[英]Efficiently plotting hundreds of millions of points in R
Is plot()
the most efficient way to plot 100 million or so data points in R? plot()
是在 R 中绘制 1 亿个左右数据点的最有效方法吗? I'd like to plot a bunch of these Clifford Attractors .我想绘制一堆这些Clifford Attractors 。 Here's an example of one I've downscaled from a very large image:这是我从非常大的图像缩小的示例:
Here is a link to some code that I've used to plot a very large 8K (7680x4320) images.这是我用来绘制非常大的 8K (7680x4320) 图像的一些代码的链接。
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.生成 50 或 1 亿个点(使用 Rcpp)不需要很长时间,也不需要获取颜色 + 透明度的十六进制值,但实际绘制和保存到磁盘的速度非常慢。
Edit: code used编辑:使用的代码
# 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()
I've recently discovered the Scattermore package for R which is about an order of magnitude faster than R's standard plot function.我最近发现了 R 的Scattermore包,它比 R 的标准绘图函数快一个数量级。 scattermoreplot()
takes ~2 minutes to plot 100m points with colour and transparency, while plot()
takes around half an hour. scattermoreplot()
需要大约 2 分钟来绘制具有颜色和透明度的 100m 点,而plot()
需要大约半小时。
I am currently exploring datashader ( http://www.datashader.org ).我目前正在探索datashader ( http://www.datashader.org )。 If you are willing to work with python, this could be an elegant solution to the problem.如果你愿意使用 python,这可能是一个优雅的问题解决方案。
Maybe geom_hex() from the ggplo2 package can be a solution?也许来自 ggplo2 包的 geom_hex() 可以是一个解决方案? https://ggplot2.tidyverse.org/reference/geom_hex.html https://ggplot2.tidyverse.org/reference/geom_hex.html
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