# ave and the "[" function in R

The `ave`

function in R is one of those little helper function I feel I should be using more. Investigating its source code showed me another twist about R and the “[” function. But first let’s look at `ave`

.

The top of `ave`

’s help page reads:

*Group Averages Over Level Combinations of Factors*

*Subsets of x[] are averaged, where each subset consist of those observations with the same factor levels.
*
As an example I look at revenue data by product and shop.

```
revenue <- c(30,20, 23, 17)
product <- factor(c("bread", "cake", "bread", "cake"))
shop <- gl(2,2, labels=c("shop_1", "shop_2"))
```

To answer the question “Which shop sells proportionally more bread?” I need to divide the revenue vector by the sum of revenue per shop, which can be calculated easily by `ave`

:

```
(shop_revenue <- ave(revenue, shop, FUN=sum))
# [1] 50 50 40 40
(revenue_split_in_shop <- revenue/shop_revenue)
# [1] 0.600 0.400 0.575 0.425 # Shop 1 sells more bread than cake
```

In other words, `ave`

has to split the revenue vector by shop and apply the `sum`

function to it. Well that’s exactly what it does. Here is the source code of `ave`

:

```
# Copyright (C) 1995-2012 The R Core Team
ave <- function (x, ..., FUN = mean)
{
if(missing(...))
x[] <- FUN(x)
else {
g <- interaction(...)
split(x,g) <- lapply(split(x, g), FUN)
}
x
}
```

However, and this is what intrigued me, if I don’t provide a grouping variable (`missing(…)`

) it will apply the function `FUN`

on `x`

itself and write its output to `x[]`

. That’s actually what the help file to `ave`

mentioned in its description. So what does it do? Here is an example again:

```
ave(revenue, FUN=sum)
# [1] 90 90 90 90
```

I get the sum of revenue repeated as many time as the vector has elements, not just once, as with `sum(revenue)`

. The trick is that the output of `FUN(x)`

is written into `x[]`

, which of course is output of a function call itself “[”(x).

I think it is the following sentence in the help file of `“[”`

(see ?“[”), which explains it: *Subsetting (except by an empty index) will drop all attributes except names, dim and dimnames.*

So there we are. I feel less inclined to use `ave`

more, as it is just short for the usual `split, lapply`

routine, but I learned something new about the subtleties of R.