Function reference • scrutiny

Consistency tests

Check the numerical consistency of summary statistics; three tests currently implemented —

GRIM

Test reported means or percentages for numerical consistency with reported sample sizes

grim(): The GRIM test (granularity-related inconsistency of means)

grim_map(): GRIM-test many cases at once

grim_map_seq(): GRIM-testing with dispersed inputs

grim_map_total_n(): GRIM-testing with hypothetical group sizes

grim_plot(): Visualize GRIM test results

grim_total() grim_ratio() grim_ratio_upper(): Possible GRIM inconsistencies

grim_granularity() grim_items(): Granularity of non-continuous scales

GRIMMER

Test reported means and standard deviations for numerical consistency with reported sample sizes

grimmer(): The GRIMMER test (granularity-related inconsistency of means mapped to error repeats)

grimmer_map(): GRIMMER-test many cases at once

grimmer_map_seq(): GRIMMER-testing with dispersed inputs

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

debit(): The DEBIT (descriptive binary) test

debit_map(): Apply DEBIT to many cases

debit_map_seq(): Using DEBIT with dispersed inputs

debit_map_total_n(): Use DEBIT with hypothetical group sizes

debit_plot(): Visualize DEBIT results

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

duplicate_count(): Count duplicate values

duplicate_count_colpair(): Count duplicate values by column

duplicate_tally(): Count duplicates at each observation

Summarize scrutiny tests

Follow up on scrutiny’s testing functions by computing specific summary statistics

audit(): Summarize scrutiny objects

audit_seq() audit_total_n(): Summarize output of sequence mappers and total-n mappers

Developer tools

Helpers for implementing error detection techniques

Function factories

Easily create new mapper functions for consistency tests

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

Rounding

Reconstruct rounding procedures

round_up_from() round_down_from() round_up() round_down(): Common rounding procedures

round_ceiling() round_floor() round_trunc() anti_trunc() round_anti_trunc(): Uncommon rounding procedures

reround(): General interface to reconstructing rounded numbers

reround_to_fraction() reround_to_fraction_level(): Generalized rounding to the nearest fraction of a specified denominator

unround(): Reconstruct rounding bounds

rounding_bias(): Compute rounding bias

Counting decimal places

decimal_places() decimal_places_scalar(): Count decimal places

decimal_places_df(): Count decimal places in a data frame

Sequence generation

seq_endpoint() seq_distance() seq_endpoint_df() seq_distance_df(): Sequence generation at decimal level

seq_test_ranking(): Rank sequence test results

seq_disperse() seq_disperse_df(): Sequence generation with dispersion at decimal level

disperse() disperse2() disperse_total(): Vary hypothetical group sizes

seq_length() `seq_length<-`(): Set sequence length

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

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

check_mapper_input_colnames(): Check that a mapper's input has correct column names

manage_helper_col(): Helper column operations

manage_key_colnames(): Enable name-independent key column identification

unnest_consistency_cols(): Unnest a test result column

audit_cols_minimal(): Compute minimal audit() summaries

check_audit_special(): Alert user if more specific audit_*() summaries are available

Data frame predicates

Test for mapper function output

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

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(): Test for subsets, supersets, and equal sets

Testing for numeric-like vectors

is_numeric_like(): Test whether a vector is numeric or coercible to numeric

Data wrangling

Automate formatting tasks with a special relevance to error detection

restore_zeros() restore_zeros_df(): Restore trailing zeros

split_by_parens(): Split columns by parentheses, brackets, braces, or similar

before_parens() inside_parens(): Extract substrings from before and inside parentheses

row_to_colnames(): Turn row values into column names

Datasets

Small example datasets to demonstrate how grim_map() and debit_map() work

pigs1: Means and sample sizes for GRIM-testing

pigs2: Percentages and sample sizes for GRIM-testing

pigs3: Binary means and standard deviations for using DEBIT

pigs4: Data with duplications

pigs5: Means, SDs, and sample sizes for GRIMMER-testing

Superseded

duplicate_detect(): Detect duplicate values