Sum row-wise values that are grouped by column name but keep all columns in R?-CodePudding

Similar questions have been asked here and here, however, my question is slightly different from those questions. In the other questions, after they sum by the groups, they are removing the 'duplicate' columns. I want to keep the duplicate columns. For example, if I have a matrix that looks like this:

myMat <- matrix(NA, 3, 4)
colnames(myMat) <- c('x', 'y', 'x', 'y')
myMat[1,] <- c(1,2,0,1)
myMat[2,] <- c(2,4,3,5)
myMat[3,] <- c(3,6,1,7)

> myMat
     x y x y
[1,] 1 2 0 1
[2,] 2 4 3 5
[3,] 3 6 1 7

I want to row-wise sum, grouped by column name. In the links above, one of the answers suggest this:

> t(rowsum(t(myMat), group = colnames(myMat), na.rm = T))
     x  y
[1,] 1  3
[2,] 5  9
[3,] 4 13

But as you can see, they are combining the columns with the same name together and, as a result, reducing the number of columns. I want to sum the values, but still have the same columns. For example, my desired output would look like this:

     x  y x  y
[1,] 1  3 1  3
[2,] 5  9 5  9
[3,] 4 13 4 13

In the above, the rows (grouped by name) are summed... but I still have each individual column. Any suggestions as to how I could do this?

CodePudding user response：

You can try ave like below (with aids of col row)

> ave(myMat,colnames(myMat)[col(myMat)], row(myMat), FUN = sum)
     x  y x  y
[1,] 1  3 1  3
[2,] 5  9 5  9
[3,] 4 13 4 13

CodePudding user response：

A simple loop would achieve this:

for(i in unique(colnames(myMat))) {
  myMat[,colnames(myMat) == i] <- rowSums(myMat[,colnames(myMat) == i])
}

myMat
#>      x  y x  y
#> [1,] 1  3 1  3
#> [2,] 5  9 5  9
#> [3,] 4 13 4 13

CodePudding user response：

You could also just take your original solution and cbind it to itself

sums =  t(rowsum(t(myMat), group = colnames(myMat), na.rm = T))
cbind(sums, sums)

     x  y x  y
[1,] 1  3 1  3
[2,] 5  9 5  9
[3,] 4 13 4 13

CodePudding user response：

Use ave and apply:

t(apply(myMat, 1, \(x) ave(x, names(x), FUN = sum)))

     x  y x  y
[1,] 1  3 1  3
[2,] 5  9 5  9
[3,] 4 13 4 13