[R-sig-Geo] raster::extract fails on brick but works on individual layers of brick
Robert J. Hijmans
r.hijmans at gmail.com
Tue Sep 2 18:41:37 CEST 2014
Frank,
I hope this issue has been solved in the development version.
install.packages("raster", repos="http://R-Forge.R-project.org")
I would appreciate feedback.
Best, Robert
On Fri, Jul 25, 2014 at 12:40 PM, Frank Davenport
<frank.davenport at gmail.com> wrote:
> Sorry for the mutliple postings but I found another solution using
> raster::disaggregate(). Essentially the same as resample but prerserves all
> the cell values. The fundamental issue was the small size of the polygons
> compared to the cells (which can be problematic when using weights()).
>
> See the discussion here:
> https://stackoverflow.com/questions/17766989/extract-data-from-raster-with-small-polygons-rounded-weights-too-small
>
>
> g2<-raster::disaggregate(g,10)
> test1<-extract(g2,dsd,fun=mean,na.rm=T,weights=T,small=T)
>
> 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
>
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