R raster package https://rspatial.github.io/raster/reference/raster-package.html
License: GNU General Public License v3.0
R package with data sets used in the material on the https://rspatial.org website to teach spatial data analysis with R.
You can install the package like this
remotes::install_github("rspatial/rspatial")
Just stumbled upon that and wondered if it is intentional:
library(raster)
raster::unique(raster(matrix(NA, 5, 5)))
> logical(0)The sort in raster::unique removes the NA, for comparison to the base unique it would maybe be useful to set na.last = "FIRST"?
Cheers
Marco
I seem to be getting different results that vary by a factor of 100 from area calculations in raster packages. Back in late 2017, I run the code below with R version 3.3.2 (2016-10-31), rgdal package version 1.2-5, and raster package version 2.6-7 and received for the variable kba_total_impact_km2_optimal a value of 70.52. I’ve recently rerun the analyses with the same code but R version 3.4.3 (2017-11-30), rgdal package version 1.3-6, and raster package version 2.8-19 and received for the variable kba_total_impact_km2_optimal a value of 0.7052. Input of optimal and temp.kba are in m units (and are also attached), hence I converted area to km2 by dividing by 1000000. I’m a bit confused and apologize if I’m missing something obvious, but I’ve never had such differences occur with different versions of R. Based on the data, the value of 70.52 km2 makes sense so I’m assuming something is not processing correctly with the latest raster package version?
I greatly appreciate any help on this and again apologies in advance if I’m missing something super simple and obvious. Input data and code are attached.
Sincerely,
Sharon Baruch-Mordo
Spatial Scientist
The Nature Conservancy
# Begin code:
optimal <- raster(file.choose())
temp.kba <- readOGR(file.choose())
# calc area of kba in country
temp.kba$area_sqkm <- area(temp.kba)/1000000
# find area that intersect with optimal scenario = 1 cells
list.areas.kba.optimal <- extract(optimal, temp.kba, na.rm=FALSE, weight=TRUE, normalizeWeights=FALSE)
sum.if <- function(x){if(length(x[,1][!is.na(x[,1])]) > 0) (sum(x[which(x[,1]==1),2])) else NA}
# how much area is impacted for each kba (sum only for those with 1, i.e., those intersecting a raster value of 1)
temp.kba$area_impacted_optimal <- unlist(lapply(list.areas.kba.optimal, FUN=sum.if)) # in sqkm as it is proportion of 1 km cell!
temp.kba$prop_optimal <- temp.kba$area_impacted_optimal/temp.kba$area_sqkm
kba_total_impact_km2_optimal <- sum(temp.kba$area_impacted_optimal,na.rm=T)
I am working with some Large RasterLayers (3636870900 elements, 27.1 Gb)
When saving them out as GeoTiff I get the following error:
raster::writeRaster(rast_tmp,
filename = paste0("rasters/test.tif"),
format = "GTiff",
datatype = "INT2U",
overwrite = T)
Error in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm = na.rm) :
long vectors not supported yet: ../../../../src/include/Rinlinedfuns.h:519
R seems to have no trouble working with these large rasters, and I only get the error when trying to save them.
Hello
I found a problem with the boundary function. Some times it convert NA values into 0's.
library(raster)
load("error.boundary.RData")
# trying boundaries 100 times
lpatc <- lapply(1:100, function(x) map * boundaries(map, type='inner', asNA=T))
# comparing all them versus the first one
ipatch <- sapply(2:100, function(x) identical(lpatc[[x]][], lpatc[[1]][]))
print(ipatch) # not all of them are identical
# comparing the bbox`
ibbox <- sapply(2:100, function(x) identical(bbox(lpatc[[x]]), bbox(lpatc[[1]])))
all(ibbox)
# so, checking values only. the raster that are different,
# are actually identical among them in their content
sapply(which(!ipatch)[-1]+1, function(x) identical(lpatc[[x]][], lpatc[[which(!ipatch)[1]+1]][]))
# getting into the values
t1 <- lpatc[[which(ipatch)[1]+1]][]
t2 <- lpatc[[which(!ipatch)[1]+1]][]
# forcing zero's in t2 into NA
t2[t2==0] <- NA
# testing if they are identical
identical(t1,t2)
# they are...
the map file is attached
Carlos Alberto
I am now creating tests to see if raster functions work for sf/sfc object.
It seems crop, mask or rasterize work well, however I found an issue with the extract function:
library(raster)
#> Loading required package: sp
library(sf)
#> Linking to GEOS 3.6.1, GDAL 2.2.4, PROJ 4.9.3
p1 <- rbind(c(-180, -20), c(-140, 55), c(10, 0), c(-140, -60), c(-180, -20))
hole <- rbind(c(-150, -20), c(-100, -10), c(-110, 20), c(-150, -20))
p1 <- list(p1, hole)
p2 <- rbind(c(-10, 0), c(140, 60), c(160, 0), c(140, -55), c(-10, 0))
p3 <- rbind(c(-125, 0), c(0, 60), c(40, 5), c(15, -45), c(-125, 0))
pols <- spPolygons(p1, p2, p3)
sf_pols <- st_as_sf(pols)
r <- raster(ncol = 90, nrow = 45)
values(r) <- 1:ncell(r)
# works
x <- raster::extract(r, pols)
# fails
y <- raster::extract(r, sf_pols)
#> Error in `[.data.frame`(x@data, i, j, ..., drop = FALSE): undefined columns selectedCreated on 2018-10-30 by the reprex package (v0.2.1.9000)
This should be TRUE:
raster::isLonLat("+init=epsg:4326")It can be worked around with rgdal, but probably best to simply match on "init=4326" as this is a very common case.
raster::isLonLat(rgdal::CRSargs(sp::CRS("+init=epsg:4326")))Hi
I read a file and after I plot it :
library(httr)
library(raster)
dsn <- 'https://framagit.org/mgageo/plotrgb/raw/master/PLOTRGB/pontorson_echangeur_satellite.tif'
fic <- tempfile('plotrgb', fileext='.tif')
download.file(dsn, fic, mode = "wb")
img <- brick(fic)
plotRGB(img)
Some small parts of the plot are white.
With an other package, there's no defects
im2 <- tiff::readTIFF(fic)
grid::grid.raster(im2)
I get the file from a WMTS server with gdal_utils.
Hi, it seems like the getData function when used to obtain GADM data isn't working. From my macbook I get the following
raster::getData("GADM", country="COL", level=1)
trying URL 'http://biogeo.ucdavis.edu/data/gadm2.8/rds/COL_adm1.rds'
Error in readRDS(filename) : unknown input format
raster seems to compile properly but then crashes when the installer tries to load it, using both CRAN and install_github()
any suggestions? Thanks
R version 3.5.0 (2018-04-23) -- "Joy in Playing"
** testing if installed package can be loaded
*** glibc detected *** /usr/lib64/R/bin/exec/R: free(): invalid pointer: 0x0000000002b91208 ***
======= Backtrace: =========
/lib64/libc.so.6(+0x75e5e)[0x7fa722160e5e]
/lib64/libc.so.6(+0x78cad)[0x7fa722163cad]
/import/home/recollins/R/x86_64-redhat-linux-gnu-library/3.5/raster/libs/raster.so(_Z23_rcpp_module_spmod_initv+0x2e7)[0x7fa713440507]
/import/home/recollins/R/x86_64-redhat-linux-gnu-library/3.5/raster/libs/raster.so(_rcpp_module_boot_spmod+0x33)[0x7fa713442183]
/usr/lib64/R/lib/libR.so(+0x1f8b7a)[0x7fa720c47b7a]
/usr/lib64/R/lib/libR.so(Rf_eval+0x1c0)[0x7fa720c4dcb0]
...
...
...
lib64/R/bin/R' --no-save --slave 2>&1 < '/tmp/RtmpmwPlUO/file37b1755bb0c3'
ERROR: loading failed
* removing ‘/import/home/recollins/R/x86_64-redhat-linux-gnu-library/3.5/raster’
Error in i.p(...) :
(converted from warning) installation of package ‘/tmp/RtmpgFc6qn/file318e219c1000/raster_2.9-1.tar.gz’ had non-zero exit status
Conversion of a raster from float to integer using writeRaster silently fails, if the datatype argument is misspelled as dataType.
library(raster)
#> Loading required package: sp
t_file <- tempfile(fileext = ".tif")
v <- c(rep(1, 25), rep(0, 50), rep(NA, 25))
m <- matrix(v, 10, 10)
r <- raster(m)
dataType(r)
#> [1] "FLT4S"
writeRaster(r, t_file, datatype = "INT2U", overwrite = TRUE)
dataType(raster(t_file))
#> [1] "INT2U"
writeRaster(r, t_file, dataType = "INT2U", overwrite = TRUE)
dataType(raster(t_file))
#> [1] "FLT4S"
According to @rsbivand the underlying problem is that dataType= is going into ..., and because
nothing needs it, it isn't spotted.
Hi there,
I have problems to compile the "raster" package. I tried install.packages("raster", dependencies = TRUE) with no success. Any suggestions? The main problem is:
g++ -m64 -std=gnu++0x -I/usr/include/R -DNDEBUG -I"/home/zfgbe/R/x86_64-redhat-linux-gnu-library/3.4/Rcpp/include" -I/usr/local/include -fpic -O2 -g -pipe -Wall -Wp,-D_FORTIFY_SOURCE=2 -fexceptions -fstack-protector --param=ssp-buffer-size=4 -m64 -mtune=generic -c RasterModule.cpp -o RasterModule.o
In file included from RasterModule.cpp:2:
spat.h:158: error: ISO C++ forbids initialization of member ‘crs’
spat.h:158: error: making ‘crs’ static
spat.h:158: error: invalid in-class initialization of static data member of non-integral type ‘std::string’
spat.h: In member function ‘std::string SpRaster::getCRS()’:
spat.h:200: error: ‘crs’ was not declared in this scope
spat.h: In member function ‘void SpRaster::setCRS(std::string)’:
spat.h:201: error: ‘crs’ was not declared in this scope
make: *** [RasterModule.o] Error 1
ERROR: compilation failed for package ‘raster’
See also stackoverflow:
https://stackoverflow.com/questions/55019040/compilation-failed-for-package-raster
Hi all.
I found a (possible) inconsistency in the definiton of the “default” raster Options concerning maxmemory and chunksize.
In practice, using the current CRAN version, after loading the package I see this:
library(raster)
#> Loading required package: sp
rasterOptions()
#> format : raster
#> datatype : FLT4S
#> overwrite : FALSE
#> progress : none
#> timer : FALSE
#> chunksize : 1e+07
#> maxmemory : 1e+09
#> tmpdir : /tmp/RtmpbkoMA1/raster//
#> tmptime : 168
#> setfileext : TRUE
#> tolerance : 0.1
#> standardnames : TRUE
#> warn depracat.: TRUE
#> header : noneHowever, if I use the “default = TRUE” argument (which as far as I understand should
reset values to default in case I changed some options and want to “revert”), I get:
rasterOptions(default = TRUE)
rasterOptions()
#> format : raster
#> datatype : FLT4S
#> overwrite : FALSE
#> progress : none
#> timer : FALSE
#> chunksize : 1e+06
#> maxmemory : 1e+07
#> tmpdir : /tmp/RtmpbkoMA1/raster//
#> tmptime : 168
#> setfileext : TRUE
#> tolerance : 0.1
#> standardnames : TRUE
#> warn depracat.: TRUE
#> header : none(so, maxmemory goes from 1e09 to 1e07, while chunksize goes from 1e07 to 1e06)
What seems to be happening is that, calling rasterOptions without arguments retrieves the options using .maxmemory and .chunksize, such as in:
Lines 395 to 407 in cfd2823
(note that here you see 1e10 instead than 1e09 for maxmemory due to changes introduced in 3c36860
in the current CRAN version we have default <- 1e+09 instead.)
Calling rasterOptions(default = TRUE) instead reinitializes the defaults using:
Lines 160 to 181 in 893db71
Since the values used in the two cases differ, we get different "default" options.
(Again, in the current CRAN version we have instead:
options(rasterChunkSize = 1000000)
options(rasterMaxMemory = 10000000)
Is this “by design”? I would expect that if I do not change explicitly the options, then
the “default” ones that are used should be identical to what I get with rasterOptions(default = TRUE)
(hope I am not missing anything obvious, here - sorry in that case)
PS: glad to see that raster found its way on GitHub! I hope I’ll be able to contribute
somehow.
The default behaviour of plotRGB doesn't honor most graphical parameters such as margins (especially for multiple plots) or titles, unless axes=TRUE is set.
These are related problems (the latter contains a small workaround):
The following could serve as a minimal reproducible example to the bug. Expected outcome would be 4 R logos with margins in between and test-titles.
library(raster)
b <- brick(system.file("external/rlogo.grd", package="raster"))
par(mfrow=c(3,2),mar=rep(1,4))
for(i in 1:4){
plotRGB(b, stretch="hist", main="testmain")
title("testtitle")
}
I have a spatialPolygonsDataFrame consisting of 3 polygons. The third polygon has the same shape as the first but has a hole where the second polygon is located.
I created a raster and a spatialPolygonsDataFrame to demonstrate the issue:
# create rasters and store them in a list
r1 <- raster(xmn=1, xmx=5, ymn=1, ymx=5, nrows=4, ncols=4)
r1[] <- 1:length(r1)
# create SpatialPolygonsDataFrame
Sr1 = Polygon(cbind(c(1,5,4,1,1),c(1,2,5,4,1)))
Sr2 = Polygon(cbind(c(2,4,3,2),c(3,2,4,3)))
SpP = SpatialPolygons(list(Polygons(list(Sr1), "s1"), Polygons(list(Sr2), "s2")),
1:2)
dat = data.frame(ID = c("s1", "s2"), value = c("a", "b"))
row.names(dat) <- c("s1", "s2")
p <- SpatialPolygonsDataFrame(SpP, data = dat,
match.ID = TRUE)
AddHoleToPolygon <-function(poly,hole){
# invert the coordinates for Polygons to flag it as a hole
coordsHole <- hole@polygons[[1]]@Polygons[[1]]@coords
newHole <- Polygon(coordsHole,hole=TRUE)
# punch the hole in the main poly
listPol <- poly@polygons[[1]]@Polygons
listPol[[length(listPol)+1]] <- newHole
punch <- Polygons(listPol,poly@polygons[[1]]@ID)
# make the polygon a SpatialPolygonsDataFrame as the entry
new <- SpatialPolygons(list(punch),proj4string=poly@proj4string)
new <- SpatialPolygonsDataFrame(new,data=as(poly,"data.frame"))
return(new)
}
punchedPoly <-AddHoleToPolygon(p[1,],p[2,])
p1 <- rbind(p, punchedPoly, makeUniqueIDs = TRUE)
p1 <- p1[2:3,]
When I use mask() to "crop" the raster r1, then the hole is created, although the triangular polygon has a value and indeed is not a real hole. But it gets "overridden" by the third polygon with the hole:
masked_hole <- mask(r1, p1)
plot(masked_hole)
When I change the order of the polygons, then no hole is created:
m3 <- mask(r1, p1[c(2,1),])
plot(m3)
The function rasterize is affected in the same manner:
r2 <- rasterize(p1, r1, field = "value")
plot(r2)
r3 <- rasterize(p1[c(2,1),], r1, field = "value")
plot(r3)
In my real data I have holes where there are no "filling" polygons and those ones I want to keep as holes.
I did a workaround of this issue by ordering the polygons by area (shape@polygons[[i]]@area, descending) and then used the new object as input for rasterize (and input for mask, respectively).
Could the behaviour of mask() and rasterize be fixed by assuring that "fillings" aren't overwritten by polygons with holes?
(stackoverflow: https://stackoverflow.com/questions/56759025/r-mask-and-rasterize-with-spatialpolygonsdataframe-having-holes)
Until a recent change in raster, it was possible to reclassify raster values using three columns matrix, where the first two columns contained the same values.
Now it does not work.
library(raster)
#> Loading required package: sp
r <- raster(ncols = 3, nrows = 3)
r[] <- c(1, 1, 1, 2, 2, 2, 3, 3, 3)
m <- c(1, 1, 100, 2, 2, 200, 3, 3, 300)
rclmat <- matrix(m, ncol = 3, byrow = TRUE)
rc <- reclassify(r, rclmat, right = NA)
# both objects are the same
r
#> class : RasterLayer
#> dimensions : 3, 3, 9 (nrow, ncol, ncell)
#> resolution : 120, 60 (x, y)
#> extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
#> crs : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
#> source : memory
#> names : layer
#> values : 1, 3 (min, max)
rc
#> class : RasterLayer
#> dimensions : 3, 3, 9 (nrow, ncol, ncell)
#> resolution : 120, 60 (x, y)
#> extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
#> crs : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
#> source : memory
#> names : layer
#> values : 1, 3 (min, max)Created on 2019-07-18 by the reprex package (v0.3.0)
I know that it is possible to use just two columns matrix in this case.
However, I can think of cases where one would want to use ranges for some values and specific values for other values.
Then, the reclassify function returns a wrong result:
library(raster)
#> Loading required package: sp
r <- raster(ncols = 3, nrows = 3)
r[] <- c(0.99, 1, 1, 2, 2, 2, 3, 3, 3)
m <- c(0.9, 1.1, 100, 2, 2, 200, 3, 3, 300)
rclmat <- matrix(m, ncol = 3, byrow = TRUE)
rc <- reclassify(r, rclmat, right = NA)
# both objects are the same
r
#> class : RasterLayer
#> dimensions : 3, 3, 9 (nrow, ncol, ncell)
#> resolution : 120, 60 (x, y)
#> extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
#> crs : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
#> source : memory
#> names : layer
#> values : 0.99, 3 (min, max)
rc
#> class : RasterLayer
#> dimensions : 3, 3, 9 (nrow, ncol, ncell)
#> resolution : 120, 60 (x, y)
#> extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
#> crs : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
#> source : memory
#> names : layer
#> values : 2, 100 (min, max)Created on 2019-07-18 by the reprex package (v0.3.0)
Came across this issue building Geocomputation with R, Chapter 13
input_ras = rasterFromXYZ(input_tidy, crs = st_crs(3035)$proj4string)
will return different output if input_ras@data@inmemory is TRUE
class : RasterBrick
dimensions : 868, 642, 557256, 4 (nrow, ncol, ncell, nlayers)
resolution : 1000, 1000 (x, y)
extent : 4031000, 4673000, 2684000, 3552000 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
data source : in memory
names : pop, women, mean_age, hh_size
min values : 1, 1, 1, 1
max values : 6, 5, 5, 5
vs.
class : RasterBrick
dimensions : 868, 642, 557256, 4 (nrow, ncol, ncell, nlayers)
resolution : 1000, 1000 (x, y)
extent : 4031000, 4673000, 2684000, 3552000 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
data source : /private/var/folders/z5/zjpcfzxd16j98nn_9wv0kp600000gn/T/RtmpgM5KqP/raster/r_tmp_2019-03-27_153858_48849_36840.grd
names : layer.1, layer.2, layer.3, layer.4
min values : 1, 1, 1, 1
max values : 6, 6, 6, 6
If the data source is the temp file on disk, the names and max values are wrong.
Simply re-running the line multiple times can get it to switch between in memory and the disk file.
Using raster 2.8-19 (R 3.5.2, Mac OS Mojave. All other packages up to date)
I was wondering if it would be possible to include additionally to ncol (Number of columns) an argument start_col? In the same sense as it is possible for ncdf (start, count argument). I saw that getValuesBlock has this posibility, but therefore the raster object already has to be present.
I don't know if I missed something.
Satellite images are composed of several bands. These bands have the same extent and origin. However, some of them have different spatial resolutions. Thus, it is not possible to read them using raster::stack(). Before, one has to raster::resample() them so that all bands share the same spatial resolutions. An interesting alternative would be add an argument to raster::stack() -- e.g. resample -- so that the bands are resampled on the fly.
raster::subs can throw errors for valid inputs, depending on the order of the key/value dataframe columns specified by the by and which arguments.
Here's a reproducible example using raster version 2.8-1, in which we have a one-cell raster r and try to recode its value. (We're trying to recode its value 5 to 0, in this case.) Whether this happens successfully depends on the order of the columns of the key/value dataframe (argument y).
r = raster::raster(nrows = 1, ncols = 1, vals = 5)
key_value_df = data.frame(by_col = c(4, 5, 6), which_col = c(0, 0, 1), another_col = c(7, 8, 9))
# SUCCESSFUL VERSION. Results in a 1x1 raster with value 0 (substituted from r's value of 5).
raster::subs(x = r, y = key_value_df, by = "by_col", which = "which_col")
#> class : RasterLayer
#> dimensions : 1, 1, 1 (nrow, ncol, ncell)
#> resolution : 360, 180 (x, y)
#> extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
#> coord. ref. : +proj=longlat +datum=WGS84
#> data source : in memory
#> names : which_col
#> values : 0, 0 (min, max)
# FAILING VERSIONS. Key/value dataframe content is the same but the columns are in a different
# order. This shouldn't matter, as we are specifying the 'by' and 'which' columns.
# Expected output is identical to line above: a 1x1 raster with value 0.
raster::subs(x = r, y = key_value_df[, c(3, 1, 2)], by = "by_col", which = "which_col")
#> Error in .local(x, y, ...): duplicate "by" values not allowed
raster::subs(x = r, y = key_value_df[, c(2, 3, 1)], by = "by_col", which = "which_col")
#> Error in `[.data.frame`(y, , by): undefined columns selected
I am happy to put together a PR for this.
Hi. I was wondering if you would consider switching to using roxygen for the documentation of the package.
That is clearly a matter of preference, but in my opinion it helps both in keeping the docs up to date with the code, and in reviewing the code (since arguments "definitions" can be readily found in the roxygen comments).
In case you are interested in this, let me know: I'd be glad to help in the transition
(maybe trying using: https://yihui.name/rd2roxygen/)
I routinely set this value much higher to avoid system users filling up temp folders.
Could it be adaptive to the current system? I think the default is much too low at any rate.
For RasterLayer extract passes to .readCells, which is slow compared to the inMemory performance for a single-layer RasterBrick, which just indexes @data@values.
You can see the difference in performance:
library(raster)
r <- disaggregate(raster(volcano), fact = 25)
if (fromDisk(r)) r <- readAll(r)
b <- brick(r)
if (fromDisk(b)) b <- readAll(b)
cells <- sample(ncell(r), 1e6)
system.time(extract(r, cells))
# user system elapsed
#3.410 0.104 3.513
system.time(extract(b, cells))
#user system elapsed
#0.169 0.000 0.169 The behaviour for RasterLayer could really benefit from the same shortcut taken for a RasterBrick.
Also in relation to #4, on the current CRAN version (2.7-15) the default setting for rasterOption maxmemory is 1e+10:
> rasterOptions()
....
chunksize : 1e+08
maxmemory : 1e+10
....this seems risky, since it would allow raster to try to allocate up to more or less 80GB for an in-memory operation. For example, this operation would be carried out in memory:
rr <- raster(ncol = 1e08, nrow = 50)
rr <- init(rr, fun = "runif")since:
> canProcessInMemory(rr, 2)
[1] TRUE, although requiring in theory 80 GB of memory:
> memreq <- 8 * ncell(rr) * 2 / 1e09
> memreq
[1] 80
Would you consider a quick patch to CRAN to restore some safer default?
I am trying to get elevation data from longitude/latitude informations using the getData function with RSTM.
I tried the example function:
getData('SRTM', lon=5, lat=45)
an it returns
trying URL 'ftp://xftp.jrc.it/pub/srtmV4/tiff/srtm_38_04.zip' trying URL 'http://hypersphere.telascience.org/elevation/cgiar_srtm_v4/tiff/zip/srtm_38_04.ZIP' trying URL 'http://srtm.csi.cgiar.org/SRT-ZIP/SRTM_V41/SRTM_Data_GeoTiff/srtm_38_04.ZIP' Error in utils::download.file(url = aurl, destfile = fn, method = "auto", : cannot open URL 'http://srtm.csi.cgiar.org/SRT-ZIP/SRTM_V41/SRTM_Data_GeoTiff/srtm_38_04.ZIP' In addition: Warning messages: 1: In utils::download.file(url = aurl, destfile = fn, method = "auto", : unable to resolve 'hypersphere.telascience.org' 2: In utils::download.file(url = aurl, destfile = fn, method = "auto", : cannot open URL 'http://srtm.csi.cgiar.org/SRT-ZIP/SRTM_V41/SRTM_Data_GeoTiff/srtm_38_04.ZIP': HTTP status was '404 Not Found'
I also tried
getData('GADM', country='AUT', level=0)
and I get:
trying URL 'http://biogeo.ucdavis.edu/data/gadm2.8/rds/AUT_adm0.rds' Error in utils::download.file(url = aurl, destfile = fn, method = "auto", : cannot open URL 'http://biogeo.ucdavis.edu/data/gadm2.8/rds/AUT_adm0.rds'
any ideas why? Or any workaround possible to get elevation data from longitude/latitude ?
Hello!
I have a set of .tif files and try to call getData('SRTM', ...). Since filenames in Linux are case-sensitive, I get a 'file not found' error.
A problem is here:
Line 406 in 9fa97f9
And after that file.exists(tiffilename) does not try to find files with lowercase extension .tif, but only uppercase .TIF
Changing extensions of all my files to .TIF solves the problem for me, bit is not an elegant solution.
Thank you!
~tatianic
I'm getting an error for yFromRow(). I could be wrong, but it seems as though it's a bug (as I'm getting this even for the 'volcano' example data).
macOS 10.14
R version 3.5.1
raster version 2.7-15
require(raster)
r <- raster(volcano)
x_centers <- xFromCol(r)
y_centers <- yFromRow(r)
Error in (function (classes, fdef, mtable) :
unable to find an inherited method for function ‘yFromRow’ for signature ‘"RasterLayer", "missing"’
EDIT: updated to correctly use storage.mode, to distinguish double and integer.
For intensive cell-based operations it could be beneficial to make these storage mode "integer".
All but the first two are "double" currently.
library(raster)
r <- raster(matrix(1:12, 3))
storage.mode(colFromCell(r, 1)) ## already integer
storage.mode(rowColFromCell(r, 1)) ## already integer
## the rest are doubles
lapply(cellFromLine(r, spLines(matrix(runif(4), ncol = 2))), storage.mode)
lapply(cellFromPolygon(r, spPolygons(matrix(runif(10), ncol = 2))), storage.mode)
storage.mode(rowFromCell(r, 1L))
storage.mode(cellFromCol(r, 1L))
storage.mode(cellFromRow(r, 1L))
storage.mode(cellFromRowCol(r, 1L, 1L))
storage.mode(cellFromRowColCombine(r, 1L, 1L))
storage.mode(cellFromXY(r, cbind(0.5, 0.5)))
storage.mode(cellsFromExtent(r, extent(0.2, 0.5, 0.3, 0.6)))
storage.mode(fourCellsFromXY(r, cbind(1, 1)))
I'd be happy to PR this, and I'm very interested in opinions.
Hello,
I was trying to rasterize polygons of IUCN distribution data for species, in order to calculate species richness.
I tried two different procedures that should give the same result, one as suggested in rasterize help (using fun = function(x, ...) length(unique(na.omit(x))), and another method consisting in rasterizing all species polygons in 1/0 and then summing the results. However, the two procedures yielded differences in some places. From my understanding, fun = function(x, ...) length(unique(na.omit(x)) fails to perform as expected.
I cannot figure out why; I posted a question on GIS/stackexchange with little success. Could you look into this?
Here is the GIS/stackexchange question with code and reproducible example:
Hi Rob,
I tried installing raster 2.7-11 following your answer to my stack exchange question. However, I get a whole list of 'Permission denied' errors (see below). Not sure if this is really an issue with raster, but I can install other packages from GitHub without trouble.
Thanks
> install_github('rspatial/raster')
Downloading GitHub repo rspatial/raster@master
from URL https://api.github.com/repos/rspatial/raster/zipball/master
Installing raster
trying URL 'https://cran.rstudio.com/bin/macosx/el-capitan/contrib/3.5/Rcpp_0.12.18.tgz'
Content type 'application/x-gzip' length 4611027 bytes (4.4 MB)
==================================================
downloaded 4.4 MB
Installing Rcpp
'/Library/Frameworks/R.framework/Resources/bin/R' --no-site-file --no-environ --no-save --no-restore --quiet CMD INSTALL \
'/private/var/folders/dn/y8q8c58n56s52sfwg26pxxk00000gp/T/RtmpbDuCVq/devtools79038dfd326/Rcpp' \
--library='/Library/Frameworks/R.framework/Versions/3.5/Resources/library' --install-tests
* installing *binary* package ‘Rcpp’ ...
mv: rename /Library/Frameworks/R.framework/Versions/3.5/Resources/library/Rcpp to /Library/Frameworks/R.framework/Versions/3.5/Resources/library/00LOCK-Rcpp/Rcpp: Permission denied
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Usage:
List: tar -tf <archive-filename>
Extract: tar -xf <archive-filename>
Create: tar -cf <archive-filename> [filenames...]
Help: tar --help
* DONE (Rcpp)
Installation failed: cannot open the connection
trying URL 'https://cran.rstudio.com/bin/macosx/el-capitan/contrib/3.5/sp_1.3-1.tgz'
Content type 'application/x-gzip' length 1873005 bytes (1.8 MB)
==================================================
downloaded 1.8 MB
Installing sp
'/Library/Frameworks/R.framework/Resources/bin/R' --no-site-file --no-environ --no-save --no-restore --quiet CMD INSTALL \
'/private/var/folders/dn/y8q8c58n56s52sfwg26pxxk00000gp/T/RtmpbDuCVq/devtools79072bafe5f/sp' \
--library='/Library/Frameworks/R.framework/Versions/3.5/Resources/library' --install-tests
* installing *binary* package ‘sp’ ...
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cp: /Library/Frameworks/R.framework/Versions/3.5/Resources/library/sp/./libs/sp.so: Permission denied
cp: /Library/Frameworks/R.framework/Versions/3.5/Resources/library/sp/./libs/sp.so.dSYM/Contents/Info.plist: Permission denied
cp: /Library/Frameworks/R.framework/Versions/3.5/Resources/library/sp/./libs/sp.so.dSYM/Contents/Resources/DWARF/sp.so: Permission denied
cp: /Library/Frameworks/R.framework/Versions/3.5/Resources/library/sp/./Meta/data.rds: Permission denied
cp: /Library/Frameworks/R.framework/Versions/3.5/Resources/library/sp/./Meta/demo.rds: Permission denied
cp: /Library/Frameworks/R.framework/Versions/3.5/Resources/library/sp/./Meta/features.rds: Permission denied
cp: /Library/Frameworks/R.framework/Versions/3.5/Resources/library/sp/./Meta/hsearch.rds: Permission denied
cp: /Library/Frameworks/R.framework/Versions/3.5/Resources/library/sp/./Meta/links.rds: Permission denied
cp: /Library/Frameworks/R.framework/Versions/3.5/Resources/library/sp/./Meta/nsInfo.rds: Permission denied
cp: /Library/Frameworks/R.framework/Versions/3.5/Resources/library/sp/./Meta/package.rds: Permission denied
cp: /Library/Frameworks/R.framework/Versions/3.5/Resources/library/sp/./Meta/Rd.rds: Permission denied
cp: /Library/Frameworks/R.framework/Versions/3.5/Resources/library/sp/./Meta/vignette.rds: Permission denied
cp: /Library/Frameworks/R.framework/Versions/3.5/Resources/library/sp/./NAMESPACE: Permission denied
cp: /Library/Frameworks/R.framework/Versions/3.5/Resources/library/sp/./NEWS.Rd: Permission denied
cp: /Library/Frameworks/R.framework/Versions/3.5/Resources/library/sp/./R/sp: Permission denied
cp: /Library/Frameworks/R.framework/Versions/3.5/Resources/library/sp/./R/sp.rdb: Permission denied
cp: /Library/Frameworks/R.framework/Versions/3.5/Resources/library/sp/./R/sp.rdx: Permission denied
Usage:
List: tar -tf <archive-filename>
Extract: tar -xf <archive-filename>
Create: tar -cf <archive-filename> [filenames...]
Help: tar --help
* DONE (sp)
Installation failed: cannot open the connection
'/Library/Frameworks/R.framework/Resources/bin/R' --no-site-file --no-environ --no-save --no-restore --quiet CMD INSTALL \
'/private/var/folders/dn/y8q8c58n56s52sfwg26pxxk00000gp/T/RtmpbDuCVq/devtools79046fb7fef/rspatial-raster-2f10ff5' \
--library='/Library/Frameworks/R.framework/Versions/3.5/Resources/library' --install-tests
* installing *source* package ‘raster’ ...
** libs
clang++ -std=gnu++11 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/Rcpp/include" -I/usr/local/include -fPIC -Wall -g -O2 -c RasterModule.cpp -o RasterModule.o
clang++ -std=gnu++11 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/Rcpp/include" -I/usr/local/include -fPIC -Wall -g -O2 -c RcppExports.cpp -o RcppExports.o
clang++ -std=gnu++11 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/Rcpp/include" -I/usr/local/include -fPIC -Wall -g -O2 -c aggregate.cpp -o aggregate.o
aggregate.cpp:3:17: warning: using directive refers to implicitly-defined namespace 'std'
using namespace std;
^
1 warning generated.
clang++ -std=gnu++11 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/Rcpp/include" -I/usr/local/include -fPIC -Wall -g -O2 -c area.cpp -o area.o
area.cpp:3:17: warning: using directive refers to implicitly-defined namespace 'std'
using namespace std;
^
1 warning generated.
clang++ -std=gnu++11 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/Rcpp/include" -I/usr/local/include -fPIC -Wall -g -O2 -c bilinear.cpp -o bilinear.o
clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/Rcpp/include" -I/usr/local/include -fPIC -Wall -g -O2 -c broom.c -o broom.o
clang++ -std=gnu++11 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/Rcpp/include" -I/usr/local/include -fPIC -Wall -g -O2 -c cellRowCol.cpp -o cellRowCol.o
clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/Rcpp/include" -I/usr/local/include -fPIC -Wall -g -O2 -c clamp.c -o clamp.o
clang++ -std=gnu++11 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/Rcpp/include" -I/usr/local/include -fPIC -Wall -g -O2 -c distance.cpp -o distance.o
clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/Rcpp/include" -I/usr/local/include -fPIC -Wall -g -O2 -c edge.c -o edge.o
clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/Rcpp/include" -I/usr/local/include -fPIC -Wall -g -O2 -c focal_fun.c -o focal_fun.o
clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/Rcpp/include" -I/usr/local/include -fPIC -Wall -g -O2 -c focal_get.c -o focal_get.o
clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/Rcpp/include" -I/usr/local/include -fPIC -Wall -g -O2 -c focal_sum.c -o focal_sum.o
clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/Rcpp/include" -I/usr/local/include -fPIC -Wall -g -O2 -c geodesic.c -o geodesic.o
clang++ -std=gnu++11 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/Rcpp/include" -I/usr/local/include -fPIC -Wall -g -O2 -c getPolygons.cpp -o getPolygons.o
clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/Rcpp/include" -I/usr/local/include -fPIC -Wall -g -O2 -c intersect.c -o intersect.o
clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/Rcpp/include" -I/usr/local/include -fPIC -Wall -g -O2 -c layerize.c -o layerize.o
clang++ -std=gnu++11 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/Rcpp/include" -I/usr/local/include -fPIC -Wall -g -O2 -c modal.cpp -o modal.o
clang++ -std=gnu++11 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/Rcpp/include" -I/usr/local/include -fPIC -Wall -g -O2 -c ppmin.cpp -o ppmin.o
clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/Rcpp/include" -I/usr/local/include -fPIC -Wall -g -O2 -c r_terrain.c -o r_terrain.o
clang++ -std=gnu++11 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/Rcpp/include" -I/usr/local/include -fPIC -Wall -g -O2 -c rasterize.cpp -o rasterize.o
clang++ -std=gnu++11 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/Rcpp/include" -I/usr/local/include -fPIC -Wall -g -O2 -c rcpp_aggregate.cpp -o rcpp_aggregate.o
clang++ -std=gnu++11 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/Rcpp/include" -I/usr/local/include -fPIC -Wall -g -O2 -c rcpp_distance.cpp -o rcpp_distance.o
clang++ -std=gnu++11 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/Rcpp/include" -I/usr/local/include -fPIC -Wall -g -O2 -c rcpp_xyCell.cpp -o rcpp_xyCell.o
clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/Rcpp/include" -I/usr/local/include -fPIC -Wall -g -O2 -c reclass.c -o reclass.o
clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/Rcpp/include" -I/usr/local/include -fPIC -Wall -g -O2 -c util.c -o util.o
clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/3.5/Resources/library/Rcpp/include" -I/usr/local/include -fPIC -Wall -g -O2 -c wirth.c -o wirth.o
clang++ -std=gnu++11 -dynamiclib -Wl,-headerpad_max_install_names -undefined dynamic_lookup -single_module -multiply_defined suppress -L/Library/Frameworks/R.framework/Resources/lib -L/usr/local/lib -o raster.so RasterModule.o RcppExports.o aggregate.o area.o bilinear.o broom.o cellRowCol.o clamp.o distance.o edge.o focal_fun.o focal_get.o focal_sum.o geodesic.o getPolygons.o intersect.o layerize.o modal.o ppmin.o r_terrain.o rasterize.o rcpp_aggregate.o rcpp_distance.o rcpp_xyCell.o reclass.o util.o wirth.o -F/Library/Frameworks/R.framework/.. -framework R -Wl,-framework -Wl,CoreFoundation
ld: warning: text-based stub file /System/Library/Frameworks//CoreFoundation.framework/CoreFoundation.tbd and library file /System/Library/Frameworks//CoreFoundation.framework/CoreFoundation are out of sync. Falling back to library file for linking.
installing to /Library/Frameworks/R.framework/Versions/3.5/Resources/library/raster/libs
** R
** inst
** byte-compile and prepare package for lazy loading
in method for ‘brick’ with signature ‘x="big.matrix"’: no definition for class “big.matrix”
in method for ‘brick’ with signature ‘x="kasc"’: no definition for class “kasc”
in method for ‘brick’ with signature ‘x="grf"’: no definition for class “grf”
in method for ‘coerce’ with signature ‘"STFDF","RasterBrick"’: no definition for class “STFDF”
in method for ‘coerce’ with signature ‘"STSDF","RasterBrick"’: no definition for class “STSDF”
in method for ‘coerce’ with signature ‘"asc","RasterLayer"’: no definition for class “asc”
in method for ‘coerce’ with signature ‘"RasterLayer","asc"’: no definition for class “asc”
in method for ‘coerce’ with signature ‘"kasc","RasterBrick"’: no definition for class “kasc”
in method for ‘coerce’ with signature ‘"kasc","RasterStack"’: no definition for class “kasc”
in method for ‘coerce’ with signature ‘"kde","RasterLayer"’: no definition for class “kde”
in method for ‘coerce’ with signature ‘"grf","RasterBrick"’: no definition for class “grf”
in method for ‘coerce’ with signature ‘"grf","RasterLayer"’: no definition for class “grf”
Creating a generic function for ‘nrow’ from package ‘base’ in package ‘raster’
Creating a generic function for ‘ncol’ from package ‘base’ in package ‘raster’
in method for ‘extent’ with signature ‘x="sf"’: no definition for class “sf”
in method for ‘extract’ with signature ‘x="Raster",y="sf"’: no definition for class “sf”
in method for ‘mask’ with signature ‘x="Raster",mask="sf"’: no definition for class “sf”
in method for ‘raster’ with signature ‘x="big.matrix"’: no definition for class “big.matrix”
in method for ‘raster’ with signature ‘x="sf"’: no definition for class “sf”
in method for ‘raster’ with signature ‘x="kasc"’: no definition for class “kasc”
in method for ‘raster’ with signature ‘x="asc"’: no definition for class “asc”
in method for ‘raster’ with signature ‘x="kde"’: no definition for class “kde”
in method for ‘raster’ with signature ‘x="grf"’: no definition for class “grf”
in method for ‘rasterize’ with signature ‘x="sf",y="Raster"’: no definition for class “sf”
Creating a generic function for ‘rowSums’ from package ‘base’ in package ‘raster’
Creating a generic function for ‘colSums’ from package ‘base’ in package ‘raster’
in method for ‘stack’ with signature ‘x="kasc"’: no definition for class “kasc”
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded
* DONE (raster)
Warning messages:
1: In file(file, ifelse(append, "a", "w")) :
cannot open file '/Library/Frameworks/R.framework/Versions/3.5/Resources/library/Rcpp/DESCRIPTION': Permission denied
2: In file(file, ifelse(append, "a", "w")) :
cannot open file '/Library/Frameworks/R.framework/Versions/3.5/Resources/library/sp/DESCRIPTION': Permission denied
Dear dev
Values in my raster files differ after writing to and reading from disk. See following code:
library(raster)
n <- 15000 # raster size
r1 <- raster(nrows=n,ncols=n) # create raster
values(r1) <- 1:ncell(r1) # fill with unique values
writeRaster(r1,"test.grd",overwrite=TRUE) # write raster to disk
r2 <- raster("test.grd") # read the same raster
print(summary(r1-r2)) # subtract, expect only 0 valuesI expect that in last print statement all the values will be 0. This is the case for small number of n (raster rows/cols, e.g. 5000) but for larger values of n, like 15000, the summary shows values between -8 to 8. Plotting the result of the subtracted grids also give weird patterns
I expect that r1 and r2 are equal to each other but it doesn't look like that they are equal. So what's happening?
R version info:
R version 3.5.1 (2018-07-02)
Platform: x86_64-redhat-linux-gnu (64-bit)
Running under: Red Hat Enterprise Linux Server 7.6 (Maipo)
Matrix products: default
BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices datasets utils methods base
other attached packages:
[1] raster_2.8-4 sp_1.3-1 localoptions_1.0.0
loaded via a namespace (and not attached):
[1] compiler_3.5.1 rgdal_1.3-6 tools_3.5.1 Rcpp_1.0.0
[5] codetools_0.2-15 grid_3.5.1 lattice_0.20-35
Hello,
I did use the function crop and mean for 434 rasters. Each one is very big, it corresponds almost like 1/4 of Brazil
now I want to calculate de standard deviation between these 434 rasters images but this error appears.
I have 66gb of space but it seems that my computer will not process .
sd_ndvi_caat_crop<-calc(ndvi_caat_crop, sd, na.rm=TRUE, aszero=FALSE)
What should I do?
Or if a use the function cellStats does it calculate in the same way? like calculating the sd of each cell of each 434 rasters images?
Very low importance, but I noticed this doesn't work.
library(raster)
r <- raster(volcano)
-r
# Error in -r : invalid argument to unary operator
0 - r ## is okThe help file says RGB requires a rasterstack or rasterbrick but actually it needs a rasterlayer and results in a rasterbrick.
Line 21 in 27d552a
Reproducible example:
library(raster)
#> Loading required package: sp
library(sf)
#> Linking to GEOS 3.7.1, GDAL 2.3.2, PROJ 5.2.0
r <- raster(ncol = 36, nrow = 18)
# works -------------------------------------------------------------------
xy1 <- c(0, 0)
d1 <- distanceFromPoints(r, xy1)
# fails -------------------------------------------------------------------
xy2 <- st_sf(geom = st_sfc(st_point(c(0, 0))))
d2 <- distanceFromPoints(r, xy2)
#> Error in .pointsToMatrix(xy): A points matrix should have 2 columnsCreated on 2019-06-03 by the reprex package (v0.3.0)
I've seen this a few times, a message like
Warning messages:
...
2: In .getCRSfromGridMap4(atts) : cannot process these parts of the CRS:
These NetCDF files https://water.weather.gov/precip/ present the warning, I've attached an example, it has 3 separate regions in three files conus, ak, pr all on the same CRS.
The CRS should be
"+proj=stere +lat_0=90 +lat_ts=60 +lon_0=-105 +x_0=0 +y_0=0 +a=6367470 +b=6367470 +units=m +no_defs"
and that is actually in the NetCDF metadata, though I'm sure other examples only have the WKT string.
I don't know the fix exactly, but getting the proj4 if present would be easiest - a more general check of the WKT probably can't be easily done without GDAL.
raster is getting the incorrect "+proj=stere +lon_0=-105 +x_0=0 +y_0=0 +lat_0=90 +ellps=WGS84" (no latitude of true scale, wrong on WGS84, no +a/+b).
library(raster)
#> Loading required package: sp
library(sf)
#> Linking to GEOS 3.6.1, GDAL 2.3.2, PROJ 4.9.3
set.seed(2019-02-26)
r = raster(nrows = 100, ncols = 100, xmn = 0, ymn = 0, xmx = 100, ymx = 100, vals = 2)
x = st_point(x = c(15, 20))
x_buf = st_sf(geom = st_sfc(st_buffer(x = x, dist = 4)))
x_buf_sp = as(x_buf, "Spatial")
# sf does not works as expected
rr = mask(r, x_buf, inverse = TRUE)
plot(rr)
rr2 = mask(r, x_buf, inverse = FALSE)
plot(rr2)# sp works as expected
# rr = mask(r, x_buf_sp, inverse = TRUE)
# plot(rr)
#
# rr2 = mask(r, x_buf_sp, inverse = FALSE)
# plot(rr2)Created on 2019-02-26 by the reprex package (v0.2.1)
Today I found out that crosstab() is not type-stable. It gives a data.frame as an output for small rasters and c("xtabs", "table") for large rasters.
This behavior can be problematic when using crosstab() to create other functions.
Would it be possible to add a new argument setting the output class? Or maybe there is a better way to deal with it?
library(raster)
#> Loading required package: sp
# small rasters -----------------------------------------------------------
r <- raster(ncols = 5, nrows = 5)
r[] <- runif(ncell(r)) * 2
s <- setValues(r, runif(ncell(r)) * 3)
small_rs = stack(r, s)
output1 = crosstab(small_rs)
output1
#> Var1 Var2 Freq
#> 1 0 0 1
#> 2 1 0 0
#> 3 2 0 2
#> 4 <NA> 0 0
#> 5 0 1 1
#> 6 1 1 2
#> 7 2 1 3
#> 8 <NA> 1 0
#> 9 0 2 1
#> 10 1 2 4
#> 11 2 2 3
#> 12 <NA> 2 0
#> 13 0 3 2
#> 14 1 3 3
#> 15 2 3 3
#> 16 <NA> 3 0
#> 17 0 <NA> 0
#> 18 1 <NA> 0
#> 19 2 <NA> 0
#> 20 <NA> <NA> 0
class(output1)
#> [1] "data.frame"
# large rasters -----------------------------------------------------------
r2 <- raster(ncols = 5000, nrows = 5000)
r2[] <- runif(ncell(r2)) * 2
s2 <- setValues(r2, runif(ncell(r2)) * 3)
large_rs = stack(r2, s2)
output2 = crosstab(large_rs)
output2
#> layer.2
#> layer.1 0 1 2 3
#> 0 1041222 2082778 2081412 1040802
#> 1 2085109 4164492 4167153 2084487
#> 2 1043505 2085358 2082713 1040969
class(output2)
#> [1] "xtabs" "table"Created on 2018-11-15 by the reprex package (v0.2.1)
When I try to get a raster object from points in sp or sf class I get always an empty raster with NAs. I have the last version of the raster and sf package, also RStudio and R are up to date.
library(tidyverse)
library(raster)
library(sf)
library(rnaturalearth)
limit <- ne_countries(country="germany", returnclass = "sf")
grd <- st_make_grid(limit,
cellsize = 0.5,
what = "centers") %>%
st_sf() %>%
mutate(dummy=rnorm(nrow(.)))
grd
r <- raster(grd)
r
values(r) #NA values
I've performed some additional tests on crosstab() and found out it does not keep values when the values are not starting from zero or when some values do not exist.
For example, I've created two rasters - one with values of 1 and 2, second with values of 1 and 3.
However, the output table shows values 1 and 2 for both layers:
library(raster)
#> Loading required package: sp
set.seed(0)
r <- raster(ncols=5, nrows=5)
values(r) <- sample(2, ncell(r), replace=TRUE)
r2 <- setValues(r, sample(c(1, 3), ncell(r), replace=TRUE))
r[1:2] <- NA
r2[2:3] <- NA
s <- stack(r, r2)
names(s) <- c("red", "blue")
x <- crosstab(s)
x
#> blue
#> red 1 2
#> 1 2 7
#> 2 7 6
x <- crosstab(s, long=TRUE)
x
#> red blue Freq
#> 1 1 1 2
#> 2 1 2 7
#> 3 2 1 7
#> 4 2 2 6Created on 2018-11-16 by the reprex package (v0.2.1)
I've tried to extract values from rasters using sf objects. It worked well for most of the cases, except one - when an sf object has only one column containing geometry. Test cases are below:
library(raster)
#> Loading required package: sp
library(sf)
#> Linking to GEOS 3.6.1, GDAL 2.3.2, PROJ 4.9.3
srtm = raster(system.file("raster/srtm.tif", package = "spDataLarge"))
data("zion_points", package = "spDataLarge")
# fails
a1 = raster::extract(srtm, zion_points)
#> Error in as(p, "SpatialPolygons"): no method or default for coercing "SpatialPointsDataFrame" to "SpatialPolygons"
# works
a2 = raster::extract(srtm, as(zion_points, "Spatial"))
# works
zion_points$id = seq_len(nrow(zion_points))
a3 = raster::extract(srtm, zion_points)Created on 2018-11-29 by the reprex package (v0.2.1)
Data.tables are usually considerably faster to process than data frames. I was looking for ways to convert raster objects to data.tables preserving as much information as I could, and I just stumbled upon this function written by Etienne Racine: https://gist.github.com/etiennebr/9515738.
Then I was wondering: any chance this function could be included in the raster package as a native one?
Hi Robert
It would be great if reclassify could keep original layer names? For now they are lost.
Thanks!!
library(raster)
#> Loading required package: sp
r <- raster(ncols=36, nrows=18)
r[] <- runif(ncell(r))
r$new <- runif(ncell(r))
rclmat <- matrix(c(0, 0.25, 1, 0.25, 0.5, 2, 0.5, 1, 3), ncol=3, byrow=TRUE)
r2 <- reclassify(r, rclmat)
names(r)
#> [1] "layer" "new"
names(r2)
#> [1] "layer.1" "layer.2"Hi,
The focal function seems to produce the following error whenever the dimension size is set to anything higher than 200:
Error in .local(x, row, nrows, ...) : validRow(x, row) is not TRUE
Calls: focal ... .local -> getValues -> getValues -> .local -> stopifnot
Execution halted
Here's the Rscript that reproduces this error:
library(raster)
r1975300<-raster("GHSPOPGPW41975GLOBER2015A540091kv10.tif")
dim=300
msize=2*dim+1
window<-matrix(1,nrow=msize, ncol=msize)
rsum1975300 <- focal(r1975300,w=window)
At dim=25 to dim=200, everything works just fine. When set to 300 we got the above error.
Just to make sure it wasn't due to a lack of memory or storage the script was run on an HPC node with 3 TB of RAM and failed the same as a node with 256 GB of RAM, with all Rtemp files being written to ample scratch storage.
R is version 3.5.2, the raster package is version 2.8.19, the sp package is version 1.3.1.
Thank you for your time!
Why raster layer name change to default when using raster::calc function, but not when using Arith-methods (that does not imply a predefined function like sum or sqrt):
Toy example copied from Reference manual (calc function):
library(raster)
r <- raster(ncols=36, nrows=18)
r[] <- 1:ncell(r)
names(r) <- "Band1"
r
rc1 <- calc(r, function(x) { x * 10 })
names(rc1) # Default name "layer"
#But when:
rc2 <- r*10
names(rc2) #It conserves the name "Band1"
names(sqrt(r)) # It doesn't.
This issue begun as a StackExchange Geographic Information System question: https://gis.stackexchange.com/questions/317591/why-raster-layer-name-change-to-default-when-using-rastercalc-function/317597#317597
When using the raster::predict function with a gbm model (dismo::gbm.step), an error occurs if there is a factor column in the data frame passed to gbm.step that is not also included in the prediction stack:
> Error in `[.data.frame`(blockvals, , f[j]) : undefined columns selected
This behavior is relatively new, introduced somewhere between versions 2.6.7 and 2.9.5 (found it when I updated). I think it is related to the inclusion of a new block of code to check for factors in gbm models (lines 73 - 85, at least in getMethod). The predict function seems to check all columns for factors, and if any factor is found, assumes that it will be present in the stack for prediction (lines 145 - 152). As not all columns included in the data frame passed to gbm.step() are necessarily used as variables (e.g., an ID column or simply a variable that was dropped), this can produce an unexpected error when predicting.
Details are below. Thanks for your time!
Code to reproduce:
# Load packages
library(dismo)
library(raster)
# Create data
set.seed(1)
id <- factor(1:1000) #factor column; not used in prediction
y <- rbinom(1000, size = 1, prob = 0.2)
x1 <- rnorm(1000)
x2 <- rbinom(1000, size = 1, prob = 0.2)
# Create data frame
factorDf <- data.frame(id, y, x1, x2)
xVarNames <- c("X1", "X2")
names(factorDf) <- c("ID", "Y", xVarNames)
# Create rasters
x1Rst <- raster(as.matrix(x1, nrow = 10))
x2Rst <- raster(as.matrix(x2, nrow = 10))
varStk <- stack(x1Rst, x2Rst)
names(varStk) <- xVarNames
# Create model
lr <- 0.0001
mdl <- gbm.step(data = factorDf, gbm.x = c("X1", "X2"), gbm.y = c("Y"),
family = "bernoulli", learning.rate = lr)
# Predict to raster fails
predict(varStk, mdl, n.trees = mdl$n.trees)
#>Error in `[.data.frame`(blockvals, , f[j]) : undefined columns selected
# Change id column from factor to numeric and everything works
noFactorDf <- data.frame(as.integer(id), y, x1, x2)
names(noFactorDf) <- c("ID", "Y", xVarNames)
mdlNoFactor <- gbm.step(data = noFactorDf, gbm.x = c("X1", "X2"), gbm.y = c("Y"),
family = "bernoulli", learning.rate = lr)
predict(varStk, mdlNoFactor, n.trees = mdlNoFactor$n.trees)
# Dropping the extra, unused column from the prediction df also works
noExtraDf <- data.frame(y, x1, x2)
names(noExtraDf) <- c("Y", xVarNames)
mdlNoExtra <- gbm.step(data = noExtraDf, gbm.x = c("X1", "X2"), gbm.y = c("Y"),
family = "bernoulli", learning.rate = lr)
predict(varStk, mdlNoExtra, n.trees = mdlNoExtra$n.trees)
R and raster info:
> bug.report(package="raster")
Package: raster
Version: 2.9-5
Maintainer: Robert J. Hijmans <[email protected]>
Built: R 3.6.0; x86_64-w64-mingw32; 2019-06-20 17:10:48 UTC; windows
R Version:
platform = x86_64-w64-mingw32
arch = x86_64
os = mingw32
system = x86_64, mingw32
status =
major = 3
minor = 6.0
year = 2019
month = 04
day = 26
svn rev = 76424
language = R
version.string = R version 3.6.0 (2019-04-26)
nickname = Planting of a Tree
Windows 10 x64 (build 17134)
Locale:
LC_COLLATE=English_United States.1252;LC_CTYPE=English_United States.1252;LC_MONETARY=English_United States.1252;LC_NUMERIC=C;LC_TIME=English_United States.1252
Search Path:
.GlobalEnv, package:dismo, package:raster, package:sp, package:stats,
package:graphics, package:grDevices, package:utils, package:datasets,
package:methods, Autoloads, package:base
Hi,
subsetting a rasterBrick/Stack to which a z attribute has been added seems to lose the attribute:
library(raster)
#> Loading required package: sp
r <- raster::brick(nrows = 100, ncols = 100, nl = 2)
r <- setZ(r, as.Date(c("2018-01-01", "2018-01-02")))
r
#> class : RasterBrick
#> dimensions : 100, 100, 10000, 2 (nrow, ncol, ncell, nlayers)
#> resolution : 3.6, 1.8 (x, y)
#> extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
#> coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
#> time : 2018-01-01, 2018-01-02
getZ(r)
#> [1] "2018-01-01" "2018-01-02"
r2 <- r[[1]]
r2
#> class : RasterLayer
#> dimensions : 100, 100, 10000 (nrow, ncol, ncell)
#> resolution : 3.6, 1.8 (x, y)
#> extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
#> coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
getZ(r2)
#> NULLI seem to recall that on earlier raster version this was not the case (but I could be mistaken). Nonetheless, I think that it could be useful to maintain the attribute on subsetting if possible.
Is this a bug?
> a <- array(runif(3*5*6),dim=c(3,5,6))
> b <- brick(a,filename="a")
Error in .local(x, ...) : unused argument (filename = "a")
filename is listed in ?brick as an optional parameter.
Found using raster_2.8-3 rgdal_1.3-4 sp_1.3-1 R version 3.5.1 on windows
At the moment FLT4S is hard-coded into the writeRaster() options when writing files to netcdf. Can this be changed so that any datatype and NAflag can be specified?
For large files which might contain integer or binary datatypes this is wasting a lot of space.
As an example, I'm working with a 42 layer stack at 0.05° which is all integer data. Writing a multilayer geotiff gives a filesize of 651kb, but writing to netcdf gives a filesize of 91mb
Reproducible example below.
library(raster)
library(ncdf4)
r <- raster(res = 20)
r <- brick(r, nl = 10)
r[] <- rbinom(ncell(r) * nlayers(r), 1, 0.25)
dataType(r)
[1] "FLT4S"
dataType(r) <- "INT1U"
dataType(r)
[1] "INT1U"
NAvalue(r)
[1] NA
NAvalue(r) <- 255
# Throws error
writeRaster(r, "./test_ncINT1U.nc", datatype = "INT1U")
Error in R_nc4_put_att_double: NetCDF: Attempt to convert between text & numbers
[1] "Error in ncatt_put, while writing attribute _FillValue with value NA"
Error in ncatt_put_inner(ncid2use, newvar$id, "_FillValue", v$missval, :
Error return from C call R_nc4_put_att_double for attribute _FillValue
# No error
# Should write as INT1U and na as 255 as this has been defined?
writeRaster(r, "./test_ncDefault.nc")
# Shows float variable with fill and missing as -3.39999995214436e+38
ncdf4::nc_open("./test_ncDefault.nc")
File ./test_ncDefault.nc (NC_FORMAT_CLASSIC):
1 variables (excluding dimension variables):
float variable[longitude,latitude,z]
_FillValue: -3.39999995214436e+38
missing_value: -3.39999995214436e+38
long_name: variable
min: 0
min: 0
min: 0
min: 0
min: 0
min: 0
min: 0
min: 0
min: 0
min: 0
max: 1
max: 1
max: 1
max: 1
max: 1
max: 1
max: 1
max: 1
max: 1
max: 1
3 dimensions:
longitude Size:18
units: degrees_east
long_name: longitude
latitude Size:9
units: degrees_north
long_name: latitude
z Size:10 *** is unlimited ***
units: unknown
long_name: z
5 global attributes:
crs: +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
crs_format: PROJ.4
Conventions: CF-1.4
created_by: R, packages ncdf4 and raster (version 2.6-7)
date: 2018-10-17 20:19:51
I am using the raster::predict() function to predict a raster using a multinomial model from nnet::multinom(). My code works (and throws the warning 'not sure if factor levels are correct') when using raster v2.8-19, and fails with error "factors[[j]] list index out of range" on raster v2.9-5.
I'm calling predict with factors = NULL and my model is produced by nnet::multinom. I believe the issue is the removal of the warning handling in commit 190c237, specifically lines 124-136(old) and 139-142(new).
I am hoping that is sufficient information to ID the problem, but if not I am happy to provide files for reproduction. I'm working with large files so it will take me some time to box up a reproducible version.
There is a bug when writing a RasterStack to NetCDF files. Normal RasterLayers work well.
Error in apply(v, 2, range, na.rm = TRUE) :
no slot of name "data" for this object of class "RasterStack"
Minimal reproducible example:
library(raster)
library(ncdf4)
r <- raster(crs="+proj=utm +zone=32 +datum=WGS84", xmn=0, xmx=10, ymn=0, ymx=10)
values(r) <- 1:ncell(r)
s <- stack(r, r/2)
xwriteRaster(s, filename="test.nc",overwrite=TRUE)
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