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

24,111

Version

1.4.3

License

GPL-3

Maintainer

Kenneth Benoit

Last Published

April 1st, 2019

Functions in quanteda (1.4.3)

convert-wrappers

Convenience wrappers for dfm convert

data_char_ukimmig2010

Immigration-related sections of 2010 UK party manifestos

data_corpus_dailnoconf1991

Confidence debate from 1991 Irish Parliament

as.corpus.corpuszip

Coerce a compressed corpus to a standard corpus

Trim a dfm using frequency threshold-based feature selection

Weight the feature frequencies in a dfm

Compute the (weighted) document frequency of a feature

as.data.frame.dfm

Convert a dfm to a data.frame

redefinition of network::as.network()

Coercion and checking functions for dfm objects

Get or set document names

Coercion and checking functions for dictionary objects

as.statistics_textmodel

Coerce various objects to statistics_textmodel

as.summary.textmodel

Assign the summary.textmodel class to a list

coef.textmodel_ca

Extract model coefficients from a fitted textmodel_ca object

compute_lexdiv_stats

Compute lexical diversity from a dfm or tokens

Coercion, checking, and combining functions for tokens objects

Count the number of documents or features

Compute the Moving-Average Type-Token Ratio (MATTR)

Recast the document units of a corpus

nest_dictionary

Utility function to generate a nested list

Randomly sample documents from a corpus

Compute the Mean Segmental Type-Token Ratio (MSTTR)

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

predict.textmodel_affinity

Prediction for a fitted affinity textmodel

Internal data sets

data_corpus_inaugural

US presidential inaugural address texts

read_dict_lexicoder

Import a Lexicoder dictionary

data_char_sampletext

A paragraph of text for testing various text-based functions

data_corpus_irishbudget2010

Irish budget speeches from 2010

Import a LIWC-formatted dictionary

Apply a dictionary to a dfm

as.coefficients_textmodel

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

Match the feature set of a dfm to given feature names

coerce a compressed corpus to a standard corpus

Weight a dfm by tf-idf

View methods for quanteda

Convert an fcm to an igraph object

Internal function to fit the likelihood scaling mixture model.

as.matrix.dist_selection

Coerce a dist_selection object to a matrix

Get or set the corpus settings

Function to assign multiple slots to a S4 object

as.matrix.simil

Coerce a simil object into a matrix

Convert the case of the features of a dfm and combine

Create a feature co-occurrence matrix

Recombine a dfm or fcm by combining identical dimension elements

Sort an fcm in alphabetical order of the features

textmodel_affinity-internal

Internal methods for textmodel_affinity

Convert quanteda dictionary objects to the YAML format

textmodel_affinity

Class affinity maximum likelihood text scaling model

Combine documents in a dfm by a grouping variable

Function extending base::attributes()

Coerce a dist object into a list

generate_groups

Generate a grouping vector from docvars

Grouping variable(s) for various functions

Get or set corpus metadata

dictionary2-class

Coerce a dictionary object into a list

Construct a corpus object

textmodel_wordfish

Wordfish text model

Coerce a dist into a dist

Coercion functions for fcm objects

textmodel_wordscores

Wordscores text model

Coerce a dfm to a matrix or data.frame

Base method extensions for corpus objects

as.list.dist_selection

Coerce a dist_selection object into a list

Create a dictionary

Return the first or last part of a corpus

Get or set document-level meta-data

textplot_scale1d

Plot a fitted scaling model

data_dfm_lbgexample

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

Convert the case of character objects

Return the first or last part of a dfm

corpus_trimsentences

Remove sentences based on their token lengths or a pattern match

Check if font is available on the system

Remove sentences based on their token lengths or a pattern match

Bootstrap a dfm

Compute keyness (internal functions)

Locate keywords-in-context

Count the number of tokens or types

Create a document-feature matrix

data_dictionary_LSD2015

Lexicoder Sentiment Dictionary (2015)

textplot_wordcloud

Plot features as a wordcloud

Pattern for feature, token and keyword matching

Select or remove tokens from a tokens object

Convert a dfm to an lsa "textmatrix"

dfm_split_hyphenated_features

Split a dfm's hyphenated features into constituent parts

Combine dfm objects by Rows or Columns

tokens_serialize

Function to serialized list-of-character tokens

Extract a subset of a dfm

Objects exported from other packages

Utility function to create a object with new set of attributes

Get word types from a tokens object

remove_empty_keys

Utility function to remove empty keys

Segment texts on a pattern match

Deprecated name for nscrabble

Extract a subset of a corpus

data-deprecated

Datasets with deprecated or defunct names

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

Replace features in dfm

Randomly sample documents or features from a dfm

Select features from a dfm or fcm

influence.predict.textmodel_affinity

Compute feature influence from a predicted textmodel_affinity object

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.

Sort a dfm by frequency of one or more margins

Select types without performing slow regex search

Virtual class "dfm" for a document-feature matrix

Check if patterns contains glob wildcard

Internal function for select_types() to escape regular expressions

Get or set document-level variables

Internal functions for dfm objects

merge_dictionary_values

Internal function to merge values of duplicated keys

Check if a glob pattern is indexed by index_types

Get the feature labels from a dfm

Count the number of sentences

Raise warning of unused dots

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

flatten_dictionary

Flatten a hierarchical dictionary into a list of character vectors

Return an error message

Count the Scrabble letter values of text

format_sparsity

format a sparsity value for printing

Defunct form of nfeat

predict.textmodel_nb

Prediction from a fitted textmodel_nb object

friendly_class_undefined_message

Print friendly object class not defined message

Count syllables in a text

predict.textmodel_wordfish

Prediction from a textmodel_wordfish method

list2dictionary

Internal function to convert a list to a dictionary

quanteda-package

An R package for the quantitative analysis of textual data

quanteda_options

Get or set package options for quanteda

Converts a Matrix to a dfm

Print a dfm object

replace_dictionary_values

Internal function to replace dictionary values

Converts a Matrix to a fcm

lowercase_dictionary_values

Internal function to lowercase dictionary values

Sample a vector by a group

Extensions for and from spacy_parse objects

summary.textmodel_wordfish

summary method for textmodel_wordfish

Naive Bayes classifier for texts

textstat_lexdiv

Calculate lexical diversity

Compute the sparsity of a document-feature matrix

Tokenize a set of texts

Split tokens by a separator pattern

Latent Semantic Analysis

[Experimental] Compute document/feature proximity

Segment tokens object by chunks of a given size

Extract a subset of a tokens

summary_character

Summary statistics on a character vector

Print a phrase object

print.statistics_textmodel

Implements print methods for textmodel_statistics

read_dict_wordstat

Import a Wordstat dictionary

print.dist_selection

Print a dist_selection object

print.summary.textmodel

print method for summary.textmodel

Internal function for textplot_wordcloud

Pattern matching using valuetype

print.textmodel_wordfish

print method for a wordfish model

read_dict_yoshikoder

Import a Yoshikoder dictionary file.

textstat_collocations

Identify and score multi-word expressions

textstat_entropy

Compute entropy of documents or features

deprecated name for dfm_weight

Deprecated form of dfm_tfidf

Convert regex and glob patterns to type IDs or fixed patterns

tokens_recompile

recompile a serialized tokens object

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

predict.textmodel_wordscores

Predict textmodel_wordscores

Internal function for special handling of multi-word dictionary values

print.coefficients_textmodel

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

summary.character

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

textplot_keyness

Plot word keyness

Set values to a dfm's S4 slots

textplot_network

Plot a network of feature co-occurrences

Internal function for select_types to search the index using fastmatch.

Replace tokens in a tokens object

Set values to a fcm's S4 slots

textmodel_wordshoal

Wordshoal text model (redirect)

textplot_influence

Influence plot for text scaling models

wordcloud_comparison

Internal function for textplot_wordcloud

tokens_compound

Convert token sequences into compound tokens

set_dfm_dimnames<-

Internal functions to set dimnames

Summarize a corpus

Plot the dispersion of key word(s)

summary.textmodel_nb

summary method for textmodel_nb objects

Get or assign corpus texts

Correspondence analysis of a document-feature matrix

tokens_wordstem

Stem the terms in an object

Recombine documents tokens by groups

Identify the most frequent features in a dfm

Similarity and distance computation between documents or features

textmodel_lsa-postestimation

Post-estimations methods for textmodel_lsa

textstat_dist_old

Similarity and distance computation between documents or features

Randomly sample documents from a tokens object

textstat_frequency

Tabulate feature frequencies

Segment tokens object by patterns

textstat_keyness

Calculate keyness statistics

textstat_readability

Calculate readability

textstat_select

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

Apply a dictionary to a tokens object

Create ngrams and skipgrams from tokens

Convert the case of tokens

[Experimental] Change direction of words in tokens

Convert a dfm to a non-quanteda format