Shadow density around data with ggplot2 (R)

Question

I am trying to have 2 "shadows" in the background of the chart below. These shadows should represent the density of the orange and blue dots separately. Does it make sense?

Here is ggplot for improvement:

Here is the code and data ( df matrix) that I used to create this graph:

  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) 

Answer 1

Below are some possible approaches using stat_density2d() with geom="polygon" and displaying or adjusting alpha transparency for density fill areas. If you want to experiment with some parameters, I think you can get very useful stories. In particular, you can configure the following options:

• n controls the smoothness of the density polygon.
• h is the bandwidth of the density estimate.
• bins controls 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) 

Answer 2

Here is my suggestion. Using shadows or polygons will be pretty ugly if you apply two colors and densities. An outline graph can be nicer to watch and it is certainly easier to work with.

I created random data as a reproducible example and used a simple density function that uses the average distance of the nearest 5 points.

 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))) 

contour graph http://img34.imageshack.us/img34/6215/1yvb.png

r contour ggplot2