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Email Hours Ranking

Source: R/email_rank.R
email_rank.Rd

This function scans a standard query output for groups with high levels of 'Weekly Email Collaboration'. Returns a plot by default, with an option to return a table with a all of groups (across multiple HR attributes) ranked by hours of digital collaboration.

Usage

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

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 Email_hours. 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(), 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_summary(), email_trend(), external_dist(), external_fizz(), external_line(), external_rank(), external_sum(), hr_trend(), hrvar_count(), hrvar_trend(), keymetrics_scan(), meeting_dist(), meeting_fizz(), meeting_line(), meeting_rank(), meeting_summary(), meeting_trend(), one2one_dist(), one2one_fizz(), one2one_freq(), one2one_line(), one2one_rank(), one2one_sum(), one2one_trend()

Other Emails: email_dist(), email_fizz(), email_line(), email_summary(), email_trend()

Examples

# Return rank table
email_rank(
  data = pq_data,
  return = "table"
)
#> # A tibble: 21 × 4
#>    hrvar               group               Email_hours     n
#>    <chr>               <chr>                     <dbl> <int>
#>  1 LevelDesignation    Director                   2.50     6
#>  2 SupervisorIndicator Manager+                   2.50     6
#>  3 FunctionType        Sales                      1.54    11
#>  4 LevelDesignation    Manager                    1.53    11
#>  5 SupervisorIndicator Manager                    1.53    11
#>  6 FunctionType        Marketing                  1.35    12
#>  7 Organization        Finance                    1.31    27
#>  8 Organization        Sales and Marketing        1.28    31
#>  9 FunctionType        Customer_Service           1.26    12
#> 10 FunctionType        G_and_A                    1.23     6
#> # ℹ 11 more rows

# Return plot
email_rank(
  data = pq_data,
  return = "plot"
)