如何在R中合并多个不同维度的矩阵

Question

我有这些不同维度的矩阵.key.related.sheet所有矩阵中的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.结果列应该只有key.related.sheet,Sample_B和trace_1,trace_2和trace_3列.有人可以帮我这个吗？

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")))

预期的产出是:

   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"

Answer 1

这可以通过组合来完成Reduce,并merge如下:

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

尤其是当你有超过三个矩阵/ dataframes,采用merge与Reduce尺度更好,然后嵌套合并.