El 21/03/2014, a las 01:15, David Winsemius <[email protected]> escribió:
>
> On Mar 20, 2014, at 4:56 PM, Rolando Valdez wrote:
>
>> Hello,
>>
>> Recently, I acquired a MacBook Pro, Core i7, 8 GB ram.
>
>
>> I Installed the newest R version, 3.0.3 from the web page. The problem is
>> when I’m plotting maps, because is going very, very slow, about 3 or 4
>> minutes just for a single map, while I’ve done this in a few seconds in
>> Windows with Core i5 and 4 GB ram.
>>
>> This is what I have:
>>
>> R version 3.0.3 (2014-03-06) -- "Warm Puppy"
>> Copyright (C) 2014 The R Foundation for Statistical Computing
>> Platform: x86_64-apple-darwin10.8.0 (64-bit)
>>
>> [R.app GUI 1.63 (6660) x86_64-apple-darwin10.8.0]
>>
>> I found a reproducible example in web and I took time with proc.time()
>>
>> ptm <- proc.time()
>
> Most people use system.time and not proc.time.
Most people use Microsoft Windows, so? Is that a reasonable argument to use it?
> When you execute proc.time you get something like:
>
>> proc.time()
> user system elapsed
> 75.736 33.765 97374.066
>
> Is that meaningful to you? (It's not to me.)
Of course is meaningful to me, It would be meaningful for anybody that have
read what means those results.
help(proc.time)
Description
proc.time determines how much real and CPU time (in seconds) the currently
running R process has already taken.
Details
proc.time returns five elements for backwards compatibility, but its print
method prints a named vector of length 3. The first two entries are the total
user and system CPU times of the current Rprocess and any child processes on
which it has waited, and the third entry is the ‘real’ elapsed time since the
process was started.
> When I wrap system.time around that set of expressions (inside RStudio on a 6
> year-old MacPro) I get:
>
> user system elapsed
> 0.065 0.001 0.066
>
> --
> David.
>
>> library(sp)
>> library(lattice) # required for trellis.par.set():
>> trellis.par.set(sp.theme()) # sets color ramp to bpy.colors()
>>
>> # prepare nc sids data set:
>> library(maptools)
>> nc <- readShapePoly(system.file("shapes/sids.shp", package="maptools")[1],
>> proj4string=CRS("+proj=longlat +datum=NAD27"))
>> arrow = list("SpatialPolygonsRescale", layout.north.arrow(),
>> offset = c(-76,34), scale = 0.5, which = 2)
>> #scale = list("SpatialPolygonsRescale", layout.scale.bar(),
>> # offset = c(-77.5,34), scale = 1, fill=c("transparent","black"), which =
>> 2)
>> #text1 = list("sp.text", c(-77.5,34.15), "0", which = 2)
>> #text2 = list("sp.text", c(-76.5,34.15), "1 degree", which = 2)
>> ## multi-panel plot with filled polygons: North Carolina SIDS
>> spplot(nc, c("SID74", "SID79"), names.attr = c("1974","1979"),
>> colorkey=list(space="bottom"), scales = list(draw = TRUE),
>> main = "SIDS (sudden infant death syndrome) in North Carolina",
>> sp.layout = list(arrow), as.table = TRUE)
>>
>> # sp.layout = list(arrow, scale, text1, text2), as.table = TRUE)
>> proc.time() - ptm
>>
>> user system elapsed
>> 2.408 0.064 2.616
>>
>> It was quick.
>>
>> Then I did a single plot with my shape:
>>
>> mapa <- readShapePoly(“Entidades_2013.shp”)
>> ptm <- proc.time()
>> spplot(mapa[1]); proc.time() - ptm
>>
>> user system elapsed
>> 87.575 0.786 88.068
>>
>> Why it take a lot of time? I worked with same shapes in Windows and never
>> took that time.
>>
>> Hope you can help me,
>>
>> Regards,
>> Rolando Valdez
>>
>> _______________________________________________
>> R-SIG-Mac mailing list
>> [email protected]
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mac
>
> David Winsemius
> Alameda, CA, USA
>
Rolando Valdez
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