View source: R/workpatterns_classify_bw.R
workpatterns_classify_bw | R Documentation |
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",
mingroup = 5,
exp_hours = NULL,
active_threshold = 0,
return = "plot"
)
data |
A data frame containing email by hours data. |
hrvar |
A string specifying the HR attribute to cut the data by. Defaults to NULL. This only affects the function when "table" is returned. |
signals |
Character vector to specify which collaboration metrics to use:
|
start_hour |
A character vector specifying starting hours, e.g.
|
end_hour |
A character vector specifying starting hours, e.g. |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
exp_hours |
Numeric value representing the number of hours the population
is expected to be active for throughout the workday. By default, this uses
the difference between |
active_threshold |
A numeric value specifying the minimum number of signals to be greater than in order to qualify as active. Defaults to 0. |
return |
Character vector to specify what to return. Valid options include:
|
A different output is returned depending on the value passed to the return
argument:
"plot"
: returns a summary grid plot of the classified 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.
"plot-hrvar"
: returns a bar plot showing the count of archetypes,
faceted by the supplied HR attribute. A 'ggplot' object.
Ainize Cidoncha ainize.cidoncha@microsoft.com
Other Working Patterns:
flex_index()
,
identify_shifts()
,
identify_shifts_wp()
,
plot_flex_index()
,
workpatterns_area()
,
workpatterns_classify()
,
workpatterns_classify_pav()
,
workpatterns_hclust()
,
workpatterns_rank()
,
workpatterns_report()
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