wpa (version 1.4.3)

workpatterns_classify: Classify working pattern personas using a rule based algorithm

Description

Apply a rule based algorithm to emails or instant messages sent by hour of day. Uses a binary week-based ('bw') method by default, with options to use the the person-average volume-based ('pav') method.

Usage

workpatterns_classify(
  data,
  hrvar = "Organization",
  values = "percent",
  signals = "email",
  start_hour = "0900",
  end_hour = "1700",
  active_threshold = 0,
  method = "bw",
  return = "plot"
)

Arguments

data

A data frame containing data from the Hourly Collaboration query.

hrvar

A string specifying the HR attribute to cut the data by. Defaults to NULL. This only affects the function when "table" is returned, and is only applicable for method = "bw".

values

Only valid if using pav method. Character vector to specify whether to return percentages or absolute values in "data" and "plot". Valid values are "percent" (default) and "abs".

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". Note that this currently only supports hourly increments.

end_hour

A character vector specifying starting hours, e.g. "1700". Note that this currently only supports hourly increments.

active_threshold

A numeric value specifying the minimum number of signals to be greater than in order to qualify as active. Defaults to 0. Only applicable for the binary-week method.

method

String to pass through specifying which method to use for classification. By default, a binary week-based (bw) method is used, with options to use the the person-average volume-based (pav) method.

return

String specifying what to return. This must be one of the following strings:

• "plot"

• "data"

• "table"

• "plot-area"

See Value for more information.

Value

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

Binary Week method

This method classifies each person-week into one of the seven archetypes:

• 0 < 3 hours on

• 1 Standard with breaks workday

• 2 Standard continuous workday

• 3 Standard flexible workday

• 4 Long flexible workday

• 5 Long continuous workday

• 6 Always on (13h+)

This is the recommended method over pav for several reasons:

1. bw ignores volume effects, where activity volume can still bias the results towards the 'standard working hours'.

2. It captures the intuition that each individual can have 'light' and 'heavy' weeks with respect to workload.

Person Average method

This method classifies each person (based on unique PersonId) into one of the six archetypes:

• Absent

• Extended Hours - Morning

• Extended Hours - Evening

• Overnight workers

• Standard Hours

• Always On

Flexibility Index

The Working Patterns archetypes as calculated using the binary-week method shares many similarities with the Flexibility Index (see flex_index()): - Both are computed directly from the Hourly Collaboration Flexible Query. - Both apply the same binary conversion of activity on the signals from the Hourly Collaboration Flexible Query.

Details

This is a wrapper around workpatterns_classify_bw() and workpatterns_classify_pav(), and calls each function depending on what is supplied to the method argument. Both methods implement a rule-based classification of either person-weeks or persons that pull apart different working patterns.

See individual sections below for details on the two different implementations.

See Also

Other Clustering: personas_hclust(), workpatterns_hclust()

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

Examples

# NOT RUN {
# Returns a plot by default
em_data %>% workpatterns_classify(method = "bw")

# Return an area plot
em_data %>% workpatterns_classify(method = "bw", return = "plot-area")

# }
# NOT RUN {
em_data %>% workpatterns_classify(method = "bw", return = "table")

em_data %>% workpatterns_classify(method = "pav")

em_data %>% workpatterns_classify(method = "pav", return = "plot-area")

# }
# NOT RUN {
# }