Calculate differences between rows faster than a for loop?

Question

I have a data set that looks like this:

ID   |   DATE    | SCORE
-------------------------
123  |  1/15/10  |  10
123  |  1/1/10   |  15
124  |  3/5/10   |           


        

Answer 1

This should work if your the dates are in order within id.

id<-c(123,123,124,124)
date<-as.Date(c('2010-01-15','2010-01-01','2010-03-05','2010-01-05'))
score<-c(10,15,20,30)
data<-data.frame(id,date,score)

data <- data[order(data$id,data$date),]
data$dayssincelast<-do.call(c,by(data$date,data$id,function(x) c(NA,diff(x))))
# Or, even more concisely
data$dayssincelast<-unlist(by(data$date,data$id,function(x) c(NA,diff(x))))

Answer 2

How does the following work for you?

 indx <- which(data$id == c(data$id[-1], NA))
 data$date[indx] - data$date[indx+1]

---

This just shifts the id's by 1 and compares them to id to check for neighboring matches.
Then for the dat subtraction, simply subtract the matches from the date of the subsequent row.

Answer 3

In the case where you need a more complex formula, you can use aggregate:

a <- aggregate(date ~ id, data=data, FUN=function(x) c(NA,diff(x)))
data$dayssincelast <- c(t(a[-1]), recursive=TRUE) # Remove 'id' column

The same sort order applies here as in @nograpes answer.