R 在多变量中使用tapply

我有一组数据，其中包含有关客户及其花费的信息，每个客户只出现一次：

customer<-c("Andy","Bobby","Oscar","Oliver","Jane","Cathy","Emma","Chris")
age<-c(25,34,20,35,23,35,34,22)
gender<-c("male","male","male","male","female","female","female","female")
moneyspent<-c(100,100,200,200,400,400,500,200)

data<-data.frame(customer=customer,age=age,gender=gender,moneyspent=moneyspent)

其中：

female   male 
  375    150

然而，我现在想找出性别和年龄组的平均花费金额，我的目标是：

 Male Age 20-30      Female Age 20-30      Male Age 30-40      Female Age 30-40
    150                     300                 150                   450

我如何修改tapply代码，使其给出这些结果

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谢谢

您可能需要使用

cut

mat <- tapply(moneyspent, list(gender, age=cut(age, breaks=c(20,30,40), 
                include.lowest=TRUE)), mean)

nm1 <- outer(rownames(mat), colnames(mat), FUN=paste)
setNames(c(mat), nm1)
#female [20,30]   male [20,30] female (30,40]   male (30,40] 
#       300            150            450            150 

或

使用plyr软件包 也会得到同样的结果

注意：Summarize和Summarise执行相同的功能

警告：加载

plyr

屏蔽了

dplyr

的摘要！在再次使用诸如“汇总”之类的功能之前，您需要分离

plyr

mat <- tapply(moneyspent, list(gender, age=cut(age, breaks=c(20,30,40), 
                include.lowest=TRUE)), mean)

nm1 <- outer(rownames(mat), colnames(mat), FUN=paste)
setNames(c(mat), nm1)
#female [20,30]   male [20,30] female (30,40]   male (30,40] 
#       300            150            450            150 

library(dplyr)
data %>% 
     group_by(gender, age=cut(age, breaks=c(20,30,40), 
              include.lowest=TRUE)) %>% 
     summarise(moneyspent=mean(moneyspent))

 library(data.table)
 setDT(data)[, list(moneyspent=mean(moneyspent)),
     by=list(gender, age=cut(age, breaks= c(20,30,40), include.lowest=TRUE))]

library(plyr)

ddply(data,.(gender, age=cut(age, breaks=c(20,30,40), 
                  include.lowest=TRUE)), summarize, moneyspent=mean(moneyspent))