Provides a week by week view of after-hours collaboration time. By default returns a week by week heatmap, highlighting the points in time with most activity. Additional options available to return a summary table.

afterhours_trend(data, hrvar = "Organization", mingroup = 5, 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.

return

Character vector specifying what to return, defaults to "plot". Valid inputs are "plot" and "table".

Value

Returns a 'ggplot' object by default, where 'plot' is passed in return. When 'table' is passed, a summary table is returned as a data frame.

Details

Uses the metric After_hours_collaboration_hours.

See also

Other Visualization: afterhours_dist(), afterhours_fizz(), afterhours_line(), afterhours_rank(), afterhours_summary(), 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_rank(), workloads_summary(), workloads_trend(), workpatterns_area(), workpatterns_rank()

Other After-hours Collaboration: afterhours_dist(), afterhours_fizz(), afterhours_line(), afterhours_rank(), afterhours_summary(), external_rank()

Examples

# Run plot
afterhours_trend(sq_data)


# Run table
afterhours_trend(sq_data, hrvar = "LevelDesignation", return = "table")
#> # A tibble: 6 × 14
#>   group     `2019-11-03` `2019-11-10` `2019-11-17` `2019-11-24` `2019-12-01`
#>   <chr>            <dbl>        <dbl>        <dbl>        <dbl>        <dbl>
#> 1 Director          3.47         3.49         2.86         3.20         2.01
#> 2 Executive         2.32         3.34         2.84         2.67         1.63
#> 3 Junior IC         3.38         3.21         2.96         2.88         1.85
#> 4 Manager           3.79         3.44         3.15         3.05         2.04
#> 5 Senior IC         3.45         3.37         2.83         2.58         2.05
#> 6 Support           3.14         2.93         2.76         2.53         1.69
#> # … with 8 more variables: `2019-12-08` <dbl>, `2019-12-15` <dbl>,
#> #   `2019-12-22` <dbl>, `2019-12-29` <dbl>, `2020-01-05` <dbl>,
#> #   `2020-01-12` <dbl>, `2020-01-19` <dbl>, `2020-01-26` <dbl>