The function staggregate_bin() aggregates climate data to the daily level,
splits these daily values into bins, and aggregates the transformed
values to the polygon level and desired temporal scale.
Usage
staggregate_bin(
data,
overlay_weights,
daily_agg,
time_agg = "month",
start_date = NA,
time_interval = "1 hour",
bin_breaks
)Arguments
- data
The raster brick with the data to be transformed and aggregated
- overlay_weights
A table of weights which can be generated using the function
overlay_weights()- daily_agg
How to aggregate hourly values to daily values prior to transformation. Options are
'sum','average', or'none'('none'will transform values without first aggregating to the daily level)- time_agg
the temporal scale to aggregate data to. Options are
'hour','day','month', or'year'('hour'cannot be selected unlessdaily_agg = 'none')- start_date
the date (and time, if applicable) of the first layer in the raster. To be input in a format compatible with lubridate::as_datetime(), e.g.
"1991-10-29"or"1991-10-29 00:00:00". The default isNAsince the rasters usually already contain temporal information in the layer names and they do not need to be manually supplied.- time_interval
the time interval between layers in the raster to be aggregated. To be input in a format compatible with seq(), e.g.
'1 day'or'3 months'. The default is'1 hour'and this argument is required if daily_agg is not'none'or if thestart_dateargument is notNA.- bin_breaks
A vector of bin boundaries to split the data by
Examples
bin_output <- staggregate_bin(
data = temp_nj_jun_2024_era5 - 273.15, # Climate data to transform and
# aggregate
overlay_weights = overlay_weights_nj, # Output from overlay_weights()
daily_agg = "average", # Average hourly values to produce daily values
# before transformation
time_agg = "month", # Sum the transformed daily values across months
start_date = "2024-06-01 00:00:00", # The start date of the supplied data,
# only required if the layer name
# format is not compatible with stagg
time_interval = "1 hour", # The temporal interval of the supplied data,
# required if daily_agg is not "none" or if the
# start_date argument is not NA
bin_breaks = c(0, 2.5, 5, 7.5, 10) # Draw 6 bins from ninf to 0, 0 to 2.5,
# 2.5 to 5, 5 to 7.5, 7.5 to 10, 10 to
# inf
)
#> Rewriting the data's temporal metadata (layer names) to reflect a dataset starting on the supplied start date and with a temporal interval of 1 hour
#> Averaging over 24 layers per day to get daily values
#> Executing binning transformation
#> Aggregating by polygon and month
head(bin_output)
#> year month poly_id bin_ninf_to_0 bin_0_to_2.5 bin_2.5_to_5 bin_5_to_7.5
#> <num> <num> <char> <num> <num> <num> <num>
#> 1: 2024 6 011 0 0 0 0
#> 2: 2024 6 033 0 0 0 0
#> 3: 2024 6 015 0 0 0 0
#> 4: 2024 6 009 0 0 0 0
#> 5: 2024 6 007 0 0 0 0
#> 6: 2024 6 041 0 0 0 0
#> bin_7.5_to_10 bin_10_to_inf
#> <num> <num>
#> 1: 0 30
#> 2: 0 30
#> 3: 0 30
#> 4: 0 30
#> 5: 0 30
#> 6: 0 30