Takes in an Hourly Collaboration query and returns a count table of working patterns, ranked from the most common to the least.
workpatterns_rank(
data,
signals = c("email", "IM"),
start_hour = "0900",
end_hour = "1700",
top = 10,
mode = "binary",
return = "plot"
)
A data frame containing hourly collaboration data.
Character vector to specify which collaboration metrics to use:
"email"
(default) for emails only
"IM"
for Teams messages only
"unscheduled_calls"
for Unscheduled Calls only
"meetings"
for Meetings only
or a combination of signals, such as c("email", "IM")
A character vector specifying starting hours,
e.g. "0900"
A character vector specifying starting hours,
e.g. "1700"
numeric value specifying how many top working patterns to display in plot,
e.g. "10"
string specifying aggregation method for plot. Valid options include:
"binary"
: convert hourly activity into binary blocks. In the plot, each
block would display as solid.
"prop"
: calculate proportion of signals in each hour over total signals
across 24 hours, then average across all work weeks. In the plot, each
block would display as a heatmap.
String specifying what to return. This must be one of the following strings:
"plot"
"table"
See Value
for more information.
A different output is returned depending on the value passed to the return
argument:
"plot"
: ggplot object. A plot with the y-axis showing the top ten
working patterns and the x-axis representing each hour of the day.
"table"
: data frame. A summary table for the top working patterns.
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()
,
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()
Other Working Patterns:
flex_index()
,
identify_shifts_wp()
,
identify_shifts()
,
plot_flex_index()
,
workpatterns_area()
,
workpatterns_classify_bw()
,
workpatterns_classify_pav()
,
workpatterns_classify()
,
workpatterns_hclust()
,
workpatterns_report()
# Plot by default
workpatterns_rank(
data = em_data,
signals = c(
"email",
"IM",
"unscheduled_calls",
"meetings"
)
)
# Plot with prop / heatmap mode
workpatterns_rank(
data = em_data,
mode = "prop"
)