I found a partial solution, I tried with X11(type=“cairo”) and reduced substantially the time plotting
> ptm <- proc.time() > spplot(map[1]); proc.time() - ptm user system elapsed 2.166 0.510 3.604 >From 88.08 to 3.6 seconds, but, I got a low quality plot. I think the Quartz plotting device gives a high quality plots, and that’s why it takes a lot of time. El 21/03/2014, a las 06:04, peter dalgaard <[email protected]> escribió: > > On 21 Mar 2014, at 08:15 , David Winsemius <[email protected]> wrote: > >> >> 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. >> >> 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.) >> >> 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 > > That'll be because you didn't print() the lattice plots, David. That saves a > bundle on graphics I/O... > > I get: > >> system.time({ > + ptm <- proc.time() > + 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 > + print(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) > + print(proc.time() - ptm) > + }) > user system elapsed > 3.906 0.118 4.166 > user system elapsed > 3.906 0.118 4.167 >> > > However, the thing that is slow for Ronaldo is not reproducible for us since > we don't have > “Entidades_2013.shp”. > > Peter D. > >> >> -- >> 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 >> >> _______________________________________________ >> R-SIG-Mac mailing list >> [email protected] >> https://stat.ethz.ch/mailman/listinfo/r-sig-mac > > -- > Peter Dalgaard, Professor > Center for Statistics, Copenhagen Business School > Solbjerg Plads 3, 2000 Frederiksberg, Denmark > Phone: (+45)38153501 > Email: [email protected] Priv: [email protected] > Rolando Valdez _______________________________________________ R-SIG-Mac mailing list [email protected] https://stat.ethz.ch/mailman/listinfo/r-sig-mac
