| Title: | Shiny Application for Topic Modeling of Scientific Literature |
| Version: | 1.1.0 |
| Description: | A 'shiny' application that guides users through an exploratory review of scientific literature using Latent Dirichlet Allocation (LDA) topic modeling. It imports bibliometric records exported from databases such as 'Scopus' and 'Web of Science', performs text preprocessing, helps select the number of topics, fits the final model, and visualizes the results and the evolution of topics over time. The methodology is described in De la Hoz-M et al. (2021) <doi:10.3390/math9141671>. |
| License: | GPL-3 |
| URL: | https://github.com/jdelahoz-m/LDAShiny |
| BugReports: | https://github.com/jdelahoz-m/LDAShiny/issues |
| Depends: | R (≥ 4.1.0) |
| Imports: | beepr, broom, colourpicker, config, dplyr, DT, ggplot2, ggrepel, golem, igraph, jsonlite, LDAvis, Matrix, openxlsx, quanteda, RColorBrewer, readxl, scales, shiny, shinybusy, shinydashboard, shinyjs, shinyWidgets, slam, SnowballC, stopwords, textmineR, tibble, tidyr, tm, utils, wordcloud |
| Suggests: | knitr, plotly, purrr, readr, rmarkdown, shinyBS, testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| Encoding: | UTF-8 |
| Language: | en-US |
| RoxygenNote: | 7.3.3 |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2026-10-29 22:39:51 UTC; Javier_de_la_Hoz |
| Author: | Javier De La Hoz-M
 [aut, cre],
Maria Camila Samper Meza
[aut],
Alexander Armando Bustamante MartÃnez
[aut] |
| Maintainer: | Javier De La Hoz-M <jdelahoz@unimagdalena.edu.co> |
| Repository: | CRAN |
| Date/Publication: | 2026-10-30 09:51:19 UTC |
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 containing: * 'data' - cleaned combined dataset
Import Bibliometrics UI
Description
Shiny module UI for importing and validating bibliometric datasets (Scopus + Web of Science) in LDAShiny.
Usage
mod_import_bibliometrics_ui(id)
Arguments
id |
Shiny module ID. |
Value
A Shiny UI element.
LDA Inference Module
Description
UI and server logic for LDA model selection. Runs multiple LDA models for a range of k and computes coherence to determine the optimal number of topics.
Usage
themes_gg
mod_inference_ui(id)
mod_inference_server(id, dtm_reactive)
Arguments
id |
Shiny module id. |
dtm_reactive |
Reactive expression returning a dgCMatrix DTM. |
Format
An object of class list of length 9.
Value
A list with: * model_list : reactive list of fitted LDA models for each k * coherence_mat : reactive data.frame with columns k and coherence * k_optimal : reactive integer, best k (max coherence)
Final LDA Model Training Module
Description
Trains the final LDA model using the processed DTM. The number of topics (K) is automatically initialized with the optimal K found in the inference module, but the user can manually modify it.
Usage
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 expression returning the DTM (dgCMatrix) |
k_optimal_reactive |
Reactive expression returning the optimal K (integer) |
bd_reactive |
Reactive expression with the original metadata (Scopus/WoS) |
Value
A list of reactive values containing the LDA model outputs.
Text Preprocessing Module for LDAShiny
Description
Provides UI and server functions to clean, tokenize, stem, remove stopwords, build a document-term matrix (DTM), filter sparse terms, and export intermediate objects for topic modeling.
Usage
mod_preprocess_ui(id)
mod_preprocess_server(id, data_reactive)
Arguments
id |
module id |
data_reactive |
reactive containing the input data frame |
Stability & Sensitivity Analysis Module
Description
Evaluates how robust the chosen K is by: 1. Training LDA with multiple random seeds for candidate K values and measuring coherence variability (stability). 2. Computing Adjusted Rand Index (ARI) between seed-pairs to quantify assignment reproducibility. 3. Testing whether the winning K changes under alternative weight schemes (sensitivity of the composite score).
Usage
mod_sensitivity_ui(id)
mod_sensitivity_server(
id,
dtm_reactive,
coherence_mat_reactive,
k_optimal_reactive
)
Arguments
id |
Shiny module id |
dtm_reactive |
Reactive expression returning the DTM (dgCMatrix) |
coherence_mat_reactive |
Reactive returning data.frame(k, coherence) |
k_optimal_reactive |
Reactive returning the best K (integer) |
Value
A list of reactive values containing stability and sensitivity results.
Topic Trend Analysis Module
Description
Performs linear regression analysis on topic intensity (Gamma mean) over years to classify topics as increasing (HOT), decreasing (COLD), or stable (EQUAL) trends.
Usage
mod_trend_ui(id)
mod_trend_server(id, df_year_topic_reactive, lda_model_reactive)
Arguments
id |
Shiny module id |
df_year_topic_reactive |
Reactive expression returning the data frame with Topic, year, and Intensity (mean Gamma) from mod_lda_train. |
lda_model_reactive |
Reactive expression returning the final LDA model (passed for completeness, but not used for labels). |
Run the LDAShiny Application
Description
Launches the LDAShiny Shiny application using the golem framework. The function temporarily raises the maximum upload size for the duration of the session and restores the previous value on exit.
Usage
run_LDAShiny(max_upload_mb = 2500, ...)
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()
}