使用cowplot将密度图与散点图完美地对齐

Question

我正在尝试建立一个用于二元作图的函数，取两个变量，它能够表示一个边际散点图和两个横向密度图。

问题是右侧的密度图与底轴不对齐。

下面是一个示例数据：

g1 = c(rnorm(200, mean=350, sd=100), rnorm(200, mean=700, sd=100))
g2 = c(rnorm(200, mean=350, sd=100), rnorm(200, mean=500, sd=100))
df_exp = data.frame(var1=log2(g1 + 1) , var2=log2(g2 + 1))

下面是函数：

    bivariate_plot <- function(df, var1, var2, density = T, box = F) {
    require(ggplot2)
    require(cowplot)
    scatter = ggplot(df, aes(eval(parse(text = var1)), eval(parse(text = var2)), color = "red")) +
            geom_point(alpha=.8)

    plot1 = ggplot(df, aes(eval(parse(text = var1)), fill = "red")) + geom_density(alpha=.5) 
    plot1 = plot1 + ylab("G1 density")

    plot2 = ggplot(df, aes(eval(parse(text = var2)),fill = "red")) + geom_density(alpha=.5) 
    plot2 = plot2 + ylab("G2 density")

    plot_grid(scatter, plot1, plot2, nrow=1, labels=c('A', 'B', 'C')) #Or labels="AUTO"


    # Avoid displaying duplicated legend
    plot1 = plot1 + theme(legend.position="none")
    plot2 = plot2 + theme(legend.position="none")

    # Homogenize scale of shared axes
    min_exp = min(df[[var1]], df[[var2]]) - 0.01
    max_exp = max(df[[var1]], df[[var2]]) + 0.01
    scatter = scatter + ylim(min_exp, max_exp)
    scatter = scatter + xlim(min_exp, max_exp)
    plot1 = plot1 + xlim(min_exp, max_exp)
    plot2 = plot2 + xlim(min_exp, max_exp)
    plot1 = plot1 + ylim(0, 2)
    plot2 = plot2 + ylim(0, 2)


    first_row = plot_grid(scatter, labels = c('A'))
    second_row = plot_grid(plot1, plot2, labels = c('B', 'C'), nrow = 1)
    gg_all = plot_grid(first_row, second_row, labels=c('', ''), ncol=1)

    # Display the legend
    scatter = scatter + theme(legend.justification=c(0, 1), legend.position=c(0, 1))



    # Flip axis of gg_dist_g2
    plot2 = plot2 + coord_flip()

    # Remove some duplicate axes
    plot1 = plot1 + theme(axis.title.x=element_blank(),
                          axis.text=element_blank(),
                          axis.line=element_blank(),
                          axis.ticks=element_blank())

    plot2 = plot2 + theme(axis.title.y=element_blank(),
                          axis.text=element_blank(),
                          axis.line=element_blank(),
                          axis.ticks=element_blank())

    # Modify margin c(top, right, bottom, left) to reduce the distance between plots
    #and align G1 density with the scatterplot
    plot1 = plot1 + theme(plot.margin = unit(c(0.5, 0, 0, 0.7), "cm"))
    scatter = scatter + theme(plot.margin = unit(c(0, 0, 0.5, 0.5), "cm"))
    plot2 = plot2 + theme(plot.margin = unit(c(0, 0.5, 0.5, 0), "cm"))

    # Combine all plots together and crush graph density with rel_heights
    first_col = plot_grid(plot1, scatter, ncol = 1, rel_heights = c(1, 3))
    second_col = plot_grid(NULL, plot2, ncol = 1, rel_heights = c(1, 3))
    perfect = plot_grid(first_col, second_col, ncol = 2, rel_widths = c(3, 1),
                        axis = "lrbl", align = "hv")

    print(perfect)
}

下面是对绘图的调用：

bivariate_plot(df = df_exp, var1 = "var1", var2 = "var2")

需要指出的是，即使更改了数据，这种对齐问题也始终存在。

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这是我的真实数据发生的情况：

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Answer 1

你的代码中有太多的错误，我不知道从哪里开始。下面的代码修复了它们，就我所理解的结果而言。

g1 = c(rnorm(200, mean=350, sd=100), rnorm(200, mean=700, sd=100))
g2 = c(rnorm(200, mean=350, sd=100), rnorm(200, mean=500, sd=100))
df_exp = data.frame(var1=log2(g1 + 1) , var2=log2(g2 + 1))


bivariate_plot <- function(df, var1, var2, density = T, box = F) {
  require(ggplot2)
  require(cowplot)
  scatter = ggplot(df, aes_string(var1, var2)) +
    geom_point(alpha=.8, color = "red")

  plot1 = ggplot(df, aes_string(var1)) + geom_density(alpha=.5, fill = "red") 
  plot1 = plot1 + ylab("G1 density")

  plot2 = ggplot(df, aes_string(var2)) + geom_density(alpha=.5, fill = "red") 
  plot2 = plot2 + ylab("G2 density")

  # Avoid displaying duplicated legend
  plot1 = plot1 + theme(legend.position="none")
  plot2 = plot2 + theme(legend.position="none")

  # Homogenize scale of shared axes
  min_exp = min(df[[var1]], df[[var2]]) - 0.01
  max_exp = max(df[[var1]], df[[var2]]) + 0.01
  scatter = scatter + ylim(min_exp, max_exp)
  scatter = scatter + xlim(min_exp, max_exp)
  plot1 = plot1 + xlim(min_exp, max_exp)
  plot2 = plot2 + xlim(min_exp, max_exp)
  plot1 = plot1 + ylim(0, 2)
  plot2 = plot2 + ylim(0, 2)

  # Flip axis of gg_dist_g2
  plot2 = plot2 + coord_flip()

  # Remove some duplicate axes
  plot1 = plot1 + theme(axis.title.x=element_blank(),
                        axis.text=element_blank(),
                        axis.line=element_blank(),
                        axis.ticks=element_blank())

  plot2 = plot2 + theme(axis.title.y=element_blank(),
                        axis.text=element_blank(),
                        axis.line=element_blank(),
                        axis.ticks=element_blank())

  # Modify margin c(top, right, bottom, left) to reduce the distance between plots
  #and align G1 density with the scatterplot
  plot1 = plot1 + theme(plot.margin = unit(c(0.5, 0, 0, 0.7), "cm"))
  scatter = scatter + theme(plot.margin = unit(c(0, 0, 0.5, 0.5), "cm"))
  plot2 = plot2 + theme(plot.margin = unit(c(0, 0.5, 0.5, 0), "cm"))

  # Combine all plots together and crush graph density with rel_heights
  perfect = plot_grid(plot1, NULL, scatter, plot2,
                      ncol = 2, rel_widths = c(3, 1), rel_heights = c(1, 3))

  print(perfect)
}

bivariate_plot(df = df_exp, var1 = "var1", var2 = "var2")

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Answer 2

这可以使用ggExtra包轻松完成，而不是使用自己的解决方案。

library(ggExtra)
library(ggplot2)
g1 = c(rnorm(200, mean=350, sd=100), rnorm(200, mean=700, sd=100))
g2 = c(rnorm(200, mean=350, sd=100), rnorm(200, mean=500, sd=100))
df_exp = data.frame(var1=log2(g1 + 1) , var2=log2(g2 + 1))
g <- ggplot(df_exp, aes(x=var1, y=var2)) + geom_point()
ggMarginal(g) 

输出：

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