workpatterns_classify_pav {wpa}

Classify working pattern personas using a rule based algorithm, using the person-average volume-based ('pav') method.

Description

Apply a rule based algorithm to emails or instant messages sent by hour of day. This uses a person-average volume-based ('pav') method.

Usage

workpatterns_classify_pav(
  data,
  values = "percent",
  signals = c("email", "IM"),
  start_hour = "0900",
  end_hour = "1700",
  return = "plot"
)

Arguments

data

A data frame containing data from the Hourly Collaboration query.

values

Character vector to specify whether to return percentages or absolute values in "data" and "plot". Valid values are:

• "percent": percentage of signals divided by total signals (default)

• "abs": absolute count of signals

signals

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")

start_hour

A character vector specifying starting hours, e.g. "0900"

end_hour

A character vector specifying starting hours, e.g. "1700"

return

Character vector to specify what to return. Valid options include:

• "plot": returns a bar 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 overlapping area plot

Value

A different output is returned depending on the value passed to the return argument:

• "plot": returns a bar 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 a summary table of the archetypes.

• "plot-area": returns an overlapping area plot. A 'ggplot' object.

Author(s)

Ainize Cidoncha [e-mail]

See Also

Other Working Patterns: flex_index(), identify_shifts_wp(), identify_shifts(), plot_flex_index(), workpatterns_area(), workpatterns_classify_bw(), workpatterns_classify(), workpatterns_hclust(), workpatterns_rank(), workpatterns_report()