15

I use ddply quite a bit but I do not consider myself an expert. I have a data frame (df) with grouping variable "Group" which has values of "A", "B" and "C" and the variable to summarize, "Var" has numeric values. If I use

ddply(df, .(Group), summarize, mysum=sum(Var))

then I get the sum of each A, B and C, which is correct. But what I want to do is to sum over each grouping of the Group variables as they are arranged in the data frame. For instance, if the data frame has

Group    Var
A        1.3
A        1.2
A        0.4
B        0.3
B        1.3
C        1.5
C        1.7
C        1.9
A        2.1
A        2.4
B        6.7

The Desired result

A        2.9
B        1.6
C        5.1
A        4.5
B        6.7

So, the desired output performs a mathematical function on each grouping of the Group variables, rather than on all instances of the individual Group variables. Can this be done in ddply?

Data

dat <- structure(list(Group = c("A", "A", "A", "B", "B", "C", "C", "C", "A", "A", "B"),
                      Var = c(1.3, 1.2, 0.4, 0.3, 1.3, 1.5, 1.7, 1.9, 2.1, 2.4, 6.7)),
                 .Names = c("Group", "Var"), class = "data.frame", row.names = c(NA, -11L))
rawr
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Joseph Kreke
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2 Answers2

19

Here's one way of doing this using the recently implemented rleid() function from data.table v1.9.6. See #686.

This generates the grouping ids as required:

require(data.table) ## v1.9.6+
DT = as.data.table(dat)
rleid(DT$Group)
# [1] 1 1 1 2 2 3 3 3 4 4 5

We can use this directly to aggregate as follows:

DT[, .(sum=sum(Var)), by=.(Group, rleid(Group))]
#    Group rleid sum
# 1:     A     1 2.9
# 2:     B     2 1.6
# 3:     C     3 5.1
# 4:     A     4 4.5
# 5:     B     5 6.7

HTH

Arun
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4

Here would be the base equivalent

dat <- structure(list(Group = c("A", "A", "A", "B", "B", "C", "C", "C", "A", "A", "B"),
                      Var = c(1.3, 1.2, 0.4, 0.3, 1.3, 1.5, 1.7, 1.9, 2.1, 2.4, 6.7)),
                 .Names = c("Group", "Var"), class = "data.frame", row.names = c(NA, -11L))

with(dat, cumsum(c(1L, Group[-length(Group)] != Group[-1])))
# [1] 1 1 1 2 2 3 3 3 4 4 5

As a function

rleid <- function(x) cumsum(c(1L, x[-length(x)] != x[-1]))

(dat <- within(dat, id <- rleid(Group)))
#    Group Var id
# 1      A 1.3  1
# 2      A 1.2  1
# 3      A 0.4  1
# 4      B 0.3  2
# 5      B 1.3  2
# 6      C 1.5  3
# 7      C 1.7  3
# 8      C 1.9  3
# 9      A 2.1  4
# 10     A 2.4  4
# 11     B 6.7  5

aggregate based on the new variable

aggregate(Var ~ ., dat, sum)
#   Group id Var
# 1     A  1 2.9
# 2     B  2 1.6
# 3     C  3 5.1
# 4     A  4 4.5
# 5     B  5 6.7

Alternatively, you can actually use rle, but it requires an atomic vector, so if you are using a factor then you need an extra step (ie, as.vector)

rleid2 <- function(x) {
  x <- as.vector(x)
  rep(seq_along(rle(x)$values), rle(x)$lengths)
}
rleid2(dat$Group)
# [1] 1 1 1 2 2 3 3 3 4 4 5

Some benchmarks:

set.seed(1)
dat2 <- dat[sample(1:nrow(dat), 1e6, TRUE), ]

identical(data.table::rleid(dat2$Group),
          rleid(dat2$Group))
# [1] TRUE

library('microbenchmark')
microbenchmark(data.table::rleid(dat2$Group),
               rleid(dat2$Group),
               rleid2(dat2$Group), unit = 'relative')


# Unit: relative
#                          expr       min        lq      mean    median        uq       max neval cld
# data.table::rleid(dat2$Group)  1.032777  1.015395  1.005023  1.020923  1.000612 0.8935531   100  a
#             rleid(dat2$Group)  1.000000  1.000000  1.000000  1.000000  1.000000 1.0000000   100  a
#            rleid2(dat2$Group) 35.747987 35.351585 28.600030 34.058992 33.147546 9.8786083   100   b
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