quanteda package - RDocumentation

Learn R Programming

⚠️There's a newer version (4.1.0) of this package.Take me there.

About

An R package for managing and analyzing text, created by Kenneth Benoit. Supported by the European Research Council grant ERC-2011-StG 283794-QUANTESS.

For more details, see https://quanteda.io.

How to Install

The normal way from CRAN, using your R GUI or

install.packages("quanteda")

Or for the latest development version:

# devtools package required to install quanteda from Github 
devtools::install_github("quanteda/quanteda")

Because this compiles some C++ and Fortran source code, you will need to have installed the appropriate compilers.

If you are using a Windows platform, this means you will need also to install the Rtools software available from CRAN.

If you are using macOS, you should install the macOS tools, namely the Clang 6.x compiler and the GNU Fortran compiler (as quanteda requires gfortran to build). If you are still getting errors related to gfortran, follow the fixes here.

How to Use

See the quick start guide to learn how to use quanteda.

How to cite

Benoit, Kenneth, Kohei Watanabe, Haiyan Wang, Paul Nulty, Adam Obeng, Stefan Müller, and Akitaka Matsuo. (2018) “quanteda: An R package for the quantitative analysis of textual data”. Journal of Open Source Software. 3(30), 774. https://doi.org/10.21105/joss.00774.

For a BibTeX entry, use the output from citation(package = "quanteda").

Leaving Feedback

If you like quanteda, please consider leaving feedback or a testimonial here.

Contributing

Contributions in the form of feedback, comments, code, and bug reports are most welcome. How to contribute:

Fork the source code, modify, and issue a pull request through the project GitHub page. See our Contributor Code of Conduct and the all-important quanteda Style Guide.
Issues, bug reports, and wish lists: File a GitHub issue.
Usage questions: Submit a question on the quanteda channel on StackOverflow.
Contact the maintainer by email.

Copy Link

Version

Install

install.packages('quanteda')

Monthly Downloads

23,966

Version

1.3.13

License

GPL-3

Maintainer

Last Published

November 1st, 2018

Functions in quanteda (1.3.13)

Coercion and checking functions for dfm objects

Coercion and checking functions for dictionary objects

as.corpus.corpuszip

Coerce a compressed corpus to a standard corpus

as.data.frame.dfm

Convert a dfm to a data.frame

Convert an fcm to an igraph object

as.matrix.dist_selection

Coerce a dist_selection object to a matrix

coef.textmodel_ca

Extract model coefficients from a fitted textmodel_ca object

Internal function to fit the likelihood scaling mixture model.

View methods for quanteda

as.matrix.simil

Coerce a simil object into a matrix

Coerce a dist object into a list

as.coefficients_textmodel

Coerce various objects to coefficients_textmodel This is a helper function used in summary.textmodel_*.

coerce a compressed corpus to a standard corpus

Bootstrap a dfm

Combine dfm objects by Rows or Columns

Coerce a dist into a dist

Convert quanteda dictionary objects to the YAML format

redefinition of network::as.network()

Coercion functions for fcm objects

as.list.dist_selection

Coerce a dist_selection object into a list

Coerce a dfm to a matrix or data.frame

convert-wrappers

Convenience wrappers for dfm convert

Function extending base::attributes()

as.statistics_textmodel

Coerce various objects to statistics_textmodel

Convert the case of character objects

Check if font is available on the system

Coercion, checking, and combining functions for tokens objects

as.summary.textmodel

Assign the summary.textmodel class to a list

Convert a dfm to a non-quanteda format

Base method extensions for corpus objects

data_corpus_dailnoconf1991

Confidence debate from 1991 Irish Parliament

Construct a corpus object

data-deprecated

Datasets with deprecated or defunct names

data_corpus_inaugural

US presidential inaugural address texts

corpus_trimsentences

Remove sentences based on their token lengths or a pattern match

Recast the document units of a corpus

data_dictionary_LSD2015

Lexicoder Sentiment Dictionary (2015)

Internal data sets

data_char_sampletext

A paragraph of text for testing various text-based functions

Utility function to create a object with new set of attributes

Randomly sample documents from a corpus

Extract a subset of a corpus

Virtual class "dfm" for a document-feature matrix

Convert a dfm to an lsa "textmatrix"

Recombine a dfm or fcm by combining identical dimension elements

Select features from a dfm or fcm

Remove sentences based on their token lengths or a pattern match

data_corpus_irishbudget2010

Irish budget speeches from 2010

Combine documents in a dfm by a grouping variable

dictionary2-class

Print a dictionary object

Weight the feature frequencies in a dfm

data_char_ukimmig2010

Immigration-related sections of 2010 UK party manifestos

Apply a dictionary to a dfm

Compute keyness (internal functions)

friendly_class_undefined_message

Print friendly object class not defined message

generate_groups

Generate a grouping vector from docvars

Get or set document-level meta-data

Count the number of documents or features

predict.textmodel_nb

Prediction from a fitted textmodel_nb object

Locate keywords-in-context

Sort a dfm by frequency of one or more margins

Segment texts on a pattern match

data_dfm_lbgexample

dfm from data in Table 1 of Laver, Benoit, and Garry (2003)

predict.textmodel_wordfish

Prediction from a textmodel_wordfish method

Internal functions for dfm objects

Import a LIWC-formatted dictionary

Objects exported from other packages

Create a dictionary

Count the number of sentences

Compute the (weighted) document frequency of a feature

Count syllables in a text

Create a document-feature matrix

Latent Semantic Analysis

Replace features in dfm

Randomly sample documents or features from a dfm

Print a phrase object

Convert the case of the features of a dfm and combine

print.statistics_textmodel

Implements print methods for textmodel_statistics

Trim a dfm using frequency threshold-based feature selection

Grouping variable(s) for various functions

Extract a subset of a dfm

Weight a dfm by tf-idf

Naive Bayes classifier for texts

Internal function for select_types() to escape regular expressions

Return the first or last part of a corpus

Check if a glob pattern is indexed by index_types

Internal function for select_types() to check if a string is a regular expression

Plot the dispersion of key word(s)

Simpler and faster version of expand.grid() in base package

Get or assign corpus texts

Count the number of tokens or types

Pattern for feature, token and keyword matching

Sort an fcm in alphabetical order of the features

Print a dfm object

Deprecated name for nscrabble

[Experimental] Compute document/feature proximity

Select types without performing slow regex search

Get the feature labels from a dfm

Summarize a corpus

print.dist_selection

Print a dist_selection object

textstat_readability

Calculate readability

Converts a Matrix to a fcm

Raise warning of unused dots

merge_dictionary_values

Internal function to merge values of duplicated keys

Return the first or last part of a dfm

Pattern matching using valuetype

summary.textmodel_nb

summary method for textmodel_nb objects

Get or set document names

influence.predict.textmodel_affinity

Compute feature influence from a predicted textmodel_affinity object

Return an error message

textmodel_wordfish

Wordfish text model

textmodel_wordscores

Wordscores text model

Deprecated form of dfm_tfidf

textplot_scale1d

Plot a fitted scaling model

textplot_wordcloud

Plot features as a wordcloud

Tokenize a set of texts

Get or set document-level variables

tokens_compound

Convert token sequences into compound tokens

print.summary.textmodel

print method for summary.textmodel

Recombine documents tokens by groups

Get or set corpus metadata

print.textmodel_wordfish

print method for a wordfish model

Identify the most frequent features in a dfm

Function to assign multiple slots to a S4 object

Get word types from a tokens object

Extensions for and from spacy_parse objects

nest_dictionary

Utility function to generate a nested list

Count the Scrabble letter values of text

Declare a compound character to be a sequence of separate pattern matches

predict.textmodel_affinity

Prediction for a fitted affinity textmodel

quanteda-package

An R package for the quantitative analysis of textual data

Correspondence analysis of a document-feature matrix

remove_empty_keys

Utility function to remove empty keys

textmodel_lsa-postestimation

Post-estimations methods for textmodel_lsa

textstat_collocations

Identify and score multi-word expressions

quanteda_options

Get or set package options for quanteda

Compute the sparsity of a document-feature matrix

textstat_frequency

Tabulate feature frequencies

Get or set the corpus settings

textmodel_wordshoal

Wordshoal text model (redirect)

summary.textmodel_wordfish

summary method for textmodel_wordfish

Set values to a fcm's S4 slots

replace_dictionary_values

Internal function to replace dictionary values

summary_character

Summary statistics on a character vector

textstat_keyness

Calculate keyness statistics

textplot_influence

Influence plot for text scaling models

textstat_lexdiv

Calculate lexical diversity

Randomly sample documents from a tokens object

Segment tokens object by patterns

summary.character

summary.character method to override the network::summary.character()

Virtual class "fcm" for a feature co-occurrence matrix The fcm class of object is a special type of fcm object with additional slots, described below.

Create a feature co-occurrence matrix

list2dictionary

Internal function to convert a list to a dictionary

Converts a Matrix to a dfm

tokens_serialize

Function to serialized list-of-character tokens

Select or remove tokens from a tokens object

textmodel_affinity-internal

Internal methods for textmodel_affinity

textmodel_affinity

Class affinity maximum likelihood text scaling model

[Experimental] Change direction of words in tokens

tokens_wordstem

Stem the terms in an object

textplot_keyness

Plot word keyness

textplot_network

Plot a network of feature co-occurrences

Convert regex and glob patterns to type IDs or fixed patterns

textstat_select

Select rows of textstat objects by glob, regex or fixed patterns

Convert various input as pattern to a vector used in tokens_select, tokens_compound and kwic.

Similarity and distance computation between documents or features

predict.textmodel_wordscores

Predict textmodel_wordscores

tokens_recompile

recompile a serialized tokens object

print.coefficients_textmodel

Print methods for textmodel features estimates This is a helper function used in print.summary.textmodel.

Internal function for select_types to search the index using fastmatch.

Replace types in tokens object

Extract a subset of a tokens

Set values to a dfm's S4 slots

Convert the case of tokens

textstat_dist_old

Similarity and distance computation between documents or features

deprecated name for dfm_weight

Apply a dictionary to a tokens object

Create ngrams and skipgrams from tokens

Internal function for textplot_wordcloud

wordcloud_comparison

Internal function for textplot_wordcloud