I set x=1:2,y=1:2，and I would like to display all x+y outcomes 2 3 4. But it just prints 2 and 4.
x<-0
for(y in 1:2){
x<-x+1
print(y+x)
}
# [1] 2
# [1] 4
If you want all combinations, you can do this with outer instead of an explicit loop:
x <- 1:2
y <- 1:2
outer(x, y, FUN='+')
## [,1] [,2]
## [1,] 2 3
## [2,] 3 4
You can then reduce this matrix to a vector with c and use unique to get unique entries:
unique(c(outer(x, y, FUN='+')))
## [1] 2 3 4
You can use expand.grid to get all combinations of x and y
dat <- expand.grid(x=x, y=y)
dat
x y
1 1 1
2 2 1
3 1 2
4 2 2
And then calculate the sums with rowSums
rowSums(dat)
[1] 2 3 3 4
Or the unique rowSums
unique(rowSums(dat))
[1] 2 3 4
If you need all the combinations then use,
i<-0
abc <- array()
for(x in 1:2){
for(y in 1:2){
i <- i + 1
abc[i] <- y+x
}
}
If you need only unique combinatinos,
unique(abc)
Related
I'm very new to R. I have two matrices of different dimensions, C (3 rows, 79 columns) and T(3 rows, 215 columns). I want my code to calculate the Spearman correlation between the first column of C and all the columns of T and return the maximum correlation with the indexes and of the columns. Then, the second column of C and all the columns of T and so on. In fact, I want to find the columns between two matrices which are most correlated. Hope it was clear.
What I did was a nested for loop, but the result is not what I search.
for (i in 1:79){
for(j in 1:215){
print(max(cor(C[,i],T[,j],method = c("spearman"))))
}
}
You don't have to loop over the columns.
x <- cor(C,T,method = c("spearman"))
out <- data.frame(MaxCorr = apply(x,1,max), T_ColIndex=apply(x,1,which.max),C_ColIndex=1:nrow(x))
head(out)
gives,
MaxCorr T_ColIndex C_ColIndex
1 1 8 1
2 1 1 2
3 1 2 3
4 1 1 4
5 1 11 5
6 1 4 6
Fake Data:
C <- matrix(rnorm(3*79),nrow=3)
T <- matrix(rnorm(3*215),nrow=3)
Maybe something like the function below can solve the problem.
pairwise_cor <- function(x, y, method = "spearman"){
ix <- seq_len(ncol(x))
iy <- seq_len(ncol(y))
t(sapply(ix, function(i){
m <- sapply(iy, function(j) cor(x[,i], y[,j], method = method))
setNames(c(i, which.max(m), max(m)), c("col_x", "col_y", "max"))
}))
}
set.seed(2021)
C <- matrix(rnorm(3*5), nrow=3)
T <- matrix(rnorm(3*7), nrow=3)
pairwise_cor(C, T)
# col_x col_y max
#[1,] 1 1 1.0
#[2,] 2 2 1.0
#[3,] 3 2 1.0
#[4,] 4 3 0.5
#[5,] 5 5 1.0
I'm looking to iterate each value in the vector by 1 until a set value is reached and saving each iteration in a vector, and further iterations do not include values past the set value. So for instance say the set value is 3. Consider this vector, A <- c(1,1,2). Then the desired outcome should be:
Outcome:
1 1 2
2 2 3
3 3
Then I want to store each line in a vector so I can plot a histogram
so with each vector outcome including the original vector.
hist(c(1,1,2))
hist(c(2,2,3))
hist(c(3,3))
Potential code:
for (i in 1:length(A)) {
A[i] <- A + 1
}
# given values
A <- c(1, 1, 2)
value <- 3
# incrementations
out_lst <- lapply(A, function(x) x : 3)
# [[1]]
# [1] 1 2 3
#
# [[2]]
# [1] 1 2 3
#
# [[3]]
# [1] 2 3
# histograms
hist_lst <- list()
max_len <- max(sapply(out_lst, function(x) length(x)))
for(l in 1:max_len) {
hist_lst[[l]] <- sapply(out_lst, function(x) x[l])
}
hist_lst
# [[1]]
# [1] 1 1 2
#
# [[2]]
# [1] 2 2 3
#
# [[3]]
# [1] 3 3 NA
par(mfrow = c(1, length(hist_lst)))
invisible(lapply(hist_lst, hist))
You can use a while loop:
funfun=function(vec,max){
y=list()
i=1
while(length(vec)!=0){
y[[i]]=vec
vec=vec+1
vec=`attributes<-`(na.omit(replace(vec,vec>max,NA)),NULL)
i=i+1
}
y
}
funfun(c(1,1,2),3)
[[1]]
[1] 1 1 2
[[2]]
[1] 2 2 3
[[3]]
[1] 3 3
you can now do
sapply(funfun(c(1,1,2),3),hist)
I have a list and a Matrix as per below:
List Y:
$`1`
V1 V2
1 1 1
2 1 2
3 2 1
4 2 2
$`2`
V1 V2
5 5 5
6 11 2
$`3`
V1 V2
7 10 1
8 10 2
9 11 1
10 5 6
Matrix Z:
[,1][,2][,3][,4][,5][,6]
[1,] 2 1 5 5 10 1
I consider below as points1, points2 and points3 in Matrix Z respectively
points1 -(2,1)
[,1][,2]
[1,] 2 1
points2 - (5,5)
[,3][,4]
[1,] 5 5
points3 - (10,1)
[,5][,5]
[1,] 10 1
I want to calculate the sum of distances between all points in list Y[[1]] and points1, all points in List Y[[2]] and points2 and all points in List Y[[3]] and points 3 in r. How can I do this?
rowsums(|y-z|^2)
Based on the description,
Map(function(y, z) rowSums(abs(y - z[col(y)])^2),
Y, split(Z, as.numeric(gl(ncol(Z), 2, ncol(Z)))))
Try the following. It uses Map to apply a function to every vector of the two lists passed to Map. Note that we cannot simply do
Map('-', Y, Z2)
because R would do the subtractions columnwise, not row by row.
f <- function(x, y){
for(i in seq_len(nrow(x)))
x[i, ] <- x[i, ] - y
x
}
Z2 <- split(Z, rep(1:3, each = 2))
Map(f, Y, Z2)
I have a list with same structure for every member as the following
config <- NULL
config[["secA"]] <- NULL
config[["secA"]]$VAL <- 0
config[["secA"]]$ARR <- c(1,2,3,4,5)
config[["secA"]]$DF <- data.frame(matrix(c(1,5,3,8),2,2))
config[["secB"]] <- NULL
config[["secB"]]$VAL <- 1
config[["secB"]]$ARR <- c(1,3,2,4,9)
config[["secB"]]$DF <- data.frame(matrix(c(2,6,1,9),2,2))
config[["secC"]] <- NULL
config[["secC"]]$VAL <- 5
config[["secC"]]$ARR <- c(4,2,1,5,8)
config[["secC"]]$DF <- data.frame(matrix(c(4,2,1,7),2,2))
and I need to obtain 3 vectors VAL, ARR and DF, each with the concatenated elements of the corresponding member. such as
# VAL: 0,1,5
# ARR: 1,2,3,4,5,1,3,2,4,9,4,2,1,5,8
# DF: 1,5,3,8,2,6,1,9,4,2,1,7
Looking at similar situations, I have the feeling I need to use a combination of do.call and cbind or lapply but I have no clue. any suggestions?
config <- NULL
config[["secA"]] <- NULL
config[["secA"]]$VAL <- 0
config[["secA"]]$ARR <- c(1,2,3,4,5)
config[["secA"]]$DF <- data.frame(matrix(c(1,5,3,8),2,2))
config[["secB"]] <- NULL
config[["secB"]]$VAL <- 1
config[["secB"]]$ARR <- c(1,3,2,4,9)
config[["secB"]]$DF <- data.frame(matrix(c(2,6,1,9),2,2))
config[["secC"]] <- NULL
config[["secC"]]$VAL <- 5
config[["secC"]]$ARR <- c(4,2,1,5,8)
config[["secC"]]$DF <- data.frame(matrix(c(4,2,1,7),2,2))
sapply(names(config[[1]]), function(x)
unname(unlist(sapply(config, `[`, x))), USE.NAMES = TRUE)
# $VAL
# [1] 0 1 5
#
# $ARR
# [1] 1 2 3 4 5 1 3 2 4 9 4 2 1 5 8
#
# $DF
# [1] 1 5 3 8 2 6 1 9 4 2 1 7
Or you can use this clist function
Unfortunately there were no other answers.
(l <- Reduce(clist, config))
# $VAL
# [1] 0 1 5
#
# $ARR
# [1] 1 2 3 4 5 1 3 2 4 9 4 2 1 5 8
#
# $DF
# X1 X2 X1 X2 X1 X2
# 1 1 3 2 1 4 1
# 2 5 8 6 9 2 7
It merges data frames and matrices, so you need to unlist to get the vector you want
l$DF <- unname(unlist(l$DF))
l
# $VAL
# [1] 0 1 5
#
# $ARR
# [1] 1 2 3 4 5 1 3 2 4 9 4 2 1 5 8
#
# $DF
# [1] 1 5 3 8 2 6 1 9 4 2 1 7
Function
clist <- function (x, y) {
islist <- function(x) inherits(x, 'list')
'%||%' <- function(a, b) if (!is.null(a)) a else b
get_fun <- function(x, y)
switch(class(x %||% y),
matrix = cbind,
data.frame = function(x, y)
do.call('cbind.data.frame', Filter(Negate(is.null), list(x, y))),
factor = function(...) unlist(list(...)), c)
stopifnot(islist(x), islist(y))
nn <- names(rapply(c(x, y), names, how = 'list'))
if (is.null(nn) || any(!nzchar(nn)))
stop('All non-NULL list elements should have unique names', domain = NA)
nn <- unique(c(names(x), names(y)))
z <- setNames(vector('list', length(nn)), nn)
for (ii in nn)
z[[ii]] <- if (islist(x[[ii]]) && islist(y[[ii]]))
Recall(x[[ii]], y[[ii]]) else
(get_fun(x[[ii]], y[[ii]]))(x[[ii]], y[[ii]])
z
}
Another approach, with slightly less code.
un_config <- unlist(config)
un_configNAM <- names(un_config)
vecNAM <- c("VAL", "ARR", "DF")
for(n in vecNAM){
assign(n, un_config[grepl(n, un_configNAM)])
}
This will return 3 vectors as the OP requested. However, generally it is more advantageous to store results in a list as rawr suggests. You of course can adopt the above code so that results are stored within a list.
l <- rep(list(NA), length(vecNAM))
i = 1
for(n in vecNAM){
l[[i]] <- un_config[grepl(n, un_configNAM)]
i = i +1
}
I want to make such a loop using R.
for i=1 output will be
1
2
3
for i=2 output will be
2
3
1
for i=3 output will be
3
1
2
Namely the outputs are successive integers. It is just when the integer reaches 4 it returns 1 and goes on. I guess I must use modular operations, how can I do that?
If you have
a <- 1:3
for a value if i, you get the the sequence with
f <- function(i) (a+i+1) %% length(a) +1
f(1)
# [1] 1 2 3
f(2)
# [1] 2 3 1
f(3)
# [1] 3 1 2
f(4)
# [1] 1 2 3
Note that it starts over again at 4
This is my solution:
f <- function(i) { x <- i:(i+2) %% 3; x[x==0] <- 3; x }
for (i in 1:5) print(f(i))
Here is a second solution:
r <- matrix(c(3,1,2, 1,2,3, 2,3,1),3)
for (i in 1:5) print(r[i %% 3 + 1,])