你可以通过以下方法计算窗口宽度为k的向量x的移动平均值:

apply(embed(x, k), 1, mean)

动物园包中的滚动平均值/最大值/中位数(rollmean) TTR中的移动平均线 马云在预测

您可以使用RcppRoll来实现用c++编写的快速移动平均线。只需调用roll_mean函数。文档可以在这里找到。

否则，这个(较慢的)for循环应该可以做到:

ma <- function(arr, n=15){
  res = arr
  for(i in n:length(arr)){
    res[i] = mean(arr[(i-n):i])
  }
  res
}

下面是一个简单的带有过滤器的函数，演示了一种方法来处理带有填充的开始和结束NAs，并使用自定义权重计算加权平均值(由过滤器支持):

wma <- function(x) { 
  wts <- c(seq(0.5, 4, 0.5), seq(3.5, 0.5, -0.5))
  nside <- (length(wts)-1)/2
  # pad x with begin and end values for filter to avoid NAs
  xp <- c(rep(first(x), nside), x, rep(last(x), nside)) 
  z <- stats::filter(xp, wts/sum(wts), sides = 2) %>% as.vector 
  z[(nside+1):(nside+length(x))]
}

为了配合坎迪奇西斯和罗德里戈·雷麦黛奥的回答;

moving_fun <- function(x, w, FUN, ...) {
  # x: a double vector
  # w: the length of the window, i.e., the section of the vector selected to apply FUN
  # FUN: a function that takes a vector and return a summarize value, e.g., mean, sum, etc.
  # Given a double type vector apply a FUN over a moving window from left to the right, 
  #    when a window boundary is not a legal section, i.e. lower_bound and i (upper bound) 
  #    are not contained in the length of the vector, return a NA_real_
  if (w < 1) {
    stop("The length of the window 'w' must be greater than 0")
  }
  output <- x
  for (i in 1:length(x)) {
     # plus 1 because the index is inclusive with the upper_bound 'i'
    lower_bound <- i - w + 1
    if (lower_bound < 1) {
      output[i] <- NA_real_
    } else {
      output[i] <- FUN(x[lower_bound:i, ...])
    }
  }
  output
}

# example
v <- seq(1:10)

# compute a MA(2)
moving_fun(v, 2, mean)

# compute moving sum of two periods
moving_fun(v, 2, sum)

下面的示例代码展示了如何使用zoo包中的rollmean函数计算居中移动平均和尾随移动平均。

library(tidyverse)
library(zoo)

some_data = tibble(day = 1:10)
# cma = centered moving average
# tma = trailing moving average
some_data = some_data %>%
    mutate(cma = rollmean(day, k = 3, fill = NA)) %>%
    mutate(tma = rollmean(day, k = 3, fill = NA, align = "right"))
some_data
#> # A tibble: 10 x 3
#>      day   cma   tma
#>    <int> <dbl> <dbl>
#>  1     1    NA    NA
#>  2     2     2    NA
#>  3     3     3     2
#>  4     4     4     3
#>  5     5     5     4
#>  6     6     6     5
#>  7     7     7     6
#>  8     8     8     7
#>  9     9     9     8
#> 10    10    NA     9