I'm trying to follow the code to construct a residual autocovariate model described here: https://github.com/jejoenje/PubsRexamples/blob/master/Crase_etal.R#L16
After creating a large raster space I get an error when assigning values to the raster. I have 1000 random points to sign to the raster so there are many points without data (NA). Any ideas?
head(xy)
[,1] [,2]
[1,] 543510.0 6968620
[2,] 543570.0 6968620
[3,] 543570.0 6968560
[4,] 543599.9 6968560
[5,] 543510.0 6968530
[6,] 543389.9 6968470
head(xy_residuals)
[,1] [,2] [,3]
1 543510.0 6968620 -0.4257671
2 543570.0 6968620 -0.4541684
3 543570.0 6968560 -0.4310492
4 543599.9 6968560 -0.4649595
5 543510.0 6968530 -0.5506348
6 543389.9 6968470 -0.4928708
summary(xy)
X Y
Min. :538800 Min. :6931480
1st Qu.:540480 1st Qu.:6932860
Median :541350 Median :6935320
Mean :541529 Mean :6943218
3rd Qu.:542670 3rd Qu.:6954003
Max. :544290 Max. :6968620
# Define raster ymn, ymx, xmn and xmx from coordinates
# ncol=xmx-xmn nrow=ymx-ymn.
rast <- raster(ncol = 5490, nrow = 37140, ymn = 6931480, ymx = 6968620,
xmn = 538800, xmx = 544290)
rast
class : RasterLayer
dimensions : 37140, 5490, 203898600 (nrow, ncol, ncell)
resolution : 1, 1 (x, y)
extent : 538800, 544290, 6931480, 6968620 (xmin, xmax, ymin, ymax)
coord. ref. : NA
Problematic call:
rast[cellFromXY(rast, xy)] <- xy_residuals[, 3]
Error in .replace(x, i = i, value = value, recycle = 1) :
cannot replace values in large Raster objects if their length is not 1 or the number of cells to be replaced**
The error message says why this happens (But perhaps this "feature" needs some improvement.) The raster is very large and may not fit in memory (RAM). That may be too conservative an assessment . You can adjust that via rasterOptions or you can try:
library(raster)
r <- raster(ncol = 10, nrow = 10, ymn = 6931480, ymx = 6968620, xmn = 538800, xmx = 544290)
m <- matrix(c(539868,542002,542409,6945031,6940012,6935997, 1, 2, 3), 3, 3)
cells <- cellFromXY(r, m[,1:2])
# create a large vector with all cells
v <- rep(NA, ncell(r))
v[cells] <- m[,3]
v <- setValues(r, v)
If that does not work, you could look at update, but that is more risky as it overwrites data on file, but that should not be a concern in this case.
r <- setValues(r, NA)
# that probably creates a file on disk. If not do
# r <- writeRaster(r, filename='test.grd')
r <- update(r, cell=cells, v=m[,3])
Related
I have a timeseries of geotiff files I'd like to stack in R using stars. Here's the first two:
urls <- paste0("/vsicurl/",
"https://sdsc.osn.xsede.org/bio230014-bucket01/neon4cast-drivers/",
"noaa/gefs-v12/cogs/gefs.20221201/",
c("gep01.t00z.pgrb2a.0p50.f003.tif", "gep01.t00z.pgrb2a.0p50.f006.tif"))
library(stars)
stars::read_stars(urls, along="time")
Errors with:
Error in c.stars_proxy(`3` = list(gep01.t00z.pgrb2a.0p50.f003.tif = "/vsicurl/https://sdsc.osn.xsede.org/bio230014-bucket01/neon4cast-drivers/noaa/gefs-v12/cogs/gefs.20221201/gep01.t00z.pgrb2a.0p50.f003.tif"), :
don't know how to merge arrays: please specify parameter along
Context: bands contain both time+band info
This fails because the dimensions do not match, which happens because the files have concatenated temporal information into the band names:
x<- lapply(urls, read_stars)
x
produces:
[[1]]
stars object with 3 dimensions and 1 attribute
attribute(s), summary of first 1e+05 cells:
Min. 1st Qu. Median Mean 3rd Qu. Max.
gep01.t00z.pgrb2a.0p50.f003.ti... 50026.01 98094.81 101138 98347.42 101845.2 104605.2
dimension(s):
from to offset delta refsys point
x 1 720 -180.25 0.5 Coordinate System importe... FALSE
y 1 361 90.25 -0.5 Coordinate System importe... FALSE
band 1 8 NA NA NA NA
values x/y
x NULL [x]
y NULL [y]
band PRES:surface:3 hour fcst,...,DLWRF:surface:0-3 hour ave fcst
[[2]]
stars object with 3 dimensions and 1 attribute
attribute(s), summary of first 1e+05 cells:
Min. 1st Qu. Median Mean 3rd Qu. Max.
gep01.t00z.pgrb2a.0p50.f006.ti... 50029.83 98101.83 101170.6 98337.52 101825 104588.2
dimension(s):
from to offset delta refsys point
x 1 720 -180.25 0.5 Coordinate System importe... FALSE
y 1 361 90.25 -0.5 Coordinate System importe... FALSE
band 1 8 NA NA NA NA
values x/y
x NULL [x]
y NULL [y]
band PRES:surface:6 hour fcst,...,DLWRF:surface:0-6 hour ave fcst
Note the band names would align except for the existence of the timestamp being tacked on, e.g. PRES:surface:3 hour fcst vs PRES:surface:6 hour fcst.
How can I best read in these files so that I have dimensions of x,y,band, and time in my stars object?
alternatives: terra?
How about terra? Note that terra is happy to read these files in directly, but treats this as 16 unique bands. Can I re-align that so that I have the original 8 bands along a new "time" dimension? (I recognize stars emphasizes 'spatio-temporal', maybe the such a cube is out of scope to terra?) Also note that terra for some reason mangles the timestamp in these band names:
x <- terra::rast(urls)
x
class : SpatRaster
dimensions : 361, 720, 16 (nrow, ncol, nlyr)
resolution : 0.5, 0.5 (x, y)
extent : -180.25, 179.75, -90.25, 90.25 (xmin, xmax, ymin, ymax)
coord. ref. : lon/lat Coordinate System imported from GRIB file
sources : gep01.t00z.pgrb2a.0p50.f003.tif (8 layers)
gep01.t00z.pgrb2a.0p50.f006.tif (8 layers)
names : PRES:~ fcst, TMP:2~ fcst, RH:2 ~ fcst, UGRD:~ fcst, VGRD:~ fcst, APCP:~ fcst, .
With terra it is pretty easy to make a time-series for each variable as I show below.
urls <- paste0("/vsicurl/",
"https://sdsc.osn.xsede.org/bio230014-bucket01/neon4cast-drivers/",
"noaa/gefs-v12/cogs/gefs.20221201/",
c("gep01.t00z.pgrb2a.0p50.f003.tif", "gep01.t00z.pgrb2a.0p50.f006.tif"))
library(terra)
r <- rast(urls)
Extract two variables of interest
nms <- names(r)
tmp <- r[[grep("TMP", nms)]]
rh <- r[[grep("RH", nms)]]
# set time
tm <- as.POSIXct("2022-12-01", tz="GMT") + c(3,6) * 3600
time(rh) <- tm
time(tmp) <- tm
And you could combine them into a SpatRasterDatset like this:
s <- sds(list(tmp=tmp, rh=rh))
An alternative path to get to the same point would be to start with a SpatRasterDataset and subset it.
sd <- sds(urls)
nl <- 1:length(sd)
nms <- names(sd[1])
tmp2 <- rast(sd[nl, grep("TMP", nms)])
time(tmp2) <- tm
rh2 <- rast(sd[nl, grep("RH", nms)])
time(rh2) <- tm
I made the subsetting work a little nicer in terra version 1.7-5
urls <- paste0("/vsicurl/",
"https://sdsc.osn.xsede.org/bio230014-bucket01/neon4cast-drivers/",
"noaa/gefs-v12/cogs/gefs.20221201/", c("gep01.t00z.pgrb2a.0p50.f003.tif", "gep01.t00z.pgrb2a.0p50.f006.tif"))
library(terra)
#terra 1.7.5
sd <- sds(urls)
tmp <- sd[,2]
tmp
#class : SpatRaster
#dimensions : 361, 720, 2 (nrow, ncol, nlyr)
#resolution : 0.5, 0.5 (x, y)
#extent : -180.25, 179.75, -90.25, 90.25 (xmin, xmax, ymin, ymax)
#coord. ref. : lon/lat Coordinate System imported from GRIB file
#sources : gep01.t00z.pgrb2a.0p50.f003.tif
# gep01.t00z.pgrb2a.0p50.f006.tif
#names : TMP:2 m above g~Temperature [C], TMP:2 m above g~Temperature [C]
#unit : C, C
#time : 2022-12-01 03:00:00 to 2022-12-01 06:00:00 UTC
As for the layer names containing the forecast time, that is just because that is what is in the tif metadata. It looks like that was a decision made when they were created from the original GRIB files.
The latitude extent going beyond the north and south poles is an interesting feature of this dataset.
Just wanted to share some additional possible solutions for comparison. With larger numbers of files some of these differences become more relevant. this expands a bit beyond my original question.
terra
Prof Hijmans gives a very nice solution in terra. He also asked about the original upstream sources, which I didn't explain properly -- these are originally GRIB files for NOAA GEFS forecast.
Notably, we can work directly from the GRIB files. GEFS is a 35-day forecast, so let's try going more than 6 hrs into the future:
library(terra)
# original GRIB sources, AWS mirror
gribs <- paste0("/vsicurl/https://noaa-gefs-pds.s3.amazonaws.com/gefs.20220314/00/atmos/pgrb2ap5/geavg.t00z.pgrb2a.0p50.f",
stringr::str_pad(seq(3,240,by=3), 3, pad="0"))
bench::bench_time({
cube <- terra::sds(gribs)
})
cube[1,63] |> plot()
very nice!
gdalcubes
gdalcubes is another package that can also leverage the gdal virtual filesystem when working with these large-ish remote files. It also lets us define an abstract cube at potentially a different resolution in space & time than the original sources (averaging or interpolating). lazy operations mean this may run a bit faster(?)
library(gdalcubes)
date <- as.Date("2023-01-26")
date_time = date + lubridate::hours(seq(3,240,by=3))
# USA box
v <- cube_view(srs = "EPSG:4326",
extent = list(left = -125, right = -66,top = 49, bottom = 25,
t0= as.character(min(date_time)), t1=as.character(max(date_time))),
dx = 0.5, dy = 0.5, dt = "PT3H")
gribs <- paste0("/vsicurl/https://noaa-gefs-pds.s3.amazonaws.com/gefs.20220314/00/atmos/pgrb2ap5/geavg.t00z.pgrb2a.0p50.f",
stringr::str_pad(seq(3,240,by=3), 3, pad="0"))
bench::bench_time({
cube <- gdalcubes::create_image_collection(gribs, date_time = date_time)
})
bench::bench_time({
raster_cube(cube, v) |>
select_bands("band63") |> # tempearture
animate(col = viridisLite::viridis, nbreaks=50, fps=10, save_as = "temp.gif")
})
stars
didn't translate a full stars example, but here at least is the band name correction; a bit more cumbersome than the examples above.
urls <- paste0("/vsicurl/",
"https://sdsc.osn.xsede.org/bio230014-bucket01/neon4cast-drivers/",
"noaa/gefs-v12/cogs/gefs.20221201/",
c("gep01.t00z.pgrb2a.0p50.f003.tif", "gep01.t00z.pgrb2a.0p50.f006.tif"))
library(stars)
#stars::read_stars(urls, along="time") # no luck!
## grab unstacked proxy object for each geotiff
x <- lapply(urls, read_stars)
# extract band-names-part
band_names <- st_get_dimension_values(x[[1]], "band") |>
stringr::str_extract("([A-Z]+):") |>
str_remove(":")
# apply corrected band-names
x1 <- lapply(x, st_set_dimensions, "band", band_names)
# at last, we can stack into a cube:
x1 <- do.call(c, c(x1, along="time"))
# and add correct date timestamps to the new time dimension
dates <- as.Date("2022-12-01") + lubridate::hours(c(3,6))
x1 <- st_set_dimensions(x1, "time", dates)
x1
I want to calculate the area under every possible combination of two classified rasters. I am using the following code
library(terra)
#First create two rasters
r1 <- r2 <- rast(nrow=100, ncol=100)
#Assign random cell values
set.seed(123)
values(r1) <- runif(ncell(r1), min=0, max=1)
values(r2) <- runif(ncell(r2), min=0, max=1)
# classify the values into two groups
m_r1 <- c(min(global(r1, "min", na.rm=TRUE)), 0.2, 1,
0.2, max(global(r1, "max", na.rm=TRUE)), 2)
m_r2 <- c(min(global(r2, "min", na.rm=TRUE)), 0.2, 1,
0.2, max(global(r2, "max", na.rm=TRUE)), 2)
#Reclassify the rasters
rclmat_r1 <- matrix(m_r1, ncol=3, byrow=TRUE)
rc_r1 <- classify(r1, rclmat_r1, include.lowest=TRUE)
rclmat_r2 <- matrix(m_r2, ncol=3, byrow=TRUE)
rc_r2 <- classify(r2, rclmat_r2, include.lowest=TRUE)
plot(rc_r1)
plot(rc_r2)
#Convert to polygons
r1_poly <- as.polygons(rc_r1, dissolve=TRUE)
r2_poly <- as.polygons(rc_r2, dissolve=TRUE)
plot(r1_poly)
plot(r2_poly)
#Perform intersections
x <- intersect(r1_poly, r2_poly)
x
#> class : SpatVector
#> geometry : polygons
#> dimensions : 2747, 2 (geometries, attributes)
#> extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84
#> names : lyr.1 lyr.1
#> type : <int> <int>
#> values : 1 1
#> 1 2
#> 2 1
As you can see from the output, one combination i.e. 2-2 is missing. Why is this happening?
When I am trying to calculate the area for each combination using expanse(x), it returns a long result. How can I get the area in km2 for the following combinations?
Combination Area (km2)
1-1
1-2
2-1
2-2
With this example it would be better to stay with raster data.
x = 10 * rc_r1 + rc_r2
a = cellSize(x, unit="km")
zonal(a, x, sum)
# lyr.1 area
#1 11 19886611
#2 12 81946082
#3 21 84763905
#4 22 323469024
By multiplying with 10, the values in the first layer become 10 (it they were 1) or 20 (if they were 2). If you then add the second layer, you get 10 + 1 or 2 and 20 + 1 or 2, so you end up with four classes: 11, 12, 21, and 22. These show the value in the first raster (first digit) and in the second raster (second digit).
When you show a SpatVector only the first three records are printed, and there is a 2-2 record. Nevertheless, intersect did not work properly and I have now fixed this.
I have two raster grids in R with different resolutions which don't line up exactly. In actual fact I have hundreds of each so any answer must be easily run many times.
I want to scale the finer resolution grid up to the coarser resolution by taking an areal weighted mean of the grid cells.
I was hoping I could use projectRaster or resample but neither give the desired output and I cannot use aggregate as I need my new grids to align to the coarser resolution grid.
For my real data my finer grid is 0.005 deg intervals and coarser is at 0.02479172 deg intervals and extents/origins don't exactly match up.
I've made an extreme version as an example why neither resample or projectRaster work
library(raster)
#> Warning: package 'raster' was built under R version 3.5.3
#> Loading required package: sp
testproj <- "+proj=lcc +lat_1=48 +lat_2=33 +lon_0=-100 +ellps=WGS84"
testmat <- matrix(1, nrow = 8, ncol = 8)
# testmat <- matrix(sample(1:10, 64, replace = T), nrow = 8, ncol = 8)
testmat[1,5] <- 400
testmat[8,4] <- -400
testsmallraster <- raster(testmat, xmn=0, xmx=8, ymn=0, ymx=8)
crs(testsmallraster) <- testproj
plot(testsmallraster)
testlarger <- raster(matrix(rep(NA,4), nrow = 2, ncol = 2), xmn=0.3, xmx=8.3, ymn=0, ymx=8)
crs(testlarger) <- testproj
tout_reproj <- projectRaster(testsmallraster, testlarger)
tout_resamp <- resample(testsmallraster, testlarger)
tout_resampngb <- resample(testsmallraster, testlarger, method = "ngb")
tout_agg <- aggregate(testsmallraster, fact = 4)
#reprojected values ignore all but 4 cells closest to new centre
values(tout_reproj)
#> [1] 1 1 1 1
#resample uses bilinear interpolation which weights the grids cells furthest from the new centre less than those closest
# I need all grid cells entirely contained in the new grid to have equal weighting
#bilinear interpolation also weights cells which do not fall within the new cell at all which I do not want
values(tout_resamp)
#> [1] 10.851852 15.777778 -7.911111 -12.366667
#aggregate gives close to the values I want but they are not in the new raster origin/resolution and therefore not splitting values that fall across grid boundaries
values(tout_agg)
#> [1] 1.0000 25.9375 -24.0625 1.0000
#using ngb was never really going to make any sense but thought I'd as it for completeness
values(tout_resampngb)
#> [1] 1 1 1 1
#desired output first cell only 0.3 of a grid cell covers the grid cell = 400 the rest equal 1
#desired output second cell 0.7 of a grid cell covers the grid cell = 400 the rest equal 1
#desired output third cell has exactly 1 grid cell of -400 and 15 of 1
#desired output fourth cell only overlap grid cells = 1
desiredoutput <- raster(matrix(c((15.7*1+0.3*400)/16,(15.3*1+0.7*400)/16,mean(c(-400, rep(1,15))),1),byrow = T, nrow = 2, ncol = 2), xmn=0.3, xmx=8.3, ymn=0, ymx=8)
values(desiredoutput)
#> [1] 8.48125 18.45625 -24.06250 1.00000
Created on 2020-07-02 by the reprex package (v0.3.0)
You can get closer to the desired result by using a similar spatial resolution for resample, and then aggregate the results
library(raster)
testproj <- "+proj=lcc +lat_1=48 +lat_2=33 +lon_0=-100 +datum=WGS84"
testmat <- matrix(1, nrow = 8, ncol = 8)
testmat[1,5] <- 400
testmat[8,4] <- -400
testsmallraster <- raster(testmat, xmn=0, xmx=8, ymn=0, ymx=8, crs=testproj)
testlarger <- raster(matrix(rep(NA,4), nrow = 2, ncol = 2), xmn=0.3, xmx=8.3, ymn=0, ymx=8, crs = testproj)
y <- disaggregate(testlarger, 4)
z <- resample(testsmallraster, y)
za <- aggregate(z, 4)
values(za)
#[1] 8.48125 18.45625 -24.06250 1.00000
for much better speed, try terra
library(terra)
a <- rast(testsmallraster)
b <- rast(testlarger)
b <- disaggregate(b, 4)
d <- resample(a, b)
da <- aggregate(d, 4)
values(da)
# layer
#[1,] 8.48125
#[2,] 18.45625
#[3,] -24.06250
#[4,] 1.00000
This probably ought to be done automatically by resample and project(Raster). raster attempts to do some of this for resample, but in this case not very satisfactorily.
When I needed to do similar resampling, this worked for me. This example is a 4-cell destination grid at 1o x 1o spacing with centroids at half degrees (to match some satellite data), and an offset half-degree grid for source data (ECMWF weather). 'Resample' does the heavy lifting of interpolating on mismatched grids. The code below is basically a manual version of a 'weights=' option that doesn't exist for resample. We need relative, not absolute, areas to be correct for weighting, so the caveat on the precision of raster::area described in the help seems of low concern.
library(raster)
wgs84 <- "+init=epsg:4326"
polar.brick.source <- array(dim = c(5, 5, 2), rep(c(1, 2), each = 25))
dimnames(polar.brick.source)[[1]] <- seq(-1, 1, by = .5)
dimnames(polar.brick.source)[[2]] <- seq(80, 82, by = .5)
dimnames(polar.brick.source)[[3]] <- c("time.a", "time.b")
# Add some outliers to see their effects.
polar.brick.source[1, 2, ] <- c(25, 50)
polar.brick.source[3, 2, 2] <- -30
polar.brick <- brick(polar.brick.source, crs = CRS(wgs84),
xmn = min(as.numeric(dimnames(polar.brick.source)[[1]])) - .25,
xmx = max(as.numeric(dimnames(polar.brick.source)[[1]])) + .25,
ymn = min(as.numeric(dimnames(polar.brick.source)[[2]])) - .25,
ymx = max(as.numeric(dimnames(polar.brick.source)[[2]])) + .25)
fine.polar.area <- raster::area(polar.brick)
polar.one.degree.source <- data.frame(
lon = c(-.5, .5, -.5, .5),
lat = c(80.5, 80.5, 81.5, 81.5),
placeholder = rep(1, 4))
polar.one.degree.raster <- rasterFromXYZ(polar.one.degree.source, crs = CRS(wgs84))
polar.one.degree.area <- raster::area(polar.one.degree.raster)
as.data.frame(polar.one.degree.area, xy = T)
fine.clip.layer <- disaggregate(polar.one.degree.raster, 2)
clipped.fine.polar <-resample(polar.brick * fine.polar.area,
fine.clip.layer)
new.weighted.wx <- aggregate(clipped.fine.polar * 4, 2)
as.data.frame(new.weighted.wx, xy = T) # look at partial results.
new.weather <- new.weighted.wx / polar.one.degree.area
as.data.frame(new.weather, xy = T)
I am writing a script that will take any three rasters, and crop them to the minimum possible extent. All three rasters will have the same resolution and projection. However, cropping to the minimum extent does not change the extents for the three rasters. I've tried setExtent and the same thing happens. If anyone can give suggestions I would really appreciate it. Here is sample code:
library(raster)
#Projection of all three rasters
newproj<- "+proj=utm +zone=4 +datum=WGS84 +units=m +no_defs +ellps=WGS84
+towgs84=0,0,0"
#Create three rasters with varying extents
raster1p<- raster(crs = newproj)
extent(raster1p)<- c(531247, 691847, 7856684, 7987884)
res(raster1p)<- c(100, 100)
values(raster1p)<- NA
raster2p<- raster(crs = newproj)
extent(raster2p)<- c(533550.8, 646550.8, 7881307, 7973807)
res(raster2p)<- c(100, 100)
values(raster2p)<- NA
raster3p<- raster(crs = newproj)
extent(raster3p)<- c(525739, 689839, 7857305, 7996505)
res(raster3p)<- c(100, 100)
values(raster3p)<- NA
#Find minimum extent
xmin1<- c(xmin(extent(raster1p)), xmin(extent(raster2p)), xmin(extent(raster3p)))
xmax1<- c(xmax(extent(raster1p)), xmax(extent(raster2p)), xmax(extent(raster3p)))
ymin1<- c(ymin(extent(raster1p)), ymin(extent(raster2p)), ymin(extent(raster3p)))
ymax1<- c(ymax(extent(raster1p)), ymax(extent(raster2p)), ymax(extent(raster3p)))
xmin_new<- min(xmin1)
xmax_new<- min(xmax1)
ymin_new<- min(ymin1)
ymax_new<- min(ymax1)
newextent=c(xmin_new, xmax_new, ymin_new, ymax_new)
#Crop rasters to minimum extent
crop(raster1p, newextent)
crop(raster2p, newextent)
crop(raster3p, newextent)
#Compare extents
extent_check<- c(extent(raster1p), extent(raster2p), extent(raster3p))
However, when I look at the extent_check to see if the extents now match, I see that the extents have not changed at all:
> extent_check
[[1]]
class : Extent
xmin : 531247
xmax : 691847
ymin : 7856684
ymax : 7987884
[[2]]
class : Extent
xmin : 533550.8
xmax : 646550.8
ymin : 7881307
ymax : 7973807
[[3]]
class : Extent
xmin : 525739
xmax : 689839
ymin : 7857305
ymax : 7996505
Any idea what I could be doing wrong? Thank you for your time
I think it is not so much about doing something wrong, but rater a misconception (although there is a mistake in your code).
Example data
library(raster)
prj <- "+proj=utm +zone=4 +datum=WGS84"
r1 <- raster(res=100, ext=extent(c(531247, 691847, 7856684, 7987884)), crs=prj, vals=NA)
r2 <- raster(res=100, ext=extent(c(533550.8, 646550.8, 7881307, 7973807)), crs=prj, vals=NA)
r3 <- raster(res=100, ext=extent(c(525739, 689839, 7857305, 7996505)), crs=prj, vals=NA)
Find the "minimum extent"
e <- intersect(intersect(extent(r1), extent(r2)), extent(r3))
Note that the result is different from yours because you use
xmin_new <- min(xmin1) and ymin_new <- min(ymin1)
Where it should be
xmin_new <- max(xmin1) and ymin_new <- max(ymin1)
Now crop
r1e <- crop(r1, e)
r2e <- crop(r2, e)
r3e <- crop(r3, e)
Inspect the resulting extents
t(sapply(c(r1e, r2e, r3e), function(i) as.vector(extent(i))))
# [,1] [,2] [,3] [,4]
#[1,] 533547.0 646547.0 7881284 7973784
#[2,] 533550.8 646550.8 7881307 7973807
#[3,] 533539.0 646539.0 7881305 7973805
They are not exactly the same, because that is not possible because the rasters do not align. Their "origins" are different
t(sapply(c(r1e, r2e, r3e), origin))
# [,1] [,2]
#[1,] 47.0 -16
#[2,] -49.2 7
#[3,] 39.0 5
To make them align, you would need to do something like this
r1e <- crop(r1, e)
r2e <- resample(r2, r1e)
r3e <- resample(r3, r1e)
t(sapply(c(r1e, r2e, r3e), function(i) as.vector(extent(i))))
# [,1] [,2] [,3] [,4]
#[1,] 533547 646547 7881284 7973784
#[2,] 533547 646547 7881284 7973784
#[3,] 533547 646547 7881284 7973784
I got a raster layer 'w_wgs' of climatic values. On the other side, I created an empty raster 'grid'.
I want to assign the values stored in 'w_wgs' into my empty raster grid. I tried merge(), overlay(), over() but these don't work.
How can I do this ? assuming these two rasters don't have the same extent nor resolution (nor equal number of cells, obviously)...but both have the same CRS (WGS84).
Here, a description of my 2 objects :
> extent(w_wgs)
class : Extent
xmin : -64.50344
xmax : 74.07016
ymin : 12.93039
ymax : 72.72534
> res(w_wgs)
[1] 0.01320 0.00895
> res(grid)
[1] 0.08 0.08
> extent(grid)
class : Extent
xmin : 5
xmax : 17.96
ymin : 40
ymax : 50
I think you need to use resample
grid <- resample(w_wgs, grid)
A small example that should be similar to your case:
x <- matrix(1:100, nr = 10, nc = 10)
a <- raster(x)
x2 <- matrix(NA, nr = 3, nc = 3)
b <- raster(x2)
# Manually changing the extent and resolution of b
b#extent#xmax <- 0.5
b#extent#ymin <- 0.3
b <- resample(a, b)
par(mfrow = c(2, 1))
plot(a)
plot(extent(b), add = T)
plot(b)