Learn R Programming

ruler (version 0.1.3)

column-pack: Column rule pack

Description

Column rule pack is a rule pack which defines a set of rules for columns as a whole, i.e. functions which convert columns of interest to logical values. It should return a data frame with the following properties:

  • Number of rows equals to one.

  • Column names should be treated as concatenation of 'check column name' + 'separator' + 'rule name'.

  • Values indicate whether the column as a whole follows the rule.

Arguments

Using rules()

Using rules() instead of funs() is recommended because:

  • It is a convenient way to ensure consistent naming of rules without manual name.

  • It adds a common prefix to all rule names. This helps in defining separator as prefix surrounded by any number of non-alphanumeric values.

Details

This format is inspired by dplyr's scoped variants of summarise() applied to non-grouped data.

The most common way to define column pack is by creating a functional sequence with no grouping and ending with one of:

  • summarise_all(.funs = rules(...)).

  • summarise_if(.predicate, .funs = rules(...)).

  • summarise_at(.vars, .funs = rules(...)).

Note that (as of dplyr version 0.7.4) when only one column is summarised, names of the output don't have a necessary structure. The 'check column name' is missing which results (after exposure) into empty string in var column of validation report. The current way of dealing with this is to name the input column (see examples).

See Also

Data pack, group pack, row pack, cell pack.

Examples

Run this code
# NOT RUN {
# Validating present columns
numeric_column_rules <- . %>% dplyr::summarise_if(
  is.numeric,
  rules(mean(.) > 5, sd(.) < 10)
)
character_column_rules <- . %>% dplyr::summarise_if(
  is.character,
  rules(. %in% letters[1:4])
)

col_packs(
  num_col = numeric_column_rules,
  chr_col = character_column_rules
)

# Dealing with one column edge case
improper_pack <- . %>% dplyr::summarise_at(
  dplyr::vars(vs),
  rules(improper_is_chr = is.character)
)

proper_pack <- . %>% dplyr::summarise_at(
  dplyr::vars(vs = vs),
  rules(proper_is_chr = is.character)
)

mtcars %>%
  expose(col_packs(improper_pack, proper_pack)) %>%
  get_report()

# }

Run the code above in your browser using DataLab