R中的数据探索:快速显示大型矩阵的热图? [英] Data exploration in R: display heatmap of large matrix, quickly?

问题描述

如何快速可视化R中的大型矩阵?

我有时会处理大型数字矩阵(例如3000 x 3000)，并且快速可视化它们是非常有用的质量控制步骤.在我以前选择的语言Matlab中，这非常容易且快速.例如，显示1000x1000矩阵需要0.5秒:

I sometimes work with large-ish numeric matrices (e.g. 3000 x 3000), and quickly visualizing them is a very helpful quality control step. This was very easy and fast in Matlab, my previous language of choice. For example, it takes 0.5 seconds to display a 1000x1000 matrix:

rand_matrix = rand(1000,1000);
tic
imagesc(rand_matrix)
toc
>> Elapsed time is 0.463903 seconds.

我希望R中具有相同的幂，但是不幸的是在R中可视化矩阵似乎很慢.例如，使用image.plot()相同的随机矩阵需要花费10秒钟以上的时间来显示:

I'd like the same powers in R, but unfortunately visualizing matrices seems very slow in R. For example, using image.plot() the same random matrix takes more than 10 seconds to display:

require(tictoc)
require(image.plot)
mm = 1000
nn = 1000
rand.matrix = matrix(runif(mm*nn), ncol=mm, nrow=nn)
tic("Visualizing matrix")
image.plot(rand.matrix)
toc()
> Visualizing matrix: 11.744 sec elapsed

随着矩阵变大，问题变得更糟.例如，一个3000x3000的矩阵在R中可视化需要几分钟，而在Matlab中则需要数秒.显然，这对于数据探索确实不起作用.我已经尝试过ggplot，但融化+ geom_raster()仍可能需要一分钟.

The problem gets worse as the matrices get bigger. For example, a 3000x3000 matrix takes minutes to visualize in R, compared to seconds in Matlab. This obviously doesn't really work for data exploration. I've tried ggplot, and melting + geom_raster() can still take up to a minute.

我在做什么错?有没有一种快速的方法可以在R中可视化矩阵?一种理想的解决方案是使用一两行.

What am I doing wrong? Is there a fast way to visualize matrices in R? An ideal solution would take one or two lines.

推荐答案

使用image(m, useRaster = TRUE)时，我会很快得到一个图:

I get a plot pretty quickly when using image(m, useRaster = TRUE):

start = Sys.time()
image(rand.matrix, useRaster = TRUE)
print(Sys.time() - start)
# Time difference of 0.326 secs

没有useRaster = TRUE会花费1.5秒，useRaster会加快速度，但仅适用于我认为简单，均匀间隔的点.

Without useRaster = TRUE this takes 1.5 seconds, useRaster speeds this up but only works for simple, evenly spaced points I think.

如果您的最终目标是使用此图生成图像文件，那么我认为直接输出为png这样的栅格格式可能是最有效的方法，尽管要准确地计算R花费多长时间会有些棘手图片文件，例如:

If your ultimate goal is to produce an image file with this plot, then I think it might be most efficient to output directly to a raster format like png, although it's a little tricky to measure exactly how long R is taking to save the image file, e.g.:

png("image_plot.png", width = 1000, height = 1000)
image(rand.matrix, useRaster = TRUE)
dev.off()

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