Question

Possible Duplicate:
How to speed up cummulative sum within group?

In the following data frame

id<-c(1,1,1,1,1,3,3,3,3)
spent<-c(10,20,30,40,50,60,70,80,90)
date<-c("11-11-07","11-11-07","23-11-07","12-12-08","17-12-08","11-11-07","23-11-07","23-       11-07","16-01-08")
df<-data.frame(id,date,spent)
df$date2<-as.Date(as.character(df$date), format = "%d-%m-%y")

  id     date spent      date2
1  1 11-11-07    10 2007-11-11
2  1 11-11-07    20 2007-11-11
3  1 23-11-07    30 2007-11-23
4  1 12-12-08    40 2008-12-12
5  1 17-12-08    50 2008-12-17
6  3 11-11-07    60 2007-11-11
7  3 23-11-07    70 2007-11-23
8  3 23-11-07    80 2007-11-23
9  3 16-01-08    90 2008-01-16

I need to find the maximum of spent for each id in each day and record in in a separate column as follow:

  id     date spent      date2   sum.spent
1  1 11-11-07    10 2007-11-11    20
2  1 11-11-07    20 2007-11-11    20 
3  1 23-11-07    30 2007-11-23    30
4  1 12-12-08    40 2008-12-12    40
5  1 17-12-08    50 2008-12-17    50
6  3 11-11-07    60 2007-11-11    60
7  3 23-11-07    70 2007-11-23    80
8  3 23-11-07    80 2007-11-23    80
9  3 16-01-08    90 2008-01-16    90 

Can anyone help me with this?

Answer 1

Here is your plyr answer:

library(plyr)
ddply(df, .(id, date), transform, sum.spent = max(spent))

This is the data.table answer (better for larger datasets):

library(data.table)
df <- data.table(df)
df[, sum.spent:=max(spent), by = list(id, date)]