Coloring kernel densities in R

Question

I'm a non-programmer who badly needs to plot a multivariate kernel density function with two variables against each other to better understand my data. After searching the internet I found a code which I used to insert my data and create the desired function.
This is my current code:

mydata = read.csv("J:/LKAB Thesis/Plots/Kernel/81241.txt", header = TRUE, sep = "\t") 

x1 <- mydata[,11] 
x2 <- mydata[,23] 
df <- data.frame(x1,x2) 


x <- densCols(x1,x2, colramp=colorRampPalette(c("black", "white"))) 

df$dens <- col2rgb(x)[1,] + 1L 

cols <-  colorRampPalette(c("#000099", "#00FEFF", "#45FE4F", 
                            "#FCFF00", "#FF9400", "#FF3100"))(32) 
df$col <- cols[df$dens] 

plot(x2~x1, data=df[order(df$dens),], pch=20, col=col, cex=1) 

And this is the resulting plot:

http://imgur.com/azb0CCj

This is exactly what I need, but why is the dense part of the plot white? It should be filled with a nice smooth red color to indicate the high density. How do I fix this?? If I change the colorRampPalette to a higher value like 256, the hole is filled but the rest of the plot is much uglier and less detailed which I don't want.

Please help, I need this to finish my thesis but bear in mind that I'm a beginner and don't really know anything about R. Unfortunately I need to use it for this plot!

Here is some sample data which can be used for this example. These are measured concentrations of two elements, Ti and Zr which I want to plot against each other and see the densities of the points.

d <- read.table("http://pastebin.com/raw.php?i=DcFdZ5Rm")

Thanks!

Answer 1

Two solutions here. In both cases, if you want to display the points (rather than the more standard density contours; see ggplot2 example), then it is important that you deal with overplotting by making your points transparent.

#Data import and cleaning
d <- read.table(
  "http://pastebin.com/raw.php?i=DcFdZ5Rm", 
  header = TRUE, 
  stringsAsFactors = FALSE
)
d[] <- lapply(d, as.numeric)

# Draw the plot using ggplot.  It's nicer. 
# Here's a scatterplot with density contours.
library(ggplot2)
(p_contours <- ggplot(d, aes(Ti, Zr)) +
  geom_point(alpha = 0.1) +
  geom_density2d()
)

# Here's a heatmap. Explore different binwidth values.
(p_heatmap <- ggplot(d, aes(Ti, Zr)) +
  geom_bin2d(binwidth = c(0.001, 0.001))
)

#If you insist on using base graphics, try something like
library(scales)
col <- densCols(d$Ti, d$Zr, colramp=colorRampPalette(c("grey67", "blue")))    
col <- alpha(col, 0.1) # Make points transparent
plot(d$Ti, d$Zr, col = col, pch = 16)    

why is the dense part of the plot white?

Because you chose a black and white colour ramp, with white representing the highest density region. Because you only specified colours for the first 32 elements in density, so some points have a missing colour value.