Filter a processed data cube using diagnostic rules

Filters observations from a processed_cube based on rule definitions. Filtering reuses the rule infrastructure used by diagnose_cube(), but applies row-level filtering logic through rule-specific filter_fn() functions.

Usage

filter_cube(data_cube, rules = NULL, diagnostics = NULL, ...)

Arguments

data_cube

A processed_cube object as returned by b3gbi::process_cube().

rules

Character vector or list of cube rule objects. Ignored if diagnostics is supplied.

diagnostics

Optional cube_diagnostics object returned by diagnose_cube(). If provided, rules are extracted from this object.

...

Additional arguments passed to rule-specific filter_fn() functions.

Value

A filtered processed_cube.

Details

The function evaluates rule-specific filter_fn() functions that return a logical vector indicating which rows should be removed. Only rules that implement a filter_fn() are applied. Rules without a filtering function are ignored.

Filtering rules operate independently from diagnostic severity levels. For example, a cube may have acceptable overall diagnostics while still containing individual observations that fail filtering criteria.

After filtering, the function attempts to rebuild the cube using b3gbi::process_cube() to ensure cube metadata remains consistent. If this function is unavailable or fails, the filtered data replaces data_cube$data directly and the original cube metadata is retained. In that case a warning is issued.

See also

Other data_exploration: diagnose_cube()

Examples

# Example cube
# ! Real cubes should be processed with b3gbi::process_cube()
processed_cube <- list(
  data = data.frame(
    obs = c(5, 2, 10, 1),
    year = c(2001, 2001, 2002, 2003),
    minCoordinateUncertaintyInMeters = c(50, 2000, NA, 10)
  ),
  resolutions = "10km"
)
class(processed_cube) <- "processed_cube"

# Filter cube based on rule
filtered_cube1 <- filter_cube(
  processed_cube,
  rules = list(rule_spatial_miss_uncertainty())
)
#> Warning: Filtered data replaced the cube data but metadata was not recomputed.
#> Install and use `b3gbi::process_cube()` to rebuild cube metadata.

# Filter cube based cube diagnostics
diag <- diagnose_cube(
  processed_cube,
  rules = list(
    rule_spatial_miss_uncertainty(),
    rule_temporal_missing_years()
  )
)
#> 
#> Data cube diagnostics
#> ----------------------
#> 🟡 NOTE - spatial_miss_uncertainty 
#>    Cube contains 1 records with missing coordinate uncertainty. 
#> 
#> 🟢 OK - temporal_missing_years 
#>    Cube contains 0 missing years. 
#> 

filtered_cube2 <- filter_cube(
  processed_cube,
  diagnostics = diag
)
#> Warning: Filtered data replaced the cube data but metadata was not recomputed.
#> Install and use `b3gbi::process_cube()` to rebuild cube metadata.

# The results are identical
identical(filtered_cube1$data, filtered_cube2$data)
#> [1] TRUE