Provides an analysis of the distribution of a selected metric. Returns a stacked bar plot by default. Additional options available to return a table with distribution elements.
A Standard Person Query dataset in the form of a data frame.
String containing the name of the metric, e.g. "Collaboration_hours"
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).
Numeric value setting the privacy threshold / minimum group size. Defaults to 5.
String specifying what to return. This must be one of the following strings:
"plot"
"table"
See Value
for more information.
A numeric vector of length three to specify the breaks for the distribution, e.g. c(10, 15, 20)
A character vector of length four to specify colour codes for the stacked bars.
String to specify what unit to use. This defaults to "hours"
but can accept any custom string. See cut_hour()
for more details.
Numeric. Specifies the lower bound (inclusive) value for the minimum label. Defaults to 0.
Numeric. Specifies the upper bound (inclusive) value for the maximum label. Defaults to 100.
String to specify the bucket label to sort by. Defaults to
NULL
(no sorting).
Character vector to override labels for the created categorical variables. Must be a named vector - see examples.
A different output is returned depending on the value passed to the return
argument:
"plot"
: 'ggplot' object. A stacked bar plot for the metric.
"table"
: data frame. A summary table for the metric.
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_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 Flexible:
create_bar_asis()
,
create_bar()
,
create_boxplot()
,
create_bubble()
,
create_density()
,
create_fizz()
,
create_hist()
,
create_inc()
,
create_line_asis()
,
create_line()
,
create_period_scatter()
,
create_rank()
,
create_sankey()
,
create_scatter()
,
create_stacked()
,
create_tracking()
,
create_trend()
,
period_change()
# Return plot
create_dist(sq_data, metric = "Collaboration_hours", hrvar = "Organization")
# Return summary table
create_dist(sq_data, metric = "Collaboration_hours", hrvar = "Organization", return = "table")
#> # A tibble: 15 × 6
#> group `< 15 hours` `15 - 20 hours` 20 - 25 h…¹ 25+ h…² Emplo…³
#> <fct> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 Biz Dev 0.52 0.48 NA NA 75
#> 2 Customer Service 0.295 0.492 0.0492 0.164 61
#> 3 Facilities 0.236 0.403 0.278 0.0833 72
#> 4 Finance-Corporate 0.471 0.309 0.176 0.0441 68
#> 5 Finance-East 0.257 0.114 0.129 0.5 70
#> 6 Finance-South 0.407 0.235 0.0494 0.309 81
#> 7 Finance-West 0.192 0.342 0.192 0.274 73
#> 8 Financial Planning 0.373 0.48 0.0133 0.133 75
#> 9 G&A Central 0.281 0.333 0.193 0.193 57
#> 10 G&A East 0.4 0.508 0.0923 NA 65
#> 11 G&A South 0.197 0.0789 0.342 0.382 76
#> 12 Human Resources 0.141 0.0563 0.310 0.493 71
#> 13 IT-Corporate 0.206 0.0441 0.368 0.382 68
#> 14 IT-East 0.290 0.0806 0.323 0.306 62
#> 15 Inventory Management 0.0333 0.0333 0.45 0.483 60
#> # … with abbreviated variable names ¹`20 - 25 hours`, ²`25+ hours`,
#> # ³Employee_Count
# Use custom labels by providing a label vector
eh_labels <- c(
"Fewer than fifteen" = "< 15 hours",
"Between fifteen and twenty" = "15 - 20 hours",
"Between twenty and twenty-five" = "20 - 25 hours",
"More than twenty-five" = "25+ hours"
)
sq_data %>%
create_dist(metric = "Email_hours",
labels = eh_labels, return = "plot")
# Sort by a category
sq_data %>%
create_dist(metric = "Collaboration_hours",
sort_by = "25+ hours")