Compute Mean for Specific Row Sequence in R-CodePudding

I have the following R code:

n = 10
t = 5
N = n * t

x <- rnorm(N)

I want to calculate the mean for every t observations. That is:

mean(x[1:5])
mean(x[6:10])
.
.
mean(x[46:50])

Similarly,

mean(x[c(1,11,21,31,41)])
mean(x[c(2,12,22,32,42)])
.
.
mean(x[c(10,20,30,40,50)])

How can I do that in a simple way?

Your help is appreciated.

CodePudding user response：

The idea is to go through all records with sapply, with each iteration with an interval of t.

sapply(seq(1, length(x), t), function(i) mean(x[i:(i   t - 1)]))

[1] -0.75555799 -0.17886981 -0.59832657  0.20791804  0.38302358
[6]  0.03449934  0.15240788 -0.17924312 -0.47540067 -0.01974705

For your second condition, we can do similar thing, but the seq would be in mean to better accommodate your need.

sapply(1:n, function(i) mean(x[c(i, (seq(n, N - n, n)   i))]))

[1] -0.251136073  0.641306302 -0.137445178 -0.775326110 -0.771252712
[6] -0.255764112 -0.005568864 -0.089227014  0.415172573 -0.200055188

Data

set.seed(12)

n = 10
t = 5
N = n * t

x <- rnorm(N)
x
 [1] -1.48056759  1.57716947 -0.95674448 -0.92000525 -1.99764210
 [6] -0.27229604 -0.31534871 -0.62825524 -0.10646388  0.42801480
[11] -0.77771958 -1.29388230 -0.77956651  0.01195176 -0.15241624
[16] -0.70346425  1.18887916  0.34051227  0.50696817 -0.29330515
[21]  0.22364142  2.00720146  1.01197912 -0.30245925 -1.02524484
[26] -0.26738483 -0.19910566  0.13112260  0.14579990  0.36206472
[31]  0.67398116  2.07203577 -0.54102865 -1.07049216 -0.37245673
[36] -0.48514135  0.27478418 -0.47951256  0.79810533 -1.00445120
[41]  0.10498423 -1.15599289  0.57813463 -1.59562566 -0.30850366
[46]  0.44946592 -0.97705328  0.18999786  0.73145336 -0.49259911

CodePudding user response：

Using dplyr, create a grouping variable, group by the variable, then find the mean of each group.

data.frame(x = rnorm(N), grp = rep(1:n, each = t)) %>%
    group_by(grp) %>%
    summarise(Mean = mean(x))