Package index
Consistency tests
Check the numerical consistency of summary statistics; three tests currently implemented —
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grim() - The GRIM test (granularity-related inconsistency of means)
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grim_map() - GRIM-test many cases at once
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grim_map_seq() - GRIM-testing with dispersed inputs
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grim_map_total_n() - GRIM-testing with hypothetical group sizes
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grim_plot() - Visualize GRIM test results
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grim_probability()grim_ratio()grim_total() - Possible GRIM inconsistencies
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grim_granularity()grim_items() - Granularity of non-continuous scales
GRIMMER
Test reported means and standard deviations for numerical consistency with reported sample sizes
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grimmer() - The GRIMMER test (granularity-related inconsistency of means mapped to error repeats)
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grimmer_map() - GRIMMER-test many cases at once
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grimmer_map_seq() - GRIMMER-testing with dispersed inputs
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grimmer_map_total_n() - GRIMMER-testing with hypothetical group sizes
DEBIT
Test reported means and standard deviations of binary data for numerical consistency with reported sample sizes
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debit() - The DEBIT (descriptive binary) test
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debit_map() - Apply DEBIT to many cases
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debit_map_seq() - Using DEBIT with dispersed inputs
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debit_map_total_n() - Use DEBIT with hypothetical group sizes
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debit_plot() - Visualize DEBIT results
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sd_binary_groups()sd_binary_0_n()sd_binary_1_n()sd_binary_mean_n() - Standard deviation of binary data
Duplicate detection
Blunt functions to tentatively discover and count duplicate numeric values; interpret results with care
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duplicate_count() - Count duplicate values
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duplicate_count_colpair() - Count duplicate values by column
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duplicate_tally() - Count duplicates at each observation
Summarize scrutiny tests
Follow up on scrutiny’s testing functions by computing specific summary statistics
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audit() - Summarize scrutiny objects
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audit_seq()audit_total_n() - Summarize output of sequence mappers and total-n mappers
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function_map() - Create new
*_map()functions -
function_map_seq() - Create new
*_map_seq()functions -
function_map_total_n() - Create new
*_map_total_n()functions -
reverse_map_seq() - Reverse the
*_map_seq()process -
reverse_map_total_n() - Reverse the
*_map_total_n()process -
check_args_disabled() - Check that disabled arguments are not specified
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check_factory_dots() - Check that no dots-argument is misspelled
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absorb_key_args() - Absorb key arguments from the user's call
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round_up_from()round_down_from()round_up()round_down() - Common rounding procedures
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round_ceiling()round_floor()round_trunc()anti_trunc()round_anti_trunc() - Uncommon rounding procedures
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reround() - General interface to reconstructing rounded numbers
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reround_to_fraction()reround_to_fraction_level() - Generalized rounding to the nearest fraction of a specified denominator
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unround() - Reconstruct rounding bounds
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rounding_bias() - Compute rounding bias
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decimal_places()decimal_places_scalar() - Count decimal places
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decimal_places_df() - Count decimal places in a data frame
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seq_endpoint()seq_distance()seq_endpoint_df()seq_distance_df() - Sequence generation at decimal level
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seq_test_ranking() - Rank sequence test results
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seq_disperse()seq_disperse_df() - Sequence generation with dispersion at decimal level
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disperse()disperse2()disperse_total() - Vary hypothetical group sizes
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seq_length()`seq_length<-`() - Set sequence length
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is_seq_linear()is_seq_ascending()is_seq_descending()is_seq_dispersed() - Is a vector a certain kind of sequence?
Documentation templates
Return standardized building blocks for documenting specific kinds of functions
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write_doc_audit() - Documentation template for
audit() -
write_doc_audit_seq() - Documentation template for
audit_seq() -
write_doc_audit_total_n() - Documentation template for
audit_total_n() -
write_doc_factory_map_conventions() - Documentation template for function factory conventions
Consistency test helpers
Call these helpers inside of your own functions that implement consistency tests at various levels
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check_mapper_input_colnames() - Check that a mapper's input has correct column names
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manage_helper_col() - Helper column operations
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manage_key_colnames() - Enable name-independent key column identification
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unnest_consistency_cols() - Unnest a test result column
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audit_cols_minimal() - Compute minimal
audit()summaries -
check_audit_special() - Alert user if more specific
audit_*()summaries are available
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is_map_df()is_map_basic_df()is_map_seq_df()is_map_total_n_df() - Is an object a consistency test output tibble?
Testing for subsets / supersets
Predicate functions for logical relations between vectors, supporting flexible data entry and tidy evaluation
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is_subset_of()is_superset_of()is_equal_set()is_proper_subset_of()is_proper_superset_of()is_subset_of_vals()is_superset_of_vals()is_equal_set_vals()is_proper_subset_of_vals()is_proper_superset_of_vals()is_subset_of_vecs()is_superset_of_vecs()is_equal_set_vecs()is_proper_subset_of_vecs()is_proper_superset_of_vecs()deprecated - Test for subsets, supersets, and equal sets
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is_numeric_like() - Test whether a vector is numeric or coercible to numeric
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restore_zeros()restore_zeros_df() - Restore trailing zeros
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split_by_parens() - Split columns by parentheses, brackets, braces, or similar
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before_parens()inside_parens() - Extract substrings from before and inside parentheses
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row_to_colnames() - Turn row values into column names
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duplicate_detect()superseded - Detect duplicate values