Package index
Consistency tests
Check the numerical consistency of summary statistics; three tests currently implemented —
-
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_probability()
grim_ratio()
grim_total()
- 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
-
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
-
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
-
decimal_places()
decimal_places_scalar()
- Count decimal places
-
decimal_places_df()
- Count decimal places in a data frame
-
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
-
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()
deprecated - Test for subsets, supersets, and equal sets
-
is_numeric_like()
- Test whether a vector is numeric or coercible to numeric
-
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
-
duplicate_detect()
superseded - Detect duplicate values