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stminsights

A Shiny Application for Inspecting Structural Topic Models

https://github.com/cschwem2er/stminsights

Science Score: 13.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ○
    .zenodo.json file
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    DOI references
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    Academic publication links
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    Committers with academic emails
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  • ○
    Scientific vocabulary similarity
    Low similarity (17.5%) to scientific vocabulary

Keywords

natural-language-processing r shiny topic-modeling
Last synced: 6 months ago · JSON representation

Repository

A Shiny Application for Inspecting Structural Topic Models

Basic Info
Statistics
  • Stars: 120
  • Watchers: 8
  • Forks: 16
  • Open Issues: 2
  • Releases: 0
Topics
natural-language-processing r shiny topic-modeling
Created over 9 years ago · Last pushed over 1 year ago
Metadata Files
Readme Changelog License

README.Rmd 

---
output: github_document
---



```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)
```

# stminsights


[![AppVeyor Build Status](https://ci.appveyor.com/api/projects/status/github/cschwem2er/stminsights?branch=master&svg=true)](https://ci.appveyor.com/project/cschwem2er/stminsights)
[![CRAN status](https://www.r-pkg.org/badges/version/stminsights)](https://cran.r-project.org/package=stminsights)
[![CRAN downloads](https://cranlogs.r-pkg.org/badges/grand-total/stminsights)](https://cran.r-project.org/package=stminsights)


```{r, include = FALSE}
#[![CRAN status](https://www.r-pkg.org/badges/version/stminsights)](https://cran.r-project.org/package=stminsights)
#[![CRAN downloads](https://cranlogs.r-pkg.org/badges/grand-total/stminsights)](https://cran.rstudio.com/web/packages/stminsights/index.html)
```





## A Shiny Application for Structural Topic Models

This app enables interactive validation, interpretation and visualization of [Structural Topic Models](https://www.structuraltopicmodel.com/) (STM).  Stminsights is focused on making your life easier after fitting your STM models. In case you are not familiar with the STM [package](https://CRAN.R-project.org/package=stm), the corresponding vignette is an excellent starting point.

## How to Install

You can download and install the latest development version of stminsights by running ``devtools::install_github('cschwem2er/stminsights')``.    

For Windows users installing from github requires proper setup of [Rtools](https://cran.r-project.org/bin/windows/Rtools/).

stminsights can also be installed from CRAN by running ``install.packages('stminsights')``.

## How to Use

After loading stminsights you can launch the shiny app in your browser:


```{r, eval = FALSE}
library(stminsights)
run_stminsights()
```

You can then upload a `.RData` file which should include:

  - one or several `stm` objects.
  - one or several `estimateEffect` objects.
  - an object `out` which was used to fit your stm models.

As an example, the following code fits two models and estimates effects for the Political Blog Corpus. Afterwards, all objects required for stminsights are stored in `stm_poliblog5k.RData`. 


```{r, eval = FALSE}
library(stm)

out <- list(documents = poliblog5k.docs,
            vocab = poliblog5k.voc,
            meta = poliblog5k.meta)

poli <- stm(documents = out$documents, 
            vocab = out$vocab,
            data = out$meta, 
            prevalence = ~ rating * s(day),
            K = 20)
prep_poli <- estimateEffect(1:20 ~ rating * s(day), poli,
                            meta = out$meta)

poli_content <-  stm(documents = out$documents, 
                     vocab = out$vocab,
                     data = out$meta, 
                     prevalence = ~ rating + s(day),
                     content = ~ rating,
                     K = 15)  
prep_poli_content <- estimateEffect(1:15 ~ rating + s(day), poli_content,
                                    meta = out$meta)

save.image('stm_poliblog5k.RData')
```


After launching stminsights and uploading the file, all objects are automatically imported and you can select which models and effect estimates to analyze.

In addition to the shiny app, several helper functions are available, e.g. ``get_effects()`` for storing effect estimates in a tidy dataframe.

## How to Deploy on Shiny Server

To deploy stminsights to your own shiny server, place the file `app.R`, which is located at `inst/app` of this package, to a folder in your server directory and you should be good to go.

## Citation

Please cite stminsights if you use it for your publications:

```
  Carsten Schwemmer (2024). stminsights: A Shiny Application for Inspecting
  Structural Topic Models. R package version 0.4.3.
  https://github.com/cschwem2er/stminsights
```

A BibTeX entry for LaTeX users is:

```
  @Manual{,
    title = {stminsights: A Shiny Application for Inspecting Structural Topic Models},
    author = {Carsten Schwemmer},
    year = {2024},
    note = {R package version 0.4.3},
    url = {https://github.com/cschwem2er/stminsights},
  }
```

Owner

  • Name: Carsten Schwemmer
  • Login: cschwem2er
  • Kind: user
  • Company: LMU - Department of Sociology

Computational Social Scientist

GitHub Events

Total
  • Watch event: 6
  • Issue comment event: 1
Last Year
  • Watch event: 6
  • Issue comment event: 1

Committers

Last synced: 9 months ago

All Time
  • Total Commits: 125
  • Total Committers: 3
  • Avg Commits per committer: 41.667
  • Development Distribution Score (DDS): 0.416
Past Year
  • Commits: 2
  • Committers: 1
  • Avg Commits per committer: 2.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Carsten Schwemmer c****r@m****e 73
Carsten Schwemmer c****r@g****m 35
jonneguyt j****t@g****m 17
Committer Domains (Top 20 + Academic)
methodds.de: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 20
  • Total pull requests: 7
  • Average time to close issues: 10 months
  • Average time to close pull requests: 5 days
  • Total issue authors: 13
  • Total pull request authors: 2
  • Average comments per issue: 2.9
  • Average comments per pull request: 2.29
  • Merged pull requests: 4
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • cschwem2er (5)
  • jonneguyt (3)
  • ZackCzq (2)
  • luerhard (1)
  • jooyoungseo (1)
  • anamaranda (1)
  • selimyaman (1)
  • shirley-jin (1)
  • lambdamoses (1)
  • srosh2000 (1)
  • MariaYR (1)
  • zhivkoplias (1)
  • mhines-usgs (1)
Pull Request Authors
  • jonneguyt (6)
  • olivroy (2)
Top Labels
Issue Labels
enhancement (5) help wanted (4)
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • cran 332 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 2
  • Total versions: 7
  • Total maintainers: 1
cran.r-project.org: stminsights

A 'Shiny' Application for Inspecting Structural Topic Models

  • Versions: 7
  • Dependent Packages: 0
  • Dependent Repositories: 2
  • Downloads: 332 Last month
Rankings
Stargazers count: 3.7%
Forks count: 4.8%
Average: 16.5%
Dependent repos count: 19.2%
Downloads: 25.9%
Dependent packages count: 28.7%
Maintainers (1)
c.schwem2er@gmail.com
Last synced: 6 months ago

Dependencies

DESCRIPTION cran
  • dplyr >= 1.0.0 imports
  • ggplot2 >= 3.3.0 imports
  • ggraph >= 2.0.0 imports
  • ggrepel >= 0.8.0 imports
  • huge >= 1.3.0 imports
  • igraph >= 1.2.0 imports
  • purrr >= 0.3.0 imports
  • readr >= 1.3.0 imports
  • scales * imports
  • shiny >= 1.5.0 imports
  • shinyBS >= 0.6.0 imports
  • shinydashboard >= 0.7.0 imports
  • shinyjs >= 1.0.0 imports
  • stats * imports
  • stm >= 1.3.5 imports
  • stringr >= 1.4.0 imports
  • tibble >= 2.1.0 imports
  • tidygraph >= 1.1.0 imports
  • knitr * suggests
  • quanteda >= 2.0.0 suggests
  • rmarkdown * suggests