Apply a rule based algorithm to emails sent by hour of day, using the binary week-based ('bw') method.
workpatterns_classify_bw(
data,
hrvar = NULL,
signals = c("email", "IM"),
start_hour = "0900",
end_hour = "1700",
active_threshold = 0,
return = "plot"
)
A data frame containing email by hours data.
A string specifying the HR attribute to cut the data by. Defaults to NULL. This only affects the function when "table" is returned.
Character vector to specify which collaboration metrics to use:
a combination of signals, such as c("email", "IM")
(default)
"email"
for emails only
"IM"
for Teams messages only
"unscheduled_calls"
for Unscheduled Calls only
"meetings"
for Meetings only
A character vector specifying start hours, e.g. "0900"
A character vector specifying finish hours, e.g. "1700"
A numeric value specifying the minimum number of signals to be greater than in order to qualify as active. Defaults to 0.
Character vector to specify what to return. Valid options include:
"plot"
: returns a heatmap plot of signal distribution by hour
and archetypes (default)
"data"
: returns the raw data with the classified
archetypes
"table"
: returns a summary table of the archetypes
"plot-area"
: returns an area plot of the percentages of archetypes
shown over time
A different output is returned depending on the value passed to the return
argument:
"plot"
: returns a heatmap plot of signal distribution by hour
and archetypes (default). A 'ggplot' object.
"data"
: returns a data frame of the raw data with the classified
archetypes
"table"
: returns a data frame of summary table of the archetypes
"plot-area"
: returns an area plot of the percentages of archetypes
shown over time. A 'ggplot' object.
Other Working Patterns:
flex_index()
,
identify_shifts_wp()
,
identify_shifts()
,
plot_flex_index()
,
workpatterns_area()
,
workpatterns_classify_pav()
,
workpatterns_classify()
,
workpatterns_hclust()
,
workpatterns_rank()
,
workpatterns_report()