Efficiently plotting hundreds of millions of points in R

Question

Is plot() the most efficient way to plot 100 million or so data points in R? I'd like to plot a bunch of these Clifford Attractors. Here's an example of one I've downscaled from a very large image:

Here is a link to some code that I've used to plot a very large 8K (7680x4320) images.

It doesn't take long to generate 50 or 100 million points (using Rcpp), nor to get the hex value for the colour + transparency, but the actual plotting and saving to disk is extremely slow.

• Is there a faster way to plot (and save) all these points?
• Is R just a bad tool for this job?
• What tools would you use to plot billions points, even if you couldn't fit them all in to ram?
• How would one have made a very high resolution plot of this type (colour + transparency) with say 1990's software and hardware?

Edit: code used

# Load packages library(Rcpp) library(viridis)  # output parameters output_width = 1920 * 4 output_height = 1080 * 4 N_points = 50e6 point_alpha = 0.05 #point transperancy  # Attractor parameters params <- c(1.886,-2.357,-0.328, 0.918)  # C++ function to rapidly generate points cliff_rcpp <- cppFunction(     "     NumericMatrix cliff(int nIter, double A, double B, double C, double D) {     NumericMatrix x(nIter, 2);     for (int i=1; i < nIter; ++i) {     x(i,0) = sin(A*x(i-1,1)) + C*cos(A*x(i-1,0));     x(i,1) = sin(B*x(i-1,0)) + D*cos(B*x(i-1,1));     }     return x;     }" )  # Function for mapping a point to a colour map2color <- function(x, pal, limits = NULL) {     if (is.null(limits))         limits = range(x)     pal[findInterval(x,                      seq(limits[1], limits[2], length.out = length(pal) + 1),                      all.inside = TRUE)] }  # Obtain matrix of points cliff_points <- cliff_rcpp(N_points, params[1], params[2], params[3], params[4])  # Calculate angle between successive points cliff_angle <- atan2(     (cliff_points[, 1] - c(cliff_points[-1, 1], 0)),     (cliff_points[, 2] - c(cliff_points[-1, 2], 0)) )  # Obtain colours for points available_cols <-     viridis(         1024,         alpha = point_alpha,         begin = 0,         end = 1,         direction = 1     )  cliff_cols <- map2color(     cliff_angle,     c(available_cols, rev(available_cols)) )   # Output image directly to disk jpeg(     "clifford_attractor.jpg",     width = output_width,     height = output_height,     pointsize = 1,     bg = "black",     quality = 100  )     plot(         cliff_points[-1, ],         bg = "black",         pch = ".",         col = cliff_cols     )  dev.off() 

Answer 1

I've recently discovered the Scattermore package for R which is about an order of magnitude faster than R's standard plot function. scattermoreplot() takes ~2 minutes to plot 100m points with colour and transparency, while plot() takes around half an hour.

Answer 2

I am currently exploring datashader (http://www.datashader.org). If you are willing to work with python, this could be an elegant solution to the problem.