Help for ggplot extenders

Your old ggplot extension can learn new tricks

The beauty of the broad solution ggdibbler provides is that it always works. It might not always look good or be exactly what you were looking for, but it always produces… something. To illustrate this, I included the ggraph example in the introduction to ggdibbler, but the idea can be implemented with extensions as well.

In the ggraph example, I illustrated how you can use the ggdibbler approach with a ggplot extension that isn’t even aware ggdibbler exists. While that method works (well enough) it can be somewhat annoying to implement, and it doesn’t give you access to the nested positions (as they use internal variables). I can understand fellow ggplot extenders might be chomping at the bit to see what their plots look like with uncertainty and nested positions.

Sometimes, when making a ggdibbler version of a ggplot2 base function, I would think to myself “What is the point of this, who on earth needs an uncertain version of stat_unique?”. I still sometimes think that, but the reason we implemented it with ALL ggplot2 functions, is not for the users, but rather for the extenders. If there is a ggplot2 extension that is the child of a base stat or base position, a geom_*_sample variation of their function will simply be the child of the ggdibbler variation of the ggplot2 stat, instead of the original ggplot2 code. The only ggplot2 extension that might not work with ggdibbler is ggdist (for the obvious reason that is is the only other ggplot extension that is designed to take distributional input).

Now, I have made all functions in base ggplot2 accept uncertain inputs, go me, but the real power of ggplot2 comes from the wealth of extension packages. Now, as fun as I am sure it would be, I am not about to spend the next 5 years making pull requests on every ggplot2 extension package so that they can all accept random variables. Largely because:

1. Going around making pull requests that forces dependency on my package sounds like the early warning signs of a personality disorder
2. (More importantly) that sounds really boring and I don’t want to.

Thankfully, the ggdibbler approach is so easy if you are the author/maintainer of a ggplot2 extension and you want it to accept random variables, you can just do it yourself.

How to extend your package to accept random variables (easiest to hardest)

geom_YOURGEOM_sample <- make_constructor(YOURGEOM, stat = "GGPLOT2STAT_sample",
                                         times = 10, seed = NULL)

#' @importFrom ggplot2 ggproto Stat***
#' @format NULL
#' @usage NULL
#' @export
Stat***Sample <- ggplot2::ggproto("Stat***Sample", ggplot2::Stat***,
                                  ### INCLUDE SETUP PARAMS IF IN PARENT STAT 
                                  setup_params = function(self, data, params) {
                                    # take one sample just to train the parameters
                                    times <- params$times
                                    params$times <- 1
                                    data <- dibble_to_tibble(data, params)
                                    params <- ggplot2::ggproto_parent(ggplot2::Stat***, self)$setup_params(data, params)
                                    params$times <- times
                                    params
                                  }
                                  ###  SETUP_DATA MUST BE IMPLEMENTED...
                                  setup_data = function(data, params) {
                                    dibble_to_tibble(data, params) 
                                    # BUT YOU ONLY NEED TO INCLUDE THIS LINE IF THE MAIN STAT USES SETUP DATA 
                                    ggproto_parent(Stat***, self)$setup_data(data, scales)
                                    },
                                  
                                  extra_params = c("na.rm", "times", "seed")
)

#' @export
#' @inheritParams ggplot2::stat_***
#' @param times A parameter used to control the number of values sampled from 
#' each distribution.
#' @param seed Set the seed for the layers random draw, allows you to plot the
#' same draw across multiple layers.
stat_***_sample <- make_constructor(Stat***Sample, geom = "***", 
                                    times = 10, seed = NULL)