I want to download the daily tmax from the NASA for a given lat lon (https://developers.google.com/earth-engine/datasets/catalog/NASA_NEX-DCP30_ENSEMBLE_STATS)
using the following tutorial https://jesjehle.github.io/earthEngineGrabR/index.html
library(devtools)
install_github('JesJehle/earthEngineGrabR')
library(earthEngineGrabR)
ee_grab_install() # had to install Anaconda before doing this step.
test_data <- ee_grab(data = ee_data_collection(datasetID = "NASA/NEX-DCP30_ENSEMBLE_STATS",
timeStart = "1980-01-01",
timeEnd = '1980-01-02',
bandSelection = 'tasmax'),
targetArea = system.file("data/territories.shp", package = "earthEngineGrabR")
)
Error: With the given product argument no valid data could be requested.
In addition: Warning message:
Error on Earth Engine servers for data product: NASA-NEX-DCP30_ENSEMBLE_STATS_s-mean_t-mean_1980-01-01to2005-12-31
Error in py_call_impl(callable, dots$args, dots$keywords): EEException: Collection.first: Error in map(ID=historical_195001):
Image.select: Pattern 'tasmax' did not match any bands.
I would like to know how to specify the bandwidth so that I do get this error and instead of using a shapefile as target area, I do I download tmax data for a single lat lon 9.55, 78.59?
You might use rgee to accomplish this. Currently, rgee has a function called rgee::ee_extract that works similar to raster::extract().
library(rgee)
library(sf)
# 1. Load a geometry
y <- st_read(system.file("shape/nc.shp", package = "sf"), quiet = TRUE) %>%
st_transform(4326)
## Move that geometry from local to earth engine
ee_y <- sf_as_ee(y)
# 2. Load your ImageCollection
x <- ee$ImageCollection("NASA/NEX-DCP30_ENSEMBLE_STATS")$
filterDate("1980-01-01","1980-01-02")$
map(function(img) img$select("tasmax_mean"))
## calculate the nominal scale
scale <- x$first()$projection()$nominalScale()$getInfo()
# 3. Extract values
tasmax_mean_data <- ee_extract(x = x,
y = y,
fun = ee$Reducer$mean(),
scale = scale,
id = "FIPS")
# 4. Merge results with the sf object
ee_nc_tasmax <- merge(y, tasmax_mean_data, by = "FIPS")
plot(ee_nc_rain['historical_198001'])
Related
Every time I run the script it always gives me an error: Error in { : task 1 failed - "could not find function "%>%""
I already check every post on this forum and tried to apply it but no one works.
Please advise any solution.
Please note: I have only 2 cores on my PC.
My code is as follows:
library(dplyr) # For basic data manipulation
library(ncdf4) # For creating NetCDF files
library(tidync) # For easily dealing with NetCDF data
library(ggplot2) # For visualising data
library(doParallel) # For parallel processing
MHW_res_grid <- readRDS("C:/Users/SUDHANSHU KUMAR/Desktop/MTech Project/R/MHW_result.Rds")
# Function for creating arrays from data.frames
df_acast <- function(df, lon_lat){
# Force grid
res <- df %>%
right_join(lon_lat, by = c("lon", "lat")) %>%
arrange(lon, lat)
# Convert date values to integers if they are present
if(lubridate::is.Date(res[1,4])) res[,4] <- as.integer(res[,4])
# Create array
res_array <- base::array(res[,4], dim = c(length(unique(lon_lat$lon)), length(unique(lon_lat$lat))))
dimnames(res_array) <- list(lon = unique(lon_lat$lon),
lat = unique(lon_lat$lat))
return(res_array)
}
# Wrapper function for last step before data are entered into NetCDF files
df_proc <- function(df, col_choice){
# Determine the correct array dimensions
lon_step <- mean(diff(sort(unique(df$lon))))
lat_step <- mean(diff(sort(unique(df$lat))))
lon <- seq(min(df$lon), max(df$lon), by = lon_step)
lat <- seq(min(df$lat), max(df$lat), by = lat_step)
# Create full lon/lat grid
lon_lat <- expand.grid(lon = lon, lat = lat) %>%
data.frame()
# Acast only the desired column
dfa <- plyr::daply(df[c("lon", "lat", "event_no", col_choice)],
c("event_no"), df_acast, .parallel = T, lon_lat = lon_lat)
return(dfa)
}
# We must now run this function on each column of data we want to add to the NetCDF file
doParallel::registerDoParallel(cores = 2)
prep_dur <- df_proc(MHW_res_grid, "duration")
prep_max_int <- df_proc(MHW_res_grid, "intensity_max")
prep_cum_int <- df_proc(MHW_res_grid, "intensity_cumulative")
prep_peak <- df_proc(MHW_res_grid, "date_peak")
I've been plotting some global rasters lately using mainly raster and tmap. I'd like to plot the maps in Robinson projection instead of lat-lon. Simple projection to Robinson however duplicates some areas on the edges of the map as you can see from the figures below (Alaska, Siberia, NZ).
Previously, I found a workaround with PROJ.4 code parameter "+over" as outlined in here and here.
With the latest changes to rgdal using GDAL > 3 and PROJ >= 6, this workaround seems to be obsolete. Has anyone found a new way on how to plot global rasters in Robinson/Eckert IV/Mollweide without duplicated areas?
I'm running R 4.0.1, tmap 3.1, stars 0.4-3, raster 3.3-7, rgdal 1.5-12, sp 1.4-2, GDAL 3.1.1 and PROJ 6.3.1 on a macOS Catalina 10.15.4
require(stars)
require(raster)
require(tmap)
require(dplyr)
# data
worldclim_prec = getData(name = "worldclim", var = "prec", res = 10)
jan_prec <- worldclim_prec$prec1
# to Robinson and plot - projection outputs a warning
jp_rob <- jan_prec %>%
projectRaster(crs = "+proj=robin +over")
tm_shape(jp_rob) + tm_raster(style = "fisher")
Warning messages:
1: In showSRID(uprojargs, format = "PROJ", multiline = "NO") :
Discarded ellps WGS 84 in CRS definition: +proj=robin +over
2: In showSRID(uprojargs, format = "PROJ", multiline = "NO") :
Discarded datum WGS_1984 in CRS definition
I tried to do the same with stars instead of raster but no resolution was found, supposedly since tmap uses stars since version 3.0.
# new grid for warping stars objects
newgrid <- st_as_stars(jan_prec) %>%
st_transform("+proj=robin +over") %>%
st_bbox() %>%
st_as_stars()
# to stars object - projection outputs no warning
jp_rob_stars <- st_as_stars(jan_prec) %>%
st_warp(newgrid)
tm_shape(jp_rob_stars) + tm_raster(style = "fisher")
Thanks for any insights - hoping someone else is thinking about this issue!
With raster you can do
library(raster)
prec <- getData(name = "worldclim", var = "prec", res = 10)[[1]]
crs <- "+proj=robin +lon_0=0 +x_0=0 +y_0=0 +datum=WGS84 +units=m"
rrob <- projectRaster(prec, crs=crs)
Create a mask
library(geosphere)
e <- as(extent(prec), "SpatialPolygons")
crs(e) <- crs(prec)
e <- makePoly(e) # add additional vertices
re <- spTransform(e, crs)
And use it
mrob <- mask(rrob, re)
The new package terra has a mask argument for that (you need version >= 0.8.3 for this, available from github)
prec <- getData(name = "worldclim", var = "prec", res = 10)[[1]]
jp <- rast(prec$prec1)
jp <- jp * 1 # to deal with NAs in this datasaet
rob <- project(jp, crs, mask=TRUE)
How can one add a new node to a SpatialLinesNetwork?
context of my problem: I have a shapefile of a bus route and another shapefile of bus stops. I want to calculate the distance between stops along the bus route. Ideally, each stop would be a node and I would use stplanr::sum_network_routes() to calculate the distance between them. The problem is that when I convert the bus route into a SpatialLinesNetwork the network only has a few nodes that are far from each other and unrelated to bus stops locations.
reproducible dataset:
# load library and data
library(stplanr)
library(sf)
# get road data
data(routes_fast)
rnet <- overline(routes_fast, attrib = "length")
# convert to sf obj
rnet <- st_as_sf(rnet)
# convert SpatialLinesDataFrame into SpatialLinesNetwork
sln <- SpatialLinesNetwork(rnet)
# identify nodes
sln_nodes = sln2points(sln)
# Here is a bus stop which should be added as a node
new_point_coordinates = c(-1.535, 53.809)
p = sf::st_sf(geometry = sf::st_sfc(sf::st_point(new_point_coordinates)), crs = st_crs(rnet))
# plot
plot(sln, col = "gray") # network
plot(sln_nodes, col="red", add = TRUE) # nodes
plot(p, add=T, col="blue") # stop to be added as a new node
This doesn't answer your question at the outset, but I believe it does resolve your "Context" by showing how your desired network distances can be calculated. This can be done with dodgr (latest dev version) like this:
library (dodgr)
library (stplanr)
library (sf)
library (sp)
dat <- st_as_sf (routes_fast)
net <- weight_streetnet (dat, wt_profile = 1)
The net object is a simple data.frame containing all edges and vertices of the network. Then adapt your code above to get the routing points as a simple matrix
rnet rnet <- overline(routes_fast, attrib = "length")
SLN <- SpatialLinesNetwork(rnet)
sln_nodes = sln2points(SLN)
xy <- coordinates (sln_nodes)
colnames (xy) <- c ("x", "y")
Node that sln2points simply returns "nodes" (in stplanr terminology), which are junction points. You can instead replace with coordinates of bus stops, or simply add those to this matrix. The following three lines convert those coordinates to unique (nearest) vertex IDs of the dodgr net object:
v <- dodgr_vertices (net)
pts <- match_pts_to_graph (v, xy)
pts <- v$id [pts]
To calculate distances between those pts on the network, just
d <- dodgr_dists (net, from = pts, to = pts)
Thanks for the question, thanks to this question and subsequent collaboration with Andrea Gilardi, I'm happy to announce that it is now possible to add new nodes to sfNetwork objects with a new function, sln_add_node().
See below and please try to test reproducible code that demonstrates how it works:
devtools::install_github("ropensci/stplanr")
#> Skipping install of 'stplanr' from a github remote, the SHA1 (33158a5b) has not changed since last install.
#> Use `force = TRUE` to force installation
library(stplanr)
#> Registered S3 method overwritten by 'R.oo':
#> method from
#> throw.default R.methodsS3
#> Warning in fun(libname, pkgname): rgeos: versions of GEOS runtime 3.7.1-CAPI-1.11.1
#> and GEOS at installation 3.7.0-CAPI-1.11.0differ
sample_routes <- routes_fast_sf[2:6, NULL]
sample_routes$value <- rep(1:3, length.out = 5)
rnet <- overline2(sample_routes, attrib = "value")
#> 2019-09-26 16:06:18 constructing segments
#> 2019-09-26 16:06:18 building geometry
#> 2019-09-26 16:06:18 simplifying geometry
#> 2019-09-26 16:06:18 aggregating flows
#> 2019-09-26 16:06:18 rejoining segments into linestrings
plot(sample_routes["value"], lwd = sample_routes$value, main = "Routes")
plot(rnet["value"], lwd = rnet$value, main = "Route network")
sln <- SpatialLinesNetwork(rnet)
#> Linking to GEOS 3.7.1, GDAL 2.4.0, PROJ 5.2.0
new_point_coordinates <- c(-1.540, 53.826)
crs <- sf::st_crs(rnet)
p <- sf::st_sf(geometry = sf::st_sfc(sf::st_point(new_point_coordinates)), crs = crs)
p_dest <- sln2points(sln)[9, ]
# We can identify the nearest point on the network at this point
# and use that to split the associated linestring:
sln_new <- sln_add_node(sln = sln, p = p)
#> although coordinates are longitude/latitude, st_nearest_feature assumes that they are planar
route_new <- route_local(sln = sln_new, from = p, to = p_dest)
plot(sln_new)
plot(p, add = TRUE)
plot(route_new, lwd = 5, add = TRUE)
#> Warning in plot.sf(route_new, lwd = 5, add = TRUE): ignoring all but the
#> first attribute
Created on 2019-09-26 by the reprex package (v0.3.0)
In case it's of use/interest, see the source code of the new small family of functions that support this new functionality here: https://github.com/ropensci/stplanr/blob/master/R/node-funs.R
I am trying to add a shapefile, when making a region for the DSsim package in R. But I keep getting the error code- Error in shapefile$shp : $ operator is invalid for atomic vectors. Can anyone help me?
The code I have is as follows:
library(DSsim)
file.path("C:/Users/user/Desktop/Lorna/Distance/WITH VARIABLES/C3p", "C3p.shp")
C3P <- shapefiles::read.shp("C3p.shp")
region.label <- make.region(region.name = "C3P", strata.name = 0, units = "km", area = 51.02, shapefile=("C3p.shp"), check.LinkID = TRUE)
The help for make.region says:
shapefile: a shapefile object of the region loaded into R using
‘read.shapefile(shape.name)’ from the shapefiles library.
but you have done:
region.label <- make.region(...., shapefile=("C3p.shp"), ....)
which is the name of the shapefile, in parentheses (these brackets).
Try using read.shapefiles instead of read.shp:
C3P <- shapefiles::read.shapefile(
"C:/Users/user/Desktop/Lorna/Distance/WITH VARIABLES/C3p"
)
Assuming the path to your shapefile is "C:/Users/user/Desktop/Lorna/Distance/WITH VARIABLES/C3p.shp" and similarly for C3p.shx etc.
and then:
region.label <- make.region(
region.name = "C3P",
strata.name = 0,
units = "km",
area = 51.02,
shapefile=C3P, # the object you created two lines previous
check.LinkID = TRUE)
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.