Track count of distinct people over time in a specified HR variable
Source:R/hrvar_trend.R
hrvar_trend.RdThis function provides a week by week view of the count of the distinct people by the specified HR attribute.The default behaviour is to return a week by week heatmap bar plot.
Arguments
- data
A Standard Person Query dataset in the form of a data frame.
- hrvar
HR Variable by which to split metrics, defaults to "Organization" but accepts any character vector, e.g. "LevelDesignation". If a vector with more than one value is provided, the HR attributes are automatically concatenated.
- return
String specifying what to return. This must be one of the following strings:
"plot""table"
See
Valuefor more information.
Value
A different output is returned depending on the value passed to the return
argument:
"plot": 'ggplot' object containing a bar plot."table": data frame containing a count table.
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_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_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
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 Data Validation:
check_query(),
extract_hr(),
flag_ch_ratio(),
flag_em_ratio(),
flag_extreme(),
flag_outlooktime(),
hr_trend(),
hrvar_count_all(),
hrvar_count(),
identify_churn(),
identify_holidayweeks(),
identify_inactiveweeks(),
identify_nkw(),
identify_outlier(),
identify_privacythreshold(),
identify_shifts(),
identify_tenure(),
track_HR_change(),
validation_report()
Examples
# Return a bar plot
hrvar_trend(pq_data, hrvar = "LevelDesignation")
# Return a summary table
hrvar_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 6 6 6 6 6
#> 2 Junior IC 10 10 10 10 10
#> 3 Manager 11 11 11 11 11
#> 4 Senior IC 20 20 20 20 20
#> 5 Support 53 53 53 53 53
#> # ℹ 5 more variables: `2022-06-05` <dbl>, `2022-06-12` <dbl>,
#> # `2022-06-19` <dbl>, `2022-06-26` <dbl>, `2022-07-03` <dbl>