Rank all groups across HR attributes for Work Week Span

This function scans a standard query output for groups with high levels of Work Week Span. Returns a plot by default, with an option to return a table with a all of groups (across multiple HR attributes) ranked by work week span.

workloads_rank(
  data,
  hrvar = extract_hr(data),
  mingroup = 5,
  mode = "simple",
  plot_mode = 1,
  return = "table"
)

Arguments

data

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

hrvar

String containing the name of the HR Variable by which to split metrics. Defaults to "Organization". To run the analysis on the total instead of splitting by an HR attribute, supply NULL (without quotes).

mingroup

Numeric value setting the privacy threshold / minimum group size. Defaults to 5.

mode

String to specify calculation mode. Must be either:

• "simple"

• "combine"

plot_mode

Numeric vector to determine which plot mode to return. Must be either 1 or 2, and is only used when return = "plot".

• 1: Top and bottom five groups across the data population are highlighted

• 2: Top and bottom groups per organizational attribute are highlighted

return

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

• "plot" (default)

• "table"

See Value for more information.

Value

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

argument:

• "plot": 'ggplot' object. A bubble plot where the x-axis represents the metric, the y-axis represents the HR attributes, and the size of the bubbles represent the size of the organizations. Note that there is no plot output if mode is set to "combine".

• "table": data frame. A summary table for the metric.

Details

Uses the metric Workweek_span. See create_rank() for applying the same analysis to a different metric.

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_asis(), create_bar(), create_boxplot(), create_bubble(), create_dist(), create_fizz(), create_inc(), create_line_asis(), create_line(), 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(), hrvar_trend(), internal_network_plot(), keymetrics_scan(), meeting_dist(), meeting_fizz(), meeting_line(), meeting_quality(), meeting_rank(), meeting_summary(), meeting_trend(), meetingtype_dist_ca(), meetingtype_dist_mt(), meetingtype_dist(), 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_summary(), workloads_trend(), workpatterns_area(), workpatterns_rank()

Other Workweek Span: workloads_dist(), workloads_fizz(), workloads_line(), workloads_summary(), workloads_trend()

Examples

# Return rank table
workloads_rank(
  data = sq_data,
  return = "table"
)
#> # A tibble: 30 × 4
#>    hrvar            group                Workweek_span     n
#>    <chr>            <chr>                        <dbl> <int>
#>  1 FunctionType     Sales                         54.0    82
#>  2 FunctionType     Marketing                     51.4   207
#>  3 Organization     Inventory Management          47.3    60
#>  4 Organization     Finance-East                  46.3    70
#>  5 Organization     Finance-West                  44.4    73
#>  6 Organization     Human Resources               44.0    71
#>  7 LevelDesignation Director                      43.7    68
#>  8 LevelDesignation Executive                     43.4     6
#>  9 Organization     G&A Central                   43.2    57
#> 10 Organization     Facilities                    43.1    72
#> # … with 20 more rows

# Return plot
workloads_rank(
  data = sq_data,
  return = "plot"
)