resizing data

Undoubtedly this question has been asked before, I just can't seem to find
the combination of search terms to produce it.  I'm trying to resize a
dataset that is pulled into R using read.table.  However, I think the same
problem can be produced using matrix:

x<-matrix(1:64,8)
x
#     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
#[1,]    1    9   17   25   33   41   49   57
#[2,]    2   10   18   26   34   42   50   58
#[3,]    3   11   19   27   35   43   51   59
#[4,]    4   12   20   28   36   44   52   60
#[5,]    5   13   21   29   37   45   53   61
#[6,]    6   14   22   30   38   46   54   62
#[7,]    7   15   23   31   39   47   55   63
#[8,]    8   16   24   32   40   48   56   64

The true order of data in the larger problem I'm working with is actually
transposed, like so:
x<-t(x)
x
#     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
#[1,]    1    2    3    4    5    6    7    8
#[2,]    9   10   11   12   13   14   15   16
#[3,]   17   18   19   20   21   22   23   24
#[4,]   25   26   27   28   29   30   31   32
#[5,]   33   34   35   36   37   38   39   40
#[6,]   41   42   43   44   45   46   47   48
#[7,]   49   50   51   52   53   54   55   56
#[8,]   57   58   59   60   61   62   63   64

I'm trying to resize the data (in this example, a matrix) to say a 16 x 4
matrix while preserving the consecutive order of the individual elements in
a left-to-right top-to-bottom fashion.  The example below is wrong because
the first row should be "1 2 3 4", how can I make this happen?  It would
also be nice to make a 4 x 16 matrix where the first row contains the values
of x[1,1:8] followed by x[2,1:8].  I'm guessing there is a 1 liner of R code
for this type of thing so I don't have to resort to nested for loops?

y<-matrix(x,nrow=16,ncol=4)
y
#      [,1] [,2] [,3] [,4]
# [1,]    1    3    5    7
# [2,]    9   11   13   15
# [3,]   17   19   21   23
# [4,]   25   27   29   31
# [5,]   33   35   37   39
# [6,]   41   43   45   47
# [7,]   49   51   53   55
# [8,]   57   59   61   63
# [9,]    2    4    6    8
#[10,]   10   12   14   16
#[11,]   18   20   22   24
#[12,]   26   28   30   32
#[13,]   34   36   38   40
#[14,]   42   44   46   48
#[15,]   50   52   54   56
#[16,]   58   60   62   64

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