[R] Speed up plotting to MSWindows graphics window
murdoch.duncan at gmail.com
Wed Apr 27 12:56:57 CEST 2011
Jonathan Gabris wrote:
> I am working on a project analysing the performance of motor-vehicles
> through messages logged over a CAN bus.
> I am using R 2.12 on Windows XP and 7
> I am currently plotting the data in R, overlaying 5 or more plots of
> data, logged at 1kHz, (using plot.ts() and par(new = TRUE)).
> The aim is to be able to pan, zoom in and out and get values from the
> plotted graph using a custom Qt interface that is used as a front end to
> R.exe (all this works).
> The plot is drawn by R directly to the windows graphic device.
> The data is imported from a .csv file (typically around 100MB) to a matrix.
> (timestamp, message ID, byte0, byte1, ..., byte7)
> I then separate this matrix into several by message ID (dimensions are
> in the order of 8cols, 10^6 rows)
> The panning is done by redrawing the plots, shifted by a small amount.
> So as to view a window of data from a second to a minute long that can
> travel the length of the logged data.
> My problem is that, the redrawing of the plots whilst panning is too
> slow when dealing with this much data.
> i.e.: I can see the last graphs being drawn to the screen in the
> half-second following the view change.
> I need a fluid change from one view to the next.
> My question is this:
> Are there ways to speed up the plotting on the MSWindows display?
> By reducing plotted point densities to *sensible* values?
> Using something other than plot.ts() - is the lattice package faster?
> I don't need publication quality plots, they can be rougher...
I don't think there are any ways to plot in the standard device that are
significantly faster than what you are doing if you want to see the
updates. (I think it would be substantially faster if you hid the
graphics window during the updates, but that won't suit you.)
I'd suggest plotting a subset of the data during the updates, then plot
the full dataset when it stops moving. For example, only plot a few
hundred points, even spaced through the time series.
> I have tried:
> -Using matrices instead of dataframes - (works for calculations but not
> enough for plots)
> -increasing the max usable memory (max-mem-size) - (no change)
> -increasing the size of the pointer protection stack (max-ppsize) - (no
> -deleting the unnecessary leftover matrices - (no change)
> -I can't use lines() instead of plot() because of the very different
> scales (rpm-10000, flags -1to3)
> I am going to do some resampling of the logged data to reduce the vector
> (removal of *less* important data and use of window.ts())
> But I am currently running out of ideas...
> So if sombody could point out something, I would be greatfull.
> Jonathan Gabris
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