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

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

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")
#> # A tibble: 72 × 5
#>    group   Hours Emails_sent IMs_sent     n
#>    <chr>   <dbl>       <dbl>    <dbl> <int>
#>  1 Biz Dev     0      0      0.000223    27
#>  2 Biz Dev     1      0      0.000227    27
#>  3 Biz Dev     2      0      0.000212    27
#>  4 Biz Dev     3      0      0           27
#>  5 Biz Dev     4      0      0           27
#>  6 Biz Dev     5      0      0.000394    27
#>  7 Biz Dev     6      0      0           27
#>  8 Biz Dev     7      0.0238 0.0226      27
#>  9 Biz Dev     8      0.105  0.102       27
#> 10 Biz Dev     9      0.0953 0.0994      27
#> # … with 62 more rows

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

Arguments

Value

See also

Examples