r - r中选定区域的双向密度图与单向密度图的组合

# data
set.seed (123)
xvar <- c(rnorm (1000, 50, 30), rnorm (1000, 40, 10), rnorm (1000, 70, 10))
yvar <-   xvar + rnorm (length (xvar), 0, 20)
myd <- data.frame (xvar, yvar)


# density plot for xvar
            upperp = 80   # upper cutoff
            lowerp = 30   # lower cutoff
            x <- myd$xvar
            plot(density(x))
            dens <- density(x)
            x11 <- min(which(dens$x <= lowerp))
            x12 <- max(which(dens$x <= lowerp))
            x21 <- min(which(dens$x > upperp))
            x22 <- max(which(dens$x > upperp))
            with(dens, polygon(x = c(x[c(x11, x11:x12, x12)]),
                y = c(0, y[x11:x12], 0), col = "green"))
             with(dens, polygon(x = c(x[c(x21, x21:x22, x22)]),
                y = c(0, y[x21:x22], 0), col = "red"))
            abline(v = c(mean(x)), lwd = 2, lty = 2, col = "red")
# density plot with yvar
    upperp = 70  # upper cutoff
    lowerp = 30   # lower cutoff
    x <- myd$yvar
    plot(density(x))
    dens <- density(x)
    x11 <- min(which(dens$x <= lowerp))
    x12 <- max(which(dens$x <= lowerp))
    x21 <- min(which(dens$x > upperp))
    x22 <- max(which(dens$x > upperp))
    with(dens, polygon(x = c(x[c(x11, x11:x12, x12)]),
        y = c(0, y[x11:x12], 0), col = "green"))
     with(dens, polygon(x = c(x[c(x21, x21:x22, x22)]),
        y = c(0, y[x21:x22], 0), col = "red"))
    abline(v = c(mean(x)), lwd = 2, lty = 2, col = "red")

我需要绘制两种密度图，我不确定是否有比以下更好的方法:

ggplot(myd,aes(x=xvar,y=yvar))+
    stat_density2d(aes(fill=..level..), geom="polygon") +
    scale_fill_gradient(low="blue", high="green") + theme_bw()

我想将所有三种类型合而为一(我不知道我是否可以在ggplot中创建双向绘图)，因此对于解决方案是在ggplot中还是在ggplot中或在基础绘图中还是混合绘图都不存在偏好。考虑到R的健壮性，我希望这是可行的项目。我个人更喜欢ggplot2。

注意:此图的下部阴影不正确，在xvar和yvar图中，红色应始终较低，绿色应始终在上部，这与xy密度图中的阴影区域相对应。

编辑:对图形的最终期望(非常感谢塞思和乔恩的回答)
(1)移除空间和轴刻度标签等以使其紧凑
(2)网格对齐，以使中间的图刻度和网格应与侧刻度和标签对齐，并且图的大小看起来相同。

最佳答案

这是将多个图与路线合并的示例:

library(ggplot2)
library(grid)

set.seed (123)
xvar <- c(rnorm (100, 50, 30), rnorm (100, 40, 10), rnorm (100, 70, 10))
yvar <-   xvar + rnorm (length (xvar), 0, 20)
myd <- data.frame (xvar, yvar)

p1 <- ggplot(myd,aes(x=xvar,y=yvar))+
  stat_density2d(aes(fill=..level..), geom="polygon") +
  coord_cartesian(c(0, 150), c(0, 150)) +
  opts(legend.position = "none")

p2 <- ggplot(myd, aes(x = xvar)) + stat_density() +
  coord_cartesian(c(0, 150))
p3 <- ggplot(myd, aes(x = yvar)) + stat_density() +
  coord_flip(c(0, 150))

gt <- ggplot_gtable(ggplot_build(p1))
gt2 <- ggplot_gtable(ggplot_build(p2))
gt3 <- ggplot_gtable(ggplot_build(p3))

gt1 <- ggplot2:::gtable_add_cols(gt, unit(0.3, "null"), pos = -1)
gt1 <- ggplot2:::gtable_add_rows(gt1, unit(0.3, "null"), pos = 0)

gt1 <- ggplot2:::gtable_add_grob(gt1, gt2$grobs[[which(gt2$layout$name == "panel")]],
                                  1, 4, 1, 4)
gt1 <- ggplot2:::gtable_add_grob(gt1, gt2$grobs[[which(gt2$layout$name == "axis-l")]],
                                 1, 3, 1, 3, clip = "off")

gt1 <- ggplot2:::gtable_add_grob(gt1, gt3$grobs[[which(gt3$layout$name == "panel")]],
                                 4, 6, 4, 6)
gt1 <- ggplot2:::gtable_add_grob(gt1, gt3$grobs[[which(gt3$layout$name == "axis-b")]],
                                 5, 6, 5, 6, clip = "off")
grid.newpage()
grid.draw(gt1)

请注意，这可用于gglot2 0.9.1，在将来的版本中，您可以更轻松地完成此操作。

最后

您可以通过以下方式做到这一点:

library(ggplot2)
library(grid)

set.seed (123)
xvar <- c(rnorm (100, 50, 30), rnorm (100, 40, 10), rnorm (100, 70, 10))
yvar <-   xvar + rnorm (length (xvar), 0, 20)
myd <- data.frame (xvar, yvar)

p1 <- ggplot(myd,aes(x=xvar,y=yvar))+
  stat_density2d(aes(fill=..level..), geom="polygon") +
  geom_polygon(aes(x, y),
               data.frame(x = c(-Inf, -Inf, 30, 30), y = c(-Inf, 30, 30, -Inf)),
               alpha = 0.5, colour = NA, fill = "red") +
  geom_polygon(aes(x, y),
               data.frame(x = c(Inf, Inf, 80, 80), y = c(Inf, 80, 80, Inf)),
               alpha = 0.5, colour = NA, fill = "green") +
  coord_cartesian(c(0, 120), c(0, 120)) +
  opts(legend.position = "none")

xd <- data.frame(density(myd$xvar)[c("x", "y")])
p2 <- ggplot(xd, aes(x, y)) +
  geom_area(data = subset(xd, x < 30), fill = "red") +
  geom_area(data = subset(xd, x > 80), fill = "green") +
  geom_line() +
  coord_cartesian(c(0, 120))

yd <- data.frame(density(myd$yvar)[c("x", "y")])
p3 <- ggplot(yd, aes(x, y)) +
  geom_area(data = subset(yd, x < 30), fill = "red") +
  geom_area(data = subset(yd, x > 80), fill = "green") +
  geom_line() +
  coord_flip(c(0, 120))

gt <- ggplot_gtable(ggplot_build(p1))
gt2 <- ggplot_gtable(ggplot_build(p2))
gt3 <- ggplot_gtable(ggplot_build(p3))

gt1 <- ggplot2:::gtable_add_cols(gt, unit(0.3, "null"), pos = -1)
gt1 <- ggplot2:::gtable_add_rows(gt1, unit(0.3, "null"), pos = 0)

gt1 <- ggplot2:::gtable_add_grob(gt1, gt2$grobs[[which(gt2$layout$name == "panel")]],
                                  1, 4, 1, 4)
gt1 <- ggplot2:::gtable_add_grob(gt1, gt2$grobs[[which(gt2$layout$name == "axis-l")]],
                                 1, 3, 1, 3, clip = "off")

gt1 <- ggplot2:::gtable_add_grob(gt1, gt3$grobs[[which(gt3$layout$name == "panel")]],
                                 4, 6, 4, 6)
gt1 <- ggplot2:::gtable_add_grob(gt1, gt3$grobs[[which(gt3$layout$name == "axis-b")]],
                                 5, 6, 5, 6, clip = "off")
grid.newpage()
grid.draw(gt1)

关于r - r中选定区域的双向密度图与单向密度图的组合，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/11546256/