Provides a week by week view of Work Week Span. By default returns a week by week heatmap, highlighting the points in time with most activity. Additional options available to return a summary table.

workloads_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 Workweek_span.

Examples

# Run plot
workloads_trend(sq_data)


# Run table
workloads_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          45.8         45.4         43.3         44.3         38.3
#> 2 Executive         40.4         49.1         45.0         46.9         36.4
#> 3 Junior IC         43.1         44.0         41.9         42.1         36.3
#> 4 Manager           45.3         44.7         43.0         43.4         36.8
#> 5 Senior IC         45.2         44.4         41.6         41.2         37.5
#> 6 Support           43.3         42.9         41.0         40.5         34.5
#> # … 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>