How to use the 'sweep' function?

Question

When I look at the source of R Packages, i see the function `sweep` used quite often.Sometimes it's used when a simpler function would have sufficed (e.g., `apply`),other times, it's impossible to know exactly what it's is doing withoutspending a fair amount of time to step through the code block it's in.The fact that I can reproduce `sweep`'s effect using a simpler function suggests thati don't understand `sweep`'s core use cases, and the fact that this function is used so often suggests that it's quite useful.The context:`sweep` is a function in R's standard library; its arguments are: sweep(x, MARGIN, STATS, FUN="-", check.margin=T, ...) # x is the data # STATS refers to the summary statistics which you wish to 'sweep out' # FUN is the function used to carry out the sweep, "-" is the defaultAs you can see, the arguments are similar to `apply` though `sweep` requiresone more parameter, `STATS`.Another key difference is that `sweep` returns an array of the *same shape* as the input array, whereas the result returned by `apply` depends on the function passed in.`sweep` in action: # e.g., use 'sweep' to express a given matrix in terms of distance from # the respective column mean # create some data: M = matrix( 1:12, ncol=3) # calculate column-wise mean for M dx = colMeans(M) # now 'sweep' that summary statistic from M sweep(M, 2, dx, FUN="-") [,1] [,2] [,3] [1,] -1.5 -1.5 -1.5 [2,] -0.5 -0.5 -0.5 [3,] 0.5 0.5 0.5 [4,] 1.5 1.5 1.5So in sum, what i'm looking for is an exemplary use case or two for `sweep`.Please, do not recite or link to the R Documentation, mailing lists, or any of the 'primary' R sources--assume I've read them. What I'm interested in is how experienced R programmers/analysts use `sweep` in their own code.

Answer 1

In my experience, I've used `sweep()` to know how "far away" a particular variable's value is from its mean, for example.

To illustrate, let's say that I wanted to know for every variable's value in the `mtcars` dataset the distance from its mean in terms of percent (i.e., I want to be able to say "Mazda RX4's mpg value is X% larger/smaller than the mean of all cars"). See and execute the code below:

```

mysweep <- sweep(mtcars, 2, apply(mtcars, 2, mean), `/`)

mysweep

```

From the above code block, we are dividing each variable's value by its mean (i.e., x/mean(x)). In other words, we are "indexing" each variable.

In terms how I've personally used the function, I've created a simplification of `sweep()` called `mop()` that takes a summary statistic FUNCTION as an argument instead of a summary statistic VALUE, which can be seen in the following link: https://github.com/robertschnitman/afp/blob/master/R/mop.R

I am willing to bet that others have used `sweep()` for more advanced purposes: I am just relaying my own experience with the function. So, hopefully, this answers your question!