在拼凑中合并两个y轴标题 [英] Merging two y-axes titles in patchwork

问题描述

关于我如何合并"的任何想法，将两个相同的y轴标题合并为一个，然后将此y轴标题放置在绘图之间的中间?我通过使用 plot_layout(guides ="collect")成功地合并了图例，但是我似乎找不到与轴相似的东西.在这种情况下，我会将名为disp_disp_disp的两个轴标题合并为一个.

Any ideas as to how I can "merge" two identical y-axes titles into one, and then place this y-axis title in the middle between the plot? I have succeded in merging legends by using plot_layout(guides = "collect") but I cannot seem to find anything similar for axes. In this case I would merge the two axes titles called disp_disp_disp into one.

mtcars

library(ggplot2)
library(patchwork)

p1 <- ggplot(mtcars) + 
  geom_point(aes(mpg, disp)) + 
  labs(x = "mpg", y = "disp_disp_disp_disp_disp")

p2 <- ggplot(mtcars) + 
  geom_boxplot(aes(gear, disp, group = gear)) + 
  labs(x = "gear", y = "disp_disp_disp_disp_disp")

p3 <- ggplot(mtcars) + 
  geom_point(aes(hp, wt, colour = mpg)) + 
  ggtitle('Plot 3')

p1 / (p2 | p3)

推荐答案

我想在绘制图形之前先去掉y轴标题，再在绘制之后再将其重新绘制会更容易一些:

I guess it would be slightly easier to strip out the y axis title before the plot is built then draw it back on after it is plotted:

library(ggplot2)
library(patchwork)

p1 <- ggplot(mtcars) + 
  geom_point(aes(mpg, disp)) + 
  labs(x = "mpg", y = "disp_disp_disp_disp_disp")

p2 <- ggplot(mtcars) + 
  geom_boxplot(aes(gear, disp, group = gear)) + 
  labs(x = "gear", y = "disp_disp_disp_disp_disp")

p3 <- ggplot(mtcars) + 
  geom_point(aes(hp, wt, colour = mpg)) + 
  ggtitle('Plot 3')

ylab <- p1$labels$y
p1$labels$y <- p2$labels$y <- " "

p1 / (p2 | p3)
grid::grid.draw(grid::textGrob(ylab, x = 0.02, rot = 90))

如果您想避免完全被grobs弄脏，另一种选择是指定纯文本ggplot并将其添加为轴文本:

Another option if you want to avoid getting your hands dirty with grobs altogether is to specify a text-only ggplot and add that as your axis text:

p4 <- ggplot(data.frame(l = p1$labels$y, x = 1, y = 1)) +
      geom_text(aes(x, y, label = l), angle = 90) + 
      theme_void() +
      coord_cartesian(clip = "off")

p1$labels$y <- p2$labels$y <- " "

p4 + (p1 / (p2 | p3)) + plot_layout(widths = c(1, 25))

这在调整大小方面也表现得更好.

This behaves a bit better on resizing too.

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