计算数据子集的统计量 [英] Calculating statistics on subsets of data

问题描述

这是我的数据的一个可重现的小示例:

Here is a small reproducible example of my data:

> mydata <- structure(list(subject = c(1, 1, 1, 2, 2, 2), time = c(0, 1, 2, 0, 1, 2), measure = c(10, 12, 8, 7, 0, 0)), .Names = c("subject", "time", "measure"), row.names = c(NA, -6L), class = "data.frame")

> mydata

subject  time  measure
1          0      10
1          1      12
1          2       8
2          0       7
2          1       0
2          2       0

我想生成一个包含该特定主题的measure平均值的新变量，因此:

I would like to generate a new variable containing the mean of measure for that particular subject, so:

subject  time  measure  mn_measure
1          0      10      10
1          1      12      10
1          2       8      10
2          0       7      2.333
2          1       0      2.333
2          2       0      2.333

除了以编程方式循环遍历所有记录或首先重新调整为宽格式之外，是否有一种简单的方法可以做到这一点?

Is there an easy way to do this, other than looping through all the records programatically or reshaping to wide format first ?

推荐答案

使用基本的 R 函数 ave()，尽管它的名称令人困惑，但可以计算各种统计信息，包括 平均:

Use the base R function ave(), which despite its confusing name, can calculate a variety of statistics, including the mean:

within(mydata, mean<-ave(measure, subject, FUN=mean))

  subject time measure      mean
1       1    0      10 10.000000
2       1    1      12 10.000000
3       1    2       8 10.000000
4       2    0       7  2.333333
5       2    1       0  2.333333
6       2    2       0  2.333333

<小时>

请注意，我使用 within 只是为了缩短代码.这是不带 within() 的等价物:

---

Note that I use within just for the sake of shorter code. Here is the equivalent without within():

mydata$mean <- ave(mydata$measure, mydata$subject, FUN=mean)
mydata
  subject time measure      mean
1       1    0      10 10.000000
2       1    1      12 10.000000
3       1    2       8 10.000000
4       2    0       7  2.333333
5       2    1       0  2.333333
6       2    2       0  2.333333

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