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r - 当使用 case_when（R 向量化）存在大量类别/类型时，R 应用多个函数

问题描述

假设我有以下形式的数据集：

City=c(1,2,2,1)
Business=c(2,1,1,2)
ExpectedRevenue=c(35,20,15,19)
zz=data.frame(City,Business,ExpectedRevenue)
zz_new=do.call("rbind", replicate(zz, n=30, simplify = FALSE))

我的实际数据集包含大约 200K 行。此外，它还包含 100 多个城市的信息。假设，对于每个城市（我也称之为“类型”），我有以下需要应用的功能：

#Writing the custom functions for the categories here

Type1=function(full_data,observation){
  NewSet=full_data[which(!full_data$City==observation$City),]
  BusinessMax = max(NewSet$ExpectedRevenue)+10*rnorm(1)
  return(BusinessMax)
}

Type2=function(full_data,observation){
  NewSet=full_data[which(!full_data$City==observation$City),]
  BusinessMax = max(NewSet$ExpectedRevenue)-100*rnorm(1)
  return(BusinessMax)
}

再一次，上面两个函数是我用来说明的非常简单的函数。这里的想法是，对于每个城市（或“类型”），我需要为数据集中的每一行运行不同的函数。在上述两个函数中，我使用 rnorm 来检查并确保我们为每一行绘制不同的值。

现在对于整个数据集，我想首先将观察结果划分为不同的城市（或“类型”）。我可以使用 (zz_new[["City"]]==1) [另见下文] 来做到这一点。然后为每个类运行各自的函数。但是，当我运行下面的代码时，我得到-Inf。

有人可以帮我理解为什么会这样吗？

对于示例数据，我希望获得 20 加 10 倍的随机值（对于 Type = 1）和 35 减 100 倍的随机值（对于 Type = 2）。每行的值也应该不同，因为我是从随机正态分布中绘制它们的。

library(dplyr) #I use dplyr here
zz_new[,"AdjustedRevenue"] = case_when(
  zz_new[["City"]]==1~Type1(full_data=zz_new,observation=zz_new[,]),
  zz_new[["City"]]==2~Type2(full_data=zz_new,observation=zz_new[,])
)

提前非常感谢。

标签： rvectorizationcase-when

解决方案

Let's take a look at your code. I rewrite your code

library(dplyr)
zz_new[,"AdjustedRevenue"] = case_when(
  zz_new[["City"]]==1~Type1(full_data=zz_new,observation=zz_new[,]),
  zz_new[["City"]]==2~Type2(full_data=zz_new,observation=zz_new[,])
)

to

zz_new %>%
  mutate(AdjustedRevenue = case_when(City == 1 ~ Type1(zz_new,zz_new),
                                     City == 2 ~ Type2(zz_new,zz_new)))

since you are using dplyr but don't use the powerful tools provided by this package.

Besides the usage of mutate one key change is that I replaced zz_new[,] with zz_new. Now we see that both arguments of your Type-functions are the same dataframe.

Next step: Take a look at your function

Type1 <- function(full_data,observation){
  NewSet=full_data[which(!full_data$City==observation$City),]
  BusinessMax = max(NewSet$ExpectedRevenue)+10*rnorm(1)
  return(BusinessMax)
}

which is called by Type1(zz_new,zz_new). So the definition of NewSet gives us

NewSet=full_data[which(!full_data$City==observation$City),]

# replace the arguments
NewSet <- zz_new[which(!zz_new$City==zz_new$City),]

Thus NewSet is always a dataframe with zero rows. Applying max to an empty column of a data.frame yields -Inf.

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