Drop plot_env from ggplot2 objects?

[clauswilke]

This comment #3619 (comment) prompted me to look into plot_env, and as far as I can see it's stored and handed around just so it can be eventually given to the function combine_vars(), which then doesn't use it:

ggplot2/R/facet-.r

Lines 544 to 587 in e9b9946

	combine_vars <- function(data, env = emptyenv(), vars = NULL, drop = TRUE) {
	possible_columns <- unique(unlist(lapply(data, names)))
	if (length(vars) == 0) return(new_data_frame())
	# For each layer, compute the facet values
	values <- compact(lapply(data, eval_facets, facets = vars, possible_columns = possible_columns))
	# Form the base data.frame which contains all combinations of faceting
	# variables that appear in the data
	has_all <- unlist(lapply(values, length)) == length(vars)
	if (!any(has_all)) {
	missing <- lapply(values, function(x) setdiff(names(vars), names(x)))
	missing_txt <- vapply(missing, var_list, character(1))
	name <- c("Plot", paste0("Layer ", seq_len(length(data) - 1)))
	abort(glue(
	"At least one layer must contain all faceting variables: {var_list(names(vars))}.\n",
	glue_collapse(glue("* {name} is missing {missing_txt}"), "\n", last = "\n")
	))
	}
	base <- unique(rbind_dfs(values[has_all]))
	if (!drop) {
	base <- unique_combs(base)
	}
	# Systematically add on missing combinations
	for (value in values[!has_all]) {
	if (empty(value)) next;
	old <- base[setdiff(names(base), names(value))]
	new <- unique(value[intersect(names(base), names(value))])
	if (drop) {
	new <- unique_combs(new)
	}
	base <- unique(rbind(base, df.grid(old, new)))
	}
	if (empty(base)) {
	abort("Faceting variables must have at least one value")
	}
	base
	}

Is this a hold-over from the times before tidy eval? Can we remove this/or assign NULL here?

ggplot2/R/plot.r

Line 96 in 5a686c3

plot_env = environment

It is already marked as deprecated in the documentation:

ggplot2/R/plot.r

Line 35 in 5a686c3

#' @param environment DEPRECATED. Used prior to tidy evaluation.

[clauswilke]

Setting plot_env to NULL doesn't work, which means it is still used somewhere as an environment. However, setting it to an empty environment seems to work without issues.

library(ggplot2)

p <- ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
  geom_point() +
  facet_wrap(~Species)

p$plot_env <- NULL
p
#> Error in exists(name, envir = env, mode = mode): use of NULL environment is defunct

p$plot_env <- rlang::new_environment()
p

^{Created on 2020-05-13 by the reprex package (v0.3.0)}

It looks like the other place where the environment is used is for scale lookup. Maybe it would be better to use the global environment that is active when the plot is printed, rather than when the plot is created? That would be more predictable anyways, I assume.

ggplot2/R/scale-type.R

Lines 28 to 39 in 5a686c3

	find_global <- function(name, env, mode = "any") {
	if (exists(name, envir = env, mode = mode)) {
	return(get(name, envir = env, mode = mode))
	}
	nsenv <- asNamespace("ggplot2")
	if (exists(name, envir = nsenv, mode = mode)) {
	return(get(name, envir = nsenv, mode = mode))
	}
	NULL
	}

[clauswilke]

On the last point, just setting plot_env = rlang::new_environment() means we cannot globally change color scales anymore. So we'd have to fix this appropriately.

library(ggplot2)

p <- ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
  geom_point() +
  facet_wrap(~Species)

scale_colour_discrete <- scale_colour_viridis_d
p

p$plot_env <- rlang::new_environment()
p

^{Created on 2020-05-13 by the reprex package (v0.3.0)}

[yutannihilation]

Thanks for investigating this. I didn't know the mechanism around plot_env.

At first, ggplot2 only looks up in global environment, but started to check the package environment by this commit:

a9f5e0a

Then, find_global() was changed to use the parent environment instead of global environment by this commit:

64666ab

I agree it's nice if we can get rid of plot_env, but the fix might be tricky if we want to preserve the current behavior. For example, I'm afraid looking up only in global environment would break this:

library(ggplot2)

do_plot <- function() {
  p <- ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
    geom_point() +
    facet_wrap(~Species)
  
  scale_colour_discrete <- scale_colour_viridis_d
  
  p
}

p <- do_plot()
p

p$plot_env <- rlang::new_environment()
p

^{Created on 2020-05-14 by the reprex package (v0.3.0)}

[clauswilke]

I get that, but the question is do we want to encourage this usage pattern? Does anybody use it? Is it even logical? See the next example. Would you have expected the plot to use the brewer color scale?

library(ggplot2)

do_plot <- function() {
  p <- ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
    geom_point() +
    facet_wrap(~Species)
  
  scale_colour_discrete <- scale_colour_viridis_d
  
  p
}

p <- do_plot()

scale_colour_discrete <- scale_colour_brewer
p

^{Created on 2020-05-14 by the reprex package (v0.3.0)}

My sense is that using the environment that is active when the plot is being printed is totally reasonable, and that doesn't require saving the environment in the plot itself. I also feel that if people write functions that return plots, then they should explicitly add the scales they want.

[yutannihilation]

do we want to encourage this usage pattern? Does anybody use it?

I really don't know, sorry. I didn't even know (or completely forget) that we can override the default scale in such a way... I think your claim is 99% valid, but I just don't see the context why ggplot2 needed to be changed to use parent environment instead of global environment.

[yutannihilation]

By the way, is this related to #2691? If we can set the default scale by setting the default theme, probably we don't need to tweak globals (or some environments) anymore.

[clauswilke]

@hadley Do you remember why scale lookup in the parent environment was added? 64666ab
Would it be terrible to get rid of this feature?

[hadley]

I have a vague recollection it was something to do with scale customisation. Maybe so that you could write your scale_colour_continuous() to override the defaults?

[thomasp85]

I’m pretty sure that was the reason. I’m all for a better way of allowing users to set a default scale

[clauswilke]

Sure, but the question is: Is it sufficient to look either in the global environment or the parent environment of the print() call, or do we need to capture the local environment that is active when the plot is defined?

In other words, in the reprex in #3994 (comment), would it be fine if the color scale being used upon printing was the brewer scale rather than the viridis scale?

[thomasp85]

I think if we went ahead and nuked the plot_env we might as well come up with a better solution for setting scale defaults than looking for specifically named functions

[clauswilke]

we might as well come up with a better solution for setting scale defaults

That creates a whole new set of problems, though. (#3962 (comment)) I should probably create an issue just for that. We will need some mechanism to override breaks, labels, expansion, etc. without adding a new scale function.

[combiz]

After implementing the p$plot_env <- rlang::new_environment() quick fix, the issue seemed resolved. However, after a recent update (or, possibly, a result of adopting the best-practices suggested in https://ggplot2.tidyverse.org/dev/articles/ggplot2-in-packages.html) I'm finding that other components of a ggplot seem to hold copies of environment objects. Consequently, saving a small list of ggplots with saveRDS or qs::qsave writes >100Gb of data to disk in my case. I've used butcher::weigh to better understand object sizes in the ggplot object: -

> butcher::weigh(p)
# A tibble: 137 x 2
   object                          size
   <chr>                          <dbl>
 1 mapping.x                 3639.     
 2 mapping.y                 3639.     
 3 layers2                   3639.     
 4 scales                       0.727  
 5 layers1                      0.574  
 6 coordinates                  0.255  
 7 facet                        0.178  
 8 data.clusters                0.00404
 9 theme.text.margin            0.00076
10 theme.axis.title.x.margin    0.00076
# … with 127 more rows
> 
> p$layers
[[1]]
geom_bar: width = NULL, na.rm = FALSE, orientation = NA
stat_identity: na.rm = FALSE
position_stack 

[[2]]
mapping: ymin = ~mean - se, ymax = ~mean + se 
geom_errorbar: na.rm = FALSE, orientation = NA, width = 0.2
stat_identity: na.rm = FALSE
position_dodge 

> p$mapping$x
<quosure>
expr: ^.data[["clusters"]]
env:  0x561f534a4500
> p$mapping$y
<quosure>
expr: ^mean
env:  0x561f534a4500
> 

It appears that quosures within the mapping and layers elements are responsible. Has there been a recent change?
Each quosure environment contains large objects unrelated to the plot or quosure expression. Any suggestions? A simple ggplot of data from a 10 row / 3 column tibble is now occupying ~12Gb on disk.