Generates a bar plot showing the prevalence of each topic across all documents.
Usage
plot_topic_probability(
gamma_data,
top_n = 10,
topic_labels = NULL,
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).
- topic_labels
Optional topic labels (default: NULL).
- 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.
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_semantic_drift(),
calculate_topic_probability(),
calculate_topic_stability(),
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()