"Stretching" summary values across rows by group

Question

While using R, I am often interested in performing operations on a data.frame in which I summarize a variable by a group, and then want to add those summary values back into the data.frame. This is most easily shown by example:

myDF <- data.frame(A = runif(5), B = c("A", "A", "A", "B", "B"))
myDF$Total <- with(myDF, by(A, B, sum))[myDF$B]
myDF$Proportion <- with(myDF, A / Total)

which produces:

          A B     Total Proportion
1 0.5272734 A 1.7186369  0.3067975
2 0.5105128 A 1.7186369  0.2970452
3 0.6808507 A 1.7186369  0.3961574
4 0.2892025 B 0.6667133  0.4337734
5 0.3775108 B 0.6667133  0.5662266

This trick -- essentially getting a vector of named values, and "spreading" or "stretching" them across the relevant rows by group -- generally works, although class(myDF$Total) is "array" unless I put the by() inside of a c().

I am wondering:

1. Is there a commonly-used name for this operation?
2. Is there another, less hacky-feeling, and/or faster way of doing this?
3. Is there a way to do this with dplyr? Maybe there is a Hadley-approved verb operation (like mutate, arrange, etc.) about which I am unaware. I know that it is easy to summarise(), but I often need to put those summaries back into the data.frame.

Answer 1

Here's a "less hacky" way to do this with base R.

set.seed(1)
myDF <- data.frame(A = runif(5), B = c("A", "A", "A", "B", "B"))

within(myDF, {
  Total <- ave(A, B, FUN = sum)
  Proportion <- A/Total
})

#           A B Proportion    Total
# 1 0.2655087 A  0.2193406 1.210486
# 2 0.3721239 A  0.3074170 1.210486
# 3 0.5728534 A  0.4732425 1.210486
# 4 0.9082078 B  0.8182865 1.109890
# 5 0.2016819 B  0.1817135 1.109890

In "dplyr" language, I guess you're looking for mutate:

myDF %>%
  group_by(B) %>%
  mutate(Total = sum(A), Proportion = A/Total)

# Source: local data frame [5 x 4]
# Groups: B
# 
#           A B    Total Proportion
# 1 0.2655087 A 1.210486  0.2193406
# 2 0.3721239 A 1.210486  0.3074170
# 3 0.5728534 A 1.210486  0.4732425
# 4 0.9082078 B 1.109890  0.8182865
# 5 0.2016819 B 1.109890  0.1817135

From the "Introduction to dplyr" vignette, you would find the following description:

As well as selecting from the set of existing columns, it's often useful to add new columns that are functions of existing columns. This is the job of mutate(). dplyr::mutate() works the same way as plyr::mutate() and similarly to base::transform(). The key difference between mutate() and transform() is that mutate allows you to refer to columns that you just created.

---

Also, since you've tagged this "data.table", you can "chain" commands together in "data.table" quite easily to do something like:

DT <- data.table(myDF)
DT[, Total := sum(A), by = B][, Proportion := A/Total][]