问题描述
# 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")
我需要绘制双向密度图，我不确定是否有比以下更好的方法:
I need to plot two way density plot, I am not sure there is better way than the following:
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二为一(我不知道我是否可以在 ggplot 中创建双向图)，对于解决方案是在 ggplot 中还是 base 中或混合中的图，并不偏好.我希望这是一个可行的项目，考虑到 R 的健壮性.我个人更喜欢 ggplot2.
I want to combine all three types in to one (I did not know if I can create two-way plot in ggplot), there is not prefrence on whether the solution be plots are in ggplot or base or mixed. I hope this is doable project, considering robustness of R. I personally prefer ggplot2.
注意:此图中的下阴影是不正确的，在 xvar 和 yvar 图中，红色应始终在下，绿色在上，对应于 xy 密度图中的阴影区域.
Note: the lower shading in this plot is not right, red should be always lower and green upper in xvar and yvar graphs, corresponding to shaded region in xy density plot.
对图表的最终期望(感谢 seth 和 jon 非常接近的答案)(1) 删除空间和轴刻度标签等以使其紧凑
(2) 对齐网格，使中间绘图刻度和网格应与侧刻度对齐，标签和绘图大小看起来相同.
Ultimate expectation on the graph (thanks seth and jon for very close answer)(1) removing space and axis tick labels etc to make it compact
(2) alignments of grids so that middle plot ticks and grids should align with side ticks and labels and size of plots look the same.
推荐答案
以下是将多个绘图与对齐方式组合在一起的示例:
Here is the example for combining multiple plots with alignment:
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，这适用于 gglot2 0.9.1，在未来的版本中，您可以更轻松地做到这一点.
note that this works with gglot2 0.9.1, and in the future release you may do it more easily.
最后
你可以这样做:
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)
这篇关于双向密度图与 r 中选定区域的单向密度图相结合的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！