按组在 R 中的数据框上运行自定义函数

问题描述

在使用自定义函数循环数据框中的组时遇到一些问题.

Having some trouble getting a custom function to loop over a group in a data frame.

以下是一些示例数据:

set.seed(42)
tm <- as.numeric(c("1", "2", "3", "3", "2", "1", "2", "3", "1", "1"))
d <- as.numeric(sample(0:2, size = 10, replace = TRUE))
t <- as.numeric(sample(0:2, size = 10, replace = TRUE))
h <- as.numeric(sample(0:2, size = 10, replace = TRUE))

df <- as.data.frame(cbind(tm, d, t, h))
df$p <- rowSums(df[2:4])

我创建了一个自定义函数来计算 w 值:

I created a custom function to calculate the value w:

calc <- function(x) {
  data <- x
  w <- (1.27*sum(data$d) + 1.62*sum(data$t) + 2.10*sum(data$h)) / sum(data$p)
  w
  }

当我在整个数据集上运行该函数时，我得到以下答案:

When I run the function on the entire data set, I get the following answer:

calc(df)
[1]1.664474

理想情况下，我想返回按 tm 分组的结果，例如:

Ideally, I want to return results that are grouped by tm, e.g.:

tm     w
1    result of calc
2    result of calc
3    result of calc

到目前为止，我已尝试将 aggregate 与我的函数一起使用，但出现以下错误:

So far I have tried using aggregate with my function, but I get the following error:

aggregate(df, by = list(tm), FUN = calc)
Error in data$d : $ operator is invalid for atomic vectors

我觉得我盯着这个看得太久了，有一个明显的答案.任何建议将不胜感激.

I feel like I have stared at this too long and there is an obvious answer. Any advice would be appreciated.

推荐答案

Using dplyr

library(dplyr)
df %>%
   group_by(tm) %>%
   do(data.frame(val=calc(.)))
#  tm      val
#1  1 1.665882
#2  2 1.504545
#3  3 1.838000

如果我们稍微改变函数以包含多个参数，这也适用于 summarise

If we change the function slightly to include multiple arguments, this could also work with summarise

 calc1 <- function(d1, t1, h1, p1){
      (1.27*sum(d1) + 1.62*sum(t1) + 2.10*sum(h1) )/sum(p1) }
 df %>%
     group_by(tm) %>%
     summarise(val=calc1(d, t, h, p))
 #  tm      val
 #1  1 1.665882
 #2  2 1.504545
 #3  3 1.838000

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