Retrieve index of newly added row - for loop in R - r

Question

I am trying to retrieve the index of a newly-added row, added via a for loop.
Starting from the beginning, I have a list of matrices of p-values, each with a variable number of rows and columns. This is because not all groups have an adequate number of treated individuals to run t-tests. The following is what prints to the console when I access this sample list:
$Group1
Normal Treatment 1 Treatment 2
Treatment 1 1 NA NA
Treatment 2 1 1 NA
Treatment 3 1 1 1
$Group2
Normal Treatment 2
Treatment 2 1 NA
Treatment 4 1 1
I would like every group to have the same number of rows and columns, in the correct order, with the missing values just filled in with NAs. This is a sample of what I would like:
$Group1
Normal Treatment 1 Treatment 2 Treatment 3
Treatment 1 1 NA NA NA
Treatment 2 1 1 NA NA
Treatment 3 1 1 1 NA
Treatment 4 NA NA NA NA
$Group2
Normal Treatment 1 Treatment 2 Treatment 3
Treatment 1 NA NA NA NA
Treatment 2 1 NA NA NA
Treatment 3 NA NA NA NA
Treatment 4 1 1 NA NA
Here is the code I have so far:
fix.results.row <- function(x, factors) {
results.matrix <- x
num <- 1
for (i in factors){
if (!i %in% rownames(results.matrix)) {
results.matrix <- rbind(results.matrix, NA)
rownames(results.matrix)[num] <- i
}
num <- num + 1
}
rownames(results.matrix) <- results.matrix[rownames(factors),,drop=FALSE]
return(results.matrix)
}
In the function above, x would be my list of matrices, and factors would be a list of all the factors in the order I want them. I have a similar function for adding columns.
My problem, as I see it, is in Group 2. If it sees that I'm missing Treatment 1, it will replace the rowname Treatment 2 with the rowname Treatment 1, so the data for Treatment 2 is now mislabeled Treatment 1. Then it reorders the variables the way I want them, but the data are already mislabeled!
If I could access the index of the newly-added row, which changes from group to group, then I could just change that specific row name. Any suggestions? Please let me know if there's any more information I need to provide. I tried to cover everything but I'm not sure if there's anything else you all need.

Answer 1

This isn't very elegant, but it might work better than using two functions to fill in the rows and columns separately.
Here, x is a list of all your matrices; factor is an optional list of desired row and column names
fix_rc <- function(x, factors) {
f <- function(x) factor(ul <- unique(unlist(x)), levels = sort(ul))
if (missing(factors))
factors <- list(f(sapply(x, rownames)),
f(sapply(x, colnames)))
template <- matrix(NA, length(factors[[1]]), length(factors[[2]]),
dimnames = factors)
lapply(x, function(xx) {
## original
# xx <- rbind(xx, template[, colnames(xx)])
# xx <- cbind(xx, template[rownames(xx), ])
# xx[rownames(template), colnames(template)]
## better http://stackoverflow.com/questions/31050787/r-how-to-match-join-2-matrices-of-different-dimensions-nrow-ncol/31051218#31051218
xx <- as.data.frame.table(xx)
template[as.matrix(xx[, 1:2])] <- xx$Freq
template
})
}
Here is the data I am using
l <- list(Group1 = matrix(c(1,1,1,NA,1,1,NA,NA,1), 3, 3,
dimnames = list(paste('Treatment', 1:3),
c('Normal', paste('Treatment', 1:2)))),
Group2 = matrix(c(1,1,NA,1), 2, 2,
dimnames = list(paste('Treatment', c(2,4)),
c('Normal','Treatment 2'))))
# $Group1
# Normal Treatment 1 Treatment 2
# Treatment 1 1 NA NA
# Treatment 2 1 1 NA
# Treatment 3 1 1 1
#
# $Group2
# Normal Treatment 2
# Treatment 2 1 NA
# Treatment 4 1 1
And you can use it like this. Note that when you don't supply factors, the function will get all the row and column names from your list of matrices
fix_rc(l)
# $Group1
# Normal Treatment 1 Treatment 2
# Treatment 1 1 NA NA
# Treatment 2 1 1 NA
# Treatment 3 1 1 1
# Treatment 4 NA NA NA
#
# $Group2
# Normal Treatment 1 Treatment 2
# Treatment 1 NA NA NA
# Treatment 2 1 NA NA
# Treatment 3 NA NA NA
# Treatment 4 1 NA 1
I'm not sure where treatment 3 in the columns in your desired output came from, but you can get that here if you want like so
fix_rc(l, factors = list(paste('Treatment', 1:6),
c('Normal', paste('Treatment', 1:3))))
# $Group1
# Normal Treatment 1 Treatment 2 Treatment 3
# Treatment 1 1 NA NA NA
# Treatment 2 1 1 NA NA
# Treatment 3 1 1 1 NA
# Treatment 4 NA NA NA NA
# Treatment 5 NA NA NA NA
# Treatment 6 NA NA NA NA
#
# $Group2
# Normal Treatment 1 Treatment 2 Treatment 3
# Treatment 1 NA NA NA NA
# Treatment 2 1 NA NA NA
# Treatment 3 NA NA NA NA
# Treatment 4 1 NA 1 NA
# Treatment 5 NA NA NA NA
# Treatment 6 NA NA NA NA

Answer 2

Not a complete solution, but if you used data frames: wouldn't it be easier to get there?
df1 <- data.frame(normal=c(1,1,1)
, treatment1=c(NA, 1,1)
, treatment2=c(NA,NA,1)
, row.names=c("Treatment1", "Treatment2", "Treatment3")
)
df2 <- data.frame(normal=c(1,1)
, treatment2=c(NA,1)
, row.names=c("Treatment2", "Treatment4")
)
df1$names <- rownames(df1)
df2$names <- rownames(df2)
df3 <- merge(df1,df2, by="names", all=TRUE)
df3
names normal.x treatment1 treatment2.x normal.y treatment2.y
1 Treatment1 1 NA NA NA NA
2 Treatment2 1 1 NA 1 NA
3 Treatment3 1 1 1 NA NA
4 Treatment4 NA NA NA 1 1
Now all you have to do is combine columns based on their names