I have a set of data which contains information about customers and how much they have spent, each customer only appears once:
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)
If I want to calculate the average amount of money spent by male and female customers, I can use tapply:
tapply(moneyspent,gender,mean)
which gives:
female male
375 150
However, I now want to find the average amount of money spent by both gender and age group and the result I am aiming for is:
Male Age 20-30 Female Age 20-30 Male Age 30-40 Female Age 30-40
150 300 150 450
How could I modifty the tapply code such that it gives these results?
THANK YOU
You may need to use 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
Other options include
library(dplyr)
data %>%
group_by(gender, age=cut(age, breaks=c(20,30,40),
include.lowest=TRUE)) %>%
summarise(moneyspent=mean(moneyspent))
Or
library(data.table)
setDT(data)[, list(moneyspent=mean(moneyspent)),
by=list(gender, age=cut(age, breaks= c(20,30,40), include.lowest=TRUE))]