BERTopic (R interface)

R wrapper around the excellent Python library BERTopic for transformer-based topic modeling without leaving R.
This package manages a separate Python environment, calls the Python API through reticulate, and gives you R-friendly helpers for fitting, transforming, exporting, visualizing, and saving/loading models.

Includes a tiny SMS spam demo dataset to get you started quickly.

Installation
Quick start
- Fit a model (demo data)
- Use your own data
Core workflow
Visualization
Model I/O
Reproducibility
Troubleshooting
API reference (R)
Citation & data

Installation

1) Install the R package

If you’re installing from source (e.g., GitHub):

# install.packages("remotes")
remotes::install_github("Feng-Ji-Lab/BERTopic")
library(BERTopic)

Our package relies on reticulate. You do not need a system-wide Python; we can set up an isolated one.

2) Install Python deps (auto-selects Conda or virtualenv)

library(reticulate)
library(BERTopic)
install_py_deps(envname = "r-bertopic", python_version = "3.10")

3) Bind the session (auto-selects the right route)

use_bertopic("r-bertopic")

Self check & session info

bertopic_available()          # TRUE if Python + bertopic importable
bertopic_session_info()       # versions & path
bertopic_self_check()  # runs a tiny round-trip fit/transform/save/load

Quick start

Fit a model (demo data)

We ship a small, cleaned subset of the UCI SMS Spam dataset:

library(BERTopic)

data(sms_spam)
head(sms_spam)

m <- bertopic_fit(sms_spam$text, calculate_probabilities = TRUE)
print(m)
summary(m)

topics_tbl <- bertopic_topics(m)
topics_tbl

Result:

> topics_tbl
# A tibble: 48 × 5
   Topic Count Name                          Representation Representative_Docs
   <dbl> <dbl> <chr>                         <list>         <list>
 1    -1   784 -1_to_you_the_in              <chr [10]>     <chr [3]>
 2     0   121 0_love_my_life_you            <chr [10]>     <chr [3]>
 3     1   101 1_chat_text_stop_sexy         <chr [10]>     <chr [3]>
 4     2    84 2_tone_ringtone_tones_week    <chr [10]>     <chr [3]>
 5     3    64 3_bus_pub_car_at              <chr [10]>     <chr [3]>
 6     4    62 4_missing_message_please_call <chr [10]>     <chr [3]>
 7     5    52 5_class_exam_school_lesson    <chr [10]>     <chr [3]>
 8     6    45 6_he_gal_ask_dude             <chr [10]>     <chr [3]>
 9     7    45 7_won_claim_draw_prize        <chr [10]>     <chr [3]>
10     8    43 8_later_sorry_call_ill        <chr [10]>     <chr [3]>

Use your own data

df <- read.csv("data/sms_spam.csv", stringsAsFactors = FALSE, encoding = "UTF-8")
docs <- as.character(df$text)

m2 <- bertopic_fit(docs, calculate_probabilities = TRUE)
pred <- predict(m2, c("I like BERTopic"), type = "both")
pred$topics
pred$probs

With precomputed embeddings (rows = docs), pass a numeric matrix:

emb <- matrix(rnorm(length(docs) * 384), length(docs), 384)
m_emb <- bertopic_fit(docs, embeddings = emb)

Core workflow

Get topic info & top terms

info <- bertopic_topics(m)
head(info)

# Top terms per topic (weights)
coef_df <- coef(m, top_n = 8)
subset(coef_df, topic %in% head(info$Topic, 3))

Transform new documents

new_docs <- c("please confirm the meeting", "win a free trip now!!!")
tr <- bertopic_transform(m, new_docs)
tr$topics
tr$probs

Export document–topic matrix

M <- bertopic_as_document_topic_matrix(m, sparse = TRUE, prefix = TRUE)
M[1:5, 1:10]

Requires calculate_probabilities = TRUE during fitting; otherwise returns NULL with a warning.

Label, update, reduce topics

# Update topic representations (after changing docs/preproc)
m <- bertopic_update_topics(m, sms_spam$text)

# Reduce/merge topics (target number or "auto")
m <- bertopic_reduce_topics(m, nr_topics = "auto", docs = sms_spam$text)

# Set custom labels (named character vector or data.frame)
labs <- c(`0` = "General ham", `1` = "Promotions/spam")
m <- bertopic_set_topic_labels(m, labs)

Topics over time

set.seed(1)
ts <- as.Date("2024-01-01") + seq_len(nrow(sms_spam)) %% 60
tot <- bertopic_topics_over_time(m, docs = sms_spam$text, timestamps = ts, nr_bins = 10)
head(tot)

Visualization

These return an htmltools::HTML object by default, or write a self-contained HTML file if you pass file =.

# Map of topics
bertopic_visualize_topics(m, file = "topics.html")

# Barchart (specific topic or default)
bertopic_visualize_barchart(m, topic_id = 0, file = "barchart_t0.html")

# Hierarchy / Heatmap / Term rank
bertopic_visualize_hierarchy(m, file = "hierarchy.html")
bertopic_visualize_heatmap(m,    file = "heatmap.html")
bertopic_visualize_term_rank(m,  file = "term_rank.html")

# Embedded documents (optionally pass a subset of docs)
bertopic_visualize_documents(m, docs = sms_spam$text[1:300], file = "docs.html")

# Topics over time
tot_py <- bertopic_topics_over_time(m, sms_spam$text, ts)
bertopic_visualize_topics_over_time(m, tot_py, top_n = 10, file = "topics_over_time.html")

Model I/O

# Save - choose one of: "pickle" (default), "safetensors", or "pt"
bertopic_save(
  m,
  path = "models/bert_model.pkl",
  serialization = "pickle",
  save_embedding_model = FALSE,
  overwrite = TRUE
)

# Load
m2 <- bertopic_load("models/bert_model.pkl")

# If you didn't save the embedding model, set one to enable `transform()`
bertopic_set_embedding_model(m2, "all-MiniLM-L6-v2")

Reproducibility

set_bertopic_seed(2025)   # seeds R, NumPy, and Python's random; also sets PYTHONHASHSEED

Troubleshooting

Python is not available
Run the installer first, then bind the session:
```
install_py_deps(); use_bertopic_virtualenv()
```
Or, if you used conda:
```
use_bertopic_condaenv("r-bertopic")
```
Python module 'bertopic' not found
You’re likely not pointing to the right env in this session. Call use_bertopic_virtualenv() or use_bertopic_condaenv() again (these are per-session).
Offline / first-run model download fails
sentence-transformers may download model weights on first use. Connect to the internet once, or pre-download by running:
```
m <- bertopic_fit(c("a","b","c"))
```
Apple Silicon (M-series)
Prefer the conda installer path; it auto-sets CONDA_SUBDIR=osx-arm64.
Headless/CI environments
Visualizations return HTML strings; writing to file via file = is safest in headless/CI contexts.

Check everything

bertopic_available()
bertopic_session_info()
bertopic_self_check()

API reference (R)

Environment / diagnostics - use_bertopic_virtualenv(env) - use_bertopic_condaenv(envname, required = TRUE) - bertopic_available() - bertopic_session_info() - bertopic_self_check(verbose = TRUE) - set_bertopic_seed(seed)

Fit / transform / export - bertopic_fit(text, embeddings = NULL, ...) - predict.bertopic_r(object, newdata, type = c("both","class","prob"), embeddings = NULL, ...) - bertopic_transform(model, new_text, embeddings = NULL) - bertopic_as_document_topic_matrix(model, sparse = TRUE, prefix = TRUE)

Info / terms / search - bertopic_topics(model) - coef.bertopic_r(object, top_n = 10) - bertopic_topic_terms(model, topic_id, top_n = 10) - bertopic_get_document_info(model, docs) - bertopic_find_topics(model, query_text, top_n = 5) - bertopic_get_representative_docs(model, topic_id, top_n = 5)

Topic operations - bertopic_update_topics(model, text) - bertopic_reduce_topics(model, nr_topics = "auto", representation_model = NULL, docs = NULL) - bertopic_set_topic_labels(model, labels)

Time & viz - bertopic_topics_over_time(model, docs, timestamps, nr_bins = NULL, datetime_format = NULL) - bertopic_visualize_topics(model, file = NULL) - bertopic_visualize_barchart(model, topic_id = NULL, file = NULL) - bertopic_visualize_hierarchy(model, file = NULL) - bertopic_visualize_heatmap(model, file = NULL) - bertopic_visualize_term_rank(model, file = NULL) - bertopic_visualize_documents(model, docs = NULL, file = NULL) - bertopic_visualize_topics_over_time(model, topics_over_time, top_n = 10, file = NULL)

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