Creates a sum total calculation using selected metrics, where the typical use case is to create different definitions of collaboration hours. Returns a stacked bar plot by default. Additional options available to return a summary table.
create_stacked(
  data,
  hrvar = "Organization",
  metrics = c("Meeting_hours", "Email_hours"),
  mingroup = 5,
  return = "plot",
  stack_colours = c("#1d627e", "#34b1e2", "#b4d5dd", "#adc0cb"),
  percent = FALSE,
  plot_title = "Collaboration Hours",
  plot_subtitle = paste("Average by", tolower(camel_clean(hrvar))),
  legend_lab = NULL,
  rank = "descending",
  xlim = NULL,
  text_just = 0.5,
  text_colour = "#FFFFFF"
)A Standard Person Query dataset in the form of a data frame.
String containing the name of the HR Variable by which to split
metrics. Defaults to "Organization". To run the analysis on the total
instead of splitting by an HR attribute, supply NULL (without quotes).
A character vector to specify variables to be used in calculating the "Total" value, e.g. c("Meeting_hours", "Email_hours"). The order of the variable names supplied determine the order in which they appear on the stacked plot.
Numeric value setting the privacy threshold / minimum group size. Defaults to 5.
Character vector specifying what to return, defaults to "plot". Valid inputs are "plot" and "table".
A character vector to specify the colour codes for the stacked bar charts.
Logical value to determine whether to show labels as
percentage signs. Defaults to FALSE.
String. Option to override plot title.
String. Option to override plot subtitle.
String. Option to override legend title/label. Defaults to
NULL, where the metric name will be populated instead.
String specifying how to rank the bars. Valid inputs are:
"descending" - ranked highest to lowest from top to bottom (default).
"ascending" - ranked lowest to highest from top to bottom.
NULL - uses the original levels of the HR attribute.
An option to set max value in x axis.
 A numeric value
controlling for the horizontal position of the text labels. Defaults to
0.5.
 String to specify
colour to use for the text labels. Defaults to 
"#FFFFFF".
Returns a 'ggplot' object by default, where 'plot' is passed in return.
When 'table' is passed, a summary table is returned as a data frame.
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_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(),
workpatterns_rank()
Other Flexible:
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_density(),
create_dist(),
create_fizz(),
create_hist(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_tracking(),
create_trend(),
period_change()
sq_data %>%
  create_stacked(hrvar = "LevelDesignation",
                 metrics = c("Meeting_hours", "Email_hours"),
                 return = "plot")
 sq_data %>%
  create_stacked(hrvar = "FunctionType",
                 metrics = c("Meeting_hours",
                             "Email_hours",
                             "Call_hours",
                             "Instant_Message_hours"),
                 return = "plot",
                 rank = "ascending")
sq_data %>%
  create_stacked(hrvar = "FunctionType",
                 metrics = c("Meeting_hours",
                             "Email_hours",
                             "Call_hours",
                             "Instant_Message_hours"),
                 return = "plot",
                 rank = "ascending")
 sq_data %>%
  create_stacked(hrvar = "FunctionType",
                 metrics = c("Meeting_hours",
                             "Email_hours",
                             "Call_hours",
                             "Instant_Message_hours"),
                 return = "table")
#> # A tibble: 8 × 7
#>   group       Meeting_hours Email_hours Call_hours Instant_Message_hours Total
#>   <chr>               <dbl>       <dbl>      <dbl>                 <dbl> <dbl>
#> 1 Engineering          9.41       10.5        4.26                 0.654  24.9
#> 2 Finance              7.83        7.88       3.87                 0.618  20.2
#> 3 G_and_A              7.72        7.75       3.74                 0.609  19.8
#> 4 IT                   7.71        9.37       3.22                 0.651  20.9
#> 5 Marketing           13.5        13.1        7.15                 0.582  34.3
#> 6 Operations          10.6         9.72       5.21                 0.680  26.2
#> 7 R_and_D              7.71        8.06       3.92                 0.634  20.3
#> 8 Sales               16.4        14.7        8.52                 0.529  40.1
#> # ℹ 1 more variable: Employee_Count <int>
sq_data %>%
  create_stacked(hrvar = "FunctionType",
                 metrics = c("Meeting_hours",
                             "Email_hours",
                             "Call_hours",
                             "Instant_Message_hours"),
                 return = "table")
#> # A tibble: 8 × 7
#>   group       Meeting_hours Email_hours Call_hours Instant_Message_hours Total
#>   <chr>               <dbl>       <dbl>      <dbl>                 <dbl> <dbl>
#> 1 Engineering          9.41       10.5        4.26                 0.654  24.9
#> 2 Finance              7.83        7.88       3.87                 0.618  20.2
#> 3 G_and_A              7.72        7.75       3.74                 0.609  19.8
#> 4 IT                   7.71        9.37       3.22                 0.651  20.9
#> 5 Marketing           13.5        13.1        7.15                 0.582  34.3
#> 6 Operations          10.6         9.72       5.21                 0.680  26.2
#> 7 R_and_D              7.71        8.06       3.92                 0.634  20.3
#> 8 Sales               16.4        14.7        8.52                 0.529  40.1
#> # ℹ 1 more variable: Employee_Count <int>