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n <- 5
a <- matrix(c(1:n**2),nrow = n, byrow = T)
output is
[,1] [,2] [,3] [,4] [,5]
[1,] 1 2 3 4 5
[2,] 6 7 8 9 10
[3,] 11 12 13 14 15
[4,] 16 17 18 19 20
[5,] 21 22 23 24 25
how do I shift the '1' to the current position of '25' to look like this:
[,1] [,2] [,3] [,4] [,5]
[1,] 2 3 4 5 6
[2,] 7 8 9 10 11
[3,] 12 13 14 15 16
[4,] 17 18 19 20 21
[5,] 22 23 24 25 1
a <- t(a); a[] <- c(a[-1], a[1]); a <- t(a)
a
# [,1] [,2] [,3] [,4] [,5]
# [1,] 2 3 4 5 6
# [2,] 7 8 9 10 11
# [3,] 12 13 14 15 16
# [4,] 17 18 19 20 21
# [5,] 22 23 24 25 1
c(a) unwinds or unlists the matrix into a vector. It does this column-first, so c(a) results in [1] 1 6 11 16 21 2 .... We want it to be row-first, though, so
t(a) transposes it, so that what was a row-first is now column-first, allowing c(a) and such to work.
c(a[-1], a[1]) is just "concatenate all except the first with the first", the classic way to put the first element of a vector at the end.
a[] <- is a way to do calcs on its values where the calcs do not preserve the "dimensionality" of the object.
After we've rearranged, we then transpose back to the original shape and row/column-order.
Here is a base R one-liner
> t(`dim<-`(t(a)[seq_along(a)%%length(a)+1],rev(dim(a))))
[,1] [,2] [,3] [,4] [,5]
[1,] 2 3 4 5 6
[2,] 7 8 9 10 11
[3,] 12 13 14 15 16
[4,] 17 18 19 20 21
[5,] 22 23 24 25 1
"I have a very long matrix, measuring 30^5 x 3 entries. I basically consists of subblocks of 10.000 30 x 3 matrices, stacked on top of one another. I want to afficiently "cbind" them, next to one another (without looping constructs), leading to a 30 x 30^4 matrix.
Just changing the matrix dimensions does not work, as R fills the new matrix per individual column.
I'm sure there is a very compact, superefficient way of doing this, and I'll slap myself on the forehead as soon as you fill me in on the obvious solution.
Thanks!"
"Just changing the matrix dimensions does not work, as R fills the new matrix per individual column."
```R
test <- matrix(c(1:18), 6, 3, byrow = FALSE)
>test
[,1] [,2] [,3]
[1,] 1 7 13
[2,] 2 8 14
[3,] 3 9 15
[4,] 4 10 16
[5,] 5 11 17
[6,] 6 12 18
dim(test) <- c(3,6)
>test
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 1 4 7 10 13 16
[2,] 2 5 8 11 14 17
[3,] 3 6 9 12 15 18
```
The output I'm looking for is:
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 1 7 13 4 10 16
[2,] 2 8 14 5 11 17
[3,] 3 9 15 6 12 18
We can create a grouping variable to split the sequence of rows, subset the matrix and then cbind
do.call(cbind, lapply(split(seq_len(nrow(test)),
as.integer(gl(nrow(test), 3, nrow(test)))), function(i) test[i,]))
# [,1] [,2] [,3] [,4] [,5] [,6]
#[1,] 1 7 13 4 10 16
#[2,] 2 8 14 5 11 17
#[3,] 3 9 15 6 12 18
I want to iterate the following matrix and print sets of 2 cell values. Is there a way to do this without a for-loop?
Input:
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 1 4 7 10 13 16
[2,] 2 5 8 11 14 17
[3,] 3 6 9 12 15 18
Expected Output:
[,1] [,2]
[1,] 1 4
[2,] 7 10
[3,] 13 16
[4,] 2 5
[5,] 8 11
[6,] 14 17
[7,] 3 6
[8,] 9 12
[9,] 15 18
This my code:
mat<-matrix(data=seq(1:18), nrow=3,ncol=6)
r <- rep(seq(1,3),each=2)
c1 <- seq(1,6,2)
c2 <- seq(2,6,2)
m <- mat[r,c(c1:c2)] # This does not work, it only output first two cells
We can get the transpose of the matrix, then convert back to matrix by specifying the ncol
matrix(t(mat), ncol=2, byrow=TRUE)
# [,1] [,2]
# [1,] 1 4
# [2,] 7 10
# [3,] 13 16
# [4,] 2 5
# [5,] 8 11
# [6,] 14 17
# [7,] 3 6
# [8,] 9 12
# [9,] 15 18
Imagine we have one matrix of 5*5 (25 elements)
m<-matrix(1:25,5,5)
> m
[,1] [,2] [,3] [,4] [,5]
[1,] 1 6 11 16 21
[2,] 2 7 12 17 22
[3,] 3 8 13 18 23
[4,] 4 9 14 19 24
[5,] 5 10 15 20 25
I want to produce large matrix with different dimensions from the matrix “m”
For example 5*8, now my second column of new matrix must have overlap with the first one and so on.
So bigger dimension (e.g 5*8) matrix would be like : (it is just example and not sure the amount of shift is correct)
[,1] [,2] [,3] [,4] ……………………[,8]
[1,] 1 4 7 10 …………………… 19
[2,] 2 5 8 11 …………………… 20
[3,] 3 6 9 12 …………………… 21
[4,] 4 7 10 13 …………………… 22
[5,] 5 8 11 14 …………………… 23
In fact in each column we have a shift back to some elements of last column in order to prevent from reaching the last element of original matrix and producing NA value.
Please anyone knows how to create such a larger matrix?
The hardest part for me is to calculate the amount of SHIFT value regarding to the size of larger matrix. The larger matrix must cover almost all elements of the original one. (it is ok to miss some last elements)
thanks
I'm not sure what you want, but this might be helpful.
rows <- 5
cols <- 8
overlap <- 1
matrix(rep(seq(1,cols)*(rows-overlap),each=rows)+seq(1,rows)-(rows-overlap),nrow=rows)
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
[1,] 1 5 9 13 17 21 25 29
[2,] 2 6 10 14 18 22 26 30
[3,] 3 7 11 15 19 23 27 31
[4,] 4 8 12 16 20 24 28 32
[5,] 5 9 13 17 21 25 29 33
overlap <- 2
matrix(rep(seq(1,cols)*(rows-overlap),each=rows)+seq(1,rows)-(rows-overlap),nrow=rows)
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
[1,] 1 4 7 10 13 16 19 22
[2,] 2 5 8 11 14 17 20 23
[3,] 3 6 9 12 15 18 21 24
[4,] 4 7 10 13 16 19 22 25
[5,] 5 8 11 14 17 20 23 26
overlap <- 3
matrix(rep(seq(1,cols)*(rows-overlap),each=rows)+seq(1,rows)-(rows-overlap),nrow=rows)
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
[1,] 1 3 5 7 9 11 13 15
[2,] 2 4 6 8 10 12 14 16
[3,] 3 5 7 9 11 13 15 17
[4,] 4 6 8 10 12 14 16 18
[5,] 5 7 9 11 13 15 17 19
the maximum efficiency of coverage is: (the best value of overlap)
> overlap<-ceiling(rows*(1-length(dna1)/(cols*rows)))+round(rows/cols)
Hi everyone who loves while hates R:
Let's say you want to turn matrix M
[,1] [,2] [,3]
[1,] 1 2 3
[2,] 4 5 6
[3,] 7 8 9
to N
[,1] [,2] [,3]
[1,] 3 2 1
[2,] 6 5 4
[3,] 9 8 7
All you need to do is
N<-M[,c(3:1)]
And N's structure is still a matrix
However, when you want to turn matrix M
[,1] [,2] [,3]
[1,] 1 2 3
to N
[,1] [,2] [,3]
[1,] 3 2 1
if you do
N<-M[,c(3:1)]
R will give you
N
[1] 3 2 1
N now is a vector! Not a matrix!
My solution is
N<-M%*%diag(3)[,c(3:1)]
which needs big space to store the identity matrix however.
Any better idea?
You're looking for this:
N<-M[,c(3:1),drop = FALSE]
Read ?Extract for more information. This is also a FAQ. This behavior is one of the most common debates folks have about the way things "should" be in R. My general impression is that many people agree that drop = FALSE might be a more sensible default, but that behavior is so old that changing it would be enormously disruptive to vast swaths of existing code.
A=t(matrix(1:25,5,5))
B=matrix(0,5,5)
for(i in 1:5){
B[i,(nrow(A)+1-i)]=1
}
A
# [,1] [,2] [,3] [,4] [,5]
# [1,] 1 2 3 4 5
# [2,] 6 7 8 9 10
# [3,] 11 12 13 14 15
# [4,] 16 17 18 19 20
# [5,] 21 22 23 24 25
A%*%B
# [,1] [,2] [,3] [,4] [,5]
# [1,] 5 4 3 2 1
# [2,] 10 9 8 7 6
# [3,] 15 14 13 12 11
# [4,] 20 19 18 17 16
# [5,] 25 24 23 22 21