[R-SIG-Mac] Dramatically slow map plotting

[Rolando Valdez]

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 <pdalgd at gmail.com> escribió:

> 
> On 21 Mar 2014, at 08:15 , David Winsemius <dwinsemius at comcast.net> 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
>>> R-SIG-Mac at r-project.org
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mac
>> 
>> David Winsemius
>> Alameda, CA, USA
>> 
>> _______________________________________________
>> R-SIG-Mac mailing list
>> R-SIG-Mac at r-project.org
>> 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: pd.mes at cbs.dk  Priv: PDalgd at gmail.com
> 

Rolando Valdez