wpa (version 1.10.0)

extract_hr: Extract HR attribute variables

Description

This function uses a combination of variable class, number of unique values, and regular expression matching to extract HR / organisational attributes from a data frame.

Usage

extract_hr(data, max_unique = 50, exclude_constants = TRUE, return = "names")

Value

A different output is returned depending on the value passed to the return

argument:

• "names": character vector identifying all the names of HR variables present in the data.

• "vars": data frame containing all the columns of HR variables present in the data.

Arguments

data

A data frame to be passed through.

max_unique

A numeric value representing the maximum number of unique values to accept for an HR attribute. Defaults to 50.

exclude_constants

Logical value to specify whether single-value HR attributes are to be excluded. Defaults to TRUE.

return

String specifying what to return. This must be one of the following strings:

• "names"

• "vars"

See Value for more information.

See Also

Other Support: camel_clean(), check_inputs(), combine_signals(), cut_hour(), extract_date_range(), heat_colours(), is_date_format(), maxmin(), p_test(), pairwise_count(), plot_WOE(), read_preamble(), rgb2hex(), totals_bind(), totals_col(), totals_reorder(), tstamp(), us_to_space(), wrap()

Other Data Validation: check_query(), flag_ch_ratio(), flag_em_ratio(), flag_extreme(), flag_outlooktime(), hr_trend(), hrvar_count(), hrvar_count_all(), hrvar_trend(), identify_churn(), identify_holidayweeks(), identify_inactiveweeks(), identify_nkw(), identify_outlier(), identify_privacythreshold(), identify_query(), identify_shifts(), identify_shifts_wp(), identify_tenure(), remove_outliers(), standardise_pq(), subject_validate(), subject_validate_report(), track_HR_change(), validation_report()

Examples

sq_data %>% extract_hr(return = "names")

sq_data %>% extract_hr(return = "vars")