Radar Chart for multiple metrics

Creates a multi-group radar (spider) chart across a set of metrics.

Core pipeline:

1. Person-level aggregation within group

2. Group-level aggregation

3. Privacy filtering via mingroup

4. Optional indexing modes: "total", "none", "ref_group", "minmax"

Optional auto-segmentation:

• If hrvar is not supplied, the function will call identify_usage_segments() and infer the resulting segment column.

Usage

create_radar(
  data,
  metrics,
  hrvar = "Organization",
  mingroup = 5,
  agg = "mean",
  index_mode = "total",
  index_ref_group = NULL,
  na.rm = FALSE,
  return = "plot"
)

Arguments

data

A Standard Person Query dataset in the form of a data frame.

metrics

Character vector of metric column names.

hrvar

Character string specifying the grouping column. Defaults to "Organization". If NULL, usage segments will be derived via identify_usage_segments().

mingroup

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

agg

String specifying aggregation method. Either "mean" (default) or "median".

index_mode

String specifying indexing mode. One of:

• "total" (default): Total = 100 for each metric

• "none": no indexing (raw group values)

• "ref_group": reference group = 100 (requires index_ref_group)

• "minmax": scale to [0, 100] within observed group ranges per metric

index_ref_group

Character string specifying reference group name when index_mode = "ref_group".

na.rm

Logical value indicating whether NA rows in required columns are removed prior to aggregation. Defaults to FALSE.

return

String specifying what to return. One of:

• "plot" (default)

• "table"

Value

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

• "plot": ggplot object (radar chart)

• "table": data frame (group-level indexed table)

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_rogers(), create_sankey(), create_scatter(), create_stacked(), create_survival(), create_tracking(), create_trend(), email_dist(), email_fizz(), email_line(), email_rank(), email_summary(), email_trend(), external_dist(), external_fizz(), external_line(), external_rank(), external_sum(), hr_trend(), hrvar_count(), hrvar_trend(), keymetrics_scan(), meeting_dist(), meeting_fizz(), meeting_line(), meeting_rank(), meeting_summary(), meeting_trend(), one2one_dist(), one2one_fizz(), one2one_freq(), one2one_line(), one2one_rank(), one2one_sum(), one2one_trend()

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_stacked(), create_survival(), create_tracking(), create_trend()

Examples

create_radar(
  data = pq_data,
  metrics = c("Collaboration_hours", "Email_hours", "Meeting_hours"),
  hrvar = "Organization",
  mingroup = 1
)


# Return the indexed table instead of a plot
create_radar(
  data = pq_data,
  metrics = c("Collaboration_hours", "Email_hours", "Meeting_hours"),
  hrvar = "LevelDesignation",
  mingroup = 1,
  return = "table"
)
#> # A tibble: 4 × 5
#>   LevelDesignation Collaboration_hours Email_hours Meeting_hours     n
#>   <chr>                          <dbl>       <dbl>         <dbl> <int>
#> 1 Executive                      101.        101.           98.6    37
#> 2 Junior IC                       99.7        99.5          97.5   136
#> 3 Senior IC                      100.        101.          103.     87
#> 4 Senior Manager                  99.1        99.4         102.     40