Distribution of Collaboration Hours as a 100% stacked bar

Source: R/collaboration_dist.R

Analyze the distribution of Collaboration Hours. Returns a stacked bar plot by default. Additional options available to return a table with distribution elements.

collaboration_dist(
  data,
  hrvar = "Organization",
  mingroup = 5,
  return = "plot",
  cut = c(15, 20, 25)
)

collab_dist(
  data,
  hrvar = "Organization",
  mingroup = 5,
  return = "plot",
  cut = c(15, 20, 25)
)

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.

return

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

  • "plot"

  • "table"

See Value for more information.

cut

A numeric vector of length three to specify the breaks for the distribution, e.g. c(10, 15, 20)

Value

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

  • "plot": 'ggplot' object. A stacked bar plot for the metric.

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

Metrics used

The metric Collaboration_hours is used in the calculations. Please ensure that your query contains a metric with the exact same name.

See also

Other Visualization: afterhours_dist(), afterhours_fizz(), afterhours_line(), afterhours_rank(), afterhours_summary(), afterhours_trend(), collaboration_area(), 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(), hrvar_trend(), 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 Collaboration: collaboration_area(), collaboration_fizz(), collaboration_line(), collaboration_rank(), collaboration_sum(), collaboration_trend()

Examples

# Return plot
collaboration_dist(sq_data, hrvar = "Organization")


# Return summary table
collaboration_dist(sq_data, hrvar = "Organization", return = "table")
#> # A tibble: 5 × 6
#>   group  `< 15 hours` `15 - 20 hours` `20 - 25 hours` `25+ hours` Employee_Count
#>   <fct>         <dbl>           <dbl>           <dbl>       <dbl>          <int>
#> 1 Custo…        0.279          0.492           0.0656       0.164             61
#> 2 Finan…        0.308          0.267           0.134        0.291            292
#> 3 Finan…        0.36           0.453           0.04         0.147             75
#> 4 Human…        0.141          0.0563          0.239        0.563             71
#> 5 IT            0.246          0.0538          0.3          0.4              130