Under step 3, when you provide the example code starting with polynomial_output <- staggregate_polynomial(, include the argument names in the function call.
So this would be:
polynomial_output <- staggregate_polynomial(
data = prcp_kansas_dec2011_era5, # A raster brick of our primary data, typically but
# not necessarily climate data. For now, data must
# start at midnight and be hourly.
overlay_weights = county_weights, # Output from Step 2, determined here by
# area-normalized cropland weights for grid cells
# within each county in Kansas
daily_agg = "sum", # How to aggregate hourly values to the daily level,
# "sum" and "average" are the only options. Here we
# want total daily precipitation.
time_agg = "month", # The temporal level to aggregate daily transformed
# values to. Current options are "day", "month", and
# "year"
degree = 3 # The highest order of the polynomial. Here this
# will create variable 3 columns: x, x^2, and x^3
)
Under step 3, when you provide the example code starting with
polynomial_output <- staggregate_polynomial(, include the argument names in the function call.So this would be: