如何跨三个多列数据帧的行计算均值? [英] How to calculate means across rows of three multi-column dataframes?

问题描述

假设我有3个数据帧，每个数据帧都是5x5的对象，

Let's say I have 3 data frames, each a 5x5 object as such:

set.seed(1)
x <-as.data.frame(matrix(rnorm(10),ncol=5,nrow=5))
colnames(x) <-c("a","b","c","d","e")

y <-as.data.frame(matrix(rnorm(10),ncol=5,nrow=5))
colnames(y) <-c("f","g","h","i","j")

z <-as.data.frame(matrix(rnorm(10),ncol=5,nrow=5))
colnames(z) <-c("k","l","m","n","o")

例如，x看起来像:

  > x
           a          b          c          d          e
1 -0.6264538 -0.8204684 -0.6264538 -0.8204684 -0.6264538
2  0.1836433  0.4874291  0.1836433  0.4874291  0.1836433
3 -0.8356286  0.7383247 -0.8356286  0.7383247 -0.8356286
4  1.5952808  0.5757814  1.5952808  0.5757814  1.5952808
5  0.3295078 -0.3053884  0.3295078 -0.3053884  0.3295078

如何有效地计算每个数据帧中同一位置的3个值的行平均值?即，例如，计算数据帧上第1/col 1行中3个值的平均值.当然，手动操作很容易.例如:

How can I efficiently calculate the means across rows for the 3 values in the same spot in each data frame? That is, calculate the mean for the 3 values in row 1/col 1 over the data frames, for instance. Easy to do manually, of course. For instance:

> mean(c(x$a[1],y$f[1],z$k[1]))
[1] 0.6014349

> mean(c(x$b[1],y$g[1],z$l[1]))
[1] -0.3071769

...等等.但是，如何在R中有效地对更大的数据帧执行此操作?我尝试过mapply()以及apply()和sweep()的变体，但是没有运气.我知道有一个简单的解决方案，但我已经陷入僵局.任何帮助将不胜感激！

... and so on. But how can I do this efficiently in R for much larger data frames? I've tried mapply() and variations on apply() and sweep(), but no luck. I know there's a simple solution but I'm having brain-lock. Any help would be greatly appreciated!

推荐答案

我觉得我必须提供一些琐碎的解决方案作为答案...

I feel like I have to supply my trivial solution as an answer...

(x+y+z)/3

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