| Title: | Interactive Topic Modeling and Bibliometric Analysis via Shiny |
| Version: | 1.0.0 |
| Description: | Provides a 'Shiny' graphical interface for the complete workflow of Latent Dirichlet Allocation (LDA) topic modelling on bibliometric data from Scopus and Web of Science. Steps include data import and deduplication, text preprocessing (stopword removal, stemming, n-grams, sparse-term filtering), statistical inference to select the optimal number of topics via coherence, final model training, and topic trend analysis over time using linear regression. All results can be exported as Excel files, RDS objects, and publication-quality plots. |
| License: | GPL-3 |
| URL: | https://github.com/JavierDeLaHoz/LDAShiny |
| Depends: | R (≥ 4.1.0) |
| Imports: | colourpicker, config (≥ 0.3.1), dplyr, DT, ggplot2, golem (≥ 0.4.0), Matrix, openxlsx, quanteda, RColorBrewer, readxl, shiny (≥ 1.7.0), shinybusy, shinydashboard, shinyjs, shinyWidgets, slam, SnowballC, stopwords, textmineR, tibble, tidyr, tm, wordcloud, broom, parallel, stats, utils, grDevices |
| Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0), withr |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.3 |
| Config/testthat/edition: | 3 |
| Language: | en-US |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2026-06-07 17:54:36 UTC; Javier_de_la_Hoz |
| Author: | Javier De La Hoz-M
 [aut, cre] |
| Maintainer: | Javier De La Hoz-M <jdelahoz@unimagdalena.edu.co> |
| Repository: | CRAN |
| Date/Publication: | 2026-06-08 06:50:14 UTC |
Get the named list of ggplot2 themes used across LDAShiny modules
Description
Get the named list of ggplot2 themes used across LDAShiny modules
Usage
lda_themes()
Value
A named list of ggplot2 theme objects.
About Server
Description
About Server
Usage
mod_about_server(id)
Arguments
id |
Shiny module id |
About UI
Description
About UI
Usage
mod_about_ui(id)
Arguments
id |
Shiny module id |
Import Bibliometrics Server
Description
Server logic for importing, validating and combining Scopus/WoS datasets.
Usage
mod_import_bibliometrics_server(id)
Arguments
id |
Shiny module ID. |
Value
A list with one element:
- data
A reactive expression returning the cleaned combined
data.frame.
Import Bibliometrics UI
Description
Shiny module UI for importing and validating bibliometric datasets from Scopus and Web of Science in LDAShiny.
Usage
mod_import_bibliometrics_ui(id)
Arguments
id |
Shiny module ID. |
Value
A Shiny UI element (tagList).
LDA Inference Module Server
Description
LDA Inference Module Server
Usage
mod_inference_server(id, dtm_reactive)
Arguments
id |
Shiny module ID. |
dtm_reactive |
A reactive expression returning a |
Value
A list with:
- model_list
Reactive list of fitted LDA models, one per k.
- coherence_mat
Reactive
data.framewith columnskandcoherence.- k_optimal
Reactive integer: k with highest mean coherence.
LDA Inference Module UI
Description
UI for running multiple LDA models over a range of topic counts (k) and selecting the best k via mean coherence.
Usage
mod_inference_ui(id)
Arguments
id |
Shiny module ID. |
Value
A Shiny UI element (tagList).
Final LDA Model Training Module
Description
Trains the final LDA model using the processed DTM. The number of topics (K) is automatically initialised from the inference module but can be overridden.
Usage
mod_lda_train()
mod_lda_train_ui(id)
mod_lda_train_server(id, dtm_reactive, k_optimal_reactive, bd_reactive)
Arguments
id |
Shiny module ID. |
dtm_reactive |
Reactive returning the DTM ( |
k_optimal_reactive |
Reactive returning the optimal K ( |
bd_reactive |
Reactive returning the original metadata |
Value
A list of reactive values with the LDA model outputs.
Text Preprocessing Module Server
Description
Text Preprocessing Module Server
Usage
mod_preprocess_server(id, data_reactive)
Arguments
id |
Shiny module ID. |
data_reactive |
A reactive expression returning the input |
Value
A list with:
- dtm
Reactive returning the filtered sparse DTM (
dgCMatrix).- tf_mat
Reactive returning the term-frequency data.frame.
Text Preprocessing Module UI
Description
Provides UI controls to clean, tokenize, stem, remove stopwords, build a Document-Term Matrix (DTM), and filter sparse terms.
Usage
mod_preprocess_ui(id)
Arguments
id |
Shiny module ID. |
Value
A Shiny UI element (tagList).
Topic Trend Analysis Module Server
Description
Topic Trend Analysis Module Server
Usage
mod_trend_server(id, df_year_topic_reactive, lda_model_reactive)
Arguments
id |
Shiny module ID. |
df_year_topic_reactive |
Reactive returning a |
lda_model_reactive |
Reactive returning the final LDA model object (used for completeness; labels are derived from data columns only). |
Value
NULL invisibly.
Topic Trend Analysis Module UI
Description
Performs linear regression on topic intensity (mean Gamma) over years and classifies topics as increasing (HOT), decreasing (COLD), or stable (EQUAL).
Usage
mod_trend_ui(id)
Arguments
id |
Shiny module ID. |
Value
A Shiny UI element (tagList).
Parse a Web of Science plain-text export file
Description
Parse a Web of Science plain-text export file
Usage
parse_wos(path)
Arguments
path |
Character. Path to the .txt file. |
Value
A data.frame with columns: doi, title, year, Journal, abstract, database.
Run the LDAShiny Application
Description
Launches the LDAShiny Shiny application using the golem framework. The function temporarily raises the maximum upload size to 500 MB for the duration of the session and restores the previous value on exit.
Usage
run_LDAShiny(max_upload_mb = 500, ...)
Arguments
max_upload_mb |
Maximum file upload size in megabytes (default 500).
Passed to |
... |
Additional named options forwarded to
|
Value
A Shiny app object (invisibly).
Examples
if (interactive()) {
run_LDAShiny()
}
Standardise a raw Scopus CSV data.frame
Description
Standardise a raw Scopus CSV data.frame
Usage
standardize_scopus(df)
Arguments
df |
A data.frame read from a Scopus CSV export. |
Value
A standardised data.frame with columns: doi, title, year, Journal, abstract, database.