The title is relatively self explanatory. I would like to know how ggplot decides its default breaks (and hence labels).
From the below code, it looks like the method is the same for each geom:
library(ggplot2)
ggplot(data=mtcars,mapping=aes(x=carb,y=hp,fill=as.factor(gear)))+
geom_bar(stat="identity",position="dodge")
ggplot(data=mtcars,mapping=aes(x=carb,y=hp,fill=as.factor(gear)))+
geom_point()
Any help would be greatly appreciated
The function scale_x_continuous() and scale_y_continuous() can be used for ggplot axis breaks settings.
ggplot2 is a plotting package that provides helpful commands to create complex plots from data in a data frame. It provides a more programmatic interface for specifying what variables to plot, how they are displayed, and general visual properties.
To add axis breaks in ggplot2 plots in R, we use scale_x_break() and scale_y_break() functions. These functions take a vector as a parameter that has breakpoints. If we need multiple breakpoints we can add those too.
The ggplot2 package is a relatively novel approach to generating highly informative publication-quality graphics. The “gg” stands for “Grammar of Graphics”.
I had the same question myself, and Google brought me to this SO question, so I thought I'd do a bit of digging.
Suppose we plot
library(ggplot2)
ggplot(mtcars, aes(x = cyl, y = mpg, size = hp)) +
geom_point()
which gives us the following plot, and we wish to know how the breaks for mpg
(10, 15, ..., 35), cyl
(4, 5, ..., 8), and hp
(100, 150, ..., 300) are derived.
Focusing on mpg
we inspect the code for scale_y_continuous
and see that it calls continuous_scale
. Then, calling up ?continuous_scale
we see, under the description for the trans
argument, that
A transformation object bundles together a transform, it's inverse, and methods for generating breaks and labels.
Then, looking up ?scales::trans_new
, we see that the default value for the breaks
argument is extended_breaks()
. Following the trail, we find that scales::extended_breaks
calls labeling::extended(rng[1], rng[2], n, only.loose = FALSE, ...)
. Applying this to our data,
with(mtcars, labeling::extended(range(mpg)[1], range(mpg)[2], m = 5))
# [1] 10 15 20 25 30 35
which is what we observe in the plot. This raises the question of why, despite
with(mtcars, labeling::extended(range(hp)[1], range(hp)[2], m = 5))
# [1] 50 100 150 200 250 300 350
we don't observe 50 and 350 in the legend. My understanding is that the answer is related to https://stackoverflow.com/a/13888731/6455166.
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