在一次调用中按组对多个变量应用多个汇总函数 [英] Apply several summary functions on several variables by group in one call

问题描述

我有以下数据框

x <- read.table(text = "  id1 id2 val1 val2
1   a   x    1    9
2   a   x    2    4
3   a   y    3    5
4   a   y    4    9
5   b   x    1    7
6   b   y    4    4
7   b   x    3    9
8   b   y    2    8", header = TRUE)

我想计算id1和id2分组的val1和val2的均值，同时计算每个id1-id2组合的行数.我可以单独执行每个计算:

I want to calculate the mean of val1 and val2 grouped by id1 and id2, and simultaneously count the number of rows for each id1-id2 combination. I can perform each calculation separately:

# calculate mean
aggregate(. ~ id1 + id2, data = x, FUN = mean)

# count rows
aggregate(. ~ id1 + id2, data = x, FUN = length)

为了在一次调用中进行两种计算，我尝试了

In order to do both calculations in one call, I tried

do.call("rbind", aggregate(. ~ id1 + id2, data = x, FUN = function(x) data.frame(m = mean(x), n = length(x))))

但是，我得到了一个乱码的输出和一个警告:

However, I get a garbled output along with a warning:

#     m   n
# id1 1   2
# id2 1   1
#     1.5 2
#     2   2
#     3.5 2
#     3   2
#     6.5 2
#     8   2
#     7   2
#     6   2
# Warning message:
#   In rbind(id1 = c(1L, 2L, 1L, 2L), id2 = c(1L, 1L, 2L, 2L), val1 = list( :
#   number of columns of result is not a multiple of vector length (arg 1)

我可以使用 plyr 包，但是我的数据集非常大，而且当数据集的大小增加时，plyr 非常慢(几乎无法使用).

I could use the plyr package, but my data set is quite large and plyr is very slow (almost unusable) when the size of the dataset grows.

如何使用 aggregate 或其他函数在一次调用中执行多个计算?

How can I use aggregate or other functions to perform several calculations in one call?

推荐答案

您可以一步完成所有工作并获得适当的标签:

You can do it all in one step and get proper labeling:

> aggregate(. ~ id1+id2, data = x, FUN = function(x) c(mn = mean(x), n = length(x) ) )
#   id1 id2 val1.mn val1.n val2.mn val2.n
# 1   a   x     1.5    2.0     6.5    2.0
# 2   b   x     2.0    2.0     8.0    2.0
# 3   a   y     3.5    2.0     7.0    2.0
# 4   b   y     3.0    2.0     6.0    2.0

这将创建一个具有两个 id 列和两个矩阵列的数据框:

This creates a dataframe with two id columns and two matrix columns:

str( aggregate(. ~ id1+id2, data = x, FUN = function(x) c(mn = mean(x), n = length(x) ) ) )
'data.frame':   4 obs. of  4 variables:
 $ id1 : Factor w/ 2 levels "a","b": 1 2 1 2
 $ id2 : Factor w/ 2 levels "x","y": 1 1 2 2
 $ val1: num [1:4, 1:2] 1.5 2 3.5 3 2 2 2 2
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : NULL
  .. ..$ : chr  "mn" "n"
 $ val2: num [1:4, 1:2] 6.5 8 7 6 2 2 2 2
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : NULL
  .. ..$ : chr  "mn" "n"

正如下面@lord.garbage 所指出的，这可以通过使用 do.call(data.frame, ...)

As pointed out by @lord.garbage below, this can be converted to a dataframe with "simple" columns by using do.call(data.frame, ...)

str( do.call(data.frame, aggregate(. ~ id1+id2, data = x, FUN = function(x) c(mn = mean(x), n = length(x) ) ) ) 
    )
'data.frame':   4 obs. of  6 variables:
 $ id1    : Factor w/ 2 levels "a","b": 1 2 1 2
 $ id2    : Factor w/ 2 levels "x","y": 1 1 2 2
 $ val1.mn: num  1.5 2 3.5 3
 $ val1.n : num  2 2 2 2
 $ val2.mn: num  6.5 8 7 6
 $ val2.n : num  2 2 2 2

这是 LHS 上多个变量的语法:

This is the syntax for multiple variables on the LHS:

aggregate(cbind(val1, val2) ~ id1 + id2, data = x, FUN = function(x) c(mn = mean(x), n = length(x) ) )

这篇关于在一次调用中按组对多个变量应用多个汇总函数的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！