累积总和，移动平均数和SQL“分组依据" R中的等价物 [英] Cumulative sums, moving averages, and SQL "group by" equivalents in R

问题描述

在R中创建移动平均值或滚动总和的最有效方法是什么?滚动功能如何与分组依据"一起使用?

What's the most efficient way to create a moving average or rolling sum in R? How do you do the rolling function along with a "group by"?

推荐答案

虽然动物园很棒，但有时候有更简单的方法.如果数据表现良好且间隔均匀，则embed()函数可有效地使您创建时间序列的多个滞后版本.如果您在VARS包中查找向量自动回归，则会看到包作者选择了此路线.

While zoo is great, sometimes there are simpler ways. If you data behaves nicely, and is evenly spaced, the embed() function effectively lets you create multiple lagged version of a time series. If you look inside the VARS package for vector auto-regression, you will see that the package author chooses this route.

例如，要计算x的3个周期滚动平均值，其中x =(1-> 20)^ 2:

For example, to calculate the 3 period rolling average of x, where x = (1 -> 20)^2:

> x <- (1:20)^2
> embed (x, 3)
      [,1] [,2] [,3]
 [1,]    9    4    1
 [2,]   16    9    4
 [3,]   25   16    9
 [4,]   36   25   16
 [5,]   49   36   25
 [6,]   64   49   36
 [7,]   81   64   49
 [8,]  100   81   64
 [9,]  121  100   81
[10,]  144  121  100
[11,]  169  144  121
[12,]  196  169  144
[13,]  225  196  169
[14,]  256  225  196
[15,]  289  256  225
[16,]  324  289  256
[17,]  361  324  289
[18,]  400  361  324
> apply (embed (x, 3), 1, mean)
 [1]   4.666667   9.666667  16.666667  25.666667  36.666667  49.666667
 [7]  64.666667  81.666667 100.666667 121.666667 144.666667 169.666667
[13] 196.666667 225.666667 256.666667 289.666667 324.666667 361.666667

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