`qstat`

qstat
qlayer()
- see sf2stat for sf-specific function qlayer_sf
a bunch of examples and ad-hoc tests…
One-liners?
Part II. Packaging and documentation 🚧 ✅
- Phase 1. Minimal working package

devtools::create(".")

… creating new stats is one of the most useful ways to extend the capabilities of ggplot2.’ – ggplot2: Elegant Graphics for Data Analysis Yes!

Current methods for defining new user-facing stat and geom functions may be considered prohibitive for in-script, on-the-fly-use. statexpress attempts to make concise definition possible - almost as concise as the pre-processing of data that you might have to do in absence of the proposed statexpress functionality. With statexpress, extending ggplot2’s capabilities by creating under-the-hood statisical transformation routines could happen not only as development activity, but also in data-analytic contexts.

Because creating new stats is so useful and can contribute so much to user’s expressiveness w/ ggplot2 and because users are likely to intuit how Stats and Geoms work together at a high level, perhaps it’s an activity that should hold a special place in the extension world - where not so much is expected of extenders as is suggested by some resources (e.g. Internals Chapter of ggplot2 which follows):

Maybe not? For a ggplot2 developer who hopes to design extensions, however, … understanding [how ggplot2 translates this plot specification into an image] is paramount. (ggplot2 internals)

… the ease with which extensions can be made means that writing one-off extensions to solve a particular plotting challenge is also viable. (More true with shortcut?)

Creating a geom_() [by defining a new Stat] from scratch is an involved process that doesn’t lend itself to simple transformations. Elio Campitelli, in blog post ‘How to make a generic stat in ggplot2’ ¹

A few approaches have been combine here.

qstat()…
qlayer()…

Let’s go!

`qstat`

A quick way to test out new Stats is to use them in combination with geom_*() user facing functions. For example,

library(tidyverse)
# Step 1. Define compute
compute_group_means <- function(data, scales){
 
  data %>%
    summarise(x = mean(x, na.rm = T),
              y = mean(y, na.rm = T))
 
}

# Step 2. Define Stat
StatMeans <- ggproto("StatMeans",
                     Stat,
                     compute_group = compute_group_means)

# Step 3. Use Stat
palmerpenguins::penguins %>%
  ggplot() +
  aes(bill_length_mm, bill_depth_mm) +
  geom_point() +
  geom_point(stat = StatMeans, size = 8)
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_point()`).

But naming things is hard; and if we’re just starting off, we might just want to have a way to check out our compute in a visual space kind of directly. So we introduce a way to test out our compute without a dedicated Stat in the global environment and without having to name it. That might look something like this two-step process:

# Step 1. Define compute
compute_group_means <- function(data, scales){
 
  data %>%
    summarise(x = mean(x, na.rm = T),
              y = mean(y, na.rm = T))
 
}

# Step 2. Use Compute
palmerpenguins::penguins %>%
  ggplot() +
  aes(bill_length_mm, bill_depth_mm) +
  geom_point() +
  geom_point(stat = qstat(compute_group_means), size = 8)

And you could start thinking about something user-facing like this:

geom_means_point <- function(...){
  geom_point(stat = qstat(compute_group_means))
  }

geom_means <- geom_means_point

This might be enabled by a simple set of convenience functions qstat_group, qstat_panel, and qstat_layer, where are specified as follows. qstat is just an alias of qstat_group (and can actually be used for panel or layer computation by spelling out compute_panel argument).

knitrExtra:::chunk_to_r("a_qstat")
#> It seems you are currently knitting a Rmd/Qmd file. The parsing of the file will be done in a new R session.

qstat <- function(compute_group = ggplot2::Stat$compute_group, ...) {

  ggproto("StatTemp", Stat, compute_group = compute_group, ...)
 
}

qstat_group <- function(compute_panel, ...) {

  ggproto("StatTemp", Stat, qstat_group = qstat_group, ...)
 
}

qstat_panel <- function(compute_panel, ...) {

  ggproto("StatTemp", Stat, compute_panel = compute_panel, ...)
 
}

qstat_layer <- function(compute_layer, ...) {

  ggproto("StatTemp", Stat, compute_layer = compute_layer, ...)
 
}

Basic example/test:

palmerpenguins::penguins %>%
  ggplot() +
  aes(bill_length_mm, bill_depth_mm) +
  geom_point() +
  geom_point(stat = qstat(compute_group_means), size = 8)
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_point()`).

`qlayer()`

You might however, also need something like qlayer. This can be useful if you want to ‘try on’ a user facing stat_*() function - in other words, don’t lock in a geom. Furthermore, you’ve got to use it in cases where user-facing geom functions don’t give you the option of changing the Stat (no stat argument) as with geom_vline/GeomVline.

qlayer is essentially a flexible cross between geom_point and stat_identity.

knitrExtra:::chunk_to_r("b_qlayer")
#> It seems you are currently knitting a Rmd/Qmd file. The parsing of the file will be done in a new R session.

qlayer <- function (mapping = NULL,
                    data = NULL,
                    stat = StatIdentity,
                    geom = GeomPoint,
                    position = position_identity(),
                    ...,
                    na.rm = FALSE,
                    show.legend = NA,
                    inherit.aes = TRUE)
{
  layer(
    data = data,
    mapping = mapping,
    stat = stat,
    geom = geom,
    position = position,
    show.legend = show.legend,
    inherit.aes = inherit.aes,
    params = rlang::list2(na.rm = na.rm, ...)
  )
}

stat_means <- function(geom = GeomPoint, ...){
 
  qlayer(geom = geom, stat = qstat(compute_group_means), ...)
 
}

ggplot(cars) +
  aes(speed, dist) +
  geom_point() +
  stat_means(size = 12) +
  stat_means(geom = "label",
             label = "means x and y")

ggplot(palmerpenguins::penguins) +
  aes(bill_length_mm, bill_depth_mm) +
  geom_point() +
  aes(color = species) +
  stat_means(size = 12) +
  stat_means(geom = "label",
             aes(label = species))
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_point()`).

Now looking at the case of GeomVline which doesn’t have a user facing function that allows Stat to be input.

compute_group_xmean <- function(data, scales){
 
  data %>% summarise(xintercept = mean(x))
 
}

geom_xmean_vline <- function(...){
 
  qlayer(stat = qstat(compute_group_xmean),
         geom = GeomVline, ...)
 
}


ggplot(faithful) +
  aes(waiting) +
  geom_histogram() +
  geom_xmean_vline()
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#> Warning: The following aesthetics were dropped during statistical transformation: x.
#> ℹ This can happen when ggplot fails to infer the correct grouping structure in
#>   the data.
#> ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
#>   variable into a factor?

Here’s a good moment to show that qstat can also be used to more fully flesh out a hypothetical stat. Given the warning we might define a quick stat within the user-facing layer, so that we are warning-free.

geom_xmean_vline <- function(...){
 
  QStat <- qstat(compute_group_xmean,
                 dropped_aes = "x")
 
  qlayer(stat = QStat,
         geom = GeomVline, ...)
 
}

ggplot(faithful) +
  aes(waiting) +
  geom_histogram() +
  geom_xmean_vline()
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

It is also possible of course to commit to a Stat, and use qlayer to speed your definitions along, if you’re layer specification is anticipated to be a tad vanilla (as mine, for example, mostly are!).

stat_means <- function(geom = "point", ...){
 
  qlayer(geom = geom, stat = StatMeans, ...)
 
}

ggplot(palmerpenguins::penguins) +
  aes(bill_length_mm, bill_depth_mm) +
  geom_point() +
  aes(color = species) +
  stat_means(size = 12) +
  stat_means(geom = "label",
             aes(label = species))
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_point()`).

see sf2stat for sf-specific function qlayer_sf

a bunch of examples and ad-hoc tests…

geom_means

test

library(tidyverse)
group_means <- function(data, scales){
 
 data |>
    summarise(x = mean(x),
              y = mean(y))
 
}

geom_means <- function(...){
  geom_point(stat = qstat(group_means), ...)
}

mtcars |>
  ggplot() +
  aes(x = wt,
      y = mpg) +
  geom_point() +
  geom_means(size = 6)

last_plot() +
  aes(color = factor(cyl))

mtcars |>
  ggplot() +
  aes(x = wt,
      y = mpg) +
  aes(color = factor(cyl)) +
  geom_point(show.legend = F) +
  geom_means(size = 6, show.legend = F)

mtcars |>
  ggplot() +
  aes(x = wt,
      y = mpg) +
  geom_point() +
  geom_means(size = 6, aes(color = NULL)) +
  aes(color = factor(cyl))

geom_center_label

group_label_at_center <- function(data, scales){
 
 data %>%
    group_by(label) %>%
    summarise(x = mean(x, na.rm = T),
              y = mean(y, na.rm = T))
 
}

geom_center_label <- function(...){
  geom_label(stat = qstat(group_label_at_center), ...)
}


palmerpenguins::penguins |>
  ggplot() +
  aes(x = bill_length_mm,
      y = bill_depth_mm) +
  geom_point() +
  aes(label = "All") +
  geom_center_label()
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_point()`).

last_plot() +
  aes(color = species, label = species)
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_point()`).

geom_center_text <- function(...){
  geom_text(stat = qstat(group_label_at_center), ...)
}


palmerpenguins::penguins |>
  ggplot() +
  aes(x = bill_length_mm,
      y = bill_depth_mm) +
  geom_point(aes(color = bill_length_mm)) +
  aes(color = species) +
  geom_center_text(color = "Black",
    aes(label = species),
                     
                    alpha = .8,
                   size = 5,
                   fontface = "bold")
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_point()`).

layer_data(i = 2)
#>       label        x        y PANEL group colour size angle hjust vjust alpha
#> 1    Adelie 38.79139 18.34636     1    -1  Black    5     0   0.5   0.5   0.8
#> 2 Chinstrap 48.83382 18.42059     1    -1  Black    5     0   0.5   0.5   0.8
#> 3    Gentoo 47.50488 14.98211     1    -1  Black    5     0   0.5   0.5   0.8
#>   family fontface lineheight
#> 1            bold        1.2
#> 2            bold        1.2
#> 3            bold        1.2

geom_post

compute_post <- function(data, scales){
 
  data %>%
    mutate(xend = x,
           yend = 0)
 
}

geom_post <- function(...){
  geom_segment(stat = qstat(compute_post), ...)
}


data.frame(outcome = 0:1, prob = c(.4, .6)) |>
  ggplot() +
  aes(x = outcome,
      y = prob) +
  geom_post() +
  geom_point() +
  labs(title = "probability by outcome")

geom_xmean

compute_xmean <- function(data, scales){
 
  data %>%
    summarize(xintercept = mean(x))
 
}

geom_xmean <- function(...){
  qlayer(stat = qstat(compute_xmean,
                      dropped_aes = c("x", "y")),
         geom = "vline", ...)
}

mtcars |>
  ggplot() +
  aes(x = wt,
      y = mpg) +
  geom_point() +
  geom_xmean(linetype = "dashed")

last_plot() +
  aes(color = factor(cyl))

geom_quantile

test of passing arguments from compute

compute_xy_quantile <- function(data, scales, q = .5){
 
  data %>%
    summarise(x = quantile(x, q),
              y = quantile(y, q))
 
}

geom_quantile <- function(...){
  geom_point(stat = qstat(compute_xy_quantile), ...)  #adding point means that aes is automatically figured out! ok
}

mtcars |>
  ggplot() +
  aes(x = wt,
      y = mpg) +
  geom_point() +
  geom_quantile(size = 8, aes(color = "q = .5"), q = .5) +
  geom_quantile(size = 8, q = 1, aes(color = "q =  1")) +
  geom_quantile(aes(color = "q =  .9"), size = 8, q = .9)

`geom_highlight()`

compute_panel_highlight <- function(data, scales, which_id = NULL){

data %>%
  arrange(highlight_condition) %>%
  mutate(group = fct_inorder(grouping))
 
}

stat_highlight <- function(geom = "line", ...){
 
  qlayer(stat = qstat_panel(compute_panel_highlight,
                      default_aes =
                        aes(color = after_stat(highlight_condition))),
             geom = geom,
             ...)
}

gapminder::gapminder %>%
  filter(continent == "Americas") %>%
  ggplot() +
  aes(x = year, y = lifeExp,
      grouping = country,
      highlight_condition =
        country == "Bolivia") +
  stat_highlight(linewidth = 3)

gapminder::gapminder %>%
  filter(year == 2002) %>%
  ggplot() +
  aes(x = gdpPercap, y = lifeExp,
      grouping = continent,
      highlight_condition =
        continent == "Europe") +
  stat_highlight(geom = "point", show.legend = F) +
  scale_color_manual(values = c("grey", "darkolivegreen"))

One-liners?

Since were organize with variable function input in first position and geom in section position, and we can do one-liners (or two) use positioning for arguments.

`geom_xmean_line()` in 137 characters

library(tidyverse)

geom_xmean_line <- function(...){qlayer(stat = qstat(function(data, scales) data |> summarize(xintercept = mean(x)) , dropped_aes = c("x", "y")), geom =  "vline", ...)}


ggplot(cars) +
  aes(speed, dist) +
  geom_point() +
  geom_xmean_line(linetype = 'dashed')

 
last_plot() +
  aes(color = dist > 50)

ggplot(cars) +
  aes(speed, dist) +
  geom_point() +
  geom_xmean_line(linetype = 'dashed',
                  data = . %>% tail,
                  aes(color = dist > 50))

ggplot(cars) +
  aes(speed, dist) +
  geom_point() +
  geom_xmean_line(data = . %>% filter(speed < 10))

`geom_xmean` in 99 characters

geom_xmean <- function(...){geom_point(stat = qstat(function(data, scales){data |> summarize(x = mean(x), y = I(.025))}), ...)}

ggplot(cars) +
  aes(speed, dist) +
  geom_point() +
  geom_xmean(size = 8, shape = "diamond")

last_plot() +
  aes(color = dist > 50)

`geom_post()` in 101 characters, `stat_expectedvalue()` in 113, `geom_expectedvalue_label()` 171…

May I buy a visually enhanced probability lesson for 400 characters? Yes please.

geom_post <- function(...){geom_segment(stat = qstat(function(data, scales){data |> mutate(xend = x, yend = 0)}), ...)}

data.frame(prob = c(.4,.6), outcome = c(0, 1)) %>%
ggplot(data = .) +
  aes(outcome, prob) +
  geom_post() +
  geom_point()

stat_expectedvalue <- function(geom = "point", ...){
 
  qlayer(stat = qstat(function(data, scales){data |> summarise(x = sum(x*y), y = 0)},
                      default_aes = aes(label = after_stat(round(x, 2)))),
             geom = geom,
             ...)
 
  } # point is default geom

last_plot() +
  stat_expectedvalue()

last_plot() +
  stat_expectedvalue(geom = "text", vjust = 0) +
  stat_expectedvalue(geom = "text", 
                     label = "The Expected Value",
                     vjust = 1)

`geom_proportion()` and `geom_proportion_label()`

rep(1, 15) |>
  c(0) %>%
  data.frame(outcome = .) |>
  ggplot() +
  aes(x = outcome) +
  geom_dotplot()
#> Bin width defaults to 1/30 of the range of the data. Pick better value with
#> `binwidth`.

geom_proportion <- function(...){geom_point(stat = qstat_panel(function(data, scales) data |> summarise(x = sum(x)/length(x), y = 0)), ...)}   # this should work for T/F too when rasa_p is in play


last_plot() +
  geom_proportion(shape = "triangle", size = 5)
#> Bin width defaults to 1/30 of the range of the data. Pick better value with
#> `binwidth`.

geom_proportion_label <- function(...){geom_text(stat = qstat_panel(function(data, scales) data |> summarise(x = sum(x)/length(x), y = 0) |> mutate(label = round(x,2))), vjust = 0, ...)}   # this should work for T/F too when rasa_p is in play

last_plot() +
  geom_proportion_label()
#> Bin width defaults to 1/30 of the range of the data. Pick better value with
#> `binwidth`.

# last_plot() +
#   geom_proportion_label() +
#   ggsample::facet_bootstrap()
#
# layer_data(i = 2)
#
#
#
# rep(0:1, 10000) %>% # very large 50/50 sample
#   data.frame(outcome = .) |>
#   ggplot() +
#   aes(x = outcome) +
#   geom_dotplot() +
#   geom_proportion() +
#   geom_proportion_label() +
#   ggsample::facet_sample(n_facets = 25,
#     n_sampled = 16) ->
# p; p
#  
#
# layer_data(p, i = 2) |>
#   ggplot() +
#   aes(x = x) +
#   geom_rug() +
#   geom_histogram()

`geom_means` in 131 characters

geom_means <- function(...){geom_point(stat = qstat(function(data, scales) data |> summarize(x = mean(x, na.rm = T), y = mean(y, na.rm = T))), ...)}

palmerpenguins::penguins %>%
  ggplot() +
  aes(bill_length_mm, bill_depth_mm) +
  geom_point() +
  geom_means(size = 7)
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_point()`).

last_plot() +
  geom_means(size = 8, shape = "diamond", aes(color = bill_depth_mm > 17))  # why does aes work here w/o mapping =
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_point()`).
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_point()`).

last_plot() +
  geom_means(size = 8,
             shape = "square",
             data = . %>% filter(species == "Chinstrap"),
             aes(color = bill_depth_mm > 17),
             alpha = .6)
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_point()`).
#> Removed 1 row containing missing values or values outside the scale range
#> (`geom_point()`).

`geom_grouplabel_at_means()`

geom_grouplabel_at_means <-  function(...){geom_label(stat = qstat(function(data, scales) data |> group_by(label) |> summarize(x = mean(x, na.rm = T), y = mean(y, na.rm = T))), ...)}

palmerpenguins::penguins %>%
  ggplot() +
  aes(bill_length_mm, bill_depth_mm, label = species, group = species) +
  geom_point() +
  geom_grouplabel_at_means(size = 7)
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_point()`).

Part II. Packaging and documentation 🚧 ✅

Phase 1. Minimal working package

To build a minimal working package, 1) we’ll need to document dependencies 2) send functions to .R files in the R folder, 3) do a package check (this will A. Document our functions and B. this will help us identify problems - for example if we’ve failed to declare a dependency) and 4) install the package locally.

Then we’ll write up a quick advertisement for what our package is able to do in the ‘traditional readme’ section. This gives us a chance to test out the package.

### Bit 2a: in the function(s) you wrote above make sure dependencies to functions using '::' syntax to pkg functions
usethis::use_package("ggplot2") # Bit 2b: document dependencies, w hypothetical ggplot2

# Bit 4: document functions and check that package is minimally viable
devtools::check(pkg = ".")

# Bit 5: install package locally
devtools::install(pkg = ".", upgrade = "never")

Bit 7. Write traditional README that uses built package (also serves as a test of build). 🚧 ✅

The goal of the {statexpress} package is to …

Install package with:

remotes::install_github("EvaMaeRey/statexpress")

Once functions are exported you can remove go to two colons, and when things are are really finalized, then go without colons (and rearrange your readme…)

rm(list = ls())
library(statexpress)
library(ggplot2)
library(tidyverse)

compute_group_balancing = function(data, scales){
    data |>
      summarize(x = mean(x), y = I(.05))
    }

geom_xbalance <- function(...){
 
  geom_point(stat = statexpress:::qstat(compute_group_balancing), ...)
 
  }

ggplot(cars) +
  aes(speed, dist) +
  geom_point() +
  geom_xbalance(size = 8, shape = "diamond")

last_plot() +
  aes(color = dist > 50)

last_plot() +
  geom_xbalance(mapping = aes(size = dist > 75))
#> Warning: Using size for a discrete variable is not advised.

# for quick knit (exiting early) change eval to TRUE
fs::dir_tree(recurse = T)
#> .
#> ├── DESCRIPTION
#> ├── NAMESPACE
#> ├── R
#> │   ├── a_qstat.R
#> │   └── b_qlayer.R
#> ├── README.Rmd
#> ├── README.md
#> ├── README0.Rmd
#> ├── README_files
#> │   └── figure-gfm
#> │       ├── cars-1.png
#> │       ├── cars-2.png
#> │       ├── cars-3.png
#> │       ├── unnamed-chunk-10-1.png
#> │       ├── unnamed-chunk-10-2.png
#> │       ├── unnamed-chunk-10-3.png
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#> │       ├── unnamed-chunk-5-1.png
#> │       ├── unnamed-chunk-5-2.png
#> │       ├── unnamed-chunk-5-3.png
#> │       ├── unnamed-chunk-5-4.png
#> │       ├── unnamed-chunk-6-1.png
#> │       ├── unnamed-chunk-6-2.png
#> │       ├── unnamed-chunk-6-3.png
#> │       ├── unnamed-chunk-7-1.png
#> │       ├── unnamed-chunk-7-2.png
#> │       ├── unnamed-chunk-8-1.png
#> │       ├── unnamed-chunk-8-2.png
#> │       ├── unnamed-chunk-8-3.png
#> │       ├── unnamed-chunk-9-1.png
#> │       ├── unnamed-chunk-9-2.png
#> │       └── unnamed-chunk-9-3.png
#> ├── man
#> └── statexpress.Rproj

knitr::knit_exit()

Elio offers a solution, stat_rasa(), but it works to mimic just a small part of the functionality offered by creating new Stats. {statexpress} aim is to allow full flexibility of designing a new Stat as with classical use, while keeping boilerplate at bay. It should be notated that a new stat_manual() function under development in base ggplot2 also achieves the limited goals Campitelli managed with stat_rasa(). ↩

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`qstat`

`qlayer()`

see sf2stat for sf-specific function qlayer_sf

a bunch of examples and ad-hoc tests…

geom_means

geom_center_label

geom_post

geom_xmean

geom_quantile

`geom_highlight()`

One-liners?

`geom_xmean_line()` in 137 characters

`geom_xmean` in 99 characters

`geom_post()` in 101 characters, `stat_expectedvalue()` in 113, `geom_expectedvalue_label()` 171…

`geom_proportion()` and `geom_proportion_label()`

`geom_means` in 131 characters

`geom_grouplabel_at_means()`

Part II. Packaging and documentation 🚧 ✅

Phase 1. Minimal working package

Bit 7. Write traditional README that uses built package (also serves as a test of build). 🚧 ✅

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Name		Name	Last commit message	Last commit date
Latest commit History 11 Commits
R		R
README_files/figure-gfm		README_files/figure-gfm
.Rbuildignore		.Rbuildignore
.gitignore		.gitignore
DESCRIPTION		DESCRIPTION
NAMESPACE		NAMESPACE
README.Rmd		README.Rmd
README.md		README.md
README0.Rmd		README0.Rmd
statexpress.Rproj		statexpress.Rproj

EvaMaeRey/statexpress

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qstat

qlayer()

see sf2stat for sf-specific function qlayer_sf

a bunch of examples and ad-hoc tests…

geom_means

geom_center_label

geom_post

geom_xmean

geom_quantile

geom_highlight()

One-liners?

geom_xmean_line() in 137 characters

geom_xmean in 99 characters

geom_post() in 101 characters, stat_expectedvalue() in 113, geom_expectedvalue_label() 171…

geom_proportion() and geom_proportion_label()

geom_means in 131 characters

geom_grouplabel_at_means()

Part II. Packaging and documentation 🚧 ✅

Phase 1. Minimal working package

Bit 7. Write traditional README that uses built package (also serves as a test of build). 🚧 ✅

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`qstat`

`qlayer()`

`geom_highlight()`

`geom_xmean_line()` in 137 characters

`geom_xmean` in 99 characters

`geom_post()` in 101 characters, `stat_expectedvalue()` in 113, `geom_expectedvalue_label()` 171…

`geom_proportion()` and `geom_proportion_label()`

`geom_means` in 131 characters

`geom_grouplabel_at_means()`

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