Skip to contents

monthly_stats_nc

Source: R/monthly_stats_nc.R
monthly_stats_nc.Rd

Computes calendar-month statistics on gridded data — either an sxts object, a NetCDF file, or a raster object. For each calendar month present in the dataset, the function calls basic_stats_nc, returning a multi-layer raster of per-cell statistics: mean, min, max, standard deviation, L-moments, probability dry, empirical quantiles, and (unless ignore_zeros = TRUE) wet/dry transition statistics. The outer loop over up to twelve calendar months is separable from the already-vectorised per-cell computations inside basic_stats_nc, so parallelisation is offered at the month level only. NetCDF input is loaded once via nc2xts and then sliced by month index; raster input is converted to sxts with automatic date parsing.

Usage

monthly_stats_nc(
  data = NULL,
  filename = NA,
  varname = NA,
  ignore_zeros = FALSE,
  zero_threshold = 0.01,
  parallel = FALSE,
  ncores = 2,
  ...
)

Arguments

data

An sxts object, or a Raster* object. Leave NULL when supplying filename and varname.

filename

Optional; path to a NetCDF file to import when data is not provided.

varname

Optional; name of the variable to extract from filename.

ignore_zeros

Logical; if TRUE, zeros are ignored in the per-cell statistics. Default FALSE.

zero_threshold

Numeric; threshold below which values are treated as zero. Default 0.01.

parallel

Logical; whether to compute per-month statistics in parallel. Default FALSE.

ncores

Integer; number of cores for parallel computation. Default 2.

...

Additional arguments passed to nc2xts when filename and varname are supplied.

Value

A named list with one element per calendar month present in the data, named after month.name. Each element is the basic_stats_nc output raster for that month.

Examples

# Synthetic sxts with 4 cells and 2 years of daily data
set.seed(123)
n <- 365 * 2
dates <- seq(as.POSIXct("2000-01-01", tz = "UTC"), by = "day", length.out = n)
vals <- matrix(pmax(0, rnorm(n * 4, mean = 3, sd = 5)), nrow = n, ncol = 4)
coords <- data.frame(x = c(0, 1, 0, 1), y = c(50, 50, 51, 51))
ts_sxts <- sxts(data = vals, order.by = dates, coords = coords,
                projection = "+proj=longlat +datum=WGS84")

ms_nc <- monthly_stats_nc(data = ts_sxts)
names(ms_nc)
#>  [1] "January"   "February"  "March"     "April"     "May"       "June"     
#>  [7] "July"      "August"    "September" "October"   "November"  "December"