# “If You Were an R Function, What Function Would You Be?”

We’ve been getting some good uptake on our piping in `R` article announcement.

The article is necessarily a bit technical. But one of its key points comes from the observation that piping into names is a special opportunity to give general objects the following personality quiz: “If you were an `R` function, what function would you be?”

• Everything that exists is an object.
• Everything that happens is a function call.

So our question is: can we add a meaningful association between the two deepest concepts in `R` objects (or references to them) and functions?

We think the answer is a resounding “yes!”

The following example (adapted from the paper) should help illustrate the idea.

Suppose we had simple linear model.

```set.seed(2019)
data_use <- base::sample(c("train", "test"),
nrow(mtcars), replace = TRUE)
mtcars_train <- mtcars[data_use == "train", , drop = FALSE]
mtcars_test <- mtcars[data_use == "test", , drop = FALSE]
model <- lm(mpg ~ disp  + wt, data = mtcars_train)
```

Now if “`model`” were an `R` function, what function would it be? One possible answer is: it would be `predict.lm()`. It would be nice if “`model(mtcars_test)`” meant “`predict(model, data = mtcars_test)`“. Or, if we accept the pipe notation “`mtcars_test %.>% model`” as an approximate substitute for (note: not an equivalent of) “`model(mtcars_test)`” we can make that happen.

The “`%.>%`” is the `wrapr` dot arrow pipe. It can be made to ask the question “If you were an `R` function, what function would you be?” as follows.

First a bit of preparation, we tell R‘s `S3` class system how to answer the question.

```apply_right.lm <-
function(pipe_left_arg,
pipe_right_arg,
pipe_environment,
left_arg_name,
pipe_string,
right_arg_name) {
predict(pipe_right_arg,
newdata = pipe_left_arg)
}
```

And now we can treat any reference to an object of class “`lm`” as a pipe destination or function.

```mtcars_test %.>% model
```

And we see our results.

```#          Mazda RX4       Mazda RX4 Wag      Hornet 4 Drive          Duster 360            Merc 280
#          23.606199           22.518582           20.477232           18.347774           20.062914
#          Merc 280C          Merc 450SE  Cadillac Fleetwood Lincoln Continental            Fiat 128
#          20.062914           16.723133           10.506642            9.836894           25.888019
#   Dodge Challenger         AMC Javelin       Porsche 914-2        Lotus Europa      Ford Pantera L
#          18.814401           19.261396           25.892974           28.719255           20.108134
#      Maserati Bora
#          18.703696
```

Notice we didn’t have to alter `model` or wrap it in a function. This solution can be used again and again in many different circumstances.

Categories: Exciting Techniques Tutorials

Tagged as: ### jmount

Data Scientist and trainer at Win Vector LLC. One of the authors of Practical Data Science with R.

1. ZJ (@dzj_evalparse) says: