This function scans a standard query output for groups with high levels of 'Manager 1:1 Time'. Returns a plot by default, with an option to return a table with a all of groups (across multiple HR attributes) ranked by manager 1:1 time.

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



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


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).


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


String to specify calculation mode. Must be either:

  • "simple"

  • "combine"


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


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

  • "plot" (default)

  • "table"

See Value for more information.


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


  • "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.


Uses the metric Meeting_hours_with_manager_1_on_1. 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_sum(), one2one_trend(), period_change(), workloads_dist(), workloads_fizz(), workloads_line(), workloads_rank(), workloads_summary(), workloads_trend(), workpatterns_area(), workpatterns_rank()

Other Managerial Relations: mgrcoatt_dist(), mgrrel_matrix(), one2one_dist(), one2one_fizz(), one2one_freq(), one2one_line(), one2one_sum(), one2one_trend()


# Return rank table
  data = sq_data,
  return = "table"
#> # A tibble: 30 × 4
#>    hrvar            group            Meeting_hours_with_manager_1_on_1     n
#>    <chr>            <chr>                                        <dbl> <int>
#>  1 FunctionType     Engineering                                  0.484    93
#>  2 FunctionType     IT                                           0.444    51
#>  3 Organization     Customer Service                             0.354    61
#>  4 Organization     G&A Central                                  0.342    57
#>  5 FunctionType     Sales                                        0.341    82
#>  6 FunctionType     Marketing                                    0.324   207
#>  7 Organization     Finance-East                                 0.316    70
#>  8 LevelDesignation Senior IC                                    0.307   103
#>  9 Organization     Human Resources                              0.301    71
#> 10 Organization     G&A East                                     0.291    65
#> # … with 20 more rows

# Return plot
  data = sq_data,
  return = "plot"