| Title: | Interactive Visualization of Topic Models |
| Version: | 0.3.2 |
| Description: | Tools to create an interactive web-based visualization of a topic model that has been fit to a corpus of text data using Latent Dirichlet Allocation (LDA). Given the estimated parameters of the topic model, it computes various summary statistics as input to an interactive visualization built with D3.js that is accessed via a browser. The goal is to help users interpret the topics in their LDA topic model. |
| Depends: | R (≥ 2.10) |
| Imports: | proxy, RJSONIO, parallel |
| License: | MIT + file LICENSE |
| Suggests: | mallet, lda, topicmodels, gistr (≥ 0.0.8.99), servr, shiny,knitr, rmarkdown, digest, htmltools |
| LazyData: | true |
| VignetteBuilder: | knitr |
| URL: | https://github.com/cpsievert/LDAvis |
| BugReports: | https://github.com/cpsievert/LDAvis/issues |
| NeedsCompilation: | no |
| Packaged: | 2015-10-23 23:58:20 UTC; cpsievert |
| Author: | Carson Sievert [aut, cre], Kenny Shirley [aut] |
| Maintainer: | Carson Sievert <cpsievert1@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2015-10-24 08:21:16 |
Twenty Newsgroups Data
Description
Twenty Newsgroups Data
Usage
TwentyNewsgroupsFormat
A list elements extracted from a topic model fit to this data
- phi
phi, a matrix with the topic-term distributions
- theta
theta, a matrix with the document-topic distributions
- doc.length
doc.length, a numeric vector with token counts for each document
- vocab
vocab, a character vector containing the terms
- term.frequency
term.frequency, a numeric vector of observed term frequencies
Source
http://qwone.com/~jason/20Newsgroups/
Create the JSON object to read into the javascript visualization
Description
This function creates the JSON object that feeds the visualization template.For a more detailed overview,seevignette("details", package = "LDAvis")
Usage
createJSON(phi = matrix(), theta = matrix(), doc.length = integer(), vocab = character(), term.frequency = integer(), R = 30, lambda.step = 0.01, mds.method = jsPCA, cluster, plot.opts = list(xlab = "PC1", ylab = "PC2"), ...)Arguments
phi | matrix, with each row containing the distribution over termsfor a topic, with as many rows as there are topics in the model, and asmany columns as there are terms in the vocabulary. |
theta | matrix, with each row containing the probability distributionover topics for a document, with as many rows as there are documents in thecorpus, and as many columns as there are topics in the model. |
doc.length | integer vector containing the number of tokens in eachdocument of the corpus. |
vocab | character vector of the terms in the vocabulary (in the sameorder as the columns of |
term.frequency | integer vector containing the frequency of each termin the vocabulary. |
R | integer, the number of terms to display in the barchartsof the interactive viz. Default is 30. Recommended to be roughlybetween 10 and 50. |
lambda.step | a value between 0 and 1.Determines the interstep distance in the grid of lambdavalues over which to iterate when computing relevance.Default is 0.01. Recommended to be between 0.01 and 0.1. |
mds.method | a function that takes |
cluster | a cluster object created from theparallel package.If supplied, computations are performed usingparLapply insteadoflapply. |
plot.opts | a named list used to customize various plot elements.By default, the x and y axes are labeled "PC1" and "PC2"(principal components 1 and 2), sincejsPCA is the defaultscaling method. |
... | not currently used. |
Details
The function first computes the topic frequencies (across the wholecorpus), and then it reorders the topics in decreasing order offrequency. The main computation is to loop through the topics and through thegrid of lambda values (determined bylambda.step)to compute theR mostrelevant terms for each topic and value of lambda.
Value
A string containing JSON content which can be written to a fileor feed intoserVis for easy viewing/sharing. One element of thisstring is the new ordering of the topics.
References
Sievert, C. and Shirley, K. (2014)LDAvis: A Method forVisualizing and Interpreting Topics, ACL Workshop on InteractiveLanguage Learning, Visualization, and Interfaces.http://nlp.stanford.edu/events/illvi2014/papers/sievert-illvi2014.pdf
See Also
Examples
## Not run: data(TwentyNewsgroups, package="LDAvis")# create the json object, start a local file server, open in default browserjson <- with(TwentyNewsgroups, createJSON(phi, theta, doc.length, vocab, term.frequency))serVis(json) # press ESC or Ctrl-C to kill# createJSON() reorders topics in decreasing order of term frequencyRJSONIO::fromJSON(json)$topic.order# You may want to just write the JSON and other dependency files# to a folder named TwentyNewsgroups under the working directoryserVis(json, out.dir = 'TwentyNewsgroups', open.browser = FALSE)# then you could use a server of your choice; for example,# open your terminal, type `cd TwentyNewsgroups && python -m SimpleHTTPServer`# then open http://localhost:8000 in your web browser# A different data set: the Jeopardy Questions+Answers data:# Install LDAvisData (the associated data package) if not already installed:# devtools::install_github("cpsievert/LDAvisData")library(LDAvisData)data(Jeopardy, package="LDAvisData")json <- with(Jeopardy, createJSON(phi, theta, doc.length, vocab, term.frequency))serVis(json) # Check out Topic 22 (bodies of water!)# If you have a GitHub account, you can even publish as a gist# which allows you to easily share with others!serVis(json, as.gist = TRUE)# Run createJSON on a cluster of machines to speed it upsystem.time(json <- with(TwentyNewsgroups, createJSON(phi, theta, doc.length, vocab, term.frequency)))# user system elapsed# 14.415 0.800 15.066library("parallel")cl <- makeCluster(detectCores() - 1)cl # socket cluster with 3 nodes on host 'localhost'system.time( json <- with(TwentyNewsgroups, createJSON(phi, theta, doc.length, vocab, term.frequency, cluster = cl)))# user system elapsed# 2.006 0.361 8.822# another scaling method (svd + tsne)library("tsne")svd_tsne <- function(x) tsne(svd(x)$u)json <- with(TwentyNewsgroups, createJSON(phi, theta, doc.length, vocab, term.frequency, mds.method = svd_tsne, plot.opts = list(xlab="", ylab="") ) )serVis(json) # Results in a different topic layout in the left panel## End(Not run)Dimension reduction via Jensen-Shannon Divergence & Principal Components
Description
Dimension reduction via Jensen-Shannon Divergence & Principal Components
Usage
jsPCA(phi)Arguments
phi | matrix, with each row containing the distribution over termsfor a topic, with as many rows as there are topics in the model, and asmany columns as there are terms in the vocabulary. |
Create an LDAvis output element
Description
Shiny server output function customized for animint plots(similar toshiny::plotOutput and friends).
Usage
renderVis(expr, env = parent.frame(), quoted = FALSE)Arguments
expr | An expression that generates a plot. |
env | The environment in which to evaluate |
quoted | Is expr a quoted expression (with |
See Also
http://shiny.rstudio.com/articles/building-outputs.html
Run shiny/D3 visualization
Description
This function is deprecated as of version 0.2
Usage
runShiny(phi, term.frequency, vocab, topic.proportion)Arguments
phi | a matrix with W rows, one for each term in the vocabulary, and Kcolumns, one for each topic, where each column sums to one. Each column is themultinomial distribution over terms for a given topic in an LDA topic model. |
term.frequency | an integer vector of length W containing the frequencyof each term in the vocabulary. |
vocab | a character vector of length W containing the unique terms inthe corpus. |
topic.proportion | a numeric vector of length K containing the proportionof each topic in the corpus. |
View and/or share LDAvis in a browser
Description
View and/or share LDAvis in a browser.
Usage
serVis(json, out.dir = tempfile(), open.browser = interactive(), as.gist = FALSE, ...)Arguments
json | character string output fromcreateJSON. |
out.dir | directory to store html/js/json files. |
open.browser | Should R open a browser? If yes, this function willattempt to create a local file server via the servr package.This is necessary since the javascript needs to access local files and mostbrowsers will not allow this. |
as.gist | should the vis be uploaded as a gist? Will prompt for aninteractive login if the GITHUB_PAT environment variable is not set. For moredetails, seehttps://github.com/ropensci/gistr#authentication. |
... | arguments passed onto |
Details
This function will place the necessary html/js/css files (located insystem.file("htmljs", package = "LDAvis")) in a directory specifiedbyout.dir, start a local file server in that directory (if necessary),and (optionally) open the default browser in this directory.Ifas.gist=TRUE, it will attempt to upload these files as a gist (in thiscase, please make sure you have the gistr package installed as well as your'github.username' and 'github.password' set inoptions.)
Value
An invisible object.
Author(s)
Carson Sievert
See Also
Examples
## Not run: # Use of serVis is documented here:help(createJSON, package = "LDAvis")## End(Not run)Shiny ui output function
Description
Shiny ui output function
Usage
visOutput(outputId)Arguments
outputId | output variable to read the plot from |
See Also
http://shiny.rstudio.com/articles/building-outputs.html