Generate the Manager-Relationship 2x2 matrix, returning a 'ggplot' object by default. Additional options available to return a "wide" or "long" summary table.
mgrrel_matrix(
data,
hrvar = NULL,
mingroup = 5,
return = "plot",
plot_colors = c("#fe7f4f", "#b4d5dd", "#facebc", "#fcf0eb"),
threshold = 15
)
Standard Person Query data to pass through. Accepts a data frame.
HR Variable by which to split metrics. Accepts a character
vector, e.g. "Organization". Defaults to NULL
.
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"
"data"
See Value
for more information.
Pass a character vector of length 4 containing HEX codes to specify colors to use in plotting.
Specify a numeric value to determine threshold (in minutes) for 1:1 manager hours. Defaults to 15.
A different output is returned depending on the value passed to the return
argument:
"plot"
: ggplot object. When NULL
is passed to hrvar
, a two-by-two
grid where the size of the grid represents total percentage of employees is
returned. Otherwise, a horizontal stacked bar plot is returned.
"table"
: data frame. A summary table is returned.
"data"
: data frame. A long table grouped at the PersonId
level with
the following columns:
PersonId
HR variable supplied to hrvar
CoattendanceRate
Meeting_hours_with_manager_1_on_1
mgr1on1
Type
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_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()
,
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 Managerial Relations:
mgrcoatt_dist()
,
one2one_dist()
,
one2one_fizz()
,
one2one_freq()
,
one2one_line()
,
one2one_rank()
,
one2one_sum()
,
one2one_trend()
# Return matrix
mgrrel_matrix(sq_data)
# Return stacked bar plot
mgrrel_matrix(sq_data, hrvar = "Organization")
## Visualize coaching style types
# Ensure dplyr is loaded
library(dplyr)
# Extract PersonId and Coaching Type
match_df <-
sq_data %>%
mgrrel_matrix(return = "data") %>%
select(PersonId, Type)
# Join and visualize baseline
sq_data %>%
left_join(match_df, by = "PersonId") %>%
keymetrics_scan(hrvar = "Type",
return = "plot")