| 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
TwentyNewsgroups
Format
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,
see vignette("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 terms for a topic, with as many rows as there are topics in the model, and as many columns as there are terms in the vocabulary. |
theta |
matrix, with each row containing the probability distribution over topics for a document, with as many rows as there are documents in the corpus, and as many columns as there are topics in the model. |
doc.length |
integer vector containing the number of tokens in each document of the corpus. |
vocab |
character vector of the terms in the vocabulary (in the same
order as the columns of |
term.frequency |
integer vector containing the frequency of each term in the vocabulary. |
R |
integer, the number of terms to display in the barcharts of the interactive viz. Default is 30. Recommended to be roughly between 10 and 50. |
lambda.step |
a value between 0 and 1. Determines the interstep distance in the grid of lambda values 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 the parallel package. If supplied, computations are performed using parLapply instead of lapply. |
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), since jsPCA is the default scaling method. |
... |
not currently used. |
Details
The function first computes the topic frequencies (across the whole
corpus), and then it reorders the topics in decreasing order of
frequency. The main computation is to loop through the topics and through the
grid of lambda values (determined by lambda.step)
to compute the R most
relevant terms for each topic and value of lambda.
Value
A string containing JSON content which can be written to a file or feed into serVis for easy viewing/sharing. One element of this string is the new ordering of the topics.
References
Sievert, C. and Shirley, K. (2014) LDAvis: A Method for Visualizing and Interpreting Topics, ACL Workshop on Interactive Language 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 browser
json <- 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 frequency
RJSONIO::fromJSON(json)$topic.order
# You may want to just write the JSON and other dependency files
# to a folder named TwentyNewsgroups under the working directory
serVis(json, out.dir = 'TwentyNewsgroups', open.browser = FALSE)
# then you could use a server of your choice; for example,
# open your terminal, type `cd TwentyNewsgroups && python -m SimpleHTTPServer`
# then open http://localhost:8000 in your web browser
# A different data set: the Jeopardy Questions+Answers data:
# Install LDAvisData (the associated data package) if not already installed:
# devtools::install_github("cpsievert/LDAvisData")
library(LDAvisData)
data(Jeopardy, package="LDAvisData")
json <- with(Jeopardy,
createJSON(phi, theta, doc.length, vocab, term.frequency))
serVis(json) # Check out Topic 22 (bodies of water!)
# If you have a GitHub account, you can even publish as a gist
# which allows you to easily share with others!
serVis(json, as.gist = TRUE)
# Run createJSON on a cluster of machines to speed it up
system.time(
json <- with(TwentyNewsgroups,
createJSON(phi, theta, doc.length, vocab, term.frequency))
)
# user system elapsed
# 14.415 0.800 15.066
library("parallel")
cl <- makeCluster(detectCores() - 1)
cl # socket cluster with 3 nodes on host 'localhost'
system.time(
json <- with(TwentyNewsgroups,
createJSON(phi, theta, doc.length, vocab, term.frequency,
cluster = cl))
)
# user system elapsed
# 2.006 0.361 8.822
# another scaling method (svd + tsne)
library("tsne")
svd_tsne <- function(x) tsne(svd(x)$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 terms for a topic, with as many rows as there are topics in the model, and as many columns as there are terms in the vocabulary. |
Create an LDAvis output element
Description
Shiny server output function customized for animint plots
(similar to shiny::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 K columns, one for each topic, where each column sums to one. Each column is the multinomial distribution over terms for a given topic in an LDA topic model. |
term.frequency |
an integer vector of length W containing the frequency of each term in the vocabulary. |
vocab |
a character vector of length W containing the unique terms in the corpus. |
topic.proportion |
a numeric vector of length K containing the proportion of 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 from createJSON. |
out.dir |
directory to store html/js/json files. |
open.browser |
Should R open a browser? If yes, this function will attempt to create a local file server via the servr package. This is necessary since the javascript needs to access local files and most browsers will not allow this. |
as.gist |
should the vis be uploaded as a gist? Will prompt for an interactive login if the GITHUB_PAT environment variable is not set. For more details, see https://github.com/ropensci/gistr#authentication. |
... |
arguments passed onto |
Details
This function will place the necessary html/js/css files (located in
system.file("htmljs", package = "LDAvis")) in a directory specified
by out.dir, start a local file server in that directory (if necessary),
and (optionally) open the default browser in this directory.
If as.gist=TRUE, it will attempt to upload these files as a gist (in this
case, please make sure you have the gistr package installed as well as your
'github.username' and 'github.password' set in options.)
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