knitr::opts_chunk$set( echo = TRUE, fig.align='center', fig.width = 6, fig.height = 4, out.width = '600px', out.height = '400px') library(ggpackets)
ggpackets
aims to make ggplot plots more modular by adding a framework for
packing groups of ggplot layers that can be stored as a single variable or
accessed from a function call. This means that you can now easily build out
template plots which still interact nicely with the ggplot syntax. To illustrate
this functionality, let's start with a trivial example.
To start, let's take a look at what it might take to build a bar plot showing
the mean value of some group with some errorbars to represent standard deviation
and the counts in each bar labelled at the base of the bar. We'll start by
building this plot using standard ggplot
.
library(ggplot2)
ggplot(mtcars) + aes(x = carb, y = mpg) + geom_bar(stat= 'summary', aes(y = mpg), fun.data = function(c) c(y = mean(c))) + geom_errorbar(stat = 'summary', aes(y = mpg), width = 0.2, fun.y = mean, fun.min = ~mean(.) - sd(.), fun.max = ~mean(.) + sd(.)) + geom_text(stat = 'summary', vjust = -0.5, color = 'white', fun.data = function(c) c( label = length(c), y = 0))
Wow! that plot got out of hand fast. And you can see that there's a lot of
repeated code. If we know that we want to use the exact same code repeatedly, we
could wrap this in a function and make it a bit more reusable, but we'd
sacrifice a lot of the customizability of building our plot from scratch for
minor modifications in each instance. Alternatively, we could use ggpackets
,
which would provide further functionality to interoperate with other ggplot
layers.
In its most straightforward form, ggpackets
allows for the packing of multiple
layers together into a single variable. This variable contains a ggppacket
object. This class handles addition with other ggpacket
objects and ggproto
objects to append them or their contents to an internal list of ggproto
layers
( ggproto
objects are the result of any of the geom_*
or stat_*
functions in
ggplot2
). When it's combined with a ggplot call to build a plot (starting with
the function ggplot()
), the ggpacket
will add its layers to the ggplot.
ggpacket
my_ggpk_layers <- ggpacket() + geom_bar(stat = 'summary', fun.data = function(c) c(y = mean(c))) + geom_errorbar(stat = 'summary', width = 0.2, fun.data = function(c) c( y = mean(c), ymin = mean(c) - sd(c), ymax = mean(c) + sd(c))) + geom_text(stat = 'summary', vjust = -0.5, color = 'white', fun.data = function(c) c(label = length(c), y = 0))
ggplot(mtcars) + aes(x = carb, y = mpg, fill = as.factor(am)) + my_ggpk_layers() + facet_grid(. ~ am, scales = 'free')
By breaking out only the layers of the plot, we still retain full control over the additional layers that might be needed and the variables that are being used to produce the plot without needing to add extensive argument handling to a function.
ggplot2
Using ggpacket()
to build out a packet of layers, we get a bunch of
flexibility to provide modifications to our base ggplot layers with only a very
minor change to our code. Let's say we instead want a bit more fine-grained
control over the explicit behavior of our custom plot. This is where the most
straightforward usage of ggpackets
might not suffice and we might turn to a
function to fullfil our needs. At this point, wrapping our original ggplot call
in a function starts to become pretty appealing.
my_plot <- function(data, x, y, fun.data = mean_se) { ggplot(data) + aes_string(x = x, y = y) + geom_bar(stat= 'summary', aes_string(y = y), fun.data = fun.data) + geom_errorbar( stat = 'summary', aes_string(y = y), width = 0.2, fun.data = fun.data) + geom_text( stat = 'summary', color = 'white', vjust = -0.5, fun.data = function(c) c(label = length(c), y = 0)) }
my_plot(mtcars, 'carb', 'wt')
Looking pretty good!
Let's consider, however, that you still want to have the flexibility to edit the color of the bars throughout your analysis, or change the function used to determine your errorbars, or facet your plot for multiple groups. One way would be to include additional arguments in your new function and then pass that through to your plot. Every additional argument requires a fair bit of tweaking to this function and it's fairly easy to imagine the scope of the function growing quickly out of hand. Alternatively, you could wrap your ggplot layers in a list and use those in a ggplot, but you'd be constrained to the parameters initially defined in the list.
ggpackets
ggpackets
aims to reduce this inevitable issue by making it as simple as
possible to build out these types of reusable code snippets.
my_ggpk <- function(...) { ggpacket(...) %+% geom_bar(.id = 'bar', fun.data = mean_se, position = position_dodge(width = 1.0), ..., stat= 'summary') %+% geom_errorbar(.id = 'errorbar', fun.data = mean_se, position = position_dodge(width = 1.0), width = 0.2, ..., stat = 'summary') %+% geom_label(.id = 'label', position = position_dodge(width = 1.0), vjust = -0.5, color = 'white', alpha = 0.3, label.size = NA, ..., stat = 'summary', fun.data = function(d) c(label = length(d), y = 0)) }
ggplot(mtcars) + aes(x = carb, y = mpg) + my_ggpk(fun.data = mean_se, bar.mapping = aes(fill = carb))
Tip:
Use
ggplot2
's lesser-used%+%
operator to composeggpacket
s in order to handle duplicate arguments without throwing warnings.ggpackets
will use the last-enterred argument to build the plot, allowing a comfortable syntax to intuitively set a default value while allowing user-flexibility.
r f <- function(...) { ggpacket(...) %+% # all ... used as default for all layers geom_bar(fill = "red") %+% # overwrites any fill from ... geom_bar(fill = "red", ...) # allows ... to now mask the default }
This format, with only a bit of extra syntax, exposes quite a lot of new
functionality. We can use additional geometries by adding them to our plot in
standard ggplot
fashion, we can alter our aesthetics intuitively and we can
modify the internals of our packed layers easily. To see this in action, let's
take a look at what we can do with this new function:
ggplot
elementsggplot(mtcars) + aes(x = carb, y = wt, fill = vs) + my_ggpk() + facet_grid(. ~ vs, scales = 'free')
ggpackets
can be used togethermy_ggpk2 <- ggpacket() + geom_ribbon(stat = 'summary', fun.data = mean_se, alpha = 0.2) + geom_line(stat = 'summary', fun.data = mean_se, alpha = 0.3, size = 2) ggplot(mtcars) + aes(x = carb, y = wt) + my_ggpk() + my_ggpk2
ggplot
annotations and themesggplot(mtcars) + aes(x = carb, y = wt, fill = carb) + my_ggpk() + ggtitle('A beautiful ggpackets plot!') + theme_light()
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