Creating Spatial Objects from get_power
If you require spatial objects to work with, it is rather simple to convert the resulting tidy data frame from get_power() to a spatial object in R using terra::rast(type = "xyz").
Converting Regional Data to a SpatRaster() Object
Using lapply and split it is easy to create stacks of raster objects from a regional query.
library("terra")
#> terra version 0.8.6 (beta-release)
# Use split to create a list of data frames split by YYYYMMDD
daily_region_ag <- split(daily_region_ag, daily_region_ag$YYYYMMDD)
# Remove date information from data frame, list names will carry YYYYMMDD
daily_region_ag <-
lapply(
X = daily_region_ag,
FUN = function(x)
x[(!names(x) %in% c("YEAR", "MM", "DD", "DOY", "YYYYMMDD"))]
)
# Convert the data into matrix() objects for terra to work with
daily_region_ag <- lapply(X = daily_region_ag, FUN = as.matrix)
# Create a list of raster bricks from each YYYYMMDD data frame
RH2M_list <- lapply(
X = daily_region_ag,
FUN = function(x)
rast(x[, 1:3],
crs = "+proj=eqc +lat_ts=0 +lat_0=0 +lon_0=0 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs",
type = "xyz")
)
T2M_list <- lapply(
X = daily_region_ag,
FUN = function(x)
rast(x[, c(1:2, 4)],
crs = "+proj=eqc +lat_ts=0 +lat_0=0 +lon_0=0 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs",
type = "xyz")
)
raster_stack <- rast(c(RH2M_list, T2M_list))
names(raster_stack) <-
paste0(names(raster_stack), "_", rep(names(daily_region_ag), times = 2))Plot all the items in the stack.
plot(raster_stack)
Converting Global Climatology to a Gridded Spatial Object
Converting global climatology to a terra objects is as simple as querying and then converting the resulting tibble to a terra object using terra::rast(type = "xyz")*. Illustrated here using the climatology_ag object previously created are two methods.
*: You may experience issues if you have <16GB RAM available converting the entire object to a terra::rast().
For low-memory systems
For low-memory systems, a computer with 8GB or less RAM, it is useful to have as much of your R global environment free from objects that take much memory and make sure other processes are not using system memory.
Using indexing pick the month or annual value and create a raster object. In this case, column 15 is the annual average, climatology_list$RH2M[, c(1:2, 15)].
# split data frame into data frames for each parameter in a list
climatology_list <- split(climatology_ag, climatology_ag$PARAMETER)
# Remove PARAMETER information from data frame, list names supply this
climatology_list <-
lapply(climatology_list, function(x) {
x["PARAMETER"] <- NULL
x
})
# Convert the data into matrix() objects for terra to work with
climatology_list <- lapply(X = climatology_list, FUN = as.matrix)
RH2M_ann <- rast(climatology_list$RH2M[, c(1:2, 15)],
crs = "+proj=eqc +lat_ts=0 +lat_0=0 +lon_0=0 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs",
type = "xyz")
RH2M_ann
#> class : SpatRaster
#> dimensions : 360, 720, 1 (nrow, ncol, nlyr)
#> resolution : 0.5, 0.5 (x, y)
#> extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
#> coord. ref. :
#> data source : memory
#> names : ANN
#> min values : 15.88
#> max values : 93.92
plot(RH2M_ann)
For systems with more available memory
For systems with RAM of ~16GB or more, it is possible to create a terra::rast() of the resulting POWER query. To do so, subset the tibble() for the desired parameter and drop the PARAMETER column.
# Remove the parameter column and conver to a matrix() object for terra
T2M <- as.matrix(subset(climatology_ag, PARAMETER == "T2M")[, -3])
# Preallocate a list and name it for filling with SpatRaster objects
T2M_list <- vector(mode = "list", length = 13)
names(T2M_list) <- colnames(T2M)[-c(1:2)]
# in a loop, convert each month into a SpatRaster object
for (i in colnames(T2M)[-c(1:2)]) {
T2M_list[[i]] <- rast(T2M[, c("LON", "LAT", i)],
crs = "+proj=eqc +lat_ts=0 +lat_0=0 +lon_0=0 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs",
type = "xyz")
}
# create a stack of SpatRaster objects
T2M <- c(rast(T2M_list))
plot(T2M)