[R-sig-Geo] raster::extract fails on brick but works on individual layers of brick
Frank Davenport
frank.davenport at gmail.com
Fri Jul 25 21:16:06 CEST 2014
Hi Lydon,
Thanks for the work around! I can use that for now, but it is still not
clear to me why the extract/mean works on each individual layer but not
on the brick. When I've used extract() on bricks in the past it will
return a mean (or whatever function) value for each layer of the brick
(but much faster than doing it individually).
In this case, since I can use extract(..,..,mean) on each individual
layer without error, which led me to believe this may be a bug.
Regardless, I appreciate the help.
Thanks again,
F
On 7/23/14 10:07 AM, Lyndon Estes wrote:
> Hi Frank,
>
> I think the mean function is getting in the way, since if you want to
> extra the values for all cells each polygon overlaps, the outputs will
> first end up in a list.
>
> test1 <- extract(g, dsd, weights = TRUE, small = TRUE)
>
> Will get the cell values for each polygon from each layer in the
> brick, along with their weight (while allowing very small polygons to
> get their underlying cell values).
>
> I would then process the mean function on the list.
>
> v <- t(sapply(test1, function(x) apply(t(sapply(1:nrow(x), function(y)
> x[y, 1:10] * x[y, 11])), 2, sum))) # matrix of annual values per
> polygon
>
> Hope this helps.
>
> Cheers, Lyndon
>
>
> On Wed, Jul 23, 2014 at 12:17 PM, Frank Davenport
> <frank.davenport at gmail.com> wrote:
>> Hello,
>>
>> I am using extract() to aggregate values from a raster to a polygon. The
>> raster is a brick object. Extract fails on the brick object but succeeds
>> when applied on each individual layer of the brick (via a for() loop). I
>> would use this as a work around but in my actual scenario the brick is much
>> bigger and the individual approach is too slow.
>>
>> The specific error message is: "Error in t(sapply(res, meanfunc)) : error
>> in evaluating the argument 'x' in selecting a method for function 't':
>> Error in apply(x, 1, function(X) { : dim(X) must have a positive length"
>>
>> I believe this has something to do with the relative sizes of the polygons
>> (small) and the grid cells. I have successfully extracted this brick to
>> another shapefile that had much larger polygons. Likewise I can also do the
>> extraction if I resample the cells to a smaller resolution (not shown
>> below).
>>
>>
>> Finally the extract will work if I set 'na.rm=F' but then produces mostly
>> NA's, even though there are no NAs in the brick. I realize this might be
>> something to do with the dataType(). However that does not explain why
>> extract works on individual layers, but not the whole brick.
>>
>> Reproducible code is below and prettier example can be found here:
>> http://rpubs.com/frank_davenport/22698
>>
>> The data necessary to run the example can be found here:
>> https://dl.dropboxusercontent.com/u/9577903/99_raster_bugreport.Rdata
>>
>> Thank you for your help and apologies if a solution is already posted.
>>
>> Frank
>>
>>> rm(list=ls())
>>> library(raster)
>> Loading required package: sp
>>> ##-Download data from here:
>> https://dl.dropboxusercontent.com/u/9577903/99_raster_bugreport.Rdata
>>> ##contains 'dsd' a spatial polygons data.frame and 'g' a raster brick
>> with 10 layers
>>> load('~/Dropbox/Public/99_raster_bugreport.Rdata')
>>>
>>> dsd
>> class : SpatialPolygonsDataFrame
>> features : 36
>> extent : 34.04665, 39.70319, -4.67742, 1.19921 (xmin, xmax, ymin,
>> ymax)
>> coord. ref. : +proj=longlat +a=6378249.145 +b=6356514.96582849 +no_defs
>> variables : 4
>> names : Province, District, Division, uidu
>> min values : CENTRAL, BUNGOMA, ABOTHUGUCHI WEST, 1
>> max values : WESTERN, VIHIGA, WINAM, 44
>>> g
>> class : RasterBrick
>> dimensions : 24, 20, 480, 10 (nrow, ncol, ncell, nlayers)
>> resolution : 0.5, 0.5 (x, y)
>> extent : 33, 43, -6, 6 (xmin, xmax, ymin, ymax)
>> coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
>> data source : in memory
>> names : X1999.03.01, X1999.03.02, X1999.03.03, X1999.03.04,
>> X1999.03.05, X1999.03.06, X1999.03.07, X1999.03.08, X1999.03.09,
>> X1999.03.10
>> min values : 22.47562, 22.44415, 22.25507, 22.23166,
>> 22.12387, 22.42477, 22.27802, 22.15134, 22.36218, 22.33447
>> max values : 41.47818, 40.53116, 41.54944, 42.33093,
>> 41.67810, 40.79260, 41.83319, 40.83359, 41.12604, 42.00555
>>
>>> #---Show the data
>>> plot(g[[1]])
>>> plot(dsd,add=T)
>>>
>>> #--Try to extract, with weights yeilds an error
>>> test1<-extract(g,dsd,fun=mean,na.rm=T,weights=T)
>> Error in t(sapply(res, meanfunc)) :
>> error in evaluating the argument 'x' in selecting a method for function
>> 't': Error in apply(x, 1, function(X) { : dim(X) must have a positive length
>>> #--Extract without weights--produces NA's for most polygons
>>> test2<-extract(g,dsd,fun=mean,na.m=T)
>>> head(test2)
>> X1999.03.01 X1999.03.02 X1999.03.03 X1999.03.04 X1999.03.05
>> X1999.03.06 X1999.03.07 X1999.03.08 X1999.03.09 X1999.03.10
>> [1,] NA NA NA NA NA
>> NA NA NA NA NA
>> [2,] NA NA NA NA NA
>> NA NA NA NA NA
>> [3,] NA NA NA NA NA
>> NA NA NA NA NA
>> [4,] NA NA NA NA NA
>> NA NA NA NA NA
>> [5,] NA NA NA NA NA
>> NA NA NA NA NA
>> [6,] NA NA NA NA NA
>> NA NA NA NA NA
>>> summary(test2)
>> X1999.03.01 X1999.03.02 X1999.03.03 X1999.03.04
>> X1999.03.05 X1999.03.06 X1999.03.07 X1999.03.08
>> Min. :24.47 Min. :25.21 Min. :24.67 Min. :25.47 Min.
>> :27.42 Min. :25.38 Min. :25.93 Min. :24.11
>> 1st Qu.:29.29 1st Qu.:30.99 1st Qu.:29.55 1st Qu.:30.83 1st
>> Qu.:31.58 1st Qu.:29.78 1st Qu.:29.51 1st Qu.:28.35
>> Median :30.31 Median :33.63 Median :31.13 Median :31.88 Median
>> :33.32 Median :30.31 Median :30.52 Median :29.71
>> Mean :29.87 Mean :32.75 Mean :30.41 Mean :31.78 Mean
>> :32.61 Mean :29.71 Mean :29.90 Mean :29.19
>> 3rd Qu.:31.86 3rd Qu.:36.08 3rd Qu.:32.59 3rd Qu.:34.37 3rd
>> Qu.:34.73 3rd Qu.:30.60 3rd Qu.:31.32 3rd Qu.:30.82
>> Max. :32.81 Max. :37.00 Max. :33.45 Max. :35.77 Max.
>> :35.42 Max. :31.98 Max. :31.69 Max. :32.53
>> NA's :30 NA's :30 NA's :30 NA's :30 NA's
>> :30 NA's :30 NA's :30 NA's :30
>> X1999.03.09 X1999.03.10
>> Min. :25.98 Min. :25.53
>> 1st Qu.:29.53 1st Qu.:30.73
>> Median :30.57 Median :31.06
>> Mean :30.12 Mean :31.24
>> 3rd Qu.:31.41 3rd Qu.:33.52
>> Max. :32.75 Max. :34.83
>> NA's :30 NA's :30
>>> #--Extract each layer seperatley--works but is SLOW
>>> glist<-vector(mode='list',length=nlayers(g))
>>>
>>> for(i in 1:length(glist)){
>> + glist[[i]]<-extract(g[[i]],dsd,fun=mean,na.rm=T,weigths=T)
>> + }
>>> sessionInfo()
>> R version 3.1.0 (2014-04-10)
>> Platform: x86_64-apple-darwin10.8.0 (64-bit)
>>
>> locale:
>> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
>>
>> attached base packages:
>> [1] stats graphics grDevices utils datasets methods base
>>
>> other attached packages:
>> [1] raster_2.2-31 sp_1.0-15
>>
>> loaded via a namespace (and not attached):
>> [1] grid_3.1.0 lattice_0.20-29 tools_3.1.0
>> [[alternative HTML version deleted]]
>>
>> _______________________________________________
>> R-sig-Geo mailing list
>> R-sig-Geo at r-project.org
>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
--
Frank Davenport, Ph.D
Climate Hazards Group
UCSB Geography
More information about the R-sig-Geo
mailing list