Pipe-esque Programming with {ggplot2}’s Plus Operator

Writing iterative code with ‘+’ rather than ‘%>%’ was a tough transition my first time around.


June 10, 2021

Writing iterative code with {ggplot2}’s plus (+) operator rather than {magrittr}’s pipe (%>%) was a tough transition my first time around.

When working on a function—say, boop()—that takes the outputs of some other function—say, beep():

beep(1) %>%

…I usually take for granted that I’ll have access to whatever beep(1) outputted, and will be able to modify that thing, inside of the boop function.

With {ggplot2}’s plus operator, though, I wasn’t sure whether this was the case. I thought that ggplot layers, added with +, had to be able to operate somewhat independently. e.g., if I’m making some function super_fancy_layer that adds a layer to a ggplot:

super_fancy_layer <- function() {

ggplot(mtcars, aes(x = hp, y = mpg)) + 
  geom_point() +

A scatter plot with horsepower (range 0 to 350) on the x axis and miles per gallon (range 10 to 35) of cars, showing a nonlinear, negative association. The key insight here is that adding super_fancy_layer(), which just outputs a list containing theme_minimal(), applies that theme to the plot via the plus operator; rather than the default gray panel background, the plot now has a white one.

…I had never thought about how super_fancy_layer() might “access” information from the earlier lines. What if I want super_fancy_layer() to modify bits and pieces of the existing plot, depending on what’s in the plot already, rather than adding a layer on top of the whole thing?1

It turns out, the maintainers of {ggplot2} have put together a pretty neat system that allows developers to access and modify previous {ggplot2} output in defining new layers.

The plan

Before we get into the details, I should mention that this post assumes a solid background in R (specifically, functions) and some awareness of the S3 object system. I’ll do my best to clarify the bits that are important here, but here’s some more extensive writing on functions, and here’s more on the S3 system.

I’ll start off highlighting the infrastructure that the {ggplot2} team has put together for developers to approach this challenge before writing a bit about how you can take advantage of it (with some additional pointers on integrating it in a package).


It starts with the + operator we all know and (maybe) love. The + you use in {ggplot2} code, like you see above, is actually a “method.” That is, the meaning of + here is defined in the {ggplot2} source code, specifically in reference to gg objects. Here’s the current source code:

"+.gg" <- function(e1, e2) {
  if (missing(e2)) {
    abort("Cannot use `+.gg()` with a single argument. Did you accidentally put + on a new line?")

  # Get the name of what was passed in as e2, and pass along so that it
  # can be displayed in error messages
  e2name <- deparse(substitute(e2))

  if      (is.theme(e1))  add_theme(e1, e2, e2name)
  else if (is.ggplot(e1)) add_ggplot(e1, e2, e2name)
  else if (is.ggproto(e1)) {
    abort("Cannot add ggproto objects together. Did you forget to add this object to a ggplot object?")

Here, e1 is what’s on the left-hand side of the + and e2 is what’s on the right-hand side. So, in the code:

ggplot(mtcars, aes(x = hp, y = mpg)) + geom_point()

e1 is ggplot(mtcars, aes(x = hp, y = mpg)) and e2 is geom_point(). Another (goofy) way of writing the above code is:

`+`(ggplot(mtcars, aes(x = hp, y = mpg)), geom_point())

In the definition of +.gg, you may notice that the function eventually returns the output of add_ggplot(e1, e2, e2name) if the thing on the left-hand side of the + is a ggplot. Okay, sure.

Here’s the current definition of add_ggplot().

add_ggplot <- function(p, object, objectname) {
  if (is.null(object)) return(p)

  p <- plot_clone(p)
  p <- ggplot_add(object, p, objectname)

Okay, so–hmm. The part here doing the work of combining what used to be e1 and e2 is ggplot_add(object, p, objectname). What’s the definition of ggplot_add?

ggplot_add <- function(object, plot, object_name) {

Huzzah! Yippee!

What this UseMethod means is that the definition of ggplot_add depends on what kind of thing object is–the thing on the right-hand side of the +. When I say what kind of thing, I mean the output of class(object). ggplot_add is exported with ggplot2, so, as developers on extensions of {ggplot2}, we get to decide what the class of our super_fancy_layer() function is, so we also get to decide how ggplot_add will work in our extensions!

Also, notice that plot (what used to be e1, or the thing on the left-hand side of the +) is also an input to ggplot_add and the output should be the result of adding those two things together. So, in implementing super_fancy_layer(),

  • we have access to what the plot previously “was”
  • we can modify that thing and output the whole plot rather than just a layer on top of it

To use a custom ggplot_add method in your own package, you’ll want to start with re-exporting the ggplot_add generic. (“Generic” is just a word for a function that uses this sort of UseMethod construction, “dispatching” to the method defined for the given input class.) To do so, add the following {roxygen2} lines somewhere in your R code and run devtools::document():

#' @importFrom ggplot2 ggplot_add
#' @export

Now, define your super_fancy_layer() function. The idea here is that, since our contents of super_fancy_layer() need access to the underlying plot data, we won’t actually define all of the function’s logic inside of super_fancy_layer() (since requiring the underlying plot data as an explicit function argument for each layer is bad ggplot form). Instead, just store what the user inputted and wait until they add the layer to a ggplot to actually do anything.

#' @export
super_fancy_layer <- function(arg1 = 1, arg2 = 2) {
  # store inputs in classed output that can 
  # be passed to a `ggplot_add` method
    "A super fancy layer.", 
    class = "fancy_layer",
    fn = "super_fancy_layer_",
    arg1 = arg1,
    arg2 = arg2

So… pretty lame so far. If I call:

## [1] "A super fancy layer."
## attr(,"class")
## [1] "fancy_layer"
## attr(,"fn")
## [1] "super_fancy_layer_"
## attr(,"arg1")
## [1] 1
## attr(,"arg2")
## [1] 2

All that I’ve done is bundled up what the user passed to me into a little fancy_layer object.

## [1] "fancy_layer"

I also added an attribute with the name fn set to "super_fancy_layer_", an arbitrary function name for the internal code you will write that actually implements the addition–more on that in a sec.

Now, think back to how that + operator works–it calls add_ggplot, which calls ggplot_add, and the code for ggplot_add depends on the class of what’s on the right-hand side of the + in the original code. We set the class of super_fancy_layer() output to fancy_layer, so all we need to do now is define the instructions for adding fancy_layers to existing ggplots.

Our definition for ggplot_add.fancy_layer will:

  • extract the fn attribute from fancy_layer output
  • extract arguments arg1 and arg2 from fancy_layer output
  • call fn with the arguments plot, arg1, and arg2
#' @method ggplot_add fancy_layer
#' @export
ggplot_add.fancy_layer <- function(object, plot, object_name) {
  # a method for the `+` operator for fancy_layer objects.
  # - "object to add" (arguments to the RHS of the `+`)
  # - plot is the existing plot (on the LHS of the `+`)
  # - object_name is the unevaluated call on the RHS of the `+`
  # extract the `fn` attribute from `fancy_layer` output
  fn <- attr(object, "fn")
  # extract arguments `arg1` and `arg2` from `fancy_layer` output
  fancy_args <- attributes(object)[!names(attributes(object)) %in% 
                                   c("class", "fn")]
  # call `fn` with the arguments `plot`, `arg1`, and `arg2`
  new_plot <- do.call(
    c(list(plot), fancy_args)
  # return the new plot

The do.call lines are a programmatic way of writing super_fancy_layer_(plot, arg1 = 1, arg2 = 2). That fn attribute is really just a string, but if there’s a function by the name of the string, do.call will call that function. The magic here is that fn (in our case, "super_fancy_layer_") can reference a function containing code defining the result of adding the left and right hand sides of the + based on both of them, rather than just the right-hand side. That is, you can peek inside of—and modify—the existing plot inside of your layer function! So, define the function in that fn attribute as you will:

super_fancy_layer_ <- function(plot, arg1, arg2) {
  # fancy code that modifies `plot` based on
  # arg1 and arg2...

…and you’re good to go. Crazy.

Bonus points: a print method

Getting the ggplot_add method working is the biggest leap here, but my first move after making that happen was to put together a print method for that new custom layer object. The default output will print out all of the attributes you attached to the object, which could be a bit overwhelming and confusing for the user.

## [1] "A super fancy layer."
## attr(,"class")
## [1] "fancy_layer"
## attr(,"fn")
## [1] "super_fancy_layer_"
## attr(,"arg1")
## [1] 1
## attr(,"arg2")
## [1] 2


To hide those internals from users, you can define your own print method–what gets printed out if a user calls super_fancy_layer() without adding it to an existing ggplot object with +?

Here’s what my print method looks like:

#' @export
print.fancy_layer <- function(x, ...) {

With this loaded, printing fancy_layers is a bit less overwhelming (if a bit underwhelming):

## A super fancy layer.

Woop woop. :-)

Wrapping up

I hope this was helpful for those who are currently encountering this problem and interesting for those who may in the future. Thanks to the {ggplot2} maintainers for putting together this infrastructure (specifically, Thomas Lin Pedersen in 2017) and to Hiroaki Yutani for writing the blog post that initially tipped me off to this. To yall maintainers, my apologies if this post encourages some poor form.

Back to top


  1. I write this blog post assuming that the reader may have stumbled here already with a problem to solve. If you’re curious when an issue like this may come up, my original use case for this kind of functionality was in a PR for {infer}, a package for {tidyverse}-aligned statistical inference. We had a {patchwork} (a few ggplots smushed together) object and wanted the layer function to act on and modify each patch in the patchwork one-by-one rather than the whole plot. You can see that PR here.↩︎