This is a popular Children's poem that is accompanied by hand actions.
We'll start by defining an object to represent little bunny Foo Foo:
```{r, eval = FALSE}
foo_foo <- little_bunny()
```
And we'll use a function for each key verb: `hop()`, `scoop()`, and `bop()`.
Using this object and these verbs, there are (at least) four ways we could retell the story in code:
1. Save each intermediate step as a new object.
2. Overwrite the original object many times.
3. Compose functions.
4. Use the pipe.
We'll work through each approach, showing you the code and talking about the advantages and disadvantages.
### Intermediate steps
The simplest approach is to save each step as a new object:
```{r, eval = FALSE}
foo_foo_1 <- hop(foo_foo, through = forest)
foo_foo_2 <- scoop(foo_foo_1, up = field_mice)
foo_foo_3 <- bop(foo_foo_2, on = head)
```
The main downside of this form is that it forces you to name each intermediate element.
If there are natural names, this is a good idea, and you should do it.
But many times, like this in this example, there aren't natural names, and you add numeric suffixes to make the names unique.
That leads to two problems:
1. The code is cluttered with unimportant names
2. You have to carefully increment the suffix on each line.
Whenever I write code like this, I invariably use the wrong number on one line and then spend 10 minutes scratching my head and trying to figure out what went wrong with my code.
You may also worry that this form creates many copies of your data and takes up a lot of memory.
Surprisingly, that's not the case.
First, note that proactively worrying about memory is not a useful way to spend your time: worry about it when it becomes a problem (i.e. you run out of memory), not before.
Second, R isn't stupid, and it will share columns across data frames, where possible.
Let's take a look at an actual data manipulation pipeline where we add a new column to `ggplot2::diamonds`:
```{r}
diamonds <- ggplot2::diamonds
diamonds2 <- diamonds %>%
dplyr::mutate(price_per_carat = price / carat)
pryr::object_size(diamonds)
pryr::object_size(diamonds2)
pryr::object_size(diamonds, diamonds2)
```
`pryr::object_size()` gives the memory occupied by all of its arguments.
The results seem counterintuitive at first:
- `diamonds` takes up 3.46 MB,
- `diamonds2` takes up 3.89 MB,
- `diamonds` and `diamonds2` together take up 3.89 MB!
How can that work?
Well, `diamonds2` has 10 columns in common with `diamonds`: there's no need to duplicate all that data, so the two data frames have variables in common.
These variables will only get copied if you modify one of them.
In the following example, we modify a single value in `diamonds$carat`.
That means the `carat` variable can no longer be shared between the two data frames, and a copy must be made.
The size of each data frame is unchanged, but the collective size increases:
```{r}
diamonds$carat[1] <- NA
pryr::object_size(diamonds)
pryr::object_size(diamonds2)
pryr::object_size(diamonds, diamonds2)
```
(Note that we use `pryr::object_size()` here, not the built-in `object.size()`.
`object.size()` only takes a single object so it can't compute how data is shared across multiple objects.)
### Overwrite the original
Instead of creating intermediate objects at each step, we could overwrite the original object:
```{r, eval = FALSE}
foo_foo <- hop(foo_foo, through = forest)
foo_foo <- scoop(foo_foo, up = field_mice)
foo_foo <- bop(foo_foo, on = head)
```
This is less typing (and less thinking), so you're less likely to make mistakes.
However, there are two problems:
1. Debugging is painful: if you make a mistake you'll need to re-run the complete pipeline from the beginning.
2. The repetition of the object being transformed (we've written `foo_foo` six times!) obscures what's changing on each line.
### Function composition
Another approach is to abandon assignment and just string the function calls together:
```{r, eval = FALSE}
bop(
scoop(
hop(foo_foo, through = forest),
up = field_mice
),
on = head
)
```
Here the disadvantage is that you have to read from inside-out, from right-to-left, and that the arguments end up spread far apart (evocatively called the [Dagwood sandwich](https://en.wikipedia.org/wiki/Dagwood_sandwich) problem).
In short, this code is hard for a human to consume.
### Use the pipe
Finally, we can use the pipe:
```{r, eval = FALSE}
foo_foo %>%
hop(through = forest) %>%
scoop(up = field_mice) %>%
bop(on = head)
```
This is my favourite form, because it focusses on verbs, not nouns.
You can read this series of function compositions like it's a set of imperative actions.
Foo Foo hops, then scoops, then bops.
The downside, of course, is that you need to be familiar with the pipe.
If you've never seen `%>%` before, you'll have no idea what this code does.
Fortunately, most people pick up the idea very quickly, so when you share your code with others who aren't familiar with the pipe, you can easily teach them.
The pipe works by performing a "lexical transformation": behind the scenes, R reassembles the code in the pipe to the function composition form used above.
## When not to use the pipe
The pipe is a powerful tool, but it's not the only tool at your disposal, and it doesn't solve every problem!
Pipes are most useful for rewriting a fairly short linear sequence of operations.
I think you should reach for another tool when:
- Your pipes are longer than (say) ten steps.
In that case, create intermediate objects with meaningful names.
That will make debugging easier, because you can more easily check the intermediate results, and it makes it easier to understand your code, because the variable names can help communicate intent.
- You have multiple inputs or outputs.
If there isn't one primary object being transformed, but two or more objects being combined together, don't use the pipe.
- You are starting to think about a directed graph with a complex dependency structure.
Pipes are fundamentally linear and expressing complex relationships with them will typically yield confusing code.
