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Diagnose data quality of a processed data cube

Source: R/diagnose_cube.R
diagnose_cube.Rd

Evaluates a set of diagnostic rules describing the data quality of a biodiversity occurrence cube. Each rule computes a metric on the cube and assigns a severity level indicating potential limitations of the data for exploratory analysis or indicator calculation.

Usage

diagnose_cube(data_cube, rules = "basic", verbose = TRUE, ...)

Arguments

data_cube

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

rules

Diagnostic rules to evaluate. Can be:

  • A character vector referring to built-in rule sets (e.g. "basic", "spatial").

  • A list of rule objects.

  • A combination of both.

verbose

Logical indicating whether a diagnostic summary should be printed.

...

Additional arguments passed to print.cube_diagnostics() in case verbose = TRUE.

Value

An object of class cube_diagnostics, containing one row per metric with the following columns:

  • dimension: Dimension of the cube being evaluated (e.g. "spatial", "temporal", "taxonomical").

  • metric: Name of the diagnostic metric.

  • value: Computed metric value.

  • severity: Severity level ("ok", "note", "important", "very_important").

  • message: Human-readable description of the diagnostic result.

The rule objects are attached as an attribute of the diagnostics object.

See also

Other data_exploration: filter_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"

# Diagnose based on default rules
diag <- diagnose_cube(processed_cube)
#> 
#> Data cube diagnostics
#> ----------------------
#> 🟡 NOTE - temporal_min_points 
#>    Cube contains observations across 3 years. 
#> 
#> 🟢 OK - temporal_missing_years 
#>    Cube contains 0 missing years. 
#> 
#> 🟠 IMPORTANT - spatial_min_cells 
#>    Cube contains observations across 0 grid cells. 
#> 
#> 🟢 OK - spatial_max_uncertainty 
#>    Cube contains 0 records where the coordinate uncertainty is larger than the grid cell resolution. 
#> 
#> 🟡 NOTE - spatial_miss_uncertainty 
#>    Cube contains 1 records with missing coordinate uncertainty. 
#> 
#> 🟠 IMPORTANT - taxon_min_taxa 
#>    Cube contains observations across 0 taxon keys. 
#> 
#> 🔴 VERY_IMPORTANT - obs_min_records 
#>    Cube contains 4 observation records (rows). 
#> 
#> 🔴 VERY_IMPORTANT - obs_min_total 
#>    Cube contains a total of 18 observations. 
#> 

# Sort diagnoses
diag <- diagnose_cube(processed_cube, sort_summary = "asc")
#> 
#> Data cube diagnostics
#> ----------------------
#> 🟢 OK - temporal_missing_years 
#>    Cube contains 0 missing years. 
#> 
#> 🟢 OK - spatial_max_uncertainty 
#>    Cube contains 0 records where the coordinate uncertainty is larger than the grid cell resolution. 
#> 
#> 🟡 NOTE - temporal_min_points 
#>    Cube contains observations across 3 years. 
#> 
#> 🟡 NOTE - spatial_miss_uncertainty 
#>    Cube contains 1 records with missing coordinate uncertainty. 
#> 
#> 🟠 IMPORTANT - spatial_min_cells 
#>    Cube contains observations across 0 grid cells. 
#> 
#> 🟠 IMPORTANT - taxon_min_taxa 
#>    Cube contains observations across 0 taxon keys. 
#> 
#> 🔴 VERY_IMPORTANT - obs_min_records 
#>    Cube contains 4 observation records (rows). 
#> 
#> 🔴 VERY_IMPORTANT - obs_min_total 
#>    Cube contains a total of 18 observations. 
#> 

# Only show at least important diagnoses
diag <- diagnose_cube(processed_cube, filter_summary = "important")
#> 
#> Data cube diagnostics
#> ----------------------
#> 🟠 IMPORTANT - spatial_min_cells 
#>    Cube contains observations across 0 grid cells. 
#> 
#> 🟠 IMPORTANT - taxon_min_taxa 
#>    Cube contains observations across 0 taxon keys. 
#> 
#> 🔴 VERY_IMPORTANT - obs_min_records 
#>    Cube contains 4 observation records (rows). 
#> 
#> 🔴 VERY_IMPORTANT - obs_min_total 
#>    Cube contains a total of 18 observations. 
#>