问题描述
我想根据它们的行名和列名对两个矩阵求和.矩阵不一定具有相同的所有行和列-某些矩阵中可能缺少某些列.
I have two matrices I want to sum based on their row and column names. The matrices will not necessarily have all rows and columns in common - some may be missing from either matrix.
例如，考虑两个矩阵A和B:
For example, consider two matrices A and B:
A= B=
a b c d a c d e
v 1 1 1 0 v 0 0 0 1
w 1 1 0 1 w 0 0 1 0
x 1 0 1 1 y 0 1 0 0
y 0 1 1 1 z 1 0 0 0
矩阵A缺少列e，矩阵B缺少列b.矩阵A缺少z行，矩阵B缺少x行.
Column e is missing from matrix A and column b is missing from matrix B.Row z is missing from matrix A and row x is missing from matrix B.
我要查找的汇总表是:
Sum=
a b c d e
v 1 1 1 0 1
w 1 1 0 2 0
x 1 0 1 1 na
y 0 1 2 1 0
z 1 na 0 0 0
最终矩阵中的行和列顺序无关紧要，只要矩阵完整即可，即具有所有数据.缺少的值不必为"Na"，而可以为"0".
The row and column ordering in the final matrix don't matter, as long as the matrix is complete, i.e. has all the data. Missing values don't have to be "Na", but could be "0" instead.
我不确定是否有一种方法不涉及for循环.任何帮助将不胜感激.
I'm not sure if there is a way to do this that doesn't involve for loops. Any help would be much appreciated.
我的解决方案
我成功地做到了这一点，方法是将矩阵转换为数据帧，按行绑定数据帧，然后将结果数据帧转换回矩阵.看起来好像可行，但也许有人可以仔细检查或让我知道是否有更好的方法.
I managed to do this easily by converting the matrices to dataframes, binding the dataframes by row and then casting the resulting dataframe back into a matrix. This looks like it works, but maybe someone could double check or let me know if there is a better way.
library(reshape2)
A_df=as.data.frame(as.table(A))
B_df=as.data.frame(as.table(B))
merged_df=rbind(A_df,B_df)
Summed_matrix=acast(merged_df, Var1 ~ Var2, sum)
merged_df看起来像这样:
merged_df looks like this:
Var1 Var2 Freq
1 v a 1
2 w a 1
3 x a 1
4 y a 0
5 v b 1
6 w b 1
etc...
推荐答案
也许您可以尝试:
cAB <- union(colnames(A), colnames(B))
rAB <- union(rownames(A), rownames(B))
A1 <- matrix(0, ncol=length(cAB), nrow=length(rAB), dimnames=list(rAB, cAB))
B1 <- A1
indxA <- outer(rAB, cAB, FUN=paste) %in% outer(rownames(A), colnames(A), FUN=paste)
indxB <- outer(rAB, cAB, FUN=paste) %in% outer(rownames(B), colnames(B), FUN=paste)
A1[indxA] <- A
B1[indxB] <- B
A1+B1 #because it was mentioned to have `0` as missing values
# a b c d e
#v 1 1 1 0 1
#w 1 1 0 2 0
#x 1 0 1 1 0
#y 0 1 2 1 0
#z 1 0 0 0 0
如果要获取NA作为缺失值
A1 <- matrix(NA, ncol=length(cAB), nrow=length(rAB), dimnames=list(rAB, cAB))
B1 <- A1
A1[indxA] <- A
B1[indxB] <- B
indxNA <- is.na(A1) & is.na(B1)
A1[is.na(A1)!= indxNA] <- 0
B1[is.na(B1)!= indxNA] <- 0
A1+B1
# a b c d e
#v 1 1 1 0 1
#w 1 1 0 2 0
#x 1 0 1 1 NA
#y 0 1 2 1 0
#z 1 NA 0 0 0
或使用reshape2
library(reshape2)
acast(rbind(melt(A), melt(B)), Var1~Var2, sum) #Inspired from the OP's idea
# a b c d e
#v 1 1 1 0 1
#w 1 1 0 2 0
#x 1 0 1 1 0
#y 0 1 2 1 0
#z 1 0 0 0 0
数据
A <- structure(c(1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L,
1L, 1L, 1L), .Dim = c(4L, 4L), .Dimnames = list(c("v", "w", "x",
"y"), c("a", "b", "c", "d")))
B <- structure(c(0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L,
0L, 0L, 0L), .Dim = c(4L, 4L), .Dimnames = list(c("v", "w", "y",
"z"), c("a", "c", "d", "e")))
这篇关于R:根据行和列名称添加矩阵的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！