Provides a week by week view of scheduled manager 1:1 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.
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, supplyNULL
(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.
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_rank()
,
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()
Other Managerial Relations:
one2one_dist()
,
one2one_fizz()
,
one2one_freq()
,
one2one_line()
,
one2one_rank()
,
one2one_sum()
Examples
# Run plot
one2one_trend(pq_data)
# Run table
one2one_trend(pq_data, hrvar = "LevelDesignation", return = "table")
#> # A tibble: 5 × 11
#> group `2022-05-01` `2022-05-08` `2022-05-15` `2022-05-22` `2022-05-29`
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Director 0.833 0.792 0.5 0.583 1
#> 2 Junior IC 0.275 0.35 0.375 0.275 0.25
#> 3 Manager 0.364 0.25 0.318 0.295 0.364
#> 4 Senior IC 0.862 0.412 0.538 0.388 0.4
#> 5 Support 0.458 0.269 0.373 0.387 0.283
#> # ℹ 5 more variables: `2022-06-05` <dbl>, `2022-06-12` <dbl>,
#> # `2022-06-19` <dbl>, `2022-06-26` <dbl>, `2022-07-03` <dbl>