I need to create a list of sequences that always goes back to the first digit in the sequence. I've written the code below but it seems clunky. Is there a solution that uses fewer characters?
(i = seq(1, 24, by = 3))
#> [1] 1 4 7 10 13 16 19 22
(i_list = purrr::map(i, ~c(.:(. + 2), .)))
#> [[1]]
#> [1] 1 2 3 1
#>
#> [[2]]
#> ...
Edit: here's a way with lapply(). Not sure why this is getting downvotes, any advice on how to improve the question welcome!
(i_list = lapply(i, function(x) c(x:(x+2), x)))
I was wondering if there's a way with replicate() so have added that tag.
In matrix, rather than list form, theres:
cbind(matrix(1:24, ncol=3,byrow=TRUE),seq(1, 24, by = 3))
[,1] [,2] [,3] [,4]
[1,] 1 2 3 1
[2,] 4 5 6 4
[3,] 7 8 9 7
[4,] 10 11 12 10
[5,] 13 14 15 13
[6,] 16 17 18 16
[7,] 19 20 21 19
[8,] 22 23 24 22
and then you'd iterate over rows of the matrix instead of elements of the list.
Or if you are into code golf:
> seq(1,24,by=3) + t(matrix(c(0,1,2,0),ncol=8,nrow=4))
[,1] [,2] [,3] [,4]
[1,] 1 2 3 1
[2,] 4 5 6 4
[3,] 7 8 9 7
[4,] 10 11 12 10
...
but then how much work do you put into constructing the RHS of the + in this case? How is your question parameterised?
This depends on i having a regular pattern (with some adjustment for step size), it doesn't work for arbitrary i sequences.
Related
I need to create a function, that will rearrange any square matrix based on the values in the matrix.
So if I have matrix like this:
M <- matrix(1:16, ncol = 4)
M
#> [,1] [,2] [,3] [,4]
#> [1,] 1 5 9 13
#> [2,] 2 6 10 14
#> [3,] 3 7 11 15
#> [4,] 4 8 12 16
After rearrangement it needs to look like this:
[,1] [,2] [,3] [,4]
[1,] 1 3 6 10
[2,] 2 5 9 13
[3,] 4 8 12 15
[4,] 7 11 14 16
So it is sorted from lowest (left upper corner) to highest (right lower corner), but the numbers are sorted on diagonal (is that the right word?) not in rows or columns.
I know how to to this "manually", but I can't figure out any rules that this rearrangement operates by.
1) row(m) + col(m) is constant along reverse diagonals so:
M <- replace(m, order(row(m) + col(m)), m)
gving:
> M
[,1] [,2] [,3] [,4]
[1,] 1 3 6 10
[2,] 2 5 9 13
[3,] 4 8 12 15
[4,] 7 11 14 16
It is not clear whether sorted on the diagonal means just that they are unravelled from the storage order onto the reverse diagonals or that they are actually sorted after that within each reverse diagonal. In the example in the question the two interpretations give the same answer; however, if you did wish to sort the result within reverse diagonal afterwards using different data then apply this:
ave(M, row(M) + col(M), FUN = sort)
2) A longer version:
M2 <- matrix(m[order(unlist(tapply(seq_along(m), row(m) + col(m), c)))], nrow(m))
Here's a function columns_to_diagonals in base R that ought to do what you're after. It uses split and unsplit with the appropriate factors.
columns_to_diagonals <- function(M) {
n <- ncol(M)
f <- matrix(rep(1:(2*n-1), c(1:n, (n-1):1)), ncol = n)
m <- split(M, f)
d <- row(M) + col(M)
matrix(unsplit(m, d), ncol = n)
}
First, we may test this on your original case:
M <- matrix(1:16, ncol = 4)
columns_to_diagonals(M)
#> [,1] [,2] [,3] [,4]
#> [1,] 1 3 6 10
#> [2,] 2 5 9 13
#> [3,] 4 8 12 15
#> [4,] 7 11 14 16
And then a larger, randomly permutated matrix, to check that this looks fine as well:
M <- matrix(sample(1:25), ncol = 5)
M
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 4 15 12 10 21
#> [2,] 19 7 5 23 6
#> [3,] 9 17 2 8 1
#> [4,] 3 11 16 25 14
#> [5,] 22 18 20 13 24
columns_to_diagonals(M)
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 4 9 15 18 20
#> [2,] 19 22 11 16 25
#> [3,] 3 17 2 8 6
#> [4,] 7 5 23 21 14
#> [5,] 12 10 13 1 24
Created on 2019-12-15 by the reprex package (v0.2.1)
Assume I have to generate 1000 Sample Pairs (Y1,Y2) (from a Normal Distribution with replacement). Each of the pairs should have 20 observations.
y1 <- rep(sample(c(1:10),10, replace = TRUE))
y2 <- rep(sample(c(1:10),10, replace = TRUE))
How would I now generate 1000 of these pairs, so that they are easy to access for further computations.
I had the idea of looping them a 1000 times and saving them in a dataframe, but this may get chaotic.
Is there a simpler/nicer way to do this? A package or a function that I am missing?
Help would be appreciated!
One way is to use replicate, i.e.
replicate(5, rep(sample(c(1:10), 10, replace = TRUE)))
# [,1] [,2] [,3] [,4] [,5]
# [1,] 3 9 2 4 5
# [2,] 4 1 10 8 1
# [3,] 5 6 1 3 7
# [4,] 1 9 9 6 5
# [5,] 5 3 4 7 9
# [6,] 4 5 4 4 5
# [7,] 2 10 9 4 9
# [8,] 3 1 10 5 3
# [9,] 7 3 10 9 10
#[10,] 10 3 10 10 1
I am trying to randomize a matrix such that each of the rows in each column are randomized individually so that in the final matrix there is no association between columns. I know that I need to use the sample() function and some sort of for(each column) loop, but I'm not exactly sure of how to go about doing it. Specifically, I am asking how to write a function that will loop through the columns of a matrix and randomize the rows of each column.
Edit: An example of what I'm trying to achieve
Original matrix:
X1 X2 X3
1 4 3 6
2 7 2 4
3 9 5 1
Sample desired output:
X1 X2 X3
1 7 3 1
2 4 5 6
3 9 2 4
As you can see, the rows in each column have been randomized separately.
If you have a matrix X, you can use apply() (ideal for matrix)
apply(X, 2, sample)
Example:
X <- matrix(1:25, 5)
# [,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
Apply the code above gives:
# [,1] [,2] [,3] [,4] [,5]
# [1,] 3 10 11 16 21
# [2,] 5 8 12 20 22
# [3,] 4 9 14 18 24
# [4,] 2 6 15 19 25
# [5,] 1 7 13 17 23
I did not set random seed via set.seed(), so you will get different result when you run it. But all you need to know is that: the result is random.
If you have a data frame X, you'd better use sapply()
sapply(X, sample)
You could use a for loop for each column.
Or you could use:
apply(x, 2, function(col) sample(col, replace=F))
I have several data objects (three-dimensional arrays), for example object1, object2, object3.
These objects are listed in a character vector:
char <- c("object1","object2","object3")
I then need to grab the list of objects for use in a function that requires its inputs to be separated by commas:
output <- FancyFunction(object1,object2,object3,OtherFancyStuff)
How can I get R to automatically change the lower line of code when I change the upper line of code? I want to be able to add or delete objects from "char" and have it change accordingly in "output".
I don't know how to do this. The best I can do is:
output <- FancyFunction(get(char[1]),get(char[2]),get(char[3]),OtherFancyStuff)
But this is useless if the size of char changes.
Any thoughts?
I suppose something like this will work:
char <- c("object1","object2","object3")
## 2-d objects for illustration as I couldn't think of an array
## function that took arbitrary input vectors `...`
object1 <- object2 <- object3 <- array(1:25, dim = c(5,5))
do.call("rbind", mget(char))
which gives
> do.call("rbind", mget(char))
[,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
[6,] 1 6 11 16 21
[7,] 2 7 12 17 22
[8,] 3 8 13 18 23
[9,] 4 9 14 19 24
[10,] 5 10 15 20 25
[11,] 1 6 11 16 21
[12,] 2 7 12 17 22
[13,] 3 8 13 18 23
[14,] 4 9 14 19 24
[15,] 5 10 15 20 25
To pass in extra arguments, you need to modify this slightly, by concatenating the other arguments on the object returned by mget(char), for example:
c(mget(char), list(arg1 = "foo", arg2 = "bar", arg3 = 1:10))
> c(mget(char), list(arg1 = "foo", arg2 = "bar", arg3 = 1:10))
$object1
[,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
$object2
[,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
$object3
[,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
$arg1
[1] "foo"
$arg2
[1] "bar"
$arg3
[1] 1 2 3 4 5 6 7 8 9 10
where arg1 etc correspond to additional arguments needed by FancyFunction and need to be named as per the formal arguments you wish these to map to in the definition of FancyFunction. The call would then resemble:
OtherFancyStuff <- list(arg1 = "foo", arg2 = "bar", arg3 = 1:10)
do.call("FancyFunction", c(mget(char), OtherFancyStuff)
Does FancyFunction need 3 arguments or is it's first argument ...? If you have control over this, it might be worth rewriting FancyFunction to take ... as the first argument and extract out of it the various arguments, any number of which can now be supplied:
FancyFunction <- function(..., other) {
dots <- list(...)
## work on components of 'dots', the length of which is number of args
## provided as ...
....
return()
}
The function do.call let's you call a function by passing a list of parameter values. In this case, you just need to create a list of the objects you want to pass and any additional parameters.
Since you already have the names of the objects you want to include in character vector form, the function mget() will return a list of objects with those names so that part is easy. You just need to add in your additional parameters.
So you can call your function with
do.call( FancyFunction, c(mget(char),OtherFancyStuff) )
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