Can't change raster's extent

Question

I want to crop an elevation raster to add it to a raster stack. It's easy, I did this before smoothly, adding a ecoregions raster to the same stack. But with the elevation one, just doesn't work. Now, there are several questions here in overflow adressing this issue and I tryed a lot of things...

First of all, we need this:

library(rgdal)
library(raster)

My stack is predictors2:

#Downloading the stack
predictors2_full<-getData('worldclim', var='bio', res=10)

#Cropping it, I don' need the whole world   
xmin=-120; xmax=-35; ymin=-60; ymax=35
limits <- c(xmin, xmax, ymin, ymax)
predictors2 <- crop(predictors2_full,limits)

Then I've downloaded the terr_ecorregions shapefile here: http://maps.tnc.org/files/shp/terr-ecoregions-TNC.zip

setwd("~/ORCHIDACEAE/Ecologicos/w2/layers/terr-ecoregions-TNC")
ecoreg = readOGR("tnc_terr_ecoregions.shp") # I've loaded...
ecoreg2 <- crop(ecoreg,extent(predictors2)) # cropped...
ecoreg2 <- rasterize(ecoreg2, predictors2)  # made the shapefile a raster
predictors4<-addLayer(predictors2,elevation,ecoreg2) # and added the raster
                                                     # to my stack

With elevation, I just can't. The Digital elevation model is based in GMTED2010, which can be downloaded here: http://edcintl.cr.usgs.gov/downloads/sciweb1/shared/topo/downloads/GMTED/Grid_ZipFiles/mn30_grd.zip

elevation<-raster("w001001.adf") #I've loaded
elevation<-crop(elevation,predictors2) # and cropped

But elevation gets a slightly different extent instead of predictors2's extent:

> extent(elevation)
class       : Extent 
xmin        : -120.0001 
xmax        : -35.00014 
ymin        : -60.00014 
ymax        : 34.99986 
> 

I tried to make then equal by all means I read about in questions here... I tried to extend so elevation's ymax would meet predictors2's ymax elevation<-extend(elevation,predictors2) #didn't work, extent remains the same

I tried the opposite... making predictors2 extent meet elevation's extent... nothing either.

But then I read that

You might not want to play with setExtent() or extent() <- extent(), as you could end with wrong geographic coordinates of your rasters - @ztl, Jun 29 '15

And I tried to get the minimal common extent of my rasters, following @zlt answer in another extent question, by doing this

# Summing your rasters will only work where they are not NA
r123 = r1+r2+r3 # r123 has the minimal common extent
r1 = crop(r1, r123) # crop to that minimal extent
r2 = crop(r2, r123)
r3 = crop(r3, r123)

For that, first I had to set the resolutions:

res(elevation)<-res(predictors2) #fixing the resolutions... This one worked.

But then, r123 = r1+r2+r didn't work:

> r123=elevation+ecoreg2+predictors2
Error in elevation + ecoreg2 : first Raster object has no values

Can anyone give me a hint on this? I really would like to add my elevation to the raster. Funny thing is, I have another stack named predictors1 with the exact same elevation's extent... And I was able to crop ecoreg and add ecoreg to both predictors1 and predictors2... Why can't I just do the same to elevation? I'm quite new to this world and runned out of ideas... I appreciate any tips.

EDIT: Solution, Thanks to @Val

I got to this:

#Getting the factor to aggregate (rasters are multiples of each other)
res(ecoreg2)/res(elevation)
[1] 20 20 #The factor is 20


elevation2<-aggregate(elevation, fact=20)
elevation2 <- crop(elevation2,extent(predictors2))

#Finally adding the layer:
predictors2_eco<-addLayer(predictors2,elevation2,ecoreg)

New problem, thought...

I can't write stack to a geotiff

 writeRaster(predictors2_eco, filename="cropped_predictors2_eco.tif", options="INTERLEAVE=BAND", overwrite=TRUE)

 Error in .checkLevels(levs[[j]], value[[j]]) : 
 new raster attributes (factor values) should be in a data.frame (inside a list)

Answer 1

I think you're having issues because you're working with rasters of different spatial resolutions. So when you crop both rasters to the same extent, they'll have a slightly different actual extent because of that. So if you want to stack rasters, you need to get them into the same resolution. Either you disaggregate the raster with the coarser resolution (i.e. increase the resolution by resampling or other methods) or you aggregate the raster with the higher resolution (i.e. decrease the resolution with for instance taking the mean over n pixel).

Please note that if you change the extent or resolution with setExtent(x), extent(x) <-, res(x) <- or similar will NOT work, since you're just changing slots in the raster object, not the actual underlying data.

So to bring the rasters into a common resolution, you need to change the data. You can use the functions (amongst others) aggregate, disaggregate and resample for that purpose. But since you're changing data, you need to be clear on what you're and the function you use is doing.

The most handy way for you should be resample, where you can resample a raster to another raster so they match in extent and resolution. This will be done using a defined method. Per default it's using nearest neighbor for computing the new values. If you're working with continuous data such as elevation, you might want to opt for bilinear which is bilinear interpolation. In this case you're actually creating "new measurements", something to be aware of.

If your two resolutions are multiples of each other, you could look into aggregate and disaggregate. In the case of disaggregate you would split a rastercell by a factor to get a higher resolution (e.g. if your first resolution is 10 degrees and your desired resolution is 0.05 degrees, you could disaggregate with a factor of 200 giving you 200 cells of 0.05 degree for every 10 degree cell). This method would avoid interpolation.

Here's a little working example:

library(raster)
library(rgeos)

shp <- getData(country='AUT',level=0)

# get centroid for downloading eco and dem data
centroid <- coordinates(gCentroid(shp))

# download 10 degree tmin
ecovar <- getData('worldclim', var='tmin', res=10, lon=centroid[,1], lat=centroid[,2])
ecovar_crop <- crop(ecovar,shp)

# output
> ecovar_crop
class       : RasterBrick 
dimensions  : 16, 46, 736, 12  (nrow, ncol, ncell, nlayers)
resolution  : 0.1666667, 0.1666667  (x, y)
extent      : 9.5, 17.16667, 46.33333, 49  (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0 
data source : in memory
names       : tmin1, tmin2, tmin3, tmin4, tmin5, tmin6, tmin7, tmin8, tmin9, tmin10, tmin11, tmin12 
min values  :  -126,  -125,  -102,   -77,   -33,    -2,    19,    20,     5,    -30,    -74,   -107 
max values  :   -31,   -21,     9,    51,    94,   131,   144,   137,   106,     60,     18,    -17 


# download SRTM elevation - 90m resolution at eqt
elev <- getData('SRTM',lon=centroid[,1], lat=centroid[,2])
elev_crop <- crop(elev, shp)

# output

> elev_crop
class       : RasterLayer 
dimensions  : 3171, 6001, 19029171  (nrow, ncol, ncell)
resolution  : 0.0008333333, 0.0008333333  (x, y)
extent      : 9.999584, 15.00042, 46.37458, 49.01708  (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0 
data source : in memory
names       : srtm_39_03 
values      : 198, 3865  (min, max)

# won't work because of different resolutions (stack is equal to addLayer)
ecoelev <- stack(ecovar_crop,elev_crop)

# resample
elev_crop_RS <- resample(elev_crop,ecovar_crop,method = 'bilinear')

# works now
ecoelev <- stack(ecovar_crop,elev_crop_RS)

# output
> ecoelev
class       : RasterStack 
dimensions  : 16, 46, 736, 13  (nrow, ncol, ncell, nlayers)
resolution  : 0.1666667, 0.1666667  (x, y)
extent      : 9.5, 17.16667, 46.33333, 49  (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0 
names       :     tmin1,     tmin2,     tmin3,     tmin4,     tmin5,     tmin6,     tmin7,     tmin8,     tmin9,    tmin10,    tmin11,    tmin12, srtm_39_03 
min values  : -126.0000, -125.0000, -102.0000,  -77.0000,  -33.0000,   -2.0000,   19.0000,   20.0000,    5.0000,  -30.0000,  -74.0000, -107.0000,   311.7438 
max values  :   -31.000,   -21.000,     9.000,    51.000,    94.000,   131.000,   144.000,   137.000,   106.000,    60.000,    18.000,   -17.000,   3006.011