This function scans a standard query output for groups with high levels of 'Weekly Email Collaboration'. Returns a plot by default, with an option to return a table with a all of groups (across multiple HR attributes) ranked by hours of digital collaboration.
Usage
email_rank(
data,
hrvar = extract_hr(data),
mingroup = 5,
mode = "simple",
plot_mode = 1,
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, supplyNULL
(without quotes).- mingroup
Numeric value setting the privacy threshold / minimum group size. Defaults to 5.
- mode
String to specify calculation mode. Must be either:
"simple"
"combine"
- plot_mode
Numeric vector to determine which plot mode to return. Must be either
1
or2
, and is only used whenreturn = "plot"
.1
: Top and bottom five groups across the data population are highlighted2
: Top and bottom groups per organizational attribute are highlighted
- return
String specifying what to return. This must be one of the following strings:
"plot"
(default)"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 bubble plot where the x-axis represents the metric, the y-axis represents the HR attributes, and the size of the bubbles represent the size of the organizations. Note that there is no plot output ifmode
is set to"combine"
."table"
: data frame. A summary table for the metric.
Details
Uses the metric Email_hours
.
See create_rank()
for applying the same analysis to a different metric.
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_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()
,
one2one_trend()
Other Emails:
email_dist()
,
email_fizz()
,
email_line()
,
email_summary()
,
email_trend()
Examples
# Return rank table
email_rank(
data = pq_data,
return = "table"
)
#> # A tibble: 22 × 4
#> hrvar group Email_hours n
#> <chr> <chr> <dbl> <int>
#> 1 Organization Operations 8.92 22
#> 2 Organization Research 8.89 52
#> 3 FunctionType Technician 8.88 274
#> 4 Level Level1 8.83 37
#> 5 LevelDesignation Executive 8.83 37
#> 6 Level Level3 8.82 87
#> 7 LevelDesignation Senior IC 8.82 87
#> 8 FunctionType Consultant 8.81 288
#> 9 Organization HR 8.80 33
#> 10 Organization Finance 8.79 68
#> # ℹ 12 more rows
# Return plot
email_rank(
data = pq_data,
return = "plot"
)