[英]density shadow around the data with ggplot2 (R)
我试图在下面的情节背景上有2个“阴影”。 这些阴影应分别代表橙色和蓝色点的密度。 是否有意义?
以下是要改进的ggplot:
这是我用来创建这个图的代码和数据(矩阵df
):
PC1 PC2 aa
A_akallopisos 0.043272525 0.0151023307 2
A_akindynos -0.020707141 -0.0158198405 1
A_allardi -0.020277664 -0.0221016281 2
A_barberi -0.023165596 0.0389906701 2
A_bicinctus -0.025354572 -0.0059122384 2
A_chrysogaster 0.012608835 -0.0339330213 2
A_chrysopterus -0.022402365 -0.0092476009 1
A_clarkii -0.014474658 -0.0127024469 1
A_ephippium -0.016859412 0.0320034231 2
A_frenatus -0.024190876 0.0238499714 2
A_latezonatus -0.010718845 -0.0289904165 1
A_latifasciatus -0.005645811 -0.0183202248 2
A_mccullochi -0.031664307 -0.0096059126 2
A_melanopus -0.026915545 0.0308399009 2
A_nigripes 0.023420045 0.0293801537 2
A_ocellaris 0.052042539 0.0126144250 2
A_omanensis -0.020387101 0.0010944998 2
A_pacificus 0.042406273 -0.0260308092 2
A_percula 0.034591721 0.0071153133 2
A_perideraion 0.052830132 0.0064495142 2
A_polymnus 0.030902254 -0.0005091421 2
A_rubrocinctus -0.033318659 0.0474995722 2
A_sandaracinos 0.055839755 0.0093724082 2
A_sebae 0.021767793 -0.0218640814 2
A_tricinctus -0.016230301 -0.0018526482 1
P_biaculeatus -0.014466403 0.0024864574 2
ggplot(data=df,aes(x=PC1, y=PC2, color=factor(aa), label=rownames(df))) + ggtitle(paste('Site n° ',Sites_names[j],sep='')) +geom_smooth(se=F, method='lm')+ geom_point() + scale_color_manual(name='mutation', values = c("darkorange2","cornflowerblue"), labels = c("A","S")) + geom_text(hjust=0.5, vjust=-1 ,size=3) + xlim(-0.05,0.07)
以下是使用stat_density2d()
和geom="polygon"
并为密度填充区域映射或设置alpha
透明度的一些可能方法。 如果你愿意尝试一些参数,我想你可以得到一些非常有用的图。 具体来说,您可能需要调整以下内容:
n
控制密度多边形的平滑度。 h
是密度估计的带宽。 bins
控制密度等级的数量。 df = read.table(header=TRUE, text=
" PC1 PC2 aa
A_akallopisos 0.043272525 0.0151023307 2
A_akindynos -0.020707141 -0.0158198405 1
A_allardi -0.020277664 -0.0221016281 2
A_barberi -0.023165596 0.0389906701 2
A_bicinctus -0.025354572 -0.0059122384 2
A_chrysogaster 0.012608835 -0.0339330213 2
A_chrysopterus -0.022402365 -0.0092476009 1
A_clarkii -0.014474658 -0.0127024469 1
A_ephippium -0.016859412 0.0320034231 2
A_frenatus -0.024190876 0.0238499714 2
A_latezonatus -0.010718845 -0.0289904165 1
A_latifasciatus -0.005645811 -0.0183202248 2
A_mccullochi -0.031664307 -0.0096059126 2
A_melanopus -0.026915545 0.0308399009 2
A_nigripes 0.023420045 0.0293801537 2
A_ocellaris 0.052042539 0.0126144250 2
A_omanensis -0.020387101 0.0010944998 2
A_pacificus 0.042406273 -0.0260308092 2
A_percula 0.034591721 0.0071153133 2
A_perideraion 0.052830132 0.0064495142 2
A_polymnus 0.030902254 -0.0005091421 2
A_rubrocinctus -0.033318659 0.0474995722 2
A_sandaracinos 0.055839755 0.0093724082 2
A_sebae 0.021767793 -0.0218640814 2
A_tricinctus -0.016230301 -0.0018526482 1
P_biaculeatus -0.014466403 0.0024864574 2")
library(ggplot2)
p1 = ggplot(data=df, aes(x=PC1, y=PC2, color=factor(aa), label=rownames(df))) +
ggtitle(paste('Site n° ',sep='')) +
stat_density2d(aes(fill=factor(aa), alpha = ..level..),
geom="polygon", color=NA, n=200, h=0.03, bins=4) +
geom_smooth(se=F, method='lm') +
geom_point() +
scale_color_manual(name='mutation',
values = c("darkorange2","cornflowerblue"),
labels = c("A","S")) +
scale_fill_manual( name='mutation',
values = c("darkorange2","cornflowerblue"),
labels = c("A","S")) +
geom_text(hjust=0.5, vjust=-1 ,size=3, color="black") +
scale_x_continuous(expand=c(0.3, 0)) + # Zooms out so that density polygons
scale_y_continuous(expand=c(0.3, 0)) + # don't reach edges of plot.
coord_cartesian(xlim=c(-0.05, 0.07),
ylim=c(-0.04, 0.05)) # Zooms back in for the final plot.
p2 = ggplot(data=df, aes(x=PC1, y=PC2, color=factor(aa), label=rownames(df))) +
ggtitle(paste('Site n° ',sep='')) +
stat_density2d(aes(fill=factor(aa)), alpha=0.2,
geom="polygon", color=NA, n=200, h=0.045, bins=2) +
geom_smooth(se=F, method='lm', size=1) +
geom_point(size=2) +
scale_color_manual(name='mutation',
values = c("darkorange2","cornflowerblue"),
labels = c("A","S")) +
scale_fill_manual( name='mutation',
values = c("darkorange2","cornflowerblue"),
labels = c("A","S")) +
geom_text(hjust=0.5, vjust=-1 ,size=3) +
scale_x_continuous(expand=c(0.3, 0)) + # Zooms out so that density polygons
scale_y_continuous(expand=c(0.3, 0)) + # don't reach edges of plot.
coord_cartesian(xlim=c(-0.05, 0.07),
ylim=c(-0.04, 0.05)) # Zooms back in for the final plot.
library(gridExtra)
ggsave("plots.png", plot=arrangeGrob(p1, p2, ncol=1), width=8, height=11, dpi=120)
这是我的建议。 当您覆盖两种颜色和密度时,使用阴影或多边形会变得非常难看。 轮廓图可以更好地查看,并且当然更容易使用。
我创建了一些随机数据作为可重复的示例,并使用了一个简单的密度函数,该函数使用最近的5个点的平均距离。
df <- data.frame(PC1 = runif(20),
PC2 = runif(20),
aa = rbinom(20,1,0.5))
point.density <- function(row){
points <- df[df$aa == row[[3]],]
x.dist <- (points$PC1 - row[[1]])^2
y.dist <- (points$PC2 - row[[2]])^2
x <- x.dist[order(x.dist)[1:5]]
y <- y.dist[order(y.dist)[1:5]]
1/mean(sqrt(x + y))
}
# you need to calculate the density for the whole grid.
res <- c(1:100)/100 # this is the resolution, so gives a 100x100 grid
plot.data0 <- data.frame(x.val = rep(res,each = length(res)),
y.val = rep(res, length(res)),
type = rep(0,length(res)^2))
plot.data1 <- data.frame(x.val = rep(res,each = length(res)),
y.val = rep(res, length(res)),
type = rep(1,length(res)^2))
plot.data <- rbind(plot.data0,plot.data1)
# we need a density value for each point type, so 2 grids
densities <- apply(plot.data,1,point.density)
plot.data <- cbind(plot.data, z.val = densities)
library(ggplot2)
# use stat_contour to draw the densities. Be careful to specify which dataset you're using
ggplot() + stat_contour(data = plot.data, aes(x=x.val, y=y.val, z=z.val, colour = factor(type)), bins = 20, alpha = 0.4) + geom_point(data = df, aes(x=PC1,y=PC2,colour = factor(aa)))
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