Plot Per-Document Per-Topic Probabilities

Generates a bar plot showing the prevalence of each topic across all documents.

Usage

plot_topic_probability(
  gamma_data,
  top_n = 10,
  use_topic_labels = FALSE,
  colors = NULL,
  ylab = "Topic Proportion",
  base_font_size = 11
)

Arguments

gamma_data

A data frame with gamma values from calculate_topic_probability().

top_n

The number of topics to display (default: 10).

use_topic_labels

Logical. If TRUE, use the topic_label column from gamma_data for axis labels (falls back to topic number when the column is absent). If FALSE (default), labels are formatted as "Topic N".

colors

Optional color palette for topics (default: NULL).

ylab

Y-axis label (default: "Topic Proportion").

base_font_size

Base font size in points for the plot theme (default: 11). Axis text and strip text will be base_font_size + 2.

Value

A ggplot2 object showing a bar plot of topic prevalence.

See also

Other topic-modeling: analyze_semantic_evolution(), assess_embedding_stability(), assess_hybrid_stability(), auto_tune_embedding_topics(), calculate_assignment_consistency(), calculate_keyword_stability(), calculate_npmi(), calculate_semantic_drift(), calculate_topic_diversity(), calculate_topic_probability(), calculate_topic_stability(), extract_topic_terms_df(), find_optimal_k(), find_topic_matches(), fit_embedding_model(), fit_hybrid_model(), fit_temporal_model(), fit_topic_prevalence_model(), generate_topic_labels(), get_topic_prevalence(), get_topic_terms(), get_topic_texts(), identify_topic_trends(), plot_model_comparison(), plot_quality_metrics(), plot_topic_effects_categorical(), plot_topic_effects_continuous(), plot_word_probability(), run_neural_topics_internal(), validate_semantic_coherence()