Another approach:
test1 <- data.frame(rbind(c(0.1,0.2),0.3,0.1))
rownames(test1) = c('y1','y2','y3')
colnames(test1) = c('x1','x2');
test2 <- data.frame(rbind(c(0.8,0.9,0.5),c(0.5,0.1,0.6)))
rownames(test2) = c('y2','y5')
colnames(test2) = c('x1','x3','x2')
> test1
x1 x2
y1 0.1 0.2
y2 0.3 0.3
y3 0.1 0.1
> test2
x1 x3 x2
y2 0.8 0.9 0.5
y5 0.5 0.1 0.6
t1.r <- rownames(test1)
t2.r <- rownames(test2)
t1.c <- colnames(test1)
t2.c <- colnames(test2)
col <- unique(union(t1.c, t2.c))
ncol <- length(col)
row <- unique(union(t1.r, t2.r))
nrow <- length(row)
m <- matrix(list(), nrow=nrow, ncol=ncol)
rownames(m) <- row
colnames(m) <- col
for (i in 1:nrow) {
for (j in 1:ncol) {
rowname <- row[i]
colname <- col[j]
v <- c()
if (!is.null(test1[rowname, colname]) && !is.na(test1[rowname, colname])) {
v <- c(test1[rowname, colname])
}
if (!is.null(test2[rowname, colname]) && !is.na(test2[rowname, colname])) {
v <- c(v, test2[rowname, colname])
}
if (!is.null(v)) {
m[rowname, colname] <- list(v)
} else {
m[rowname, colname] <- NA
}
}
}
> m
x1 x2 x3
y1 0.1 0.2 NA
y2 Numeric,2 Numeric,2 0.9
y3 0.1 0.1 NA
y5 0.5 0.6 0.1
> m["y2",]
$x1
[1] 0.3 0.8
$x2
[1] 0.3 0.5
$x3
[1] 0.9
--
Giorgio Garziano
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