ggplot2（R）数据周围的密度阴影 [英] density shadow around the data with ggplot2 (R)
问题描述
我试图在下面的情节的背景上有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？
以下是改进的ggplot：
这里是代码和数据（矩阵 df ）我曾经创造这个阴谋：
pre $ 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 = （突变），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以及映射或设置 alpha 密度填充区域的透明度。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表。具体而言，您可能需要调整以下内容：
-
n控制平滑度密度多边形。
-
h是密度估计的带宽。 -
bins控制密度级别的数量。
df = read。表格（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 -0.02 4190876 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 （aa（fill = factor（aa），alpha = ..level ..），
geom =polygon，color = NA，n（n''，sep =''））+
stat_density2d = $ 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'，
值= 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））+＃缩小以便密度多边形
scale_y_continuous（expand = c（0.3,0））+＃不会达到剧情的边缘。
coord_cartesian（xlim = c（-0.05，0.07），
ylim = c（-0.04，0.05））＃放大回到最终情节。
$ b 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 ，其中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 ='突变'，
values = c（darkorange2，cornflowerblue），
labels = c（A，S））+
geom_text（hjust = 0.5， （扩展= c（0.3，0））+＃缩小以便密度多边形
scale_y_continuous（expand = c（0.3，0））+＃不要触及阴谋的边缘。
coord_cartesian（xlim = c（-0.05，0.07），
ylim = c（-0.04，0.05））＃放大回到最终情节。
library（gridExtra）
ggsave（plots.png，plot = arrangeGrob（p1，p2，ncol = 1），width = 8，height = 11，dpi = 120）
I am trying to have 2 "shadows" on the background of the below plot. These shadows should represent the density of the orange and blue points separately. Does it make sense?
Here is the ggplot to improve:
Here is the code and the data (matrix df) I used to create this plot:
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)
Here are some possible approaches using stat_density2d() with geom="polygon" and mapping or setting alpha transparency for the density fill regions. If you are willing to experiment with some the parameters, I think you can get some very useful plots. Specifically, you may want to adjust the following:
ncontrols the smoothness of the density polygon.his the bandwidth of the density estimation.binscontrols the number of density levels.
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)
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