Masking raster from data in SpatialGridDataFrame and SpatialPolygonsDataFrame - r

I am trying to mask a raster file by including only some specific area (‘Koeppen Geiger’ climatic zones) with several locations. I got an error message running the final line of code:
Error in (function (classes, fdef, mtable) : unable to find an
inherited method for function ‘mask’ for signature
‘"SpatialGridDataFrame", "SpatialPolygonsDataFrame"’
.
##Read Countries file
library(sp)
library(maptools)
library(rworldmap)
countries = readShapeSpatial("D:/Studies/PhD/SCI/modeling/ne_10m_admin_0_countries/ne_10m_admin_0_countries.shp") [enter link description here][1]
asia.zone = countries[countries$ADMIN=="South Korea"|
countries$ADMIN=="North Korea"|
countries$ADMIN=="Japan"|
countries$ADMIN=="China"|
countries$ADMIN=="Taiwan",]
##Read Koeppen Geiger’ climatic zones
tst <- read.csv('D:/Studies/PhD/SCI/modeling/Koeppen-Geiger-ASCII.csv',as.is=TRUE) [enter link description here][1]
tst.l <- tst [tst$Cls=="Cfc"|
tst$Cls=="Cfa"|
tst$Cls=="Cfb"|
tst$Cls=="Cwa"|
tst$Cls=="Cwb"|
tst$Cls=="Aw"|
tst$Cls=="As"|
tst$Cls=="Am"|
tst$Cls=="Dwd"|
tst$Cls=="Dwb"|
tst$Cls=="Dwa"|
tst$Cls=="Dwc",]
#convert to sp SpatialPointsDataFrame
coordinates(tst.l) = c("Lon", "Lat")
# promote to SpatialPixelsDataFrame
gridded(tst.l) <- TRUE
# promote to SpatialGridDataFrame
tst.lsGDF = as(tst.l, "SpatialGridDataFrame")
# mask the specific climate zone from some locations
asia.zone2 <- mask(tst.lsGDF,asia.zone)

If you look up ?mask you will see that it has been implemented for Raster* objects, not for SpatialGridDataFrame objects. So you need to coerce your data to a Raster object. Something like this might work:
library(raster)
setwd("D:/Studies/PhD/SCI/modeling/")
countries <- shapefile("vne_10m_admin_0_countries/ne_10m_admin_0_countries.shp")
asia.zone <- countries[countries$ADMIN %in% c("South Korea", "North Korea","Japan", "China", "Taiwan"), ]
tst <- read.csv("Koeppen-Geiger-ASCII.csv", stringsAsFactor=FALSE)
tst.l <- tst [tst$Cls %in% c("Cfc", "Cfa", "Cfb", "Cwa", "Cwb", "Aw", "As", "Am", "Dwd", "Dwb", "Dwa", "Dwc"),]
coordinates(tst.l) = c("Lon", "Lat")
# promote to SpatialPixelsDataFrame
gridded(tst.l) <- TRUE
r <- raster(tst.l)
asia.zone2 <- mask(r, asia.zone)

Related

R - extract part of .nc file and convert into raster (similar to WorldClim format)

I have a netcdf file I made which contains percentage values.
The file has 1 variable, 5 dimensions and 0 NetCDF attributes.
The dimensions are
"lon" "lat" "month" "CR" "yearSumm"
They were created using
lon <- ncdim_def("lon", "modis_degrees", -179.5:179.5, unlim=FALSE,
create_dimvar=TRUE, calendar=NA, longname="Longitude")
lat <- ncdim_def("lat", "modis_degrees", -89.5:89.5, unlim=FALSE,
create_dimvar=TRUE, calendar=NA, longname="Latitude")
month <- ncdim_def("month", "month_name", 1:13, unlim=FALSE,
create_dimvar=TRUE, calendar=NA, longname="Month.and.Annual.Data")
CR <- ncdim_def("CR", "CR_numeric", 1:12, unlim=FALSE,
create_dimvar=TRUE, calendar=NA, longname="Cloud.Regime")
yearSumm <- ncdim_def("yearSumm", "yearOrSummType", 1:21, unlim=FALSE,
create_dimvar=TRUE, calendar=NA, longname="Year.and.Summary.Data")
I want to extract 13 layers (each latxlong with each cell a percentage value) from this and make them into a raster file like the bioclimatic data you can download from worldclim
I have tried extracting the data I want into an array, to then make a raster. I did that using
CR_RFO <- ncvar_get(CRnc, attributes(CRnc$var)$names[1])
CR_Ann <- as.array(CR_RFO[1:360, 1:180, 13, 1:12, 18])
This seems to have selected the data I want.
I then tried to make that into raster format.
raster(CR_Ann)
Error in (function (classes, fdef, mtable) :
unable to find an inherited method for function ‘raster’ for signature ‘"array"’
> CR_R <- as.raster(CR_Ann)
Error in array(if (d[3L] == 3L) rgb(t(x[, , 1L]), t(x[, , 2L]), t(x[, :
a raster array must have exactly 3 or 4 planes
> CR_R <- raster(CR_Ann)
Error in (function (classes, fdef, mtable) :
unable to find an inherited method for function ‘raster’ for signature ‘"array"’
> CR_R <- stack(CR_Ann)
Error in data.frame(values = unlist(unname(x)), ind, stringsAsFactors = FALSE) :
arguments imply differing number of rows: 777600, 0
> CR_R <- brick(CR_Ann)
Eventually brick worked, but I don't think that is actually what I want.
When I looked up the WorldClim files I downloaded, it is a zip file of .tifs
I also had tried
# set path and filename
ncpath <- "data/"
ncname <- "CR_RFO"
ncfname <- paste(ncpath, ncname, ".nc", sep="")
dname <- "Ann" # note: Ann means Annual
CR_raster <- brick(ncfname, varname="CR_RFO")
CR_raster; class(CR_raster)
which resulted in the error
CR_RFO has more than 4 dimensions, I do not know what to do with these data
I suspect I am going about it from the wrong angle, and maybe even have made my netcdf file incorrectly, as lat and long are not variables like in some of the examples I have read.
How can I extract these 13 lat x long layers and output them as .tif as per worldclim?
This is how I have ended up doing what I think I needed to. I haven't tested this in place of worldclim data yet, but I have successfully made the geotiff files.
CRnc <- nc_open("data/CR_RFO.nc")
CR_RFO <- ncvar_get(CRnc, attributes(CRnc$var)$names[1])
Repeat from here for each tif I want, selecting the correct number in the 4th place in the index, and changing the file names accordingly.
CR1_Ann <- as.matrix(CR_RFO[1:360, 1:180, 13, 1, 18])
CR1_Ann <- t(CR1_Ann)
CR1_Ann <- flipud(CR1_Ann)
CR1_Annr <- raster(CR1_Ann, ymn = -89.5, ymx = 89.5, xmn = -179.5, xmx = 179.5)
#plot(CR1_Annr)
writeRaster(CR1_Annr, "./data/CR_Ann/CR1_Ann", format = "GTiff")
This is not an elegant solution, so if anyone has a better way, please share.

'st_intersects' applied to an object of class "data.frame"

I am trying to convert latitude/longitude to Census Tract or FIPS using R studio. I am getting the following error message when I am running "system.time"
"Error in UseMethod("st_intersects") :
no applicable method for 'st_intersects' applied to an object of class "data.frame"
Timing stopped at: 0 0 0"
Entire Code:
library(sf)
list.files("./tl_2010_22_tract10")
census_tracts <- st_read("./tl_2010_22_tract10/tl_2010_22_tract10.shp")
head(census_tracts)
library(readxl)
DAT <- read_excel("~/DAT.xlsx")
View(DAT)
latlong_dat = data.frame(DAT)
latlong_sf <- latlong_dat
filter(!is.na(LAT), !is.na(LONG))
st_as_sf(latlong_dat, coords = c("LONG", "LAT"), crs =
st_crs(census_tracts))
head(latlong_dat)
system.time({intersected <- st_intersects(latlong_sf, census_tracts)})
Screenshot of code
DAT FILE

Extracting elevation data with getData in the Raster package

I am trying to get some elevations for bird locations I have in NZ. I thought I might use the code provided as an answer to a similar question (Extracting elevation from website for lat/lon points in Australia, using R), unfortunately I get errors when using the extract function in the raster package, even though the code is almost identical.
library(raster)
m <- data.frame(lon = c(172.639847, 173.283966), lat = c(-43.525650, -41.270634))
x <- getData('alt', country = "NZL")
cbind(m, alt = extract(x, m))
plot(x)
points(m)
ERROR:
cbind(m, alt = extract(x, m))
Error in (function (classes, fdef, mtable) :
unable to find an inherited method for function ‘extract’ for signature ‘"list", "data.frame"’
Can anyone tell me what is going wrong? I have been searching for hours but cannot find a solution.
Thanks, Sam
I don't know exactly why, but the subtle reason is that, compared to Australia, getData returns a different data structure for New Zealand. It returns a list, where the RasterLayers are in the first (and second) list element:
library(raster)
## Australia ==============
m <- data.frame(lon = c(146.9442, 146.4622), lat = c(-36.0736, -36.0491))
aus <- getData('alt', country = "AUS")
class(aus)
# [1] "RasterLayer"
# attr(,"package")
# [1] "raster"
cbind(m, alt = extract(aus, m))
## New Zealand ============
m <- data.frame(lon = c(172.639847, 173.283966), lat = c(-43.525650, -41.270634))
nzl <- getData("alt", country = "NZL")
> class(nzl) # is a list!
# [1] "list"
> class(nzl[[1]])
# [1] "RasterLayer"
# attr(,"package")
# [1] "raster"
cbind(m, alt = extract(nzl[[1]], m))
cbind(m, alt = extract(nzl[[2]], m))

Minimum elevation within km

Trying to find the minimum elevation within 10km of a certain latitude and longitude using R.
So far I have
dem <- getData("SRTM", lat=42.90, lon=-78.85, path = datadir)
plot(dem)
I know I need to create spatial points and eventually buffer/extract the information.
When I try:
buffdem <- buffer(dem, width=10000)
It does not work because I don't have any points.
I tried
dem <- getData("SRTM", lat=42.90, lon=-78.85, path = datadir)
coords <- data.frame(
x = rnorm(100),
y = rnorm(100)
)
coordinates(dem)
spdf = SpatialPointsDataFrame(coords, dem)
I get the following error:
Error in validObject(.Object) : invalid class
“SpatialPointsDataFrame” object: invalid object for slot "data" in
class "SpatialPointsDataFrame": got class "RasterLayer", should be or
extend class "data.frame"
I think this accomplishes what you need:
library(raster)
#elevation <- getData("SRTM", lat=42.90, lon=-78.85)
#poi <- cbind(lon=-78.85, lat=42.90)
using a smaller example data set for quicker download:
elevation <- getData('alt', country='CHE')
poi <- cbind(8.13, 46.47)
e <- extract(elevation, poi, buffer=10000)
sapply(e, min, na.rm=TRUE)
By the way, this is a duplicate of this and this question.

Create spatial objects in R useful for coordinates() and spsample()

I'm trying to use this code, adapted from dataset meuse
data<-list(var1,var2,x,y)
coordinates(data)=~x+y
grid = spsample(data, type = "regular", cellsize = c(0.05,0.05))
vt <- variogram(var1 ~ var2,data=data)
vt.fit <- fit.variogram(vt, vgm(0.2, "Sph", 800, 0.05))
gstatobj <- gstat(id = 'var1', formula = var1 ~ var2, model=vt.fit, set = list(gls=1))
My goal is creating a grid, like meuse.grid. But coordinates doesn't work... list isn't the right command.
What shall I use?
Is correct the way I'm using to create the grid?
the following reproducible example shows jlhoward's comment is right, and Darko's reply is wrong:
library(gstat)
var1 = 1:3; var2 = 1:3; x = 1:3; y = 1:3
data<-list(var1,var2,x,y)
coordinates(data) = ~x+y
Error in (function (classes, fdef, mtable) :
unable to find an inherited method for function ‘coordinates<-’ for signature ‘"list"’
data<-data.frame(var1,var2,x,y)
coordinates(data) = ~x+y
class(data)
[1] "SpatialPointsDataFrame"
attr(,"package")
[1] "sp"
you may have been confused by doing this again, which would give:
coordinates(data) = ~x+y
Error in `coordinates<-`(`*tmp*`, value = ~x + y) :
setting coordinates cannot be done on Spatial objects, where they have already been set
but leaves the existing (and correct) data in tact.

Resources