Provides a week by week view of collaboration time, visualised as line charts. By default returns a line chart for collaboration hours, with a separate panel per value in the HR attribute. Additional options available to return a summary table.

collaboration_line(data, hrvar = "Organization", mingroup = 5, return = "plot")

collab_line(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

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

  • "plot"

  • "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 faceted line 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_dist(), collaboration_fizz(), 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 Collaboration: collaboration_area(), collaboration_dist(), collaboration_fizz(), collaboration_rank(), collaboration_sum(), collaboration_trend()

Examples

# Return a line plot
collaboration_line(sq_data, hrvar = "LevelDesignation")


# Return summary table
collaboration_line(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          20.8         21.8         18.2         20.5         11.8
#> 2 Executive         17.6         21.1         20.2         17.3         14.0
#> 3 Junior IC         22.1         21.8         20.4         19.3         12.9
#> 4 Manager           24.3         23.6         22.0         21.0         13.8
#> 5 Senior IC         23.1         22.4         19.3         20.0         13.1
#> 6 Support           21.0         20.6         19.3         18.3         12.1
#> # … 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>