workpatterns_rank {wpa}

Create a rank table of working patterns

Description

Takes in an Hourly Collaboration query and returns a count table of working patterns, ranked from the most common to the least.

Usage

workpatterns_rank(
  data,
  signals = c("email", "IM"),
  start_hour = "0900",
  end_hour = "1700",
  top = 10,
  mode = "binary",
  return = "plot"
)

Arguments

data

A data frame containing hourly collaboration data.

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"

top

numeric value specifying how many top working patterns to display in plot, e.g. "10"

mode

string specifying aggregation method for plot. Valid options include:

• "binary": convert hourly activity into binary blocks. In the plot, each block would display as solid.

• "prop": calculate proportion of signals in each hour over total signals across 24 hours, then average across all work weeks. In the plot, each block would display as a heatmap.

return

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

• "plot"

• "table"

See Value for more information.

Value

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

• "plot": ggplot object. A plot with the y-axis showing the top ten working patterns and the x-axis representing each hour of the day.

• "table": data frame. A summary table for the top working patterns.

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(), create_bar_asis(), create_boxplot(), create_bubble(), create_dist(), create_fizz(), create_inc(), create_line(), create_line_asis(), 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(), meetingtype_dist_ca(), meetingtype_dist_mt(), 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_area()

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

Examples

# Plot by default
workpatterns_rank(
  data = em_data,
  signals = c(
    "email",
    "IM",
    "unscheduled_calls",
    "meetings"
  )
  )

# Plot with prop / heatmap mode
workpatterns_rank(
  data = em_data,
  mode = "prop"
)