I have a 1:50 vector and I need to make a moving sum (equal to the moving average), ie in the case of the last 5 observations, the new vector would be c(sum(1:5), sum(2:6), sum(3:7), ..., sum(45:49), sum(46:50)) . The aggregate function has aggregate(presidents, nfrequency = 1, FUN = weighted.mean, w = c(1, 1, 0.5, 1)) that was the closest I got to the solution without using a for
How to make mobile sum in R?
6
asked by anonymous 17.12.2018 / 15:51
2 answers
7
I know two good packages to do this. The zoo (as Rui quoted in the comment) and RcppRoll .
> zoo::rollsum(1:20, k = 5)
[1] 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90
> RcppRoll::roll_sum(1:20, n = 5)
[1] 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90
In terms of performance, RcppRoll is much faster:
> bench::mark(
+ zoo::rollsum(1:50, k = 5),
+ RcppRoll::roll_sum(1:50, n = 5)
+ )
# A tibble: 2 x 14
expression min mean median max 'itr/sec' mem_alloc n_gc n_itr total_time result memory time gc
<chr> <bch:t> <bch:tm> <bch:t> <bch:> <dbl> <bch:byt> <dbl> <int> <bch:tm> <list> <list> <lis> <lis>
1 zoo::roll… 909.4µs 3.45ms 1.71ms 40.3ms 290. 18.91KB 0 155 535ms <int … <Rpro… <bch… <tib…
2 RcppRoll:… 40.5µs 150.75µs 89.49µs 14.6ms 6634. 3.34KB 0 3316 500ms <dbl … <Rpro… <bch… <tib…
17.12.2018 / 16:49
5
There are a few ways you can calculate the mobile sum in the r :
R-base
diff(c(0, cumsum(1:10)), 5)
# 15 20 25 30 35 40
This proposal can be generalized as a function:
soma_movel <- function(x, n) {
diff(c(0, cumsum(x)), n)
}
Zoo
The zoo package, as raised in the comments, has a function for this, but it does not perform very well
zoo::rollsum(1:10, 5)
# 15 20 25 30 35 40
Comparison
set.seed(123)
vetor <- rnorm(1e5) # 100 mil números
# As funções retornam valores iguais?
all.equal(zoo::rollsum(vetor, 5), soma_movel(vetor, 5))
# [1] TRUE
zoo , the solution with base still loses to the solution with RcppRoll presented by Daniel in 5 times.
microbenchmark::microbenchmark(
zoo = zoo::rollsum(vetor, 5),
base = soma_movel(vetor, 5),
cpp = RcppRoll::roll_sum(vetor, n = 5),
times = 30
)
Unit: microseconds
expr min lq mean median uq max neval cld
zoo 200659.545 204218.475 208418.887 206276.601 209928.673 255552.267 30 b
base 2229.273 2536.157 3379.694 2633.918 2755.286 7725.985 30 a
cpp 452.116 514.725 6966.097 558.089 577.333 188068.577 30 a
17.12.2018 / 17:03