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Visualize Career Cluster Profiles

Source: R/career_clustering.R
plot_cluster_profiles.Rd

Creates comprehensive visualizations of career trajectory clusters showing key metrics, cluster sizes, and comparative profiles across segments.

Usage

plot_cluster_profiles(
  cluster_result,
  metrics = c("employment_rate", "career_advancement_index",
    "employment_stability_index"),
  plot_type = "radar",
  language = "en",
  color_palette = "Set2",
  title = NULL,
  theme_base = theme_minimal()
)

Arguments

cluster_result

List. Output from cluster_career_trajectories() function

metrics

Character vector. Metrics to visualize. Options: "employment_rate", "career_advancement_index", "contract_quality_score", "employment_stability_index", "career_complexity_index", "avg_employment_spell", "job_turnover_rate", "all". Default: c("employment_rate", "career_advancement_index", "employment_stability_index")

plot_type

Character. Type of plot: "radar" (spider/radar chart), "bar" (grouped bars), "heatmap" (cluster × metric heatmap), "all" (multiple plots). Default: "radar"

language

Character. Label language: "en" (English), "it" (Italian), "both". Default: "en"

color_palette

Character. Color palette name or vector of colors. Default: "Set2"

title

Character. Plot title. Default: auto-generated

theme_base

ggplot2 theme. Base theme for plots. Default: theme_minimal()

Value

A ggplot2 object or list of ggplot2 objects (if plot_type = "all")

Details

Plot Types:

  • radar: Spider/radar chart showing cluster profiles across metrics (best for 3-6 metrics)

  • bar: Grouped bar chart comparing clusters on each metric

  • heatmap: Heatmap showing cluster × metric matrix with color intensity

  • all: Returns a list with all three plot types

Metric Normalization: All metrics are normalized to 0-1 scale for fair comparison across different units.

See also

cluster_career_trajectories for creating career clusters

Examples

if (FALSE) { # \dontrun{
# Cluster careers
career_metrics <- calculate_comprehensive_career_metrics(employment_data)
clusters <- cluster_career_trajectories(career_metrics)

# Radar chart of cluster profiles
plot_cluster_profiles(
  clusters,
  metrics = c("employment_rate", "career_advancement_index",
              "employment_stability_index", "contract_quality_score"),
  plot_type = "radar",
  language = "en"
)

# Bar chart with Italian labels
plot_cluster_profiles(
  clusters,
  metrics = "all",
  plot_type = "bar",
  language = "it"
)

# Heatmap visualization
plot_cluster_profiles(
  clusters,
  plot_type = "heatmap",
  color_palette = "RdYlGn"
)

# Generate all plot types
all_plots <- plot_cluster_profiles(clusters, plot_type = "all")
print(all_plots$radar)
print(all_plots$bar)
print(all_plots$heatmap)
} # }