Finding the max value for each id, grouped by date [duplicate]

Answer 1

Here is a simple approach using ave():

df$sum.spent <- ave(df$spent, df$id, df$date2, FUN = max)
df
#   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

It's also simple using data.table():

library(data.table)
# data.table 1.8.2  For help type: help("data.table")
dfDT <- data.table(df, key="id,date2")
dfDT[, sum.spent:=max(spent), by=key(dfDT)]
#    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

Answer 2

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)]