On Fri, Oct 23, 2009 at 1:54 AM, Jim Lemon <j...@bitwrit.com.au> wrote:
> On 10/23/2009 06:07 AM, Lasse Kliemann wrote:
>>
>> I wish to save a scatter plot comprising approx. 2 million points
>> in order to include it in a LaTeX document.
>>
>> Using 'pdf(...)' produces a file of size about 20 MB, which is
>> useless.
>>
>> Using 'cairo_pdf(...)' produces a smaller file, around 3 MB. This
>> is still too large. Not only that the document will be too large,
>> but also PDF viewers choke on this. Moreover, Cairo has problems
>> with text: by default text looks ugly, like scaled bitmaps. After
>> hours of trying different settings, I discovered that choosing a
>> different font family can help, e.g.: 'par(family="Mono")'. This
>> gives good-looking text. Yet, the problem with the file size
>> remains.
>>
>> There exists the hint to produdc EPS instead and then convert to
>> PDF using 'epstopdf'. The resulting PDF files are slightly
>> smaller, but still too large, and PDF viewers still don't like
>> it.
>>
>> So I gave PNG a try. PNG files are much smaller and PDF viewers
>> have no trouble with them. However, fonts look ugly. The same
>> trick that worked for Cairo PDF has no effect for PNG. When I
>> view the PNGs with a dedicated viewer like 'qiv', even the fonts
>> look good. But not when included in LaTeX; I simply use
>> '\includegraphics{...}' and run the document through 'pdflatex'.
>>
>> I tried both, creating PNG with 'png(...)' and converting from
>> PDF to PNG using 'convert' from ImageMagick.
>>
>> So my questions are:
>>
>> - Is there a way to produce sufficiently lean PDFs directly in R,
>> even when the plot comprises several million points?
>>
>> - How to produce a PNG that still looks nice when included in a
>> LaTeX PDF document?
>>
>> Any hints will be greatly appreciated.
>>
>
> Hi Lasse,
> I may be right off the track, but I can't make much sense of 2 million
> points in a scatterplot. If you are interested in the density of points
> within the plot, you could compute this using something like the bkde2
> function in the KernSmooth package and then plot that using something like
> "image".
Which (and a bit more) are done by ?smoothScatter (originally in a
Bioconductor package, but now available in a default installation of
R).
-Deepayan
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