垂直和水平对齐打印R ggplot2

我有一个特别的问题（至少我喜欢这样认为：））。我想在两列两行中对齐三个绘图。对齐应在中央图（见图；图C）之后，其中图A应在图C的x轴之后对齐，图D应在图C的y轴之后对齐。注意，没有图B，该图应保持为空

数据：

我希望D区离C区很近

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Deni

正如@Axeman提到的，这是由图例引起的，

cowplot:：get_legend（）

可以解决这个问题（请参见

？cowplot:：get_legend（）

，了解您的具体情况）：

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legend_p1您是否对A和C的x轴使用相同的值？看这个例子，它都很好地结合在一起：
library（ggplot2）；图书馆（cowplot）；“希望有人能复制一些东西”，嗯，我想你应该给出一个可复制的例子。是否收到有关对齐的消息或警告？@m-dz，无论x轴值如何，打印区域都将/应该对齐。不过，图例可能会带来问题。@Axeman，看来我不明白
cowplot
对齐是如何工作的，谢谢你纠正我。从图中可以看出，它正在对齐整个图，包括图例和轴标签。更容易获得。@m-dz是的，这些轴值相同，C和D的y轴值相同。在我的情况下，你刚刚采用了相同的方法，但校准不起作用。非常感谢！这看起来更符合我的真实需求。你能告诉我进行各自数据转换的最佳方法吗？没问题！我不知道你所说的“各自的数据转换”是什么意思，你能解释一下吗，或者给你上面的问题添加一个例子吗？我的意思是，有没有一种巧妙的方法可以将原始帖子中的数据调整到你为“可读”绘图而创建的数据？不是真的，通过使绘图“可读”，我的意思是调整
相对宽度
等等。，您还可以缩短
GO
和
GOd
，这样它们就不会占用太多空间。还有什么。。。如果绘图
D
仅显示体积比较，您可以尝试删除x轴。这一切只是为了修复情节的“视觉”方面。谢谢你的帮助！还有一件事，实际上是两件：）在绘图时，我想让绘图D尽可能靠近绘图C，可以这样做吗？现在他们几乎分开了。还有一个“上帝”的标签在a区后徘徊，我想把它去掉。再次感谢。
a <- data.frame(
  id = 1:15,
  GO = c(
    "phosphoglycerate kinase", "phosphoglycerate kinase", 
    "phosphoglycerate kinase", "phosphoglycerate kinase", "phosphoglycerate kinase", 
    "phenylalanine-tRNA ligase", "phenylalanine-tRNA ligase", "phenylalanine-tRNA ligase", 
    "phenylalanine-tRNA ligase", "phenylalanine-tRNA ligase", "allantoicase", 
    "allantoicase", "allantoicase", "allantoicase", "allantoicase"),
  variable = c(
    "d64", "d31", "d16", "d9", "d0", "d64", "d31", "d16", "d9", "d0", "d64", "d31", "d16", "d9", "d0"),
  value = c(
    154.28239, 226.04355, 245.67728, 271.82375, 270.83519, 289.01809, 491.66461,
    485.28291, 351.3759, 510.96043, 22.75253, 31.66546, 129.50564, 206.6651, 32.43769),
  relAbundByGO = c(
    13.201624, 19.342078, 21.022096, 23.259395, 23.174806, 13.57975, 23.101262,
    22.801413, 16.509683, 24.007892, 5.378513, 7.485456, 30.614078, 48.853948, 7.668005),
  GOd = c(
    "phosphoglycerate kinase", "phosphoglycerate kinase", 
    "phosphoglycerate kinase", "phosphoglycerate kinase", "phosphoglycerate kinase", 
    "phenylalanine-tRNA ligase", "phenylalanine-tRNA ligase", "phenylalanine-tRNA ligase", 
    "phenylalanine-tRNA ligase", "phenylalanine-tRNA ligase", "allantoicase", 
    "allantoicase", "allantoicase", "allantoicase", "allantoicase"
  ))

b <- data.frame(
  id = 1:15,
  Compound = c(
    "C5-C10", "C5-C10", "C5-C10", 
    "C5-C10", "C5-C10", "C10-C20", "C10-C20", "C10-C20", "C10-C20", 
    "C10-C20", "BTEX", "BTEX", "BTEX", "BTEX", "BTEX"),
  Degradation = c(
    100, 100, 23.5, 5.6, 0, 100, 100, 67.2, 19, 0.6, 100, 100, 88.7, 43.3, 0.1),
  st ()dev = c(
    0, 0, 35, 12.4, 0, 0, 0, 19.3, 13.1, 0.6, 0, 0, 33.4, 43.4, 0.2),
  day = c(
    "NSWOD-0", "NSWOD-64", "NSOD-9", 
    "NSOD-16", "NSOD-31", "NSWOD-0", "NSWOD-64", "NSOD-9", "NSOD-16", 
    "NSOD-31", "NSWOD-0", "NSWOD-64", "NSOD-9", "NSOD-16", "NSOD-31"))

a$GO <- factor(a$GO, levels = a$GO) #keep the same order as in table
a$variable <- factor(a$variable, levels = c("d0", "d64", "d9", "d16", "d31"))
p1 <- ggplot(data = a, aes(x=variable, y=GO)) + 
  geom_tile(aes(fill=relAbundByGO), colour = "white") + ylab("Gene ontology") + 
  scale_fill_gradient(name="Relative\nabundance of TPM", low = "green", high = "red", limits=c(0, 100), na.value="transparent") +
  scale_x_discrete("Sample", 
                   breaks = c("d0", "d64", "d9", "d16", "d31"),
                   labels = c("CTRL-0", "CTRL-64", "CEWAF-9","CEWAF-16","CEWAF-31")) + 
  theme(legend.position="left")
p1

a$GO <- factor(a$GO, levels = a$GO) #keep the same order as in table
p2 <- ggplot(data = a, aes(x=GO, y=value)) +
  geom_bar(stat = "identity") +
  theme(axis.text.x = element_text(angle = 0)) +
  ylab("Cumulative TPM abundance") +
  theme(axis.text.y=element_blank(),
        axis.title.y=element_blank()) +
  coord_flip()
p2

b$day <- factor(b$day, levels = b$day) #keep the same order as in table
p3 <- ggplot(b, aes(x=day, y=Degradation, color=Compound, group=Compound)) + 
  geom_point(size=4, shape=21, fill="white") + 
  geom_line(size=0.7) + 
  ylab("Hydrocarbon content (%)") + 
  geom_errorbar(aes(ymax=Degradation + stdev, ymin=Degradation-stdev), linetype="dashed", lwd=.6, width=.4) +
  theme_bw(base_size = 12, base_family = "Helvetica") +
  theme(axis.text.x=element_blank()
        ,axis.ticks=element_blank()
        ,axis.title.x=element_blank(),
        legend.position="left") + 
  scale_color_discrete(name="Hydrocarbon\ngroup",
                       breaks=c("C5-C10", "C10-C20", "BTEX", "PAHs"))
p3

legend_p1 <- get_legend(p1)
legend_p3 <- get_legend(p3)

p1 <- p1 + theme(legend.position='none')
p3 <- p3 + theme(legend.position='none')

cowplot::plot_grid(
  cowplot::plot_grid(legend_p3, legend_p1, ncol = 1),
  cowplot::plot_grid(p3, NULL, p1, p2,  ncol = 2, nrow = 2, rel_widths = c(1, 0.75, 1, 0.75), labels = c('A', '', 'C', 'D'), align = "hv"),
  rel_widths = c(0.16, 1))

legend_p1 <- get_legend(p1)
legend_p3 <- get_legend(p3)

p1 <- p1 + theme(legend.position='none')
p3 <- p3 + theme(legend.position='none')

cowplot::plot_grid(
  cowplot::plot_grid(legend_p1, legend_p3, ncol = 1),
  cowplot::plot_grid(p3, NULL, p1, p2,  ncol = 2, nrow = 2, rel_widths = c(1, 0.75, 1, 0.75), labels = c('A', '', 'C', 'D'), align = "hv"),
  rel_widths = c(0.1, 1))

a <- data.frame(
  id = 1:15,
  GO = c(
    "phosphoglycerate kinase", "phosphoglycerate kinase", 
    "phosphoglycerate kinase", "phosphoglycerate kinase", "phosphoglycerate kinase", 
    "phenylalanine-tRNA ligase", "phenylalanine-tRNA ligase", "phenylalanine-tRNA ligase", 
    "phenylalanine-tRNA ligase", "phenylalanine-tRNA ligase", "allantoicase", 
    "allantoicase", "allantoicase", "allantoicase", "allantoicase"),
  variable = c(
    "d64", "d31", "d16", "d9", "d0", "d64", "d31", "d16", "d9", "d0", "d64", "d31", "d16", "d9", "d0"),
  value = c(
    154.28239, 226.04355, 245.67728, 271.82375, 270.83519, 289.01809, 491.66461,
    485.28291, 351.3759, 510.96043, 22.75253, 31.66546, 129.50564, 206.6651, 32.43769),
  relAbundByGO = c(
    13.201624, 19.342078, 21.022096, 23.259395, 23.174806, 13.57975, 23.101262,
    22.801413, 16.509683, 24.007892, 5.378513, 7.485456, 30.614078, 48.853948, 7.668005),
  GOd = c(
    "phosphoglycerate kinase", "phosphoglycerate kinase", 
    "phosphoglycerate kinase", "phosphoglycerate kinase", "phosphoglycerate kinase", 
    "phenylalanine-tRNA ligase", "phenylalanine-tRNA ligase", "phenylalanine-tRNA ligase", 
    "phenylalanine-tRNA ligase", "phenylalanine-tRNA ligase", "allantoicase", 
    "allantoicase", "allantoicase", "allantoicase", "allantoicase"
  ))
b <- data.frame(
  id = 1:15,
  Compound = c(
    "C5-C10", "C5-C10", "C5-C10", 
    "C5-C10", "C5-C10", "C10-C20", "C10-C20", "C10-C20", "C10-C20", 
    "C10-C20", "BTEX", "BTEX", "BTEX", "BTEX", "BTEX"),
  Degradation = c(
    100, 100, 23.5, 5.6, 0, 100, 100, 67.2, 19, 0.6, 100, 100, 88.7, 43.3, 0.1),
  st ()dev = c(
    0, 0, 35, 12.4, 0, 0, 0, 19.3, 13.1, 0.6, 0, 0, 33.4, 43.4, 0.2),
  day = c(
    "NSWOD-0", "NSWOD-64", "NSOD-9", 
    "NSOD-16", "NSOD-31", "NSWOD-0", "NSWOD-64", "NSOD-9", "NSOD-16", 
    "NSOD-31", "NSWOD-0", "NSWOD-64", "NSOD-9", "NSOD-16", "NSOD-31"))