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Preserve order in split-apply-comb tasks

Possible duplicate:
How to ddply () without sorting?

I have the following dataframe

dd1 = data.frame(cond = c("D","A","C","B","A","B","D","C"), val = c(11,7,9,4,3,0,5,2))
dd1
  cond val
1    D  11
2    A   7
3    C   9
4    B   4
5    A   3
6    B   0
7    D   5
8    C   2

and now it is necessary to calculate the cumulative amounts related to the factor level in cond. The results should look like this:

> dd2 = data.frame(cond = c("D","A","C","B","A","B","D","C"), val = c(11,7,9,4,3,0,5,2), cumsum=c(11,7,9,4,10,4,16,11))
> dd2
  cond val cumsum
1    D  11     11
2    A   7      7
3    C   9      9
4    B   4      4
5    A   3     10
6    B   0      4
7    D   5     16
8    C   2     11

It is important to get the result data frame in the same order as the original data frame because other variables are bound to it.

I tried ddply(dd1, .(cond), summarize, cumsum = cumsum(val))

it but didn't give the expected result.

thank

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3 answers


Use ave

instead.



dd1$cumsum <- ave(dd1$val, dd1$cond, FUN=cumsum)
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If you do it by hand, this is an option, then split()

, and unsplit()

with a suitable lapply()

inbetween will make it for you.

dds <- split(dd1, dd1$cond)
dds <- lapply(dds, function(x) transform(x, cumsum = cumsum(x$val)))
unsplit(dds, dd1$cond)

The last line gives



> unsplit(dds, dd1$cond)
  cond val cumsum
1    D  11     11
2    A   7      7
3    C   9      9
4    B   4      4
5    A   3     10
6    B   0      4
7    D   5     16
8    C   2     11

I have separated three steps, but they could be stuffed or put into a function if you do a lot of that.

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A data.table

solution:

require(data.table)
dt <- data.frame(dd1)
dt[, c.val := cumsum(val),by=cond]
> dt
#    cond val c.val
# 1:    D  11    11
# 2:    A   7     7
# 3:    C   9     9
# 4:    B   4     4
# 5:    A   3    10
# 6:    B   0     4
# 7:    D   5    16
# 8:    C   2    11
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