Efficient use of vectors - r

Question

I am attempting to copy one vector to another using the following syntax:
data<-NULL
for( i in 1:nrow(line)){
data=append(data,line[i*4])
}
From what I have seen, the use of append in this way results in a lot of copying of data, which makes R very slow. What is the syntax for copying the 4th element of one array to another, given that the list you are copying from is of a given size?

Answer 1

Here are three methods with their benchmarks. You can see that preallocating the vector as in the method2 function is quite a bit faster, while the lapply method is middle, and your function is the slowest.
Of course, these are 1D vectors as opposed to arrays of n-D, but I would expected the benchmarks would be similar or even more pronounced.
method1 <- function(line) {
data<-NULL
for( i in 1:length(line)){
data=append(data,line[i])
}
}
method2 <- function(line) {
data <- vector(mode="numeric", length = length(line))
for (i in 1:length(line)) {
data[i] <- line[i]
}
}
library(microbenchmark)
r <- rnorm(1000)
microbenchmark(method2(r), unit="ms")
#> Unit: milliseconds
#> expr min lq mean median uq max neval
#> method2(r) 2.18085 2.279676 2.428731 2.371593 2.500495 5.24888 100
microbenchmark(lapply(r, function(x) { data<-append(data, x) }), unit="ms")
#> Unit: milliseconds
#> expr min lq
#> lapply(r, function(x) { data <- append(data, x) }) 3.014673 3.091299
#> mean median uq max neval
#> 3.287216 3.150052 3.260199 6.036501 100
microbenchmark(method1(r), unit="ms")
#> Unit: milliseconds
#> expr min lq mean median uq max neval
#> method1(r) 3.938684 3.978002 5.71831 4.020001 4.280521 98.58584 100
Didn't realize OP wanted only every fourth. Why not just use a data frame or data.table?
d <- data.frame(matrix(rnorm(1000), ncol=1))
microbenchmark(d2 <- d[seq(1,nrow(d), 4),])
#> Unit: microseconds
#> expr min lq mean median uq
#> d2 <- d[seq(1, nrow(d), 4), ] 64.846 65.9915 73.08007 67.225 73.8225
#> max neval
#> 220.438 100
library(data.table)
dt <- data.table(d)
microbenchmark(d2 <- dt[seq(1,nrow(d), 4),])
#> Unit: microseconds
#> expr min lq mean median uq
#> d2 <- dt[seq(1, nrow(d), 4), ] 298.163 315.2025 324.8793 320.554 330.416
#> max neval
#> 655.124 100

Answer 2

If you're trying to extract every fourth element from a vector, you could index using seq to grab the correct elements:
data <- letters[seq(4, length(letters), by=4)]
data
# [1] "d" "h" "l" "p" "t" "x"
Growing the vector one at a time as you show in your question will be slow because you will need to keep re-allocating your vector (see the second circle of The R Inferno for details). However, even pre-allocating your vector and constructing it with a for loop will be slow compared to constructing it in a single vectorized indexing operation.
To get a sense of the speed improvements, consider a comparison to the sort of method you've described, except using pre-allocation:
for.prealloc <- function(x) {
data <- vector(mode="numeric", length = floor(length(x)/4))
for (i in 1:floor(length(x)/4)) {
data[i] <- x[i*4]
}
data
}
josilber <- function(x) x[seq(4, length(x), by=4)]
r <- rnorm(10000)
all.equal(for.prealloc(r), josilber(r))
# [1] TRUE
library(microbenchmark)
microbenchmark(for.prealloc(r), josilber(r))
# Unit: microseconds
# expr min lq mean median uq max neval
# for.prealloc(r) 1846.014 2035.7890 2351.9681 2094.804 2244.56 5283.285 100
# josilber(r) 95.757 97.4125 125.9877 113.179 138.96 259.606 100
The approach I propose is 20x faster than using for and a pre-allocated vector (and it will be even faster than using append and a non-pre-allocated vector).