check_key_length: Ckecks if length of dataframe could accomodate all possible...

In seanob01PHS/list.utils: Common Utility Packages in LIST

Ckecks if length of dataframe could accomodate all possible key combinations.

Description

This function finds the number of unique values in each of the key columns and then checks if the number of rows in the dataframe provided is consistent with the predicate that the dataframe is complete in its keys. Say the number of unique values for key_col_1 and key_col_2 were nUnique_1 and nUnique_2 etc. and there are m key columns. If the dataframe was complete in those keys it would have exactly nUninque_1 * nUnique_2 * ...* nUnique_m rows. This function verifies this, that df has as many rows as the product of the number of unique values in its keys.

Usage

check_key_length(df, key_cols = NULL, non_key_cols = NULL)

Arguments

df	Dataframe input to check for key completeness.
key_cols	Character vector of key columns.
non_key_cols	Character vector of non-key columns. You cannot provide both key_cols and non_key_cols.

Details

NOTE check_key_length(df) does NOT imply df is complete in its keys (there could be duplicates or garbage rows). rather !check_key_length(df) does imply df is non-complete in its keys.

By default if neither key_cols or non_key_cols are provided, all columns are interpreted as key columns.

Value

Booolean vector.

Examples

# a IS complete in keys and thus satisfies check_key_length(a)
> a <- tibble(key_1 = c(1,1,2,2),
              key_2 = c(1,2,1,2))
> check_key_length(a)
TRUE

# b IS NOT complete in keys but still satisfies check_key_length(b)
> b <- tibble(key_1 = c(1,1,2,2),
              key_2 = c(1,1,1,2))
> check_key_length(b)
TRUE

# c fails check_key_length(c), thus it is not complete in its keys.
> c <- tibble(key_1 = c(1,1,2),
              key_2 = c(1,2,1))
> check_key_length(c)
FALSE

seanob01PHS/list.utils documentation built on March 26, 2022, 12:41 p.m.