wpa (version 1.9.0)

workpatterns_area: Create an area plot of emails and IMs by hour of the day

Description

Uses the Hourly Collaboration query to produce an area plot of Emails sent and IMs sent attended by hour of the day.

Usage

workpatterns_area(
  data,
  hrvar = "Organization",
  mingroup = 5,
  signals = c("email", "IM"),
  return = "plot",
  values = "percent",
  start_hour = "0900",
  end_hour = "1700"
)

Value

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

argument:

• "plot": ggplot object. An overlapping area plot (default).

• "table": data frame. A summary table.

Arguments

data

A data frame containing data from the Hourly Collaboration query.

hrvar

HR Variable by which to split metrics. Accepts a character vector, defaults to "Organization" but accepts any character vector, e.g. "LevelDesignation"

mingroup

Numeric value setting the privacy threshold / minimum group size, defaults to 5.

signals

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

return

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

• "plot"

• "table"

See Value for more information.

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

start_hour

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

end_hour

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

See Also

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_rank()

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

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

Examples

# Create a sample small dataset
orgs <- c("Customer Service", "Financial Planning", "Biz Dev")
em_data <- em_data[em_data$Organization %in% orgs, ]

# Return visualization of percentage distribution
workpatterns_area(em_data, return = "plot", values = "percent")

# Return visualization of absolute values
# \donttest{
workpatterns_area(em_data, return = "plot", values = "abs")
# }

# Return summary table
# \donttest{
workpatterns_area(em_data, return = "table")
# }