在R中聚合具有多个函数参数的函数 [英] Aggregate function in R with multiple function arguments

问题描述

我有样本data.set，其中包含不同季节的气候数据：

I have sample data.set containing climate data for different seasons:

df <- data.frame(season=rep(1:5,2),year=rep(1:2,each=5),
      temp=c(2,4,3,5,2,4,1,5,4,3),ppt=c(4,3,1,5,6,2,1,2,2,2),
      samples=c(22,25,24,31,31,29,28,31,30,32))

我可以简单地确定每年每个季节的气候变量平均值：

I can determine the mean of my climate variables for each season for each year simply:

aggregate(df[,c('temp','ppt')], by = list(df$season,df$year), function(x) mean(x,na.rm=T))

但是，我想使用变量样本作为我的权重，确定每个季节|年份组合的加权平均值。

However, I want to determine the weighted mean of each season|year combo using variable samples as my weights.

基本上我想用 aggregate（）中的平均值函数> weighted.mean 。这将需要在我的函数中添加第二个参数，该参数需要使用我的 x 进行更改。

Essentially I want to replace my mean function in aggregate() with weighted.mean. That would require adding a second argument to my function that needs to change with my x.

    function(x,w) weighted.mean(x,w,na.rm=T))

不过，我不确定如何让的权重参数（'w'）加权.mean（）因汇总数据的每个子集而异。

Though, I'm not sure how to let the weight argument ('w') of weighted.mean() vary with each subset of the aggregated data.

我可以在 aggregate 函数？

任何建议都很棒！

推荐答案

尝试使用 dplyr 中的 summarise_each 。它允许使用 group_by 进行先前的分组，并应用于多个列：

Try summarise_each from dplyr. It allows for the prior grouping with group_by and application to multiple columns:

library(dplyr)
df %>% group_by(season, year) %>%
        summarise_each(funs(weighted.mean(., samples,na.rm=T)), temp,ppt)
# Source: local data frame [10 x 5]
# Groups: season, year [10]
# 
#    season  year  temp   ppt samples
#    (int) (int) (dbl) (dbl)   (dbl)
# 1       1     1     2     4      22
# 2       2     1     4     3      25
# 3       3     1     3     1      24
# 4       4     1     5     5      31
# 5       5     1     2     6      31
# 6       1     2     4     2      29
# 7       2     2     1     1      28
# 8       3     2     5     2      31
# 9       4     2     4     2      30
# 10      5     2     3     2      32

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