带有 data.table 的滚动加权平均值

Question

我想为 data.table 计算每组的滚动加权平均值，如下所示:

DT <- data.table(group = rep(c(1,2), each = 5), value = 1:10, weight = 11:20)
   group value weight
 1:     1     1     11
 2:     1     2     12
 3:     1     3     13
 4:     1     4     14
 5:     1     5     15
 6:     2     6     16
 7:     2     7     17
 8:     2     8     18
 9:     2     9     19
10:     2    10     20

我在这个问题 Rolling over function with 2 vector arguments 中使用 runner 包找到了一个可行的解决方案:

my_weighted_mean <- function(data) {
  weighted.mean(data[, 1], w = data[, 2])
}

DT[, weighted_mean := runner::runner(x = .SD, f = my_weighted_mean , k = 3, na_pad = TRUE), .SDcols = c("value", "weight"), by = list(group)]

但是代码很慢。

我想它应该与 frollapply 一起使用，但下面的不行，因为我不明白如何将 frollapply 与两列函数一起使用:

 DT[, weighted_mean := frollapply(value, FUN = weighted.mean, n = 3, w = weights), by = list(group)]

寻找更好的性能(以及没有运行器的解决方案)

Answer 1

“frollapply with a two column function”:不是在值上滚动，而是在索引上滚动，内部函数可以根据需要使用尽可能多的列。

 DT[, weighted_mean := frollapply(seq_len(.N),
                                  FUN = function(ind) weighted.mean(value[ind], weight[ind]),
                                  n = 3),
    by = .(group)]
#     group value weight weighted_mean
#     <num> <int>  <int>         <num>
#  1:     1     1     11            NA
#  2:     1     2     12            NA
#  3:     1     3     13      2.055556
#  4:     1     4     14      3.051282
#  5:     1     5     15      4.047619
#  6:     2     6     16            NA
#  7:     2     7     17            NA
#  8:     2     8     18      7.039216
#  9:     2     9     19      8.037037
# 10:     2    10     20      9.035088