我有这些不同维度的矩阵。所有矩阵中的key.related.sheet列都有一些常见值和一些唯一值。我想匹配这些常见的行并合并所有三个矩阵，但我也想包含唯一的行。结果列应仅包含key.related.sheet，Sample_B和trace_1，trace_2和trace_3列。有人可以帮帮我吗？
aa<-structure(c("S05-F13-P01:S05-F13-P01", "S05-F13-P01:S08-F10-P01",
"S05-F13-P01:S08-F11-P01", "S05-F13-P01:S09-F66-P01", "S05-F13-P01",
"S08-F10-P01", "S08-F11-P01", "S09-F66-P01", "1.25", "0.227",
"-0.183", "-0.217"), .Dim = c(4L, 3L), .Dimnames = list(NULL,
c("key.related.sheet", "sample_B", "trace_1")))
bb<-structure(c("S05-F13-P01:S08-F10-P01", "S05-F13-P01:S08-F11-P01",
"S05-F13-P01:S09-F66-P01", "S05-F13-P01:S09-F67-P01", "S08-F10-P01",
"S08-F11-P01", "S09-F66-P01", "S09-F67-P01", "0.227", "-0.183",
"-0.217", "0.292", "Unknown", "Unknown", "Unknown", "Unknown"
), .Dim = c(4L, 4L), .Dimnames = list(NULL, c("key.related.sheet",
"sample_B", "trace_2", "type")))
cc<-structure(c("S05-F13-P01:S08-F11-P01", "S05-F13-P01:S09-F66-P01",
"S05-F13-P01:S09-F67-P01", "S05-F13-P01:S09-F68-P01", "S05-F13-P01:S09-F01-P01",
"S08-F11-P01", "S09-F66-P01", "S09-F67-P01", "S09-F68-P01", "S09-F01-P01",
"-0.183", "-0.217", "0.292", "-0.314", "0.0418"), .Dim = c(5L,
3L), .Dimnames = list(NULL, c("key.related.sheet", "sample_B",
"trace_3")))
预期输出为：
key.related.sheet sample_B trace_1 trace_2 trace_3
"S05-F13-P01:S05-F13-P01" "S05-F13-P01" "1.25"
"S05-F13-P01:S08-F10-P01" "S08-F10-P01" "0.227" "0.227"
"S05-F13-P01:S08-F11-P01" "S08-F11-P01" "-0.183" "-0.183" "-0.183"
"S05-F13-P01:S09-F66-P01" "S09-F66-P01" "-0.217" "-0.217" "-0.217"
"S05-F13-P01:S09-F67-P01" "S09-F67-P01" "0.292" "0.292"
"S05-F13-P01:S09-F68-P01" "S09-F68-P01" "-0.314"
"S05-F13-P01:S09-F01-P01" "S09-F01-P01" "0.0418"
答案 0 :(得分：5)
可以使用Reduce和merge的组合完成此操作，如下所示：
Reduce(function(x, y) merge(x, y, all=TRUE), list(aa, bb[,-4], cc))
结果：
key.related.sheet sample_B trace_1 trace_2 trace_3
1 S05-F13-P01:S05-F13-P01 S05-F13-P01 1.25 <NA> <NA>
2 S05-F13-P01:S08-F10-P01 S08-F10-P01 0.227 0.227 <NA>
3 S05-F13-P01:S08-F11-P01 S08-F11-P01 -0.183 -0.183 -0.183
4 S05-F13-P01:S09-F66-P01 S09-F66-P01 -0.217 -0.217 -0.217
5 S05-F13-P01:S09-F67-P01 S09-F67-P01 <NA> 0.292 0.292
6 S05-F13-P01:S09-F01-P01 S09-F01-P01 <NA> <NA> 0.0418
7 S05-F13-P01:S09-F68-P01 S09-F68-P01 <NA> <NA> -0.314
特别是当你有三个以上的矩阵/数据框时，使用merge和Reduce缩放比嵌套合并更好。
答案 1 :(得分：2)
您还可以使用基数为R merge的{{1}}方法进行完整加入。
all = TRUE这里合并完成了w.r.t.所有常见列，即> merge(merge(aa,bb,all=TRUE),cc,all=TRUE)
key.related.sheet sample_B trace_1 trace_2 type trace_3
1 S05-F13-P01:S05-F13-P01 S05-F13-P01 1.25 <NA> <NA> <NA>
2 S05-F13-P01:S08-F10-P01 S08-F10-P01 0.227 0.227 Unknown <NA>
3 S05-F13-P01:S08-F11-P01 S08-F11-P01 -0.183 -0.183 Unknown -0.183
4 S05-F13-P01:S09-F66-P01 S09-F66-P01 -0.217 -0.217 Unknown -0.217
5 S05-F13-P01:S09-F67-P01 S09-F67-P01 <NA> 0.292 Unknown 0.292
6 S05-F13-P01:S09-F01-P01 S09-F01-P01 <NA> <NA> <NA> 0.0418
7 S05-F13-P01:S09-F68-P01 S09-F68-P01 <NA> <NA> <NA> -0.314
和key.related.sheet - 但这应该没问题，因为sample_B取决于sample_B？
使用key.related.sheet，您可以获得与使用dplyr的Adams回答相同的输出。然后合并就在w.r.t完成。左侧和右侧加入伙伴中的by="key.related.sheet"和key.related.sheet列同时出现在结果中（即，您的数据都是重复的）
答案 2 :(得分：1)
您可以将矩阵转换为data.frame并使用dplyr包中的full_join命令将它们连接在一起
library(dplyr)
for(i in c("aa","bb", "cc")) assign(i, data.frame(get(i)))
aa %>% full_join(bb, by="key.related.sheet") %>% full_join(cc,
by="key.related.sheet")
key.related.sheet sample_B.x trace_1 sample_B.y trace_2 type sample_B trace_3
1 S05-F13-P01:S05-F13-P01 S05-F13-P01 1.25 <NA> <NA> <NA> <NA> <NA>
2 S05-F13-P01:S08-F10-P01 S08-F10-P01 0.227 S08-F10-P01 0.227 Unknown <NA> <NA>
3 S05-F13-P01:S08-F11-P01 S08-F11-P01 -0.183 S08-F11-P01 -0.183 Unknown S08-F11-P01 -0.183
4 S05-F13-P01:S09-F66-P01 S09-F66-P01 -0.217 S09-F66-P01 -0.217 Unknown S09-F66-P01 -0.217
5 S05-F13-P01:S09-F67-P01 <NA> <NA> S09-F67-P01 0.292 Unknown S09-F67-P01 0.292
6 S05-F13-P01:S09-F68-P01 <NA> <NA> <NA> <NA> <NA> S09-F68-P01 -0.314
7 S05-F13-P01:S09-F01-P01 <NA> <NA> <NA> <NA> <NA> S09-F01-P01 0.0418
答案 3 :(得分：1)
两个嵌套合并并删除无关列
merge(merge(aa,bb[, -4], by=c("key.related.sheet", "sample_B") ,all=TRUE),
cc, by=c("key.related.sheet", "sample_B") ,all=TRUE)
key.related.sheet sample_B trace_1 trace_2 trace_3
1 S05-F13-P01:S05-F13-P01 S05-F13-P01 1.25 <NA> <NA>
2 S05-F13-P01:S08-F10-P01 S08-F10-P01 0.227 0.227 <NA>
3 S05-F13-P01:S08-F11-P01 S08-F11-P01 -0.183 -0.183 -0.183
4 S05-F13-P01:S09-F66-P01 S09-F66-P01 -0.217 -0.217 -0.217
5 S05-F13-P01:S09-F67-P01 S09-F67-P01 <NA> 0.292 0.292
6 S05-F13-P01:S09-F01-P01 S09-F01-P01 <NA> <NA> 0.0418
7 S05-F13-P01:S09-F68-P01 S09-F68-P01 <NA> <NA> -0.314