cowplot (version 0.9.2)

axis_canvas: Generates a canvas onto which one can draw axis-like objects.

Description

This function takes an existing ggplot2 plot and copies one or both of the axis into a new plot. The main idea is to use this in conjunction with insert_xaxis_grob() or insert_yaxis_grob() to draw custom axis-like objects or margin annotations. Importantly, while this function works for both continuous and discrete scales, notice that discrete scales are converted into continuous scales in the returned axis canvas. The levels of the discrete scale are placed at continuous values of 1, 2, 3, etc. See Examples for an example of how to convert a discrete scale into a continuous scale.

Usage

axis_canvas(plot, axis = "y", data = NULL, mapping = aes(), xlim = NULL,
  ylim = NULL, coord_flip = FALSE)

Arguments

plot

The plot defining the x and/or y axis range for the axis canvas.

axis

Specifies which axis to copy from plot. Can be "x", "y", or "xy".

data

(optional) Data to be displayed in this layer.

mapping

(optional) Aesthetic mapping to be used in this layer.

xlim

(optional) Vector of two numbers specifying the limits of the x axis. Ignored if the x axis is copied over from plot.

ylim

(optional) Vector of two numbers specifying the limits of the y axis. Ignored if the y axis is copied over from plot.

coord_flip

(optional) If true, flips the coordinate system and applies x limits to the y axis and vice versa. Useful in combination with ggplot2's coord_flip() function.

Examples

# NOT RUN {
# annotate line graphs with labels on the right
library(dplyr)
library(tidyr)
x <- seq(0, 10, .1)
d <- data.frame(x,
                linear = x,
                squared = x*x/5,
                cubed = x*x*x/25) %>%
  gather(fun, y, -x)

pmain <- ggplot(d, aes(x, y, group = fun)) + geom_line()  +
  scale_x_continuous(expand = c(0, 0))

paxis <- axis_canvas(pmain, axis = "y") +
  geom_text(data = filter(d, x == max(x)), aes(y = y, label = paste0(" ", fun)),
            x = 0, hjust = 0, vjust = 0.5)
ggdraw(insert_yaxis_grob(pmain, paxis, grid::unit(.25, "null")))

# discrete scale with integrated color legend
pmain <- ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
  geom_violin(trim = FALSE) + guides(fill = "none") +
  scale_x_discrete(labels = NULL) +
  theme_minimal()

label_data <- data.frame(x = 1:nlevels(iris$Species),
                         Species = levels(iris$Species))
paxis <- axis_canvas(pmain, axis = "x", data = label_data, mapping = aes(x = x)) +
  geom_tile(aes(fill = Species, y = 0.5), width = 0.9, height = 0.3) +
  geom_text(aes(label = Species, y = 0.5), hjust = 0.5, vjust = 0.5, size = 11/.pt)
ggdraw(insert_xaxis_grob(pmain, paxis, grid::unit(.07, "null"),
                         position = "bottom"))

# add marginal density distributions to plot
pmain <- ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width, color=Species)) + geom_point()

xdens <- axis_canvas(pmain, axis = "x") +
  geom_density(data=iris, aes(x=Sepal.Length, fill=Species), alpha=0.7, size=.2)

# need to set `coord_flip = TRUE` if you plan to use `coord_flip()`
ydens <- axis_canvas(pmain, axis = "y", coord_flip = TRUE) +
  geom_density(data=iris, aes(x=Sepal.Width, fill=Species), alpha=0.7, size=.2) +
  coord_flip()

p1 <- insert_xaxis_grob(pmain, xdens, grid::unit(.2, "null"), position = "top")
p2 <- insert_yaxis_grob(p1, ydens, grid::unit(.2, "null"), position = "right")
ggdraw(p2)
# }

Run the code above in your browser using DataCamp Workspace