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SELF-DOCUMENTING PLOTS IN GGPLOT2
Posted on March 10, 2022 by Higher Order Functions in R bloggers | 0 Comments
[This article was first published on Higher Order Functions, and kindly
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When I am showing off a plotting technique in ggplot2, I sometimes like to
include the R code that produced the plot as part of the plot. Here is an
example I made to demonstrate the
debug
debug parameter in
element_text()
element_text():
library(ggplot2)
self_document(
ggplot(mtcars, aes(x = mpg)) +
geom_histogram(bins = 20, color = "white") +
labs(title = "A basic histogram") +
theme(axis.title = element_text(debug = TRUE))
)
library(ggplot2) self_document( ggplot(mtcars, aes(x = mpg)) +
geom_histogram(bins = 20, color = "white") + labs(title = "A basic histogram") +
theme(axis.title = element_text(debug = TRUE)) )
library(ggplot2)
self_document(
ggplot(mtcars, aes(x = mpg)) +
geom_histogram(bins = 20, color = "white") +
labs(title = "A basic histogram") +
theme(axis.title = element_text(debug = TRUE))
)
Let’s call these “self-documenting plots”. If we’re feeling nerdy, we might also
call them “qquines”, although they are not true quines.
In this post, we will build up a
self_document()
self_document() function from scratch. Here are the problems we need to sort
out:
* how to put plotting code above a title
* how to capture plotting code and convert it into text
CREATING THE CODE ANNOTATION
As a first step, let’s just treat our plotting code as a string that is ready to
use for annotation.
p_text <- 'ggplot(mtcars, aes(x = mpg)) +
geom_histogram(bins = 20, color = "white") +
labs(title = "A basic histogram")'
p_plot <- ggplot(mtcars, aes(x = mpg)) +
geom_histogram(bins = 20, color = "white") +
labs(title = "A basic histogram")
p_text <- 'ggplot(mtcars, aes(x = mpg)) + geom_histogram(bins = 20, color =
"white") + labs(title = "A basic histogram")' p_plot <- ggplot(mtcars, aes(x =
mpg)) + geom_histogram(bins = 20, color = "white") + labs(title = "A basic
histogram")
p_text <- 'ggplot(mtcars, aes(x = mpg)) +
geom_histogram(bins = 20, color = "white") +
labs(title = "A basic histogram")'
p_plot <- ggplot(mtcars, aes(x = mpg)) +
geom_histogram(bins = 20, color = "white") +
labs(title = "A basic histogram")
In order to have a titled plot along with this annotation, we need some way to
combine these two graphical objects together (the code and the plot produced by
ggplot2). I like the patchwork package for this job. Here we use
wrap_elements()
wrap_elements() to capture the plot into a “patch” that patchwork can annotate.
library(patchwork)
wrap_elements(p_plot) +
plot_annotation(title = p_text)
library(patchwork) wrap_elements(p_plot) + plot_annotation(title = p_text)
library(patchwork)
wrap_elements(p_plot) +
plot_annotation(title = p_text)
Let’s style this title to use a monospaced font. I use Windows and like
Consolas, so I will use that font.
# Use default mono font if "Consolas" is not available
extrafont::loadfonts(device = "win", quiet = TRUE)
monofont <- ifelse(
extrafont::choose_font("Consolas") == "",
"mono",
"Consolas"
)
title_theme <- theme(
plot.title = element_text(
family = monofont, hjust = 0, size = rel(.9),
margin = margin(0, 0, 5.5, 0, unit = "pt")
)
)
wrap_elements(p_plot) +
plot_annotation(title = p_text, theme = title_theme)
# Use default mono font if "Consolas" is not available
extrafont::loadfonts(device = "win", quiet = TRUE) monofont <- ifelse(
extrafont::choose_font("Consolas") == "", "mono", "Consolas" ) title_theme <-
theme( plot.title = element_text( family = monofont, hjust = 0, size = rel(.9),
margin = margin(0, 0, 5.5, 0, unit = "pt") ) ) wrap_elements(p_plot) +
plot_annotation(title = p_text, theme = title_theme)
# Use default mono font if "Consolas" is not available
extrafont::loadfonts(device = "win", quiet = TRUE)
monofont <- ifelse(
extrafont::choose_font("Consolas") == "",
"mono",
"Consolas"
)
title_theme <- theme(
plot.title = element_text(
family = monofont, hjust = 0, size = rel(.9),
margin = margin(0, 0, 5.5, 0, unit = "pt")
)
)
wrap_elements(p_plot) +
plot_annotation(title = p_text, theme = title_theme)
One problem with this setup is that the plotting code has to be edited in two
places: the plot
p_plot
p_plot and the title
p_text
p_text. As a result, it’s easy for these two pieces of code to fall out of sync
with each other, turning our self-documenting plot into a lying liar plot.
The solution is pretty easy: Tell R that
p_text
p_text is code with
parse()
parse() and evaluate the code with
eval()
eval():
wrap_elements(eval(parse(text = p_text))) +
plot_annotation(title = p_text, theme = title_theme)
wrap_elements(eval(parse(text = p_text))) + plot_annotation(title = p_text,
theme = title_theme)
wrap_elements(eval(parse(text = p_text))) +
plot_annotation(title = p_text, theme = title_theme)
This works. It gets the job done. But we find ourselves in a clumsy workflow,
either having to edit R code inside of quotes or editing the plot interactively
and then having to wrap it in quotes. Let’s do better.
CAPTURING PLOTTING CODE AS A STRING
Time for some nonstandard evaluation. I will use the rlang package, although in
principle we could use functions in base R to accomplish these goals.
First, we are going to use
rlang::expr()
rlang::expr() to capture/quote/defuse the R code as an expression. We can print
the code as code, print it as text, and use
eval()
eval() to show the plot.
p_code <- rlang::expr(
ggplot(mtcars, aes(x = mpg)) +
geom_histogram(bins = 20, color = "white") +
labs(title = "A basic histogram")
)
# print the expressions
p_code
#> ggplot(mtcars, aes(x = mpg)) + geom_histogram(bins = 20, color = "white") +
#> labs(title = "A basic histogram")
# expression => text
rlang::expr_text(p_code)
#> [1] "ggplot(mtcars, aes(x = mpg)) + geom_histogram(bins = 20, color =
\"white\") + \n labs(title = \"A basic histogram\")"
eval(p_code)
p_code <- rlang::expr( ggplot(mtcars, aes(x = mpg)) + geom_histogram(bins = 20,
color = "white") + labs(title = "A basic histogram") ) # print the expressions
p_code #> ggplot(mtcars, aes(x = mpg)) + geom_histogram(bins = 20, color =
"white") + #> labs(title = "A basic histogram") # expression => text
rlang::expr_text(p_code) #> [1] "ggplot(mtcars, aes(x = mpg)) +
geom_histogram(bins = 20, color = \"white\") + \n labs(title = \"A basic
histogram\")" eval(p_code)
p_code <- rlang::expr(
ggplot(mtcars, aes(x = mpg)) +
geom_histogram(bins = 20, color = "white") +
labs(title = "A basic histogram")
)
# print the expressions
p_code
#> ggplot(mtcars, aes(x = mpg)) + geom_histogram(bins = 20, color = "white") +
#> labs(title = "A basic histogram")
# expression => text
rlang::expr_text(p_code)
#> [1] "ggplot(mtcars, aes(x = mpg)) + geom_histogram(bins = 20, color = \"white\") + \n labs(title = \"A basic histogram\")"
eval(p_code)
Then, it should be straightforward to make the self-documenting plot, right?
p_code <- rlang::expr(
ggplot(mtcars, aes(x = mpg)) +
geom_histogram(bins = 20, color = "white") +
labs(title = "A basic histogram")
)
wrap_elements(eval(p_code)) +
plot_annotation(title = rlang::expr_text(p_code), theme = title_theme)
p_code <- rlang::expr( ggplot(mtcars, aes(x = mpg)) + geom_histogram(bins = 20,
color = "white") + labs(title = "A basic histogram") )
wrap_elements(eval(p_code)) + plot_annotation(title = rlang::expr_text(p_code),
theme = title_theme)
p_code <- rlang::expr(
ggplot(mtcars, aes(x = mpg)) +
geom_histogram(bins = 20, color = "white") +
labs(title = "A basic histogram")
)
wrap_elements(eval(p_code)) +
plot_annotation(title = rlang::expr_text(p_code), theme = title_theme)
Hey, it reformatted the title! Indeed, in the process of capturing the code, the
code formatting was lost. To get something closer to the source code we
provided, we have to reformat the captured code before we print it.
The styler package provides a suite of functions for reformatting code. We can
define our own coding styles/formatting rules to customize how styler works. I
like the styler rules used by Garrick Aden-Buie in his grkstyle package, so I
will use
grkstyle::grk_style_text()
grkstyle::grk_style_text() to reformat the code.
p_code <- rlang::expr(
ggplot(mtcars, aes(x = mpg)) +
geom_histogram(bins = 20, color = "white") +
labs(title = "A basic histogram")
)
wrap_elements(eval(p_code)) +
plot_annotation(
title = rlang::expr_text(p_code) |>
grkstyle::grk_style_text() |>
# reformatting returns a vector of lines,
# so we have to combine them
paste0(collapse = "\n"),
theme = title_theme
)
p_code <- rlang::expr( ggplot(mtcars, aes(x = mpg)) + geom_histogram(bins = 20,
color = "white") + labs(title = "A basic histogram") )
wrap_elements(eval(p_code)) + plot_annotation( title = rlang::expr_text(p_code)
|> grkstyle::grk_style_text() |> # reformatting returns a vector of lines, # so
we have to combine them paste0(collapse = "\n"), theme = title_theme )
p_code <- rlang::expr(
ggplot(mtcars, aes(x = mpg)) +
geom_histogram(bins = 20, color = "white") +
labs(title = "A basic histogram")
)
wrap_elements(eval(p_code)) +
plot_annotation(
title = rlang::expr_text(p_code) |>
grkstyle::grk_style_text() |>
# reformatting returns a vector of lines,
# so we have to combine them
paste0(collapse = "\n"),
theme = title_theme
)
PUTTING IT ALL TOGETHER
When we write our
self_document()
self_document() function, the only change we have to make is using
rlang::enexpr()
rlang::enexpr() instead
rlang::expr()
rlang::expr(). The en-variant is used when we want to en-quote exactly what the
user provided. Aside from that change, our
self_document()
self_document() function just bundles together all of the code we developed
above:
self_document <- function(expr) {
monofont <- ifelse(
extrafont::choose_font("Consolas") == "",
"mono",
"Consolas"
)
p <- rlang::enexpr(expr)
title <- rlang::expr_text(p) |>
grkstyle::grk_style_text() |>
paste0(collapse = "\n")
patchwork::wrap_elements(eval(p)) +
patchwork::plot_annotation(
title = title,
theme = theme(
plot.title = element_text(
family = monofont, hjust = 0, size = rel(.9),
margin = margin(0, 0, 5.5, 0, unit = "pt")
)
)
)
}
self_document <- function(expr) { monofont <- ifelse(
extrafont::choose_font("Consolas") == "", "mono", "Consolas" ) p <-
rlang::enexpr(expr) title <- rlang::expr_text(p) |> grkstyle::grk_style_text()
|> paste0(collapse = "\n") patchwork::wrap_elements(eval(p)) +
patchwork::plot_annotation( title = title, theme = theme( plot.title =
element_text( family = monofont, hjust = 0, size = rel(.9), margin = margin(0,
0, 5.5, 0, unit = "pt") ) ) ) }
self_document <- function(expr) {
monofont <- ifelse(
extrafont::choose_font("Consolas") == "",
"mono",
"Consolas"
)
p <- rlang::enexpr(expr)
title <- rlang::expr_text(p) |>
grkstyle::grk_style_text() |>
paste0(collapse = "\n")
patchwork::wrap_elements(eval(p)) +
patchwork::plot_annotation(
title = title,
theme = theme(
plot.title = element_text(
family = monofont, hjust = 0, size = rel(.9),
margin = margin(0, 0, 5.5, 0, unit = "pt")
)
)
)
}
And let’s confirm that it works.
library(ggplot2)
self_document(
ggplot(mtcars, aes(x = mpg)) +
geom_histogram(bins = 20, color = "white") +
labs(title = "A basic histogram")
)
library(ggplot2) self_document( ggplot(mtcars, aes(x = mpg)) +
geom_histogram(bins = 20, color = "white") + labs(title = "A basic histogram") )
library(ggplot2)
self_document(
ggplot(mtcars, aes(x = mpg)) +
geom_histogram(bins = 20, color = "white") +
labs(title = "A basic histogram")
)
Because we developed this function on top of rlang, we can do some tricks like
injecting a variable’s value when capturing the code. For example, here I use
!! color
!! color to replace the
color
color variable with the actual value.
color <- "white"
self_document(
ggplot(mtcars, aes(x = mpg)) +
geom_histogram(bins = 20, color = !! color) +
labs(title = "A basic histogram")
)
color <- "white" self_document( ggplot(mtcars, aes(x = mpg)) +
geom_histogram(bins = 20, color = !! color) + labs(title = "A basic histogram")
)
color <- "white"
self_document(
ggplot(mtcars, aes(x = mpg)) +
geom_histogram(bins = 20, color = !! color) +
labs(title = "A basic histogram")
)
And if you are wondering, yes, we can
self_document()
self_document() a
self_document()
self_document() plot.
self_document(
self_document(
ggplot(mtcars, aes(x = mpg)) +
geom_histogram(bins = 20, color = "white") +
labs(title = "A basic histogram")
)
)
self_document( self_document( ggplot(mtcars, aes(x = mpg)) + geom_histogram(bins
= 20, color = "white") + labs(title = "A basic histogram") ) )
self_document(
self_document(
ggplot(mtcars, aes(x = mpg)) +
geom_histogram(bins = 20, color = "white") +
labs(title = "A basic histogram")
)
)
ALAS, COMMENTS ARE LOST
One downside of this approach is that helpful comments are lost.
self_document(
ggplot(mtcars, aes(x = mpg)) +
geom_histogram(bins = 20, color = !! color) +
# get rid of that grey
theme_minimal() +
labs(title = "A basic histogram")
)
self_document( ggplot(mtcars, aes(x = mpg)) + geom_histogram(bins = 20, color =
!! color) + # get rid of that grey theme_minimal() + labs(title = "A basic
histogram") )
self_document(
ggplot(mtcars, aes(x = mpg)) +
geom_histogram(bins = 20, color = !! color) +
# get rid of that grey
theme_minimal() +
labs(title = "A basic histogram")
)
I am not sure how to include comments. One place where comments are stored and
printed is in function bodies:
f <- function() {
ggplot(mtcars, aes(x = mpg)) +
geom_histogram(bins = 20, color = !! color) +
# get rid of that grey
theme_minimal() +
labs(title = "A basic histogram")
}
print(f, useSource = TRUE)
#> function() {
#> ggplot(mtcars, aes(x = mpg)) +
#> geom_histogram(bins = 20, color = !! color) +
#> # get rid of that grey
#> theme_minimal() +
#> labs(title = "A basic histogram")
#> }
#> <environment: 0x000001746d339b68>
f <- function() { ggplot(mtcars, aes(x = mpg)) + geom_histogram(bins = 20, color
= !! color) + # get rid of that grey theme_minimal() + labs(title = "A basic
histogram") } print(f, useSource = TRUE) #> function() { #> ggplot(mtcars, aes(x
= mpg)) + #> geom_histogram(bins = 20, color = !! color) + #> # get rid of that
grey #> theme_minimal() + #> labs(title = "A basic histogram") #> } #>
<environment: 0x000001746d339b68>
f <- function() {
ggplot(mtcars, aes(x = mpg)) +
geom_histogram(bins = 20, color = !! color) +
# get rid of that grey
theme_minimal() +
labs(title = "A basic histogram")
}
print(f, useSource = TRUE)
#> function() {
#> ggplot(mtcars, aes(x = mpg)) +
#> geom_histogram(bins = 20, color = !! color) +
#> # get rid of that grey
#> theme_minimal() +
#> labs(title = "A basic histogram")
#> }
#> <environment: 0x000001746d339b68>
I have no idea how to go about exploiting this feature for self-documenting
plots, however.
--------------------------------------------------------------------------------
Last knitted on 2022-03-10. Source code on GitHub.1
1. sessioninfo::session_info()
#> ─ Session info
───────────────────────────────────────────────────────────────
#> setting value
#> version R Under development (unstable) (2022-03-02 r81842 ucrt)
#> os Windows 10 x64 (build 22000)
#> system x86_64, mingw32
#> ui RTerm
#> language (EN)
#> collate English_United States.utf8
#> ctype English_United States.utf8
#> tz America/Chicago
#> date 2022-03-10
#> pandoc NA
#>
#> ─ Packages
───────────────────────────────────────────────────────────────────
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↩
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