我有下面的代码，我运行了很多次，因为它在我的多个模型中，具有不同的参数，这个特殊的代码是最耗时的，有更快的方法吗？
membership_labels = unlist(lapply(comun$membership,
function(x) membership_table[
membership_table$membership == x,]$membership_label))
描述数据：
comun$membership：
c(4, 6, 9, 6, 7, 7, 6, 3, 3, 6, 3, 9, 7, 3, 3, 3, 3, 3, 7, 6,
3, 7, 7, 3, 7, 7, 6, 6, 7, 6, 7, 3, 5, 7, 7, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
8, 3, 3, 3, 3, 9, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 9, 7, 9, 3, 19, 8, 6, 9, 8, 6, 19, 2, 2, 3, 2, 19, 7, 3,
3, 7, 7, 2, 9, 3, 3, 19, 2, 3, 7, 11, 3, 17, 7, 9, 3, 9, 6, 7,
6, 3, 17, 8, 3, 3, 19, 3, 3, 6, 1, 7, 9, 7, 7, 6, 3, 9, 6, 6,
6, 7, 8, 4, 6, 6, 3, 7, 6, 6, 8, 6, 6, 3, 6, 3, 3, 3, 6, 3, 6,
2, 3, 3, 3, 18, 6, 3, 3, 9, 3, 3, 3, 3, 8, 3, 6, 9, 6, 6, 6,
6, 6, 6, 8, 3, 3, 6, 6, 3, 3, 3, 6, 1, 5, 7, 7, 7, 1, 7, 9, 7,
7, 7, 7, 7, 7, 7, 7, 9, 7, 7, 7, 8, 3, 3, 17, 1, 1, 1, 7, 7,
17, 6, 6, 6, 6, 6, 6, 6, 6, 3, 6, 7, 7, 7, 7, 6, 6, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 6, 7, 7, 7, 6, 7, 7, 7, 7, 6,
7, 6, 7, 6, 7, 6, 7, 2, 7, 6, 6, 7, 6, 12, 12, 8, 2, 6, 7, 6,
6, 7, 3, 6, 12, 6, 6, 8, 7, 7, 7, 7, 7, 7, 1, 7, 7, 7, 1, 16,
7, 7, 6, 12, 7, 7, 2, 6, 6, 7, 3, 3, 6, 8, 9, 9, 6, 3, 6, 6,
6, 18, 6, 3, 3, 6, 3, 17, 6, 8, 6, 6, 8, 10, 3, 14, 16, 6, 1,
7, 16, 7, 5, 1, 11, 1, 19, 7, 3, 2, 7, 5, 8, 7, 14, 2, 17, 7,
7, 11, 7, 1, 7, 1, 6, 1, 16, 6, 1, 7, 6, 7, 14, 2, 7, 2, 10,
7, 7, 1, 2, 7, 7, 7, 5, 1, 7, 9, 1, 1, 1, 12, 1, 5, 13, 1, 9,
2, 2, 2, 4, 2, 4, 2, 3, 2, 2, 3, 9, 9, 6, 6, 3, 6, 18, 15, 7,
12, 7, 6, 7, 6, 17, 6, 7, 6, 7, 7, 6, 8, 5, 2, 7, 2, 8, 18, 6,
8, 6, 6, 6, 12, 12, 6, 11, 2, 12, 8, 8, 8, 6, 3, 7, 3, 1, 1,
8, 7, 6, 9, 6, 18, 6, 6, 6, 7, 7, 5, 2, 18, 6, 5, 7, 6, 6, 1,
14, 6, 6, 9, 9, 7, 3, 6, 6, 9, 6, 3, 3, 6, 3, 6, 6, 3, 9, 9,
3, 7, 3, 6, 6, 9, 6, 6, 3, 9, 6, 9, 11, 11, 1, 1, 5, 5, 7, 10,
7, 9, 13, 10, 10, 2, 1, 5, 1, 16, 10, 10, 5, 10, 7, 10, 10, 5,
10, 10, 1, 7, 5, 10, 1, 2, 2, 10, 10, 1, 1, 14, 16, 7, 1, 1,
1, 7, 7, 16, 1, 5, 6, 1, 9, 1, 6, 5, 9, 9, 9, 9, 1, 18, 18, 16,
1, 6, 1, 1, 1, 1, 6, 10, 1, 1, 1, 1, 1, 11, 1, 1, 10, 9, 1, 10,
1, 1, 7, 1, 1, 1, 10, 10, 1, 1, 1, 9, 6, 7, 5, 1, 1, 1, 1, 1,
1, 1, 9, 1, 1, 12, 1, 1, 6, 1, 1, 1, 1, 9, 1, 1, 1, 9, 1, 1,
1, 9, 10, 1, 5, 1, 1, 1, 6, 1, 6, 6, 16, 12, 6, 2, 5, 1, 12,
1, 6, 1, 12, 5, 1, 5, 12, 7, 5, 2, 10, 2, 1, 7, 1, 1, 5, 9, 10,
5, 10, 10, 1, 5, 10, 1, 9, 2, 1, 6, 10, 1, 6, 1, 12, 9, 1, 3,
6, 13, 9, 5, 1, 15, 7, 2, 12, 1, 12, 6, 5, 1, 2, 1, 1, 6, 2,
7, 1, 5, 2, 6, 7, 7, 5, 6, 5, 5, 2, 1, 18, 8, 6, 1, 14, 1, 1,
16, 15, 8, 1, 12, 2, 1, 4, 2, 4, 13, 2, 14, 16, 17, 4, 2, 4,
4, 4, 2, 13, 2, 13, 16, 4, 10, 1, 2, 10, 13, 13, 14, 7, 2, 7,
10, 5, 10, 5, 10, 1, 10, 5, 10, 7, 4, 1, 5, 1, 6, 5, 11, 7, 7,
7, 10, 5, 10, 10, 4, 10, 4, 10, 6, 10, 5, 10, 5, 19, 1, 19, 2,
10, 5, 10, 5, 10, 10, 5, 5, 10, 5, 14, 10, 6, 10, 2, 4, 4, 4,
19, 17, 4, 2, 2, 2, 2, 2, 9, 7, 9, 6, 9, 5, 8, 6, 2, 1, 1, 9,
1, 1, 1, 5, 1, 1, 5, 14, 7, 18, 1, 1, 12, 1, 1, 1, 6, 3, 15,
1, 10, 6, 2, 6, 10, 15, 2, 12, 1, 5, 1, 1, 1, 11, 6, 1, 2, 18,
15, 5, 8, 5, 9, 1, 13, 3, 8, 11, 8, 6, 5, 7, 6, 8, 3, 8, 2, 1,
15, 1, 15, 2, 9, 9, 9, 6, 13, 2, 10, 11, 10, 8, 6, 4, 2, 4, 4,
4, 4, 2, 4, 2, 2, 4, 10, 4, 10, 4, 13, 4, 4, 4, 2, 14, 13, 10,
5, 10, 10, 10, 5, 2, 6, 1, 11, 1, 5, 1, 5, 2, 1, 2, 15, 5, 1,
5, 10, 10, 10, 10, 4, 10, 5, 10, 5, 10, 10, 10, 10, 10, 4, 10,
5, 10, 11, 10, 2, 10, 5, 10, 5, 4, 5, 10, 10, 10, 5, 10, 5, 5,
10, 5, 10, 10, 10, 10, 5, 10, 5, 10, 5, 10, 2, 2, 10, 1, 1, 5,
1, 10, 10, 5, 10, 5, 10, 10, 11, 5, 1, 6, 10, 5, 10, 5, 10, 5,
10, 5, 10, 6, 1, 2, 1, 1, 1, 15, 2, 4, 4, 4, 10, 10, 4, 10, 10,
4, 4, 10, 6, 4, 4, 4, 10, 4, 10, 4, 4, 4, 6, 2, 8, 10, 4, 10,
14, 6, 2, 6, 10, 9, 1, 13, 1, 1, 1, 5, 1, 1, 2, 1, 2, 1, 1, 1,
1, 5, 5, 1, 5, 8, 6, 1, 2, 2, 5, 6, 1, 11, 1, 5, 15, 5, 10, 10,
10, 10, 10, 1, 10, 6, 2, 4, 2, 4, 4, 2, 4, 4, 2, 4, 2, 4, 2,
4, 10, 2, 10, 4, 4, 19, 15, 10, 4, 13, 2, 17, 4, 17, 4, 4, 4,
2, 4, 4, 2, 4, 2, 4, 4, 4, 4, 2, 4, 16, 2, 4, 4, 2, 2, 4, 4,
4, 4, 2, 2, 4, 4, 4, 15, 4, 4, 5, 11, 10, 2, 2, 2, 14, 2, 18,
5, 2, 8, 4, 10, 10, 4, 5, 4, 2, 10, 10, 11, 2, 15, 10, 11, 1,
4, 4, 14, 4, 2, 1, 4, 4, 4, 14, 8, 8, 8, 4, 2, 4, 1, 11, 1, 4,
2, 2, 6, 2, 6, 9, 1, 6, 2, 8, 6, 2, 2, 2, 6, 9, 2, 4, 2, 2, 14,
6, 9, 8, 2, 2, 2, 2, 4, 4, 6, 4, 4, 15, 2, 2, 2, 4, 2, 2, 8,
2, 2, 2, 2, 4, 4, 4, 2, 4, 4, 2, 4, 4, 4, 2, 4, 4, 4, 4, 4, 4,
2, 4, 6, 4, 2, 4, 2, 2, 4, 4, 4, 2, 4, 4, 4, 4, 4, 2, 2, 4, 2,
4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 4, 4, 2, 4, 2, 4, 2, 17, 2,
2, 4, 2, 2, 4, 2, 4, 2, 4, 4, 2, 2, 4, 2, 4, 2, 2, 4, 4, 4, 2,
10, 5, 4, 4, 4, 2, 2, 2, 2, 2, 6, 9, 2, 9, 4, 11, 4, 2, 15, 2,
4, 10, 4, 1, 4, 4, 2, 2, 2, 8, 6, 4, 4, 2, 8, 2, 9, 2, 1, 4,
2, 8, 2, 8, 4, 10, 4, 4, 4, 5, 10, 4, 10, 4)
membership_table：
membership community_count community_maj_count membership_label
<int> <int> <int> <int>
1 1 276 171 2
2 2 319 206 2
3 3 482 322 -2
4 4 404 293 2
5 5 161 88 2
6 6 271 110 -2
7 7 332 167 -2
8 8 56 46 2
9 9 77 37 2
10 10 434 244 2
11 11 27 12 -2
12 12 24 11 2
13 13 19 8 2
14 14 17 8 -2
15 15 18 16 2
16 16 14 5 -2
17 17 13 8 -2
18 18 16 13 2
19 19 12 9 -2
带dput:
structure(list(membership = 1:19, community_count = c(276L, 319L,
482L, 404L, 161L, 271L, 332L, 56L, 77L, 434L, 27L, 24L, 19L,
17L, 18L, 14L, 13L, 16L, 12L), community_maj_count = c(171L,
206L, 322L, 293L, 88L, 110L, 167L, 46L, 37L, 244L, 12L, 11L,
8L, 8L, 16L, 5L, 8L, 13L, 9L), membership_label = c(2L, 2L, -2L,
2L, 2L, -2L, -2L, 2L, 2L, 2L, -2L, 2L, 2L, -2L, 2L, -2L, -2L,
2L, -2L)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,
-19L))
需要注意的是，membership_table中的成员资格从1开始是连续的，但并不总是如此，它可以从任何索引开始，并且之间有间隔。
临时答案：
membership_index_list <- list()
membership_index_list[membership_table$membership] = membership_table$membership_label
membership_labels <- membership_index_list[comun$membership]
将我的注释更改为使用match而不是%in%:
membership_table$membership_label[match(comun$membership, membership_table$membership)]
# [1] 2 -2 2 -2 -2 -2 -2 -2 -2 -2 -2 2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 2 -2 -2 -2 -2 -2 -2
# [40] -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2
# [79] -2 -2 -2 -2 -2 2 -2 -2 -2 -2 2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 2 -2 2 -2 -2 2 -2 2 2 -2 -2
### ...truncated...
# [1366] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 -2 2 2 2 2 -2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 -2 2 2 2 2
# [1405] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
根据您的使用情况和对数据完整性的信心，当comun$membership中有membership_table中找不到的内容时，您可能会看到NA。例如，
membership_table$membership_label[match(c(99, head(comun$membership)), membership_table$membership)]
# [1] NA 2 -2 2 -2 -2 -2
其中CCD_ 10不在可用的CCD_。