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 raster::rasterFromXYZ().
Converting Regional Data to a raster Object
Using lapply and split it is easy to create stacks of raster objects from a regional query.
library(raster)
# Use split to create a list of data frames split by YYYYMMDD
daily_region_ag <- split(daily_region_ag, daily_region_ag$YYYYMMDD)
# Remove date information from data frame, list names will carry YYYYMMDD
daily_region_ag <-
lapply(daily_region_ag, function(x)
x[(!names(x) %in% c("YEAR", "MM", "DD", "DOY", "YYYYMMDD"))])
# Create a list of raster bricks from each YYYYMMDD data frame
raster_list <- lapply(daily_region_ag, rasterFromXYZ,
crs = "+proj=eqc +lat_ts=0 +lat_0=0 +lon_0=0 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs")
stack_names <-
paste0(names(raster_list), rep(c("_RH2M", "_T2M"), 2))
raster_stack <- stack(unlist(raster_list))
names(raster_stack) <- stack_namesPlot all the items in the stack.
Converting Global Climatology to a raster Object
Converting global climatology to a raster objects is as simple as querying and then converting the resulting tibble to a raster object using raster::rasterFromXYZ()*. Illustrated here using the climatology_ag object previously created are two methods.
*: You may experience issues if you have <16GB RAM available converting the entire object to a raster::brick().
For low-memory systems
For low-memory systems, a computer with 8GB or less RAM, it is useful to have as much of your R global environment free from objects that take much memory and make sure other processes are not using system memory.
Using indexing pick the month or annual value and create a raster object.
# split data frame into data frames for each parameter in a list
climatology_list <- split(climatology_ag, climatology_ag$PARAMETER)
# Remove PARAMETER information from data frame, list names supply this
climatology_list <-
lapply(climatology_list, function(x) {
x["PARAMETER"] <- NULL
x
})
RH2M_ann <- rasterFromXYZ(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")
RH2M_ann
#> class : RasterLayer
#> dimensions : 360, 720, 259200 (nrow, ncol, ncell)
#> resolution : 0.5, 0.5 (x, y)
#> extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
#> 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 +towgs84=0,0,0
#> source : memory
#> names : ANN
#> values : 15.88, 93.92 (min, max)
plot(RH2M_ann)
For systems with more available memory
For systems with RAM of ~16GB or more, it is possible to create a raster::brick() of the resulting POWER query. To do so, subset the tibble() for the desired parameter and drop the PARAMETER column.
T2M_brick <- rasterFromXYZ(subset(climatology_ag, PARAMETER == "T2M")[-3])
T2M_brick
#> class : RasterBrick
#> dimensions : 360, 720, 259200, 13 (nrow, ncol, ncell, nlayers)
#> resolution : 0.5, 0.5 (x, y)
#> extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
#> crs : NA
#> source : memory
#> names : JAN, FEB, MAR, APR, MAY, JUN, JUL, AUG, SEP, OCT, NOV, DEC, ANN
#> min values : -40.88, -44.85, -57.06, -62.95, -65.00, -64.50, -66.26, -66.74, -65.38, -57.44, -44.34, -39.79, -54.75
#> max values : 34.08, 32.84, 32.77, 34.30, 37.09, 39.34, 39.13, 38.65, 35.04, 32.46, 34.20, 33.83, 32.23
plot(T2M_brick)