Track count of distinct people over time in a specified HR variable

Source: R/hrvar_trend.R

This function provides a week by week view of the count of the distinct people by the specified HR attribute.The default behaviour is to return a week by week heatmap bar plot.

hrvar_trend(data, hrvar = "Organization", return = "plot")

Arguments

data

A Standard Person Query dataset in the form of a data frame.

hrvar

HR Variable by which to split metrics, defaults to "Organization" but accepts any character vector, e.g. "LevelDesignation". If a vector with more than one value is provided, the HR attributes are automatically concatenated.

return

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

  • "plot"

  • "table"

See Value for more information.

Value

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

  • "plot": 'ggplot' object containing a bar plot.

  • "table": data frame containing a count table.

See also

Other Visualization: afterhours_dist(), afterhours_fizz(), afterhours_line(), afterhours_rank(), afterhours_summary(), afterhours_trend(), collaboration_area(), collaboration_dist(), collaboration_fizz(), collaboration_line(), collaboration_rank(), collaboration_sum(), collaboration_trend(), create_bar(), create_bar_asis(), create_boxplot(), create_bubble(), create_dist(), create_fizz(), create_inc(), create_line(), create_line_asis(), create_period_scatter(), create_rank(), create_sankey(), create_scatter(), create_stacked(), create_tracking(), create_trend(), email_dist(), email_fizz(), email_line(), email_rank(), email_summary(), email_trend(), external_dist(), external_fizz(), external_line(), external_network_plot(), external_rank(), external_sum(), hr_trend(), hrvar_count(), internal_network_plot(), keymetrics_scan(), meeting_dist(), meeting_fizz(), meeting_line(), meeting_quality(), meeting_rank(), meeting_summary(), meeting_trend(), meetingtype_dist(), meetingtype_dist_ca(), meetingtype_dist_mt(), meetingtype_summary(), mgrcoatt_dist(), mgrrel_matrix(), one2one_dist(), one2one_fizz(), one2one_freq(), one2one_line(), one2one_rank(), one2one_sum(), one2one_trend(), period_change(), workloads_dist(), workloads_fizz(), workloads_line(), workloads_rank(), workloads_summary(), workloads_trend(), workpatterns_area(), workpatterns_rank()

Other Data Validation: check_query(), extract_hr(), flag_ch_ratio(), flag_em_ratio(), flag_extreme(), flag_outlooktime(), hr_trend(), hrvar_count(), hrvar_count_all(), 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

# Return a bar plot
hrvar_trend(sq_data, hrvar = "LevelDesignation")


# Return a summary table
hrvar_trend(sq_data, hrvar = "LevelDesignation", return = "table")
#> # A tibble: 6 × 8
#>   group     `2019-12-15` `2019-12-22` `2019-12-29` `2020-01-05` `2020-01-12`
#>   <chr>            <dbl>        <dbl>        <dbl>        <dbl>        <dbl>
#> 1 Director            43           43           43           43           43
#> 2 Executive            4            4            4            4            4
#> 3 Junior IC           58           58           58           58           58
#> 4 Manager            200          200          200          200          200
#> 5 Senior IC           67           67           67           67           67
#> 6 Support            257          257          257          257          257
#> # ℹ 2 more variables: `2020-01-19` <dbl>, `2020-01-26` <dbl>