什么硬件限制了R的绘制速度？ [英] What hardware limits plotting speed in R?

问题描述

我想提高绘图的速度，并且对R图形和ggplot软件包感到满意（并且有很多代码需要） - 所以我只想知道如何配置我的系统来加速绘图。特别是：

1. R处理速度受处理器限制，内存，显卡？


2. 是否有特定的硬件组件或配置会提高绘图速度？
   

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更新：在评论中回答问题：

规格：Ubuntu 11.04，英特尔酷睿双核，8GB内存，但我更感兴趣的是图形计算或图形渲染是有限的，如果是这样，我该如何使用这些信息。

我的图有很多对象，但我不知道绘图的计算成本是。在绘图时（我在完成任何必要的分析后绘制图表），我没有做任何具体分析，尽管我知道有些是在飞行中完成的，就像绘制平滑线或将数据转换成位置一样。

解决方案

除非您有计算机密集型单图，否则加速多重绘图的好方法是采用并行处理。例如，假设你有一个数据框，并且你想用某个变量（或多个变量）对其进行分解，并为每个分区做图。

有很多方法注册一个并行后端，所以我不会进入。例如，请参阅此小插图： http：//cran.r -project.org/web/packages/doSMP/vignettes/gettingstartedSMP.pdf

然后检查 ddply 在Hadley的 plyr 包中，并使用 .parallel = TRUE 选项。基本上就是这样。

以下是一个独立的示例：

 ＃这是我选择注册并行后端的特定库。还有其他人。有关详细信息，请参阅新的Parallel R书。 
 library（doMC）
 registerDoMC（）
 getDoParWorkers（）#This列出了你有多少工人（希望超过1！）
 
 library（ggplot2）
 ddply（
 mtcars，.variables =vs，.fun = function（x）{
＃现在绘图
 example_plot<  -  ggplot（x，aes（y （x = vs [1]，。pdf，sep =mpg，x = wt））+ geom_point（）+ geom_smooth（se = FALSE）
 #save your plot 
 ggsave ），example_plot）
}，
 .parallel = TRUE 
）
  

这将保存两个文件，0.pdf和1.pdf，它们是<$ c $ 和变量的级别（即唯一值） c> mtcars dataframe。如果你用变量 country name 分解它，那么保存的文件将是这些国家的名称。 0.pdf和1.pdf如下：

I would like to increase the speed of plotting, and I am happy with (and have lots of code requiring) the R graphics and ggplot packages - so I am only interested in knowing how I can configure my system to speed up plotting.

Specifically:

1. Is the speed of plotting in R limited by the processor, memory, graphics card?
2. Are there particular hardware components or configurations would increase plotting speed?

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Update: Answers to questions in comments:

specs: Ubuntu 11.04, intel Core Duo, 8GB ram, but I am more generally interested in wether the graphical computation or the graphical rendering is limiting, and if so, how can I use this information.

My plots have lots of objects, but I have no idea what the computational costs of plotting is. I don't do any specific analyses while plotting (I am plotting after completing any required analyses), although I understand that some is done 'on the fly', as when plotting a smoothed line or even translating data into locations.

解决方案

Unless you have computer-intensive single plots, a great way to speed up multiple plotting is with parallel processing. For example, suppose you have a dataframe and you want to break it down by a certain variable (or variables) and do plots for each partition.

There are many ways to register a parallel backend so I won't go into that. See, for example, this vignette: http://cran.r-project.org/web/packages/doSMP/vignettes/gettingstartedSMP.pdf

Then check out the function ddply in Hadley's plyr package and use the .parallel = TRUE option. That's basically it. Then just do plotting normally.

Here's a self-contained example:

#this is the particular library I chose to register a parallel backend. There are others. See the new "Parallel R" book for details.
library(doMC)
registerDoMC()
getDoParWorkers() #This lists how many workers you have (hopefully more than 1!)

library(ggplot2)
ddply(
        mtcars, .variables = "vs", .fun = function(x) {
        #do your plotting now 
        example_plot <- ggplot(x, aes(y = mpg, x = wt)) + geom_point() + geom_smooth(se = FALSE)
        #save your plot
        ggsave(paste(x$vs[1],".pdf",sep = ""), example_plot)
        },
        .parallel = TRUE
)

This will save two files, 0.pdf and 1.pdf, which are the levels (ie the unique values) of the vs variable of the mtcars dataframe. If you broke it down by a variable country name then the files saved would be the names of the countries. 0.pdf and 1.pdf are as below:

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