Pipes let you transform the way you call deeply nested functions. Using a pipe doesn't affect what the code does; behind the scenes it is run in (almost) the exact same way. What the pipe does is change how _you_ write, and read, code.
You've been using the pipe for a while now, so you already understand the basics. The point of this chapter is to explore the pipe in more detail. You'll learn the alternatives that the pipe replaces, and the pros and cons of the pipe. Importantly, you'll also learn situations in which you should avoid the pipe.
The pipe, `%>%`, comes from the __magrittr__ package by Stefan Milton Bache. This package provides a handful of other helpful tools if you explicitly load it. We'll explore some of those tools to close out the chapter.
This chapter focusses on `%>%` which is normally loaded for you by packages in the tidyverse. Here we'll focus on it alone, so we'll make it available directly from magrittr. We'll also extract the `diamonds` dataset out of ggplot2 to use in some examples.
The point of the pipe is to help you write code in a way that easier to read and understand. To see why the pipe is so useful, we're going to explore a number of ways of writing the same code. Let's use code to tell a story about a little bunny named Foo Foo:
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:
We'll work through each approach, showing you the code and talking about the advantages and disadvantages. Note that these are made up functions; please don't expect this code to do something.
The main downside of this form is that it forces you to name each intermediate element. If there are natural names, this form feels natural, and you should use it. But in this example, there aren't natural names, and we're adding numeric suffixes just to make the names unique. That leads to two problems: the code is cluttered with unimportant names, and 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 worry that this form creates many intermediate copies of your data and takes up a lot of memory, but that's not necessary. First, 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: if you're working with data frames, R will share columns where possible. Let's take a look at an actual data manipulation pipeline where we add a new column to the `diamonds` dataset from ggplot2:
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 individual size of each data frame will be unchanged, but the collective size increases:
(Note that we use `pryr::object_size()` here, not the built-in `object.size()`. `object.size()` isn't quite smart enough to recognise that the columns are shared across multiple data frames.)
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
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, however, most people pick up the idea very quickly, so when you share you code with others who aren't familiar with the pipe, you can easily teach them.
The pipe works by doing "lexical transformation". Behind the scenes, magrittr reassemble the code in the pipe to a form that works by overwriting an intermediate object. When you run a pipe like the one above, magrittr does something like this:
There are a relatively wide class of functions with this behaviour.
This includes `try()`, `supressMessages()`, and `suppressWarnings()`
in base R.
## 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 get longer than five or six lines. 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.
The pipe is provided by the magrittr package, by Stefan Milton Bache. Most of the packages you work with in this book will automatically provide `%>%` for you. You might want to load magrittr yourself if you're using another package, or you want to access some of the other pipe variants that magrittr provides.
