 2L, 1L, 10L, 14L, 15L, 22L, 1L, 
12L, 16L, 17L, 18L, 1L, 6L, 7L, 8L, 1L, 11L, 1L, 24L, 1L, 23L, 
1L, 13L, 1L, 21L, 1L, 20L, 1L, 3L, 4L, 5L, 1L, 19L, 1L, 9L, 1L, 
2L, 1L, 10L, 14L, 15L, 22L, 1L, 12L, 16L, 17L, 18L, 1L, 6L, 7L, 
8L, 1L, 11L, 1L, 24L, 1L, 23L, 1L, 13L, 1L, 21L, 1L, 20L, 1L, 
3L, 4L, 5L, 1L, 19L, 1L, 9L, 1L, 2L, 1L, 10L, 14L, 15L, 22L, 
1L, 12L, 16L, 17L, 18L, 1L, 6L, 7L, 8L, 1L, 11L, 1L, 24L, 1L, 
23L, 1L, 13L, 1L, 21L, 1L, 20L, 1L, 3L, 4L, 5L, 1L, 19L, 1L, 
9L, 1L, 2L, 1L, 10L, 14L, 15L, 22L, 1L, 12L, 16L, 17L, 18L, 1L, 
6L, 7L, 8L, 1L, 11L, 1L, 24L, 1L, 23L, 1L, 13L, 1L, 21L, 1L, 
20L, 1L, 3L, 4L, 5L, 1L, 19L, 1L, 9L, 1L, 2L, 1L, 10L, 14L, 15L, 
22L, 1L, 12L, 16L, 17L, 18L, 1L, 6L, 7L, 8L, 1L, 11L, 1L, 24L, 
1L, 23L, 1L, 13L, 1L, 21L, 1L, 20L, 1L, 3L, 4L, 5L, 1L, 19L, 
1L, 9L, 1L, 2L, 1L, 10L, 14L, 15L, 22L, 1L, 12L, 16L, 17L, 18L, 
1L, 6L, 7L, 8L, 1L, 11L, 1L, 24L, 1L, 23L, 1L, 13L, 1L, 21L, 
1L, 20L, 1L, 3L, 4L, 5L, 1L, 19L, 1L, 9L, 1L, 2L, 1L, 10L, 14L, 
15L, 22L, 1L, 12L, 16L, 17L, 18L, 1L, 6L, 7L, 8L, 1L, 11L, 1L, 
24L, 1L, 23L, 1L, 13L, 1L, 21L, 1L, 20L, 1L, 3L, 4L, 5L, 1L, 
19L, 1L, 9L, 1L, 2L, 1L, 10L, 14L, 15L, 22L, 1L, 12L, 16L, 17L, 
18L, 1L, 6L, 7L, 8L, 1L, 11L, 1L, 24L, 1L, 23L, 1L, 13L, 1L, 
21L, 1L, 20L, 1L, 3L, 4L, 5L, 1L, 19L, 1L, 9L, 1L, 2L, 1L, 10L, 
14L, 15L, 22L, 1L, 12L, 16L, 17L, 18L, 1L, 6L, 7L, 8L, 1L, 11L, 
1L, 24L, 1L, 23L, 1L, 13L, 1L, 21L, 1L, 20L, 1L, 3L, 4L, 5L, 
1L, 19L, 1L, 9L, 1L, 2L, 1L, 10L, 14L, 15L, 22L, 1L, 12L, 16L, 
17L, 18L, 1L, 6L, 7L, 8L, 1L, 11L, 1L, 24L, 1L, 23L, 1L, 13L, 
1L, 21L, 1L, 20L, 1L, 3L, 4L, 5L, 1L, 19L, 1L, 9L, 1L, 2L, 1L, 
10L, 14L, 15L, 22L, 1L, 12L, 16L, 17L, 18L, 1L, 6L, 7L, 8L, 1L, 
11L), .Label = c("All", "B cell", "CD14+ CD16+", "CD14++ CD16-", 
"CD14++ CD16+", "CD16-CD56+ NK", "CD16-CD56+ NK bright", "CD16+CD56+ NKdim", 
"CD3-", "CD3+CD56- Tcells", "CD4+CD25++", "CD4+CD8- Tc", "CD45+", 
"CD56+CD3- NK cells", "CD56+CD3+ NKT cells", "CD8-CD4- Tc", "CD8+CD4- Tc", 
"CD8+CD4+ Tc", "Lymphocytes", "Monocytes", "Neutrophils", "non NK(T) cells", 
"Singlets", "Time"), class = "factor"), Set = structure(c(2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 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, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 15L, 15L, 
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 17L, 17L, 17L, 17L, 17L, 
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 
17L, 17L, 17L, 17L, 17L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 
18L, 18L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 20L, 
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 21L, 21L, 21L, 21L, 
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 
21L, 21L, 21L, 21L, 21L, 21L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 
22L, 22L, 22L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 
23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 
23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 
24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 
24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 
24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 25L, 25L, 25L, 
25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 
25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 
25L, 25L, 25L, 25L, 25L, 25L, 25L, 26L, 26L, 26L, 26L, 26L, 26L, 
26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 
26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 
26L, 26L, 26L, 26L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
16L), .Label = c("d31 wk 9.5 LMI1 168", "PIP D04 LMI1 975", "PIP D06 LMI1 363", 
"PIP d08 LMI1 wk7 539", "PIP d10 LMI1 wk10.4 540", "PIP D12 DPMC LMI1 789", 
"PIP d13 6.2wk LMI1", "PIP D15 LMI1 316", "PIP D19 LMI1 319", 
"PIP D21 LMI1 518", "PIP D22 LMI1 519", "PIP D23 LMI1 520", "PIP D26 LMI1 912", 
"PIP d39 wk 9.2 LMI1 094", "PIP d46 wk 8 LMI1 550", "d56 LMI1 14.3wk 2018-06-19 771", 
"PIP D05P LMI1 981", "PIP D07 LMI1 367", "PIP d11 LMI1 wk14 541", 
"PIP d14 LMI1 14wk 136", "PIP D18 LMI1 318", "PIP D20 LMI1 321", 
"PIP D24 LMI1 521", "PIP D25 LMI1 527", "PIP D27 LMI1 911", "PIP d47 wk 15.3 LMI1 554"
), class = "factor")), .Names = c("Gate", "Set"), class = "data.frame", row.names = c(NA, 
-972L))

и df2: 

structure(list(ID = structure(c(1L, 2L, 3L, 4L, 6L, 5L, 7L, 8L, 
9L, 10L, 11L, 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, 100L, 101L, 102L, 103L, 104L, 105L, 
106L, 107L, 12L, 13L, 14L, 15L, 16L, 17L, 18L), .Label = c("d01", 
"d02", "d03", "d04", "d05B", "d05P", "d06", "d07", "d08", "d09", 
"d10", "d100", "d101", "d102", "d103", "d104", "d105", "d106", 
"d11", "d12", "d13", "d14", "d15", "d16", "d17", "d18", "d19", 
"d20", "d21", "d22", "d23", "d24", "d25", "d26", "d27", "d28", 
"d29", "d30", "d31", "d32", "d33", "d34", "d35", "d36", "d37", 
"d38", "d39", "d40", "d41", "d42", "d43", "d44", "d45", "d46", 
"d47", "d48", "d49", "d50", "d51", "d52", "d53", "d54", "d55", 
"d56", "d57", "d58", "d59", "d60", "d61", "d62", "d63", "d64", 
"d65", "d66", "d67", "d68", "d69", "d70", "d71", "d72", "d73", 
"d74", "d75", "d76", "d77", "d78", "d79", "d80", "d81", "d82", 
"d83", "d84", "d85", "d86", "d87", "d88", "d89", "d90", "d91", 
"d92", "d93", "d94", "d95", "d96", "d97", "d98", "d99"), class = "factor"), 
    Weeks = c(6.7, 8.4, 6.3, 8, 15.9, 15.9, 8.9, 16.3, 7, 8, 
    10.6, 14, 10, 6.3, 14, 6.9, NA, 16, 16, 7.3, 13, 5, 6, 7, 
    14.4, 14.4, 7, 13.4, 6, 6, 14.3, 9.7, 14.3, 5.7, 7.3, 8.1, 
    6, 15.4, 7.4, 9.3, 13.6, 5.9, 5.4, 7.7, 13, 13.3, 8, 15.4, 
    16, 7, 14.1, 6.7, 13.1, 6, 6, 10.6, 14.4, 5, 6.7, 7.9, 12.4, 
    7.6, 14, 6, 13.6, 13.6, 7.3, 9, 16, 16.3, 4.7, 5, 5.9, 6, 
    8, 11.6, 6, 6.4, 8.1, 8.1, 7, 7.3, 9.6, 13.3, 6, 5, 11, 11, 
    17, 6.3, 8.3, 11.3, 14.3, 4.9, 5.9, 8.4, 5.4, 7, 15.6, 6.1, 
    4.7, 10.9, 10.4, 6, 7.3, 8.9, 11)), .Names = c("ID", "Weeks"
), class = "data.frame", row.names = c(NA, -107L))

Ответы [ 2 ]

0 голосов
/ 22 января 2019

Один из вариантов - использовать regex_left_join из библиотеки fuzzyjoin, что позволяет объединять наборы данных в совпадениях регулярных выражений записей столбцов: 

library(fuzzyjoin)
library(dplyr)
regex_left_join(df1, df2, by = c("Set" = "ID"), ignore_case = T) %>%
    select(-ID)
#  Gate                  Set Weeks
#1    1     PIP D04 LMI1 975     8
#2    6     PIP D06 LMI1 363     9
#3 Time PIP d08 LMI1 wk7 539    19

Пример данных 

df1 <- read.table(text =
    "  Gate  Set
1  1  'PIP D04 LMI1 975'
2  6  'PIP D06 LMI1 363'
3 Time  'PIP d08 LMI1 wk7 539'", header = T)

df2 <- read.table(text =
    "    ID     Weeks
1  d01       6
2  d04       8
3  d06       9
4  d08       19", header = T)

Обновление

Использование обновленных образцов данных 

regex_left_join(df1, df2, by = c("Set" = "ID"), ignore_case = T) %>%
    select(-ID)
                    Gate                            Set Weeks
1                    All               PIP D04 LMI1 975   8.0
2                   Time               PIP D04 LMI1 975   8.0
3                    All               PIP D04 LMI1 975   8.0
4               Singlets               PIP D04 LMI1 975   8.0
5                    All               PIP D04 LMI1 975   8.0
6                  CD45+               PIP D04 LMI1 975   8.0
7                    All               PIP D04 LMI1 975   8.0
8            Neutrophils               PIP D04 LMI1 975   8.0
9                    All               PIP D04 LMI1 975   8.0
10             Monocytes               PIP D04 LMI1 975   8.0
....
0 голосов
/ 22 января 2019

Использование data.table

df1[, ID := tolower(gsub(".+([dD]\\d{2}).+", "\\1", Set))]
df1
#   Gate                  Set  ID
#1:    1     PIP D04 LMI1 975 d04
#2:    6     PIP D06 LMI1 363 d06
#3: Time PIP d08 LMI1 wk7 539 d08
df2[df1, on = .(ID)]
#    ID Weeks Gate                  Set
#1: d04     8    1     PIP D04 LMI1 975
#2: d06     9    6     PIP D06 LMI1 363
#3: d08    19 Time PIP d08 LMI1 wk7 539

# with merge()
merge(df1, df2, by = "ID")

Данные 

df1 <- fread("Gate,  Set
  1,  PIP D04 LMI1 975
  6,  PIP D06 LMI1 363
 Time,  PIP d08 LMI1 wk7 539")

df2 <- fread("ID     Weeks
d01       6
d04       8
d06       9
d08       19")
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