R - 分组数据，但将不同的功能应用于不同的列

Question

I'd like to group this data but apply different functions to some columns when grouping.

ID  type isDesc isImage
1   1    1      0
1   1    0      1
1   1    0      1
4   2    0      1
4   2    1      0
6   1    1      0
6   1    0      1
6   1    0      0

I want to group by ID , columns isDesc and isImage can be summed, but I would like to get the value of type as it is. type will be the same through the whole dataset. The result should look like this:

ID  type isDesc isImage
1   1    1      2
4   2    1      1
6   1    1      1

Currently I am using

library(plyr)
summarized = ddply(data, .(ID), numcolwise(sum))

but it simply sums up all the columns. You don't have to use ddply but if you think it's good for the job I'd like to stick to it. data.table library is also an alternative

Answer 1

Using data.table :

require(data.table)
dt <- data.table(data, key="ID")
dt[, list(type=type[1], isDesc=sum(isDesc), 
                  isImage=sum(isImage)), by=ID]

#    ID type isDesc isImage
# 1:  1    1      1       2
# 2:  4    2      1       1
# 3:  6    1      1       1

Using plyr :

ddply(data , .(ID), summarise, type=type[1], isDesc=sum(isDesc), isImage=sum(isImage))
#   ID type isDesc isImage
# 1  1    1      1       2
# 2  4    2      1       1
# 3  6    1      1       1

Edit: Using data.table 's .SDcols , you can do this in case you've too many columns that are to be summed, and other columns to be just taken the first value.

dt1 <- dt[, lapply(.SD, sum), by=ID, .SDcols=c(3,4)]
dt2 <- dt[, lapply(.SD, head, 1), by=ID, .SDcols=c(2)]
> dt2[dt1]
#    ID type isDesc isImage
# 1:  1    1      1       2
# 2:  4    2      1       1
# 3:  6    1      1       1

You can provide column names or column numbers as arguments to .SDcols. Ex: .SDcols=c("type") is also valid.