quanteda package - RDocumentation

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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.

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Version

Install

install.packages('quanteda')

Monthly Downloads

17,344

Version

1.3.14

License

GPL-3

Maintainer

Kenneth Benoit

Last Published

November 19th, 2018

Functions in quanteda (1.3.14)

coerce a compressed corpus to a standard corpus

as.statistics_textmodel

Coerce various objects to statistics_textmodel

Convert the case of character objects

as.matrix.simil

Coerce a simil object into a matrix

Remove sentences based on their token lengths or a pattern match

Check if font is available on the system

data_corpus_irishbudget2010

Irish budget speeches from 2010

as.matrix.dist_selection

Coerce a dist_selection object to a matrix

Create a dictionary

Extract a subset of a corpus

Convert quanteda dictionary objects to the YAML format

redefinition of network::as.network()

data_dfm_lbgexample

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

Combine documents in a dfm by a grouping variable

quanteda_options

Get or set package options for quanteda

Function extending base::attributes()

corpus_trimsentences

Remove sentences based on their token lengths or a pattern match

Weight the feature frequencies in a dfm

Apply a dictionary to a dfm

tokens_wordstem

Stem the terms in an object

Utility function to create a object with new set of attributes

Replace features in dfm

View methods for quanteda

Convert a dfm to a non-quanteda format

Check if a glob pattern is indexed by index_types

Coercion and checking functions for dictionary objects

generate_groups

Generate a grouping vector from docvars

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.

Weight a dfm by tf-idf

Base method extensions for corpus objects

Compute the (weighted) document frequency of a feature

Extensions for and from spacy_parse objects

Get the feature labels from a dfm

Sort an fcm in alphabetical order of the features

data-deprecated

Datasets with deprecated or defunct names

Compute keyness (internal functions)

Internal function to fit the likelihood scaling mixture model.

data_char_sampletext

A paragraph of text for testing various text-based functions

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

Convert a dfm to an lsa "textmatrix"

Internal data sets

as.list.dist_selection

Coerce a dist_selection object into a list

Randomly sample documents from a corpus

Coerce a dfm to a matrix or data.frame

print.dist_selection

Print a dist_selection object

Pattern for feature, token and keyword matching

Import a LIWC-formatted dictionary

Convert an fcm to an igraph object

Internal functions for dfm objects

Coerce a dist object into a list

textstat_dist_old

Similarity and distance computation between documents or features

Convert regex and glob patterns to type IDs or fixed patterns

Bootstrap a dfm

predict.textmodel_wordfish

Prediction from a textmodel_wordfish method

data_dictionary_LSD2015

Lexicoder Sentiment Dictionary (2015)

predict.textmodel_wordscores

Predict textmodel_wordscores

Create a document-feature matrix

textplot_scale1d

Plot a fitted scaling model

Randomly sample documents or features from a dfm

Combine dfm objects by Rows or Columns

textplot_network

Plot a network of feature co-occurrences

Segment texts on a pattern match

Select features from a dfm or fcm

coef.textmodel_ca

Extract model coefficients from a fitted textmodel_ca object

Create a feature co-occurrence matrix

data_corpus_dailnoconf1991

Confidence debate from 1991 Irish Parliament

data_char_ukimmig2010

Immigration-related sections of 2010 UK party manifestos

Count the Scrabble letter values of text

Recast the document units of a corpus

data_corpus_inaugural

US presidential inaugural address texts

Segment tokens object by patterns

Convert the case of the features of a dfm and combine

Extract a subset of a dfm

Latent Semantic Analysis

Get or set document names

influence.predict.textmodel_affinity

Compute feature influence from a predicted textmodel_affinity object

dictionary2-class

Print a dictionary object

Get or set corpus metadata

Trim a dfm using frequency threshold-based feature selection

convert-wrappers

Convenience wrappers for dfm convert

Internal function for select_types() to escape regular expressions

Replace types in tokens object

Grouping variable(s) for various functions

Locate keywords-in-context

Construct a corpus object

Recombine a dfm or fcm by combining identical dimension elements

list2dictionary

Internal function to convert a list to a dictionary

Count the number of tokens or types

Similarity and distance computation between documents or features

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

merge_dictionary_values

Internal function to merge values of duplicated keys

Get or set document-level meta-data

Converts a Matrix to a dfm

Compute the sparsity of a document-feature matrix

Virtual class "dfm" for a document-feature matrix

print.coefficients_textmodel

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

format_sparsity

format a sparsity value for printing

replace_dictionary_values

Internal function to replace dictionary values

Print a dfm object

Count syllables in a text

Summarize a corpus

Get or set document-level variables

Return the first or last part of a corpus

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

Naive Bayes classifier for texts

Converts a Matrix to a fcm

friendly_class_undefined_message

Print friendly object class not defined message

Internal function for select_types to search the index using fastmatch.

textmodel_wordfish

Wordfish text model

textplot_wordcloud

Plot features as a wordcloud

Deprecated name for nscrabble

Count the number of sentences

textplot_influence

Influence plot for text scaling models

Select types without performing slow regex search

print.statistics_textmodel

Implements print methods for textmodel_statistics

remove_empty_keys

Utility function to remove empty keys

Count the number of documents or features

Print a phrase object

textstat_keyness

Calculate keyness statistics

Set values to a dfm's S4 slots

summary.character

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

Apply a dictionary to a tokens object

Return the first or last part of a dfm

textplot_keyness

Plot word keyness

textstat_frequency

Tabulate feature frequencies

print.textmodel_wordfish

print method for a wordfish model

tokens_recompile

recompile a serialized tokens object

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

Objects exported from other packages

quanteda-package

An R package for the quantitative analysis of textual data

Return an error message

Get or set the corpus settings

summary.textmodel_nb

summary method for textmodel_nb objects

Set values to a fcm's S4 slots

Recombine documents tokens by groups

textmodel_lsa-postestimation

Post-estimations methods for textmodel_lsa

print.summary.textmodel

print method for summary.textmodel

nest_dictionary

Utility function to generate a nested list

textmodel_wordscores

Wordscores text model

textmodel_affinity

Class affinity maximum likelihood text scaling model

Function to assign multiple slots to a S4 object

summary_character

Summary statistics on a character vector

Identify the most frequent features in a dfm

Convert the case of tokens

Correspondence analysis of a document-feature matrix

Create ngrams and skipgrams from tokens

Plot the dispersion of key word(s)

predict.textmodel_affinity

Prediction for a fitted affinity textmodel

textstat_readability

Calculate readability

predict.textmodel_nb

Prediction from a fitted textmodel_nb object

summary.textmodel_wordfish

summary method for textmodel_wordfish

Randomly sample documents from a tokens object

Extract a subset of a tokens

textstat_lexdiv

Calculate lexical diversity

Get or assign corpus texts

Get word types from a tokens object

textmodel_wordshoal

Wordshoal text model (redirect)

Internal function for textplot_wordcloud

Tokenize a set of texts

tokens_compound

Convert token sequences into compound tokens

textstat_select

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

textmodel_affinity-internal

Internal methods for textmodel_affinity

deprecated name for dfm_weight

Raise warning of unused dots

textstat_collocations

Identify and score multi-word expressions

Deprecated form of dfm_tfidf

Select or remove tokens from a tokens object

Pattern matching using valuetype

[Experimental] Compute document/feature proximity

wordcloud_comparison

Internal function for textplot_wordcloud

[Experimental] Change direction of words in tokens

tokens_serialize

Function to serialized list-of-character tokens

Coercion and checking functions for dfm objects

as.corpus.corpuszip

Coerce a compressed corpus to a standard corpus

as.data.frame.dfm

Convert a dfm to a data.frame

Coerce a dist into a dist

Coercion, checking, and combining functions for tokens objects

Coercion functions for fcm objects

as.summary.textmodel

Assign the summary.textmodel class to a list

as.coefficients_textmodel

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

Sort a dfm by frequency of one or more margins