This package allows easy access to some common ggplot2
tasks.
library(ggplot2) library(patchwork) library(labelled) library(ggeasy)
Rotating the x
axis labels is a very frequently looked up task, and we can make it easier. If we create a simple ggplot2
plot
p <- ggplot(mtcars, aes(hp, mpg)) + geom_point()
then by default, this looks like
p + labs(title = "ggplot2 default")
We can perform various rotations though
p1 <- p + easy_rotate_x_labels() + labs(title = "default rotation") p2 <- p + easy_rotate_x_labels(angle = 45, side = "right") + labs(title = "angle = 45") p3 <- p + easy_rotate_x_labels("startattop") + labs(title = "text starts at top") p4 <- p + easy_rotate_x_labels("startatbottom") + labs(title = "text starts at bottom") (p1 + p2) / (p3 + p4)
Removing legends is made easier by the easy_remove_legend
function. When called without arguments, all legends are removed (equivalent to theme(legend.position = "none")
). Alternatively, the names of aesthetics for which legends should be removed can be passed.
p <- ggplot(mtcars, aes(wt, mpg, colour = cyl, size = hp)) + geom_point() p1 <- p + labs(title = "With all legends") p2 <- p + easy_remove_legend() + labs(title = "Remove all legends") p3 <- p + easy_remove_legend(size) + labs(title = "Remove size legend") p4 <- p + easy_remove_legend(size, color) + labs(title = "Remove both legends specifically") (p1 + p2) / (p3 + p4)
Grid lines can be completely removed, or removed in only one direction
p <- ggplot(mtcars, aes(hp, mpg)) + geom_point() p1 <- p + easy_remove_gridlines() + labs(title = "Remove all gridlines") p2 <- p + easy_remove_gridlines(major = FALSE) + labs(title = "Remove minor gridlines") p3 <- p + easy_remove_gridlines(minor = FALSE) + labs(title = "Remove minor gridlines") p4 <- p + easy_remove_x_gridlines() + labs(title = "Remove x gridlines") # or # p + easy_remove_gridlines(axis = "x") # p + easy_remove_y_gridlines() (p1 + p2) / (p3 + p4)
Changing plot labels to a specified string isn't particularly difficult (labs(x = "my label")
) but wouldn't it be even nicer if you could just add labels to your data.frame
columns (e.g. using labelled::var_labels()
) and have these reflected in your plot. easy_labs()
makes this possible.
## create a copy of the iris data iris_labs <- iris ## add labels to the columns lbl <- c('Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width', 'Flower\nSpecies') var_label(iris_labs) <- split(lbl, names(iris_labs))
These are visible if you use View(iris_labs)
in RStudio
p <- ggplot(iris_labs, aes(x = Sepal.Length, y = Sepal.Width)) + geom_line(aes(colour = Species)) p1 <- p + labs(title = "default labels") p2 <- p + easy_labs() + labs(title = "Replace titles with column labels") p3 <- p + easy_labs(x = 'My x axis') + labs(title = "Manually add x axis label") iris_labs_2 <- iris_labs var_label(iris_labs_2$Species) <- "Sub-genera" p4 <- p + geom_point(data = iris_labs_2, aes(fill = Species), shape = 24) + easy_labs() + labs(title = "Additional labels can be added in other aesthetics") (p1 + p2) / (p3 + p4)
easy_labs also extends to facetting
p4 + geom_point(data = iris_labs_2, aes(fill = Species), shape = 24) + facet_wrap(~Species) + easy_labs() + labs(title = "Facetting works")
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