workpatterns_area: Create an area plot of emails and IMs by hour of the day
In wpa: Tools for Analysing and Visualising Viva Insights Data

workpatterns_area

R Documentation

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"
)

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"

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.

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()

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

workpatterns_area(em_data, return = "plot", values = "abs")


# Return summary table

workpatterns_area(em_data, return = "table")

wpa documentation built on Aug. 21, 2023, 5:11 p.m.