**mages' blog**, and kindly contributed to R-bloggers)

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.

**leave a comment**for the author, please follow the link and comment on their blog:

**mages' blog**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...