I have a very large dataframe and my goal is to list the cumulative USD by user ID. The dataframe looks like this, but it is much larger:

```
dt<-sample(seq(as.Date("2013-01-01"),as.Date("2013-05-01"),by="days"),10)
s<-c(rep(5252525,5),rep(1313131,5))
usd<-round(rnorm(10,100),2)
money<-data.frame(dt,s,usd)
money<-money[order(money$dt),]
money$Cumulative<-NA
users<-unique(money$s)

```

I started with a for loop, but it was very slow:

```
for (i in 1:length(users)){
    temp=which(money$s==users[i])
    money$Cumulative[temp]=cumsum(money$usd[temp])
}

```

I read on StackOverflow that I could use data.table to improve overall speed, and this helped somewhat:

```
money<-data.table(money)
setkey(money,s)

for (i in 1:length(users)){
    temp=which(money$s==users[i])
    money$Cumulative[temp]=cumsum(money$usd[temp])
}

```

I'd like to make this calculation even faster. What should I do next?