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()
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
Dramatically slow map plotting
5 messages · David Winsemius, Peter Dalgaard, Rolando Valdez
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
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
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
1 day later
El 21/03/2014, a las 01:15, David Winsemius <dwinsemius at comcast.net> escribi?:
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 R-SIG-Mac at r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-mac
David Winsemius Alameda, CA, USA
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