The goal of this repository is to keep track of some neat ggplot2 tricks I’ve learned. This assumes you’ve familiarised yourself with the basics of ggplot2 and can construct some nice plots of your own. If not, please peruse the book at your leasure.
I’m not incredibly adapt in gloriously typesetting plots and expertly
finetuning themes and colour palettes, so you’d have to forgive me. The
mpg dataset is very versatile for plotting, so you’ll be seeing a lot
of that as you read on. Extension packages are great, and I’ve dabbled
myself, but I’ll try to limit myself to vanilla ggplot2 tricks here.
For now, this will be mostly a README-only bag of tricks, but I may decide later to put them into separate groups in other files.
By loading the library and setting a plotting theme. The first trick
here is to use theme_set() to set a theme for all your plots
throughout a document. If you find yourself setting a very verbose theme
for every plot, here is the place where you set all your common
settings. Then never write a novel of theme elements ever again1!
library(ggplot2)
library(scales)
theme_set(
# Pick a starting theme
theme_gray() +
# Add your favourite elements
theme(
axis.line = element_line(),
panel.background = element_rect(fill = "white"),
panel.grid.major = element_line("grey95", linewidth = 0.25),
legend.key = element_rect(fill = NA)
)
)There are some extensions that offer half-geom versions of things. Of the ones I know, gghalves and the see package offer some half-geoms.
Here is how to abuse the delayed evaluation system to make your own. This can come in handy if you’re not willing to take on an extra dependency for just this feature.
The easy case is the boxplot. You can either set xmin or xmax to
after_scale(x) to keep the right and left parts of a boxplot
respectively. This still works fine with position = "dodge".
# A basic plot to reuse for examples
p <- ggplot(mpg, aes(class, displ, colour = class)) +
guides(colour = "none", fill = "none") +
labs(y = "Engine Displacement [L]", x = "Type of car")
p + geom_boxplot(aes(xmin = after_scale(x)))
The same thing that works for boxplots, also works for errorbars.
p + geom_errorbar(
stat = "summary",
fun.data = mean_se,
aes(xmin = after_scale(x))
)
We can once again do the same thing for violin plots, but the layer
complains about not knowing about the xmin aesthetic. It does use that
aesthetic, but only after the data has been setup, so it is not
intended to be a user accessible aesthetic. We can silence the warning
by updating the xmin default to NULL, which means it won’t complain,
but also doesn’t use it if absent.
update_geom_defaults("violin", list(xmin = NULL))
p + geom_violin(aes(xmin = after_scale(x)))
Not left as an exercise for the reader this time, but I just wanted to show how it would work if you were to combine two halves and want them a little bit offset from one another. We’ll abuse the errorbars to serve as staples for the boxplots.
# A small nudge offset
offset <- 0.025
# We can pre-specify the mappings if we plan on recycling some
right_nudge <- aes(
xmin = after_scale(x),
x = stage(class, after_stat = x + offset)
)
left_nudge <- aes(
xmax = after_scale(x),
x = stage(class, after_stat = x - offset)
)
# Combining
p +
geom_violin(right_nudge) +
geom_boxplot(left_nudge) +
geom_errorbar(left_nudge, stat = "boxplot", width = 0.3)
Let’s say you have better colour intuition than I have, and three
colours aren’t enough for your divergent colour palette needs. A
painpoint is that it is tricky to get the midpoint right if your limits
aren’t perfectly centered around it. Enter the rescaler argument in
league with scales::rescale_mid().
my_palette <- c("dodgerblue", "deepskyblue", "white", "hotpink", "deeppink")
p <- ggplot(mpg, aes(displ, hwy, colour = cty - mean(cty))) +
geom_point() +
labs(
x = "Engine displacement [L]",
y = "Highway miles per gallon",
colour = "Centered\nvalue"
)
p +
scale_colour_gradientn(
colours = my_palette,
rescaler = ~ rescale_mid(.x, mid = 0)
)
An alternative is to simply center the limits on x. We can do that by providing a function to the scale’s limits.
p +
scale_colour_gradientn(
colours = my_palette,
limits = ~ c(-1, 1) * max(abs(.x))
)
Putting text annotations on facetted plots is a pain, because limits can vary on a per-panel basis, so it is very difficult to find the correct position. An extension that explores alleviating this pain is the tagger extension, but we can do a similar thing in vanilla ggplot2.
Luckily, there is a mechanic in ggplot2’s position axes that let’s
-Inf and Inf be interpreted as the scale’s minimum and maximum limit
respectively2. You can exploit this by choosing x = Inf, y = Inf to
put the labels in a corner. You can also use -Inf instead of Inf to
place at the bottom instead of top, or left instead of right.
We need to match the hjust/vjust arguments to the side of the plot.
For x/y = Inf, they would need to be hjust/vjust = 1, and for
x/y = -Inf they need to be hjust/vjust = 0.
p + facet_wrap(~ class, scales = "free") +
geom_text(
# We only need 1 row per facet, so we deduplicate the facetting variable
data = ~ subset(.x, !duplicated(class)),
aes(x = Inf, y = Inf, label = LETTERS[seq_along(class)]),
hjust = 1, vjust = 1,
colour = "black"
)
Unfortunately, this places the text straight at the border of the panel,
which may offend our sense of beauty. We can get slightly fancier by
using geom_label(), which lets us more precisely control the spacing
between the text and the panel borders by setting the label.padding
argument.
Moreover, we can use label.size = NA, fill = NA to hide the textbox
part of the geom. For illustration purposes, we now place the tag at the
top-left instead of top-right.
p + facet_wrap(~ class, scales = "free") +
geom_label(
data = ~ subset(.x, !duplicated(class)),
aes(x = -Inf, y = Inf, label = LETTERS[seq_along(class)]),
hjust = 0, vjust = 1, label.size = NA, fill = NA,
label.padding = unit(5, "pt"),
colour = "black"
)
Let’s say we’re tasked with making a bunch of similar plots, with different datasets and columns. For example, we might want to make a series of barplots3 with some specific pre-sets: we’d like the bars to touch the x-axis and not draw vertical gridlines.
One well-known way to make a bunch of similar plots is to wrap the plot construction into a function. That way, you can use encode all the presets you want in your function.
I case you might not know, there are various methods to program with
the aes()
function,
and using {{ }} (curly-curly) is one of the more flexible ways 4.
barplot_fun <- function(data, x) {
ggplot(data, aes(x = {{ x }})) +
geom_bar(width = 0.618) +
scale_y_continuous(expand = c(0, 0, 0.05, 0)) +
theme(panel.grid.major.x = element_blank())
}
barplot_fun(mpg, class)
One drawback of this approach is that you lock-in any aesthetics in the
function arguments. To go around this, an even simpler way is to simply
pass ... directly to aes().
barplot_fun <- function(data, ...) {
ggplot(data, aes(...)) +
geom_bar(width = 0.618) +
scale_y_continuous(expand = c(0, 0, 0.1, 0)) +
theme(panel.grid.major.x = element_blank())
}
barplot_fun(mpg, class, fill = factor(cyl))
Another method of doing a very similar thing, is to use plot
‘skeletons’. The idea behind a skeleton is that you can build a plot,
with or without any data argument, and add in the specifics later.
Then, when you actually want to make a plot, you can use the %+% to
fill in or replace the dataset, and + aes(...) to set the relevant
aesthetics.
barplot_skelly <- ggplot() +
geom_bar(width = 0.618) +
scale_y_continuous(expand = c(0, 0, 0.1, 0)) +
theme(panel.grid.major.x = element_blank())
my_plot <- barplot_skelly %+% mpg + aes(class, fill = factor(cyl))
my_plot
One neat thing about these skeletons is that even when you’ve already
filled in the data and mapping arguments, you can just replace them
again and again.
my_plot %+% mtcars + aes(factor(carb), fill = factor(cyl))
Ribcage5
The idea here is to not skeletonise the entire plot, but just a
frequently re-used set of parts. For example, we might want to label our
barplot, and pack together all the things that make up a labelled
barplot. The trick to this is to not add these components together
with +, but simply put them in a list(). You can then + your list
together with the main plot call.
labelled_bars <- list(
geom_bar(width = 0.618),
geom_text(
stat = "count",
aes(y = after_stat(count),
label = after_stat(count),
fill = NULL),
vjust = -1
),
scale_y_continuous(expand = c(0, 0, 0.1, 0)),
theme(panel.grid.major.x = element_blank())
)
ggplot(mpg, aes(class, fill = factor(cyl))) +
labelled_bars +
ggtitle("The `mpg` dataset")
ggplot(mtcars, aes(factor(carb), fill = factor(cyl))) +
labelled_bars +
ggtitle("The `mtcars` dataset")
Session info
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.2.2 (2022-10-31 ucrt)
#> os Windows 10 x64 (build 22000)
#> system x86_64, mingw32
#> ui RTerm
#> language (EN)
#> collate English_United Kingdom.utf8
#> ctype English_United Kingdom.utf8
#> tz Europe/Berlin
#> date 2023-02-05
#> pandoc 2.19.2
#>
#> ─ Packages ───────────────────────────────────────────────────────────────────
#> package * version date (UTC) lib source
#> assertthat 0.2.1 2019-03-21 [] CRAN (R 4.2.0)
#> cli 3.4.1 2022-09-23 [] CRAN (R 4.2.2)
#> colorspace 2.0-3 2022-02-21 [] CRAN (R 4.2.0)
#> DBI 1.1.3 2022-06-18 [] CRAN (R 4.2.2)
#> digest 0.6.29 2021-12-01 [] CRAN (R 4.2.0)
#> dplyr 1.0.10 2022-09-01 [] CRAN (R 4.2.1)
#> evaluate 0.19 2022-12-13 [] CRAN (R 4.2.2)
#> fansi 1.0.3 2022-03-24 [] CRAN (R 4.2.0)
#> farver 2.1.1 2022-07-06 [] CRAN (R 4.2.1)
#> fastmap 1.1.0 2021-01-25 [] CRAN (R 4.2.0)
#> generics 0.1.3 2022-07-05 [] CRAN (R 4.2.1)
#> ggplot2 * 3.4.0 2022-11-04 [] CRAN (R 4.2.2)
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#> knitr 1.41 2022-11-18 [] CRAN (R 4.2.2)
#> labeling 0.4.2 2020-10-20 [] CRAN (R 4.2.0)
#> lifecycle 1.0.3 2022-10-07 [] CRAN (R 4.2.2)
#> magrittr 2.0.3 2022-03-30 [] CRAN (R 4.2.0)
#> munsell 0.5.0 2018-06-12 [] CRAN (R 4.2.0)
#> pillar 1.8.1 2022-08-19 [] CRAN (R 4.2.1)
#> pkgconfig 2.0.3 2019-09-22 [] CRAN (R 4.2.0)
#> R6 2.5.1 2021-08-19 [] CRAN (R 4.2.0)
#> ragg 1.2.2 2022-02-21 [] CRAN (R 4.2.0)
#> rlang 1.0.6 2022-09-24 [] CRAN (R 4.2.1)
#> rmarkdown 2.19 2022-12-15 [] CRAN (R 4.2.2)
#> rstudioapi 0.14 2022-08-22 [] CRAN (R 4.2.2)
#> scales * 1.2.1 2022-08-20 [] CRAN (R 4.2.2)
#> sessioninfo 1.2.2 2021-12-06 [] CRAN (R 4.2.0)
#> stringi 1.7.6 2021-11-29 [] CRAN (R 4.2.0)
#> stringr 1.5.0 2022-12-02 [] CRAN (R 4.2.2)
#> systemfonts 1.0.4 2022-02-11 [] CRAN (R 4.2.0)
#> textshaping 0.3.6 2021-10-13 [] CRAN (R 4.2.0)
#> tibble 3.1.8 2022-07-22 [] CRAN (R 4.2.1)
#> tidyselect 1.2.0 2022-10-10 [] CRAN (R 4.2.2)
#> utf8 1.2.2 2021-07-24 [] CRAN (R 4.2.0)
#> vctrs 0.5.0 2022-10-22 [] CRAN (R 4.2.2)
#> withr 2.5.0 2022-03-03 [] CRAN (R 4.2.0)
#> xfun 0.36 2022-12-21 [] CRAN (R 4.2.2)
#> yaml 2.3.5 2022-02-21 [] CRAN (R 4.2.0)
#>
#>
#> ──────────────────────────────────────────────────────────────────────────────
Footnotes
-
Well, you need to do it once at the start of your document. But then never again! Except in your next document. Just write a
plot_defaults.Rscript andsource()that from your document. Copy-paste that script for every project. Then, truly, never again :heart:. ↩ -
Unless you self-sabotage your plots by setting
oob = scales::oob_censor_anyin the scale for example. ↩ -
In your soul of souls, do you really want to make a bunch of barplots though? ↩
-
The alternative is to use the
.datapronoun, which can be.data$varif you want to lock in that column in advance, or.data[[var]]whenvaris passed as a character. ↩ -
This bit was originally called ‘partial skeleton’, but as a ribcage is a part of a skeleton, this title sounded more evocative. ↩